Forest Hydrology – Results of Research in Germany and Russia Forest Hydrology – Results of Research in Germany and Russia

IHP/HWRP - BERICHTE

HEFT 6 KOBLENZ 2007

Forest hydrology – results of research in Germany and Russia Forest hydrology – results of research in Germany and Russia

AUS DER ARBEIT DES HEFT 6/2007 HEFT DEUTSCHEN IHP/HWRP - NATIONALKOMITEES

ISSN 1614-1180 IHP – INTERNATIONAL HYDROLOGICAL PROGRAMME OF UNESCO HWRP – HYDROLOGY AND WATER RESOURCES PROGRAMME OF WMO IHP/HWRP - BERICHTE

Forest hydrology – results of research in Germany and Russia

Editors Part I: H. Puhlmann, R. Schwarze

Editors Part II: S.F. Federov, S.V. Marunich (dec.)

Koblenz 2007

IHP – International Hydrological Programme of UNESCO

HWRP – Hydrology and Water Resources Programme of WMO

BfG – Federal Institute of Hydrology, Koblenz, Germany

A German contribution to Phase VI of the International Hydrological Programme of UNESCO, Theme 3: Land Habitat Hydrology.

Authors Part I: Burkhard Beudert (Section 2.1) Bavarian Forest National Park, Germany Beate Klöcking (Section 2.1) Bureau for Applied Hydrology, Munich, Germany Benjamin Marcq (Section 2.3) Forest Research Institute Baden-Wuerttemberg, Freiburg, Germany Jörg Niederberger (Section 2.3) Forest Research Institute Baden-Wuerttemberg, Freiburg, Germany Heike Puhlmann (Sections 2.2, 2.3) Forest Research Institute Baden-Wuerttemberg, Freiburg, Germany Robert Schwarze (Sections 1.1, 2.1) Institute of Hydrology and Meteorology, Dresden University of Technology, Germany Klaus-Hermann von Wilpert (Sections 1.2, 1.3, 2.2, 2.3) Forest Research Institute Baden-Wuerttemberg, Freiburg, Germany

Part II: Stepan Fedorovich Federov State Hydrological Institute, St. Peterburg, Russia Sergey Victorovich Marunich (dec.) State Hydrological Institute, St. Peterburg, Russia

Herausgeber: Deutsches Nationalkomitee für das International Hydrological Programme (IHP) der UNESCO und das Hydrology and Water Resources Programme (HWRP) der WMO Koblenz 2007 ISSN 1614-1180

Fax +49 (0)261 1306 5422

Foreword

Forest plays a key role for many aspects of water resource management and environmental protection. The impact of forest on water quality and the characteristics of hydrological processes in forested catchments are of paramount importance for sustainable human development and the preservation of natural habitats. This impacts not only directly on the socio-economic situation of people living in forested environments, but also affects whole societies via the close links of forestry, sustainable water resource management and other aspects of human economic behaviour such as agriculture, energy supply and trade.

This report aims at helping us to clarify some of the main characteristics of forest hydrology in order to provide a sound basis for planning and operational purposes for both water managers and forest officials. It is based on scientific research in the Black Forest and Bavarian Forest, located in Germany (Part I), as well as a thorough study of the hydrological characteristics of Valday forests in Russia (Part II).

The Forest Research Institute Baden-Wuerttemberg, Freiburg has a long tradition of studying forest hydrology in the Black Forest with the main objective of improving water resources management and water ecology in forested areas. With this end in view, the status of water quality and how it has changed has been studied in various forested catchments. To ensure the quality of drinking water, whether it comes from ground or surface water, seepage or discharge water quality in the forest has to be controlled. Climate change and the risk of water stress are also aspects that require consideration. Extensive dieback of coniferous trees is often the result not only of anthropogenic activities but also of ecological variations such as the change of vegetation following attacks by vermin, the bark-beetle for example. This latter aspect was studied in the Bavarian Forest by the Dresden Technical University.

In the Valday, extensive studies of forest hydrology were carried out over a long period. Focus was mostly on the water balance, runoff formation and effects of clear-cutting and reforestation on the hydrology of the catchment. Considerable attention was paid to the methods of measuring precipitation and its interpolation within the catchment area, and to the determination of evapotranspiration in forested and non-forested regions.

The report Forest hydrology – results of research in Germany and Russia has been produced jointly by the Valday branch of the State Hydrological Institute (SHI), Russia and the German IHP/HWRP National Committee. Already in the 1990s the German IHP/HWRP National Committee and the SHI cooperated well. Focus was on the exchange of information and study results regarding experimental hydrology with reference to hydrological processes in small research basins. Fortunately, the continuation of this cooperation has resulted in the publication of this report.

iii Forest hydrology – results of research in Germany and Russia

The publication Forest hydrology – results of research in Germany and Russia is a contribution to Phase VI of the IHP of UNESCO, Theme 3: Land Habitat Hydrology. With this publication a contribution is made to the determination of parameters and processes in forested catchments, but also to the clarification and significance of man-made or ecologically induced variations.

Many thanks are extended to the authors of this report, Burkhard Beudert, Beate Klöcking, Benjamin Marcq, Jörg Niederberger, Heike Puhlmann, Robert Schwarze, Klaus-Hermann von Wilpert (Part I) and Stepan Federovich Federov, Sergey Victorovich Marunich (dec.) (Part II).

Dr J. Cullmann Director of the German IHP/HWRP Secretariat

Contents

Foreword ……...…………………………………………………………………….. ix

Part I Results of research in Germany …………...…………………..…. 1

Chapter 1 Objectives of hydrological research in forested catchments …..……. 3 1.1 Runoff formation and water balances of forested catchments .………………. 3 1.1.1 Runoff formation ……………………………………………………. 3 1.1.2 Results from regional studies of land-use influence on runoff formation ...... 7 1.1.3 Hydrological implications of forest damage ...……………………… 11 1.1.4 Residence and transit times …………………………………………. 17 1.2 Chemical deposition and seepage water quality in forests …………………... 23 1.2.1 Soil acidification ……………………………………………………. 25 1.2.2 Base saturation ……………………………………………………… 29 1.2.3 Nitrogen saturation ………………………………………………….. 30 1.2.4 Element budgets and their disturbances …………………………….. 31 1.2.5 Exceedance of critical loads ………………………………………… 33 1.2.6 Effect of deposition on seepage and groundwater quality …...……... 35 1.3 Silvicultural management of water quality …………………………………... 36 1.3.1 Tree species selection for controlling seepage water quality …...…... 37 1.3.2 Thinning strategies for controlling seepage water quality …...……... 38 1.3.3 Forest liming for enhancing seepage water quality ….……………… 39

Chapter 2 Selected case studies of forest-hydrological research in German low mountain ranges ………………………………………………………. 41 2.1 Große Ohe: impact of bark beetle infestation on the water and matter budget of a forested catchment ………………………….…………………………… 41 2.1.1 Abstract ……………………………………………………………... 41 2.1.2 Research area ………………………………………………………... 42 2.1.3 Monitoring programme ……………………………………………... 43 2.1.4 Process analysis ……………………………………………………... 46 2.1.4.1 Methods …………………………………………………… 46 2.1.4.2 Results …………………………………………………….. 47 2.1.5 Modelling …………………………………………………………… 58 2.1.5.1 The river basin model ……………………………………... 58 2.1.5.2 Model validation …………………………………………... 59 2.1.5.3 Scenario simulations ………………………………………. 60 2.1.5.4 Conclusions ……………………………………………….. 62

v Forest hydrology – results of research in Germany and Russia

2.2 Conventwald: silvicultural management of seepage water quality …………... 63 2.2.1 Research focus ………………………………………………………. 63 2.2.2 Site characteristics …………………………………………………... 64 2.2.3 Instrumentation – configuration of the stratified approach …………. 67 2.2.4 Measurements – results and discussions ……………………………. 70 2.2.4.1 Precipitation and deposition load …………………………. 70 2.2.4.2 Element concentrations in the soil matrix, the seepage and the catchment outlet ……………………………………….. 71 2.2.4.3 Soil hydrological dynamics ……………………………….. 76 2.2.5 Modelling water flow ……………………………………………….. 78 2.2.5.1 Model boundary conditions ……………………………….. 79 2.2.5.2 Soil hydraulic characteristics ……………………………… 80 2.2.5.3 Model calibration ………………………………………….. 81 2.2.5.4 Results …………………………………………………….. 82 2.2.6 Modelling ion fluxes ………………………………………………... 87 2.2.6.1 Model setup ……………………………………………….. 87 2.2.6.2 Spatial effects on ion fluxes ……………………………….. 87 2.2.7 Discussion …………………………………………………………... 90 2.2.7.1 Site drift …………………………………………………… 90 2.2.7.2 Spatial variability of water and matter fluxes ……………... 90 2.2.7.3 Forest management options to improve seepage water quality ……………………………………………………... 90 2.3 Kleine Kinzig: forest liming to enhance the water quality in the catchment of a drinking-water reservoir ……………………………………………………. 91 2.3.1 Research focus ………………………………………………………. 91 2.3.2 Site characteristics …………………………………………………... 91 2.3.3 Instrumentation and sampling ………………………………………. 94 2.3.3.1 Climate and deposition ……………………………………. 95 2.3.3.2 Surface water, perched groundwater and soil water ………. 95 2.3.3.3 Soil chemistry ……………………………………………... 96 2.3.3.4 Electrical Resistivity Tomography ………………………... 96 2.3.4 Results ………………………………………………………………. 97 2.3.4.1 Runoff generation and matter transport …………………… 97 2.3.4.2 Soil chemical status ……………………………………….. 100 2.3.4.3 Element concentrations in the perched groundwater ……… 106 2.3.4.4 Element concentrations in the springs …………………….. 108 2.3.4.5 Element concentrations in the brooks ……………………... 108 2.3.4.6 Soil water chemistry ………………………………………. 110 2.3.5 Discussion …………………………………………………………... 113 2.3.5.1 Observed liming effects …………………………………… 113 2.3.5.2 Perspective ………………………………………………… 113

References …………………………………………………………………………... 115 List of figures ……………………………………………………………………….. 125 List of tables ………………………………………………………………………… 131

vi Contents

Part II – Results of research in Russia ……………………………………. 133

Physiographic description .………………………………………………………… 135

Map of study area ………………………………………………….………………. 137

Chapter 1 Forest and precipitation ……………………………………..…..……. 139 1.1 General ……………………………………………………………………….. 139 1.2 Determination of the quantity of precipitation on the surface of the catchment …………………………………………………………………….. 139 1.3 Methodology for precipitation measurements in the forest ………………...... 140 1.4 Liquid and solid precipitation on forest glades ………………………………. 141 1.5 Interception of precipitation by the forest canopy, penetration of precipitation under the canopy ……………………………………………………………... 146 1.6 Determination of liquid precipitation under the forest canopy and quantity of intercepted precipitation ……………………………………………………… 151 1.7 Stemflow ……………………………………………………………………... 152 1.8 Estimation of precipitation in a field catchment ……………………………... 153 1.9 The impact of forest on precipitation ………………………………………… 155

Chapter 2 Evaporation from forest ………………………………………………. 167 2.1 General ……………………………………………………………………….. 167 2.2 Potential evapotranspiration …………………………………………………. 178 2.3 Evaporation from a field catchment ………………………………………….. 183 2.3.1 Evaporation measurements from catchment surfaces ………………. 183 2.3.2 Estimation of evaporation from the Usadievsky small experimental catchment using the method with soil evaporimeters ………………. 185 2.4 Calculation of evaporation from the Taezhny forest experimental catchment . 188 2.5 Structure of evapotranspiration from the forest ………………………….…... 192 2.5.1 Evaporation under the forest canopy ………………………………... 193 2.5.2 Evaporation of liquid precipitation intercepted by forest plants ……. 195 2.5.3 Water loss for transpiration …………………………………………. 200 2.6 Ratios between the components of forest evapotranspiration for individual periods ………………………………………………………………………... 215 2.7 Relationship between annual evapotranspiration rates from forested and open territories ……………………………………………………………………... 220

Chapter 3 Water regime of soils in the unsaturated zone ………………………. 223 3.1 General ………………………….……………………………………………. 223 3.2 Estimation of soil humidity and moisture content in soil ...………………….. 225 3.3 Critical water storage in soils and subsoils ………………...………………… 226 3.4 Variations in the regime of soil moisture content and water table affected by forest clear-cutting …………………………………………………………… 229

vii Forest hydrology – results of research in Germany and Russia

Chapter 4 Impact of forest on runoff …………………………………………….. 233 4.1 Impact of forest on mean long-term runoff characteristics …………………... 233 4.2 Experimental studies on the impact of forest on runoff from small catchments …………………………………………………………………… 237 4.3 Soil moisture content and runoff from forest catchments ..…………………... 240 4.4 Runoff changes in the experimental catchments during an observation period 249

Chapter 5 Changes in water regimes of catchments affected by forestry practices ………………………………………………………………... 253 5.1 General ……………………………………………………………………….. 253 5.2 Impact of clear-cutting on the conditions of precipitation accumulation ……. 253 5.3 Evaporation from the forest after clear-cutting and during forest regeneration 254 5.4 Distribution of some water and heat balance components in the felling area .. 260 5.5 Changes in the soil moisture content and the groundwater table after clear- cutting ………………………………………………………………………... 260 5.6 Runoff change caused by forest cutting ……………………………………… 261 5.7 Water regime transformation in the catchment ………………………………. 263 5.8 Heat regime changes in catchments affected by forestry practices ………….. 268

Annex ………………………………………………………………………………... 275 References …………………………………………………………………………... 285 List of figures ……………………………………………………………………….. 289 List of tables ………………………………………………………………………… 293

IHP/HWRP-Berichte ………………………………………………………………. 299

viii

Part I

Results of research in Germany

Objectives of hydrological research in forested catchments

1 Objectives of hydrological research in forested catchments

1.1 Runoff formation and water balances of forested catchments

1.1.1 Runoff formation

The runoff formation can be described as the combination of various runoff processes in which precipitation water is transferred to the brook or stream, either on the forest floor, in the soil or as groundwater. During the runoff formation, the precipitation water is redistributed into evaporation, surface runoff and infiltration, the latter leading to transpiration, deep seepage and lateral flow [Mendel, 2000] (Figure 1.1). The runoff concentration in a catchment mainly depends on the flow velocity of the water in the soil zone (hydraulic conductivity) and on the soil surface (furrows and small channels) [Mößmer et al., 2003]. The runoff concentration is faster in catchments with dense superficial stream networks because the flow paths with low flow velocities (e.g. flow through the unsaturated soil zone and groundwater flow) are shorter and precipitation is, after relatively short distances, concentrated in flow paths with comparatively high flow velocities (superficial channels). Dense superficial stream networks are typically found in catchments with compacted or otherwise low-conductive soils and steep slopes. Here, surface runoff can be the dominating runoff process, especially after heavy rainfalls. In contrast, catchments with profound and uncompacted soils have a high water infiltration and retention potential, and hence lateral flow, seepage and groundwater recharge are the dominating runoff processes. Generally, the ratio of soil-internal runoff processes versus surface runoff increases with decreasing topographic relief, e.g. a flat catchment has a larger soil-internal runoff ratio than a steep headwater catchment. This effect correlates with catchment size for mountainous regions. The vegetation cover largely influences the runoff formation in a catchment. In comparison with agricultural plots, forests retain larger volumes of water, slow down the runoff formation and significantly decrease discharge peaks during floods [LWF, 2003; Mendel, 2000]. This ability of the forests is mainly attributed to the interception of precipitation water in the canopy. The interception capacity of a forest depends on the precipitation type, its intensity and density as well as on the forest age, the species composition, the density and thinning degree [Wetzel, 2005]. On average, forests retain about 4 to 6 mm of precipitation water through interception. Although interception is generally higher in summer, it also plays an

3 Forest hydrology – results of research in Germany and Russia

Figure 1.1 Water balance of a forest ecosystem [from Hegg et al., 2004b] important role in winter since snow evaporates faster from the crown canopy than from the forest floor or from open land [Markart and Kohl, 2006]. Interception either reduces the amount of precipitation water that can generate runoff (in this case, the intercepted water is evaporated from the canopy), or it retards the runoff formation. The latter is the case if the interception storage is depleted or is emptied by the wind, and the intercepted precipitation reaches, retarded, the forest floor either as direct throughfall through the canopy, as canopy drip or as stemflow. The relative importance of the canopy drip and the stemflow varies with stand structure and tree species composition. Due to their umbrella-like crown structures, spruces have a comparatively high canopy drip ratio because precipitation water is drained along the outer crown periphery (Figure 1.2). In contrast, beeches have a funnel-like crown and therefore conduct the precipitation water away, mainly along the stem (Figure 1.2). In comparison to beeches, where stemflow reaches 10 to 20 % of the precipitation water, the stemflow of the spruce plays a negligible role. In any case, interception leads to an intermediate storage of the precipitation and consequently to a spatio-temporal reallocation of the precipitation, which also influences the local water balance. If the water reaches the forest floor, it either infiltrates into the soil or becomes surface runoff. The partitioning into infiltration and runoff is a function of the ratio between infiltration capacity and precipitation intensity [Waldenmeyer, 2003]. During long and intense rain events, the infiltration capacity can be exceeded, either by complete saturation of the soil or if the rainfall intensity exceeds the hydraulic conductivity of the soil, and then surface runoff occurs. The infiltration capacity depends on relief properties (mainly slope), the saturation degree of the soil and its hydraulic characteristics (mainly conductivity). The hydraulic

4 Objectives of hydrological research in forested catchments

Figure 1.2 Canopy drip and stemflow from a spruce and a beech tree [from Hegg et al., 2004b] conductivity of forest soils is generally larger than that of agriculturally used soils, due to their comparatively low compaction and large macropore proportion. The existence of macropores relies on an intact rooting network as well as an intensive soil-biological activity in the forest soil. Therefore, the intense and deep rooting in forest soils mainly explains their generally high hydraulic conductivity. Not only the high conductivity of forest soils generally hinders the formation of surface runoff; due to their large root water uptake, trees regularly empty the soil water storage between successive rain events and thus the infiltration capacity of the soil increases again [Hegg et al., 2004a]. Furthermore, the high porosity, retention capacity and surface roughness of the humus layer allow surface runoff only under extreme conditions, e.g. extreme precipitation or very steep slopes [Witzig et al., 2004]. Once the water is infiltrated, it flows through the soil according to the prevailing hydraulic gradient and eventually contributes to groundwater recharge and the generation of baseflow. Infiltrated water may partly follow the direction of the slope; this is especially the case if the infiltrating water meets impermeable layers. Then, the infiltrated water flows parallel to the slope as lateral flow or interflow [Witzig et al., 2004]. The above-mentioned processes of runoff formation – interception, throughfall/stemflow, surface runoff, infiltration, interflow and baseflow – interact with each other and, depending on the catchment properties as well as on the meteorological conditions, one or the other will dominate [Waldenmeyer, 2003].

5 Forest hydrology – results of research in Germany and Russia

The water storage in a catchment plays an important role for runoff formation as it may retard or even hinder the formation of runoff. In addition to the already mentioned interception storage, retention in superficial depressions, in the litter and humus layer, and especially in the topsoil are of importance [UBA, 2000]. The effectiveness of the water retention in the topsoil depends on the texture of the soil, with deeply developed and fine-textured soils having the highest retention capacity [Hegg, 2006]. In comparison with open land, the water balance of forested catchments is characterised by a high water consumption and storage capacity, which balances the runoff formation. Thereby, forests hinder or at least significantly reduce surface runoff and enhance subsurface runoff, which makes subsurface runoff the dominating runoff forming process [Uhlenbrook and Leibundgut, 1997]. On the other hand, forests in the low mountain ranges often grow on rather shallow soils and thus fast subsurface runoff dominates here [UBA, 2000; Wetzel, 2005]. Numerous studies document the potential of forests to reduce or hinder surface runoff and thereby slow down the runoff concentration and decrease discharge peaks during floods. As a very early evidence, Engler [1919] showed that stormflow peaks were about 30 to 50% lower in completely forested catchments than in partly or non-forested catchments. This effect can be mainly attributed to the high surface roughness of both the forest canopy and the forest floor. This roughness is especially large in multi-layered forests (mixed species/age composition, understorey, moss layer, litter layer, humus layer, mineral soil) [Markart and Kohl, 2006]. However, already Engler [1919], and much later also Markart and Kohl [2006], pointed out that the difference between the different land-use forms decreases with increasing rainfall intensity. Figure 1.3 supports this thesis and includes the role of the soil properties on the runoff formation. Catchment A with good water-storing soils and a low pre-moisture condition has a significantly larger influence on the runoff formation than catchment B with the same soil properties but a much higher pre-moisture condition. Catchment C has soils with a low retention capacity. Here, an effect of the forest on the runoff formation is not evident, even under dry pre-conditions [Hegg et al., 2004a]. If the forest disappears, be it completely or in part, e.g. as a result of clear-cutting, windthrow or vermin infestation, the tree water uptake decreases and subsequently the water level of the free slope water (or perched groundwater) rises. This consequently leads not only to an increased runoff formation, it also enhances erosion and slope slides, which in turn further reduces the retention capacity of the soil.

Figure 1.3 Effect of forests on the runoff formation [from Hegg et al., 2004a]

6 Objectives of hydrological research in forested catchments

1.1.2 Results from regional studies of land-use influence on runoff formation

The Lange Bramke catchment in the Hartz mountains is one of the best known hydrological research catchments in Germany (area 0.76 km², 543 to 700 m a.s.I.). The forest in the catchment was completely cleared after World War II and reforested in 1951 with spruce (Picea abies Karst.). Presently, about 90% of the catchment is covered by monocultural spruce stands. Using long-term observations of the catchment runoff (1948 to 1998), Schwarze et al. [1994, modified] investigated the effects of the stand development on the catchment water balance using the DIFGA method (cf. section 2.1.4 for a model description). DIFGA allows the partitioning of a measured time series of the discharge at a catchment outlet into various runoff components. Furthermore, DIFGA calculates actual and mean monthly water balances. Figure 1.4 shows the proportions of the various runoff components (RD = direct runoff, RG1 = fast baseflow, RG2 = slow baseflow) and the evapotranspiration in relation to the annual precipitation sum as calculated with DIFGA. It is important to note that the precipitation time series does not show a trend whereas all the runoff components display a significant decreasing trend.

100 1196 1359 1125 1322 1184 1134 1270 1331 1204 1399 P in mm/a 90 % off P 80

70 32,4 36,1

60 42,8 43,9 ETR/P 21,2 46,5 48,0 21,3 50,6 51,8 RD/P 50 16,0 52,9 16,0 RG1/P 15,0 14,6 57,4 13,1 RG2/P 40 13,6 12,9 11,3 P

30 29,3 34,3 31,1 30,4 26,5 28,8 29,7 25,9 27,1 20 25,6

10 13,2 12,0 10,1 9,7 9,8 9,7 7,7 8,7 5,6 7,1 0 1949-53 1954-58 1959-63 1964-68 1969-73 1974-78 1979-83 1984-88 1989-93 1994-98

Figure 1.4 Proportions of the various runoff components (RD = direct runoff, RG1 = fast baseflow, RG2 = slow baseflow) and the evapotranspiration in relation to the annual precipitation sum; DIFGA application for the Lange Bramke catchment, 1948 to 1998

Figure 1.5 shows results of the trend analyses for the evapotranspiration and the various runoff components. Detailed results can be found in Schwarze et al. [1994, modified]. The evapotranspiration ratio significantly increased from 34 to 53% of P along with the forest growth, whilst simultaneously the runoff ratio decreased from 66 to 47% of P. Interestingly, the decrease of the total runoff is mainly caused by the decrease in the direct runoff component, which contributes 45% of the total runoff reduction. Obviously, the developing spruce stand demonstrably slows down the runoff formation and therefore has the potential to

7 Forest hydrology – results of research in Germany and Russia

Figure 1.5 Trends in the ratios of total runoff, the various runoff components and the evapotranspiration in relation to the precipitation; DIFGA results for the Lange Bramke catchment reduce storm flow peaks. However, the reduction of the runoff ratio from 22% immediately after reforestation to 12% at the end of the observation period arises mainly from the

8 Objectives of hydrological research in forested catchments reduction of small flood events rather than extreme floods. For the period from 1948 to 1998, 50 flood events were analysed. For the 20 highest floods, no correlation between the runoff ratio and the age of the forest stand could be established. However, for the smaller flood events, a decrease of the runoff ratio with increasing stand age could be observed. This conforms with the findings in Figure 1.3, where the influence of the forest on the runoff formation decreases with increasing precipitation height, and hence increasing flood volume. Along with the successive development of the spruce stands, the water uptake by the trees increases and consequently the groundwater recharge decreases. Thus, the ratio of the fast baseflow component RG2 continually decreases with increasing stand age. Schwarze et al. [1994, modified] carried out several comparative field studies in order to quantify systematic differences in the water balances of forested and agriculturally used catchments. They included six catchments in the Ore Mountains (sub-catchments of the Saidenbach reservoir and the Neunzehnhain reservoir) and two small catchments of the river Váh in the Slovakian Highlands (the Rybarik catchment and the Lesny catchment). In both the German and the Slovakian field studies forested and agriculturally used catchments lie in immediate vicinity of each other and hence are comparable with respect to all environmental conditions (climate, geology, morphology, etc.) except land use (Table 1.1).

Table 1.1 Main characteristics of Saxonian (Saidenbach, Neunzehnhain) and Slovakian research catchments (Rybarik, Lesny)

Catchments

Saidenbach Neunzehnhain Rybarik Lesny observation period 1969-96 1979-96 surface area (km²) 60.7 (4 sub-basins) 13.7 (2 sub-basins) 0.12 0.864 altitude (m a.s.l.) 549 599 384 370 slope (%) 9.3 9.1 2.6 2.6 P (mm/yr) precipitation 931 959 763 763 R (mm/yr) runoff 459 420 196 153 R/P 0.49 0.44 0.26 0.20 main land use agriculture forest agriculture forest geology/soils thin autochthonous soils on gneiss-mica several meters of pleistocene slate allochthonous solifluidal cover of debris with loam

In the German and the Slovakian studies, similar effects of the land use on the catchment water balance and the dynamics of the various runoff components were observed. Figure 1.6 shows monthly mean values of the relative proportion of the various runoff components (calculated with DIFGA) in relation to the monthly precipitation sum. It can be concluded that: • Total runoff (long-term average) is significantly higher in agricultural than in forested areas. • In the summer half-year, runoff totals are equal for the two land-use forms, whereas during the winter half-year the runoff coefficient is 15 to 20% higher in agricultural areas, due to the missing soil cover.

9 Forest hydrology – results of research in Germany and Russia

Figure 1.6 Annual development of runoff coefficients for different runoff components from DIFGA applications for forested and agricultural catchments in the Váh region of the Slovakian highlands (Rybarik, Lesny) and the German Ore Mountains (Neunzehnhain, Saidenbach)

• In comparison with agricultural land use, the annual course of the runoff formation is more damped under forests. • Agricultural use accelerates the runoff, due to higher direct flow proportions than in forested areas. The differences between agricultural and forested catchments are most pronounced during winter with the direct runoff (QD1, QD2) coefficient in agricultural areas being almost double that for forested land. • Direct runoff (QD1, QD2) is almost 50% of the total in agricultural regions and therefore is the dominant component which distinctly controls the dynamics and development of catchment runoff throughout the year.

10 Objectives of hydrological research in forested catchments

• Fast baseflow (QG1) dominates in forested catchments with more than 40% of the total runoff. The recharge rate of QG1 is considerably higher than in agricultural areas, particularly during snowmelt. This leads to a higher slow component flow (QG1, QG2) in early summer in forested areas. Consequently, the beginning of the low flow season (midsummer and autumn), which is characterised by the predominance of the slow groundwater flow QG2, is delayed by several weeks in forests compared to agricultural areas. • Slow baseflow (QG2) is the only component which permanently supplies runoff under both land use forms, even in extremely dry years. Therefore, it is a measure of the minimum groundwater yield from a catchment. On average, the investigated agricultural areas exhibit slightly higher QG2 proportions. However, when looking at the intra-annual fluctuations, it can be seen that a significantly lower runoff formation is observed in agricultural catchments during late summer and early autumn. Consequently, especially in autumn following dry summers, the groundwater yield may be greatly reduced. This effect is enforced by the simultaneously reduced QG1 proportion, as discussed above. Most of these conclusions coincide with the findings in the Lange Bramke catchment, where the observed gradual change in the runoff component dynamics can be attributed to the change in land use from agriculture to forestry [cf. Schwarze et al., 1994 (modified), 1999].

1.1.3 Hydrological implications of forest damage

A further focus of hydrological investigations is damage to forests and its implications for the water balance. In the 1970s to the 1990s, the vitality and production of forests throughout Europe were severely affected by the deposition of air pollutants and acid rain. In Germany, particularly the forests in mountain ridge positions were damaged. As an example, hydrological investigations in the Ore Mountains in Eastern Germany, and specifically in the Natzschung catchment (74.5 km² at gauge Rothenthal), are presented here [for details cf. Schwarze, 2004b]. Hydrological measurements in the Natzschung catchment started in 1928. From 1960 to the early 1980s, the forest was severely damaged by air pollutants. Figure 1.7 shows the development of the affected areas in the catchment. In 1980, about 90% of the forests belonged to the highest level of forest damage. Since 1983, these areas have been successively reforested. In 1994, the catchment was covered by the following land-use forms: mixed forest 17.0%, deciduous forests 10.7%, coniferous forests 18.4%, shrubland 25.5%, meadows/pastures 25.1% and farmland 3.2%. The reason for the high proportion of shrubland is that this category includes damaged areas which, by 1994, had not yet been reforested but on which secondary vegetation had developed by natural succession. A runoff separation with DIFGA, carried out for the period from 1928 to 1995, indicates a continuous increase of the runoff, and particularly the direct runoff component, between 1960 and around 1980 (Figure 1.9, top). Simultaneously, the evapotranspiration was significantly lower due to the vitality loss of the damaged stands. Around 1980, the evapotranspiration started to increase again, caused by both the alternative vegetation which developed during the course of the natural succession (mainly mountain ash and birch) and the reforested stands (mainly larch, Colorado spruce, Omorika spruce and Murray pines). Due to natural succession, the ratio of coniferous to deciduous trees shifted from 90:10, prior to forest dieback, to 80:20. Regardless of the changes in the stand structure, the ratio of the direct runoff remained at an increased level. After the runoff separation with DIFGA, the catchment water balance was simulated with the water balance model AKWA-M [Münch,

11 Forest hydrology – results of research in Germany and Russia

Figure 1.7 Development of forest damage from 1960 to 1994 in the Natzschung catchment (gauge Rothenthal) [from Fichtner, 1995; modified]

1994] in order to quantify the impact of the change in land use on the catchment discharge. The model was applied in various scenario runs with the model parameters being derived from mapped or otherwise available catchment properties. The land-use parameters described a vegetation development which specifically took into account the dieback of the formerly forested areas and their gradual transformation into shrubland. The agricultural land use (farmland, meadow, pasture) was considered to be constant over the modelling period. The transpiration of the forested areas and the shrublands was calculated using the approach of Running and Coughlan [1988]. The vitality of the forest stands was characterised in AKWA- M by their maximum interception capacity, their leaf area index, their rooting depth, their tree stem size, their impact categories as well as parameters which describe the annual course of the aforementioned parameters. These parameters were adapted for various vitality classes, which described changes in the forest vitality caused by both natural vitality fluctuations and irrevocable forest damage. The real evapotranspiration was used as a measure for the forest vitality. Figure 1.8 displays the development of the normalised vitality (value range 0 to 120%). The total evapotranspiration in the catchment is composed of the evapotranspiration of the various sub-areas with their specific land-use dynamics. For the years 1955 to 1960 (period prior to the first forest damage), the AKWA-M simulations yielded the following mean contributions of the different land-use forms to the total catchment evapotranspiration: agriculture 3%, pasture 25%, mixed forests 17%, deciduous forests 11% and coniferous forests 44%. These values were used as starting values (year 1960) in Figure 1.7. The development of forest damage was described in AKWA-M by the variation of the vegetation parameters described above. The simulated annual evapotranspiration was cumulated over the catchment area by weighting the different land-use forms with their respective area size. Despite spreading forest damage, the evapotranspiration of the forested areas increased until 1967, to 114% of the starting value (year 1960). This can be explained by the more favourable energy and moisture supply in these years, which resulted in comparatively high evapotranspiration rates and which compensated the evapotranspiration reduction due to forest damage. From 1967, the spruce stands collapsed very rapidly (Figure 1.7). This caused a decrease of the total catchment evapotranspiration to 43% of the reference value (year 1960) in the mid-1970s. Although forest damage reached its maximum only in 1980, the

12 Objectives of hydrological research in forested catchments

Figure 1.8 Cumulative vitality of the vegetation in the Natzschung catchment weighted by the area proportion of the respective land-use class evapotranspiration started to increase already in 1975. This was due to the development of an alternative vegetation on the cleared stand-wood areas, as already discussed above. Since the total catchment evapotranspiration is very much influenced by the vitality of the coniferous forest stands, the "stand vitality" can be expressed by a combination of the total catchment evapotranspiration and the degree of forest damage. The reference vitality (year 1960, prior to the first forest damage) is set at 100%. The vitality of the stands was then classified as follows: • 80 to 120% natural fluctuations due to varying meteorological conditions • 20 to 80% heavily to slightly affected vitality • 0 to 20% clear-cut or complete stand dieback and extremely affected vegetation. The development of the vitality of the different land-use types as used in the AKWA-M scenario is shown in Figure 1.8 where the vitalities are weighted by their area percentage in the catchment and added up to a "total catchment vitality". Figure 1.9 shows the results of the two AKWA-M scenarios "with land-use dynamics" and "without forest dieback" and compares them with the results of the runoff separation with DIFGA. It can be seen that the AKWA-M scenario "with land-use dynamics" simulates the impact of forest damage on the runoff formation very well. The AKWA-M scenario "without forest dieback" simulates the runoff components for the case that the forest is intact over the whole modelling period. The results of this model scenario prove that, due to the damage to the vegetation, the evapotranspiration decreased by 15% and, at the same time, the runoff increased in the same order of magnitude. The simulations presented above showed a significant impact of the extensive dieback or re- growth of large forested areas on the water balance and the runoff formation of a catchment.

13 Forest hydrology – results of research in Germany and Russia

Figure 1.9 Water balance components of the Natzschung catchment for the period from 1961 to 1992, calculated with a) runoff separation method DIFGA, b) AKWA-M scenario "with land-use dynamics", c) AKWA-M scenario "without forest 14 dieback" Objectives of hydrological research in forested catchments

However, typical land-use changes, e.g. nature-oriented forest conversion, bankside protective shrubs along brooks, conversion of fields into forests, are most often gradual processes, which might take place only in small areas or not in optimal locations, due to political or economic reasons. The impact of such land-use changes is often over-estimated. Schwarze et al. [2004a] used AKWA-M to evaluate the impact of gradual land-use changes on the water balance of the Flöha catchment in the Ore mountains (gauge Borstendorf, catchment size 643 km³). Presently, the land-use in the catchment is, without considering the settled areas, agriculture on 24% of the area, grassland on 36% and forest, mainly spruce, on 40%. In scenario simulations, the present land use was modified according to Table 1.2.

Table 1.2 Overview of the land-use scenarios applied in the AKWA-M simulations

Variant Modification 0 present land-use scenario 1 1-1 fields converted to 20 % more grassland 1-2 fields converted to 20 % more forest 1-3 grassland converted to 20 % more forest 1-4 coniferous forest converted to 40 % more mixed forest 1-5 area of sealed areas (settlements) doubled scenario 2 2-1 fields converted to 27 % more grassland 2-2 fields converted to 24 % more forest 2-3 grassland converted to 40 % more forest 2-4 coniferous forest converted to 60 % more mixed forest

The water balance calculated with the runoff separation method DIFGA was used as a reference state for the scenario simulations. Figure 1.10 shows the DIFGA results and the simulations with AKWA-M for the present land-use (variant 0). The AKWA-M results agree relatively well with the observations (DIFGA). This is also indicated by the statistical measures summarised in Table 1.3 (calculation basis: monthly values). For the slow groundwater component RG2, the variation coefficient cv varies between 0.295 (-1% compared to the reference variant 0) and 0.306 (+2.6%). The conversion from grassland to mixed forest (variant 2-3) resulted in the lowest groundwater recharge (118 mm/a) and conversion from coniferous forest to mixed forest (variant 2-4) in the highest groundwater recharge (122 mm/a). For the fast groundwater runoff RG1, cv varies between 0.279 (-1%) and 0.284 (+0.7%). Analogous to RG2, the conversion from grassland to mixed forest (variant 2-3) resulted in the lowest runoff (225 mm/a) and the conversion from coniferous forest to mixed forest (variant 2-4) in the highest runoff (232 mm/a). The direct runoff has cv values between 0.373 (-3.9%) and 0.399 (+2.8%). Not astonishingly, the highest direct runoff occurs in the scenario variant 1-5 (doubling of the settled areas) with 87 mm/a. The conversion from grassland to mixed forest (variant 2-3) results in the largest reduction of RD (-4.8%) with 79 mm/a compared to 83 mm/a for the present land use. To conclude, it can be said that the differences in the water balances between the various land-use scenarios are marginal. The maximum deviations from the reference state are in the range of ± 5 mm/a for the runoff components and between -2 and +11 mm/a for the evapotranspiration. This means that the considered land-use changes will most likely not result in significant changes in the catchment water balances and runoff formation. Our findings agree with the results of

15 Forest hydrology – results of research in Germany and Russia

Lahmer [2004] for the Stepenitz catchment in Northern Brandenburg and the Obere Stör catchment in Schleswig-Holstein.

Figure 1.10 Comparison of water balance terms calculated with DIFGA from the observed discharge and simulation results of the AKWA-M scenario 0 (present land use)

Table 1.3 Water balance components as calculated with DIFGA and with the various AKWA-M scenarios and their statistical measures (x = arithmetic average, s = standard deviation, cv = variation coefficient = s/x)

variant RG2 RG1 RD ETR x s cv x s cv x s cv x s cv DIFGA 121 36.0 0.297 230 64.2 0.280 82 32.0 0.389 535 75.5 0.141 0 121 35.9 0.298 230 64.8 0.282 83 32.2 0.388 534 30.3 0.057 1-1 120 35.9 0.298 230 64.8 0.282 84 32.5 0.388 534 30.7 0.057 2-1 120 35.9 0.298 229 64.8 0.282 84 32.5 0.387 534 30.8 0.058 1-2 119 35.9 0.302 227 64.3 0.284 82 32.2 0.393 540 30.6 0.057 2-2 118 35.8 0.303 226 64.3 0.284 82 32.2 0.394 542 30.8 0.057 1-3 119 36.0 0.302 227 64.3 0.283 81 31.9 0.393 540 30.1 0.056 2-3 118 36.0 0.306 225 64.0 0.284 79 31.6 0.399 546 30.1 0.055 1-4 121 35.9 0.296 231 64.7 0.280 83 32.2 0.387 532 30.0 0.056 2-4 122 35.9 0.295 232 64.7 0.279 83 32.2 0.387 531 29.8 0.056 1-5 120 35.7 0.298 228 64.3 0.282 87 32.5 0.373 533 30.2 0.057

16 Objectives of hydrological research in forested catchments

1.1.4 Residence and transit times

As already mentioned, forest soils are characterised by a large proportion of macropores, which generally reduces the duration of the water storage in the topsoil and leads to a fast drainage of the water into deeper soil layers. However, beyond the sphere of influence of the macropores, the texture of the soil plays an increasing role. The mean residence time of the water is higher in fine-textured soils with a low hydraulic conductivity. Similarly, soils with a large depth development increase the residence time of the water in the soil zone. Furthermore, Hegg et al. [2004a, b] showed that the type of vegetation growing on a soil significantly influences the soil-water retention. They found that both the shape of the crown and the rooting depth governed the residence time of the water in the soil zone as well as the amount of seepage water. For example, beeches with their generally very deep rooting system can take up water from a much longer seepage path than is the case with spruces, which usually have a rather shallow rooting system. During infiltration and flow through the soil zone, the water can react with the humus layer and the mineral soil. The intensity of these reactions depends on the duration of the contact between water and the soil aggregates. For example, the element concentrations of the infiltrating water is largely modified in clay rich soils with low hydraulic conductivity. On the other hand, seepage water from soils with high hydraulic conductivity shows a less modified chemical composition. An exception are karst formations where the water can be heavily modified despite generally high flow velocities [Greiner and Schmidt, 2006]. The vegetation growing on the soil also modifies the chemical composition of the seepage water. The amount of the element uptake as well as the selection of specific elements depends on the vegetation species, its vitality and growth status [Greiner and Schmidt, 2006]. In the following, results of the Wernersbach forest-hydrological study are presented as an example for a complex analysis of the runoff dynamics and the residence times. The ongoing investigations in the Wernersbach catchment started in 1967 and have been carried out by the Institute of Hydrology and Meteorology of the TU Dresden. A comprehensive description of the experimental site is given in Bernhofer [2002]. The tracer-hydrological studies are discussed in detail by Schwarze et al. [1995]and Schwarze [2002, 2004a, 2005b]. The study aims at describing the processes of the matter cycling and transport in a forested catchment. For this, all relevant information regarding flow paths, residence times and source areas are collected. The measuring concept and the subsequent analyses conform to Figure 2.4. The present database comprises long time series of the brook and spring discharges, precipitation and various other hydrological and meteorological data, including isotope measurements, all with a high resolution in space and time.

Study area The Wernersbach catchment is situated in the Tharandt Forest near Dresden. It is 4.57 km² in size and is completely covered by forest (35% pure spruce stands, 47% mixed spruce stands with less than 10% deciduous trees, 18% mixed spruce stands with more than 10% deciduous trees). The underlying bedrock is a rhyolithe, which formed in the Upper Carboniferous and which is partly overlaid by thin sandstone formations. During low flow periods, the catchment runoff is almost exclusively generated by about 40 springs. These springs can be grouped into (i) point springs in the joint and fault system of the rhyolithe, and (ii) diffuse contact springs above the transition zone from the highly conductive sandstone to the almost impermeable rhyolithe.

17 Forest hydrology – results of research in Germany and Russia

Isotope measurements Since 1959, the concentration of the environmental isotope tritium in the precipitation has been measured in Freiberg (about 15 km from the Wernersbach catchment) (Figure 1.11). The measurements show the tritium contamination of the atmosphere caused by the nuclear weapon tests after World War II. Since the Nuclear Test Ban Treaty in 1963, the tritium concentrations continually decreased due to the radioactive decay of tritium without, however, reaching its former level. Atmospheric processes lead to a periodicity in the tritium concentrations within a year.

10000 [T.U.]

1000

100

1 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004

Figure 1.11 Tritium concentration in the precipitation, Freiberg station [from Hebert, 1990, modified]. The unit T.U. (tritium unit) is equivalent to 1 T.U. = 1 3H atom per 1018 1H atoms.

The tritium concentration in the catchment runoff was first observed in 1971 and has been continuously measured since 1991. Special attention was paid to the tritium concentrations in the low flow period (Figure 1.12). Further tritium measurements were carried out for several sub-catchments of the Wernersbach catchment as well as for various springs and the groundwater. Besides tritium, 18O concentrations in the precipitation and discharges have been measured since 1989 (Figure 1.13). For the subsequent analyses, these measurements were supplemented with data from the Berlin and Hof stations for the years 1982 to 1989. The isotope 18O is very stable and therefore its atmospheric concentration is relatively constant over long periods and shows only variations within a year.

18 Objectives of hydrological research in forested catchments

20 [T.U.] 15

0 Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. Sep. Mrz. 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97

Figure 1.12 Tritium concentrations in the Wernersbach brook for low-flow periods (selection for 1990 to 1997)

-5

-10 18O in ‰ δ

-15

18 O Output in Q 18 O Input in P

-20 Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. Okt. 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96

Figure 1.13 Input and output concentrations of 18O for the Wernersbach catchment. The 18O content is not measured in terms of an absolute concentration, but rather as a deviation from a defined standard concentration.

19 Forest hydrology – results of research in Germany and Russia

Results of the isotope analyses The isotope measurements were analysed using the software MULTIS [Richter and Szcymczak, 1995; Richter et al., 1993], which interprets the difference between the input concentrations and the output concentration (attenuation, phase shift, cf. Figure 1.13) under consideration of various common flow models. The analyses included measurements at three rhyolithe springs (Q6, Q9, and Q10), a sandstone spring (Q21) and in the brook water at the catchment outlet. Firstly, tritium measurements were used to estimate the mean residence time of the slow groundwater runoff RG2. For this, the output time series included both the brook water at the catchment outlet, for times when the fast runoff components approached zero, as well as springs from the cleft groundwater (rhyolithe springs Q6, Q9, and Q10). Firstly, the tritium concentration in the groundwater recharge was estimated from the tritium concentrations in the precipitation (Figure 1.14). As an example, Figure 1.15 shows how the tritium concentration is altered during the passage of infiltration water through the soil zone.

Figure 1.14 Tritium input into the groundwater in the Wernersbach catchment as compared to the tritium concentrations in the precipitation at the Freiberg station

According to the 3H analyses, the slowest runoff component in the discharge at the catchment outlet has a mean residence time of 38 years, referring to the total catchment area (Figure 1.16). However, the mean residence time of the spring water can be expected to be in the range of 10 years. The value of 38 years results from the mixing of very old spring water (spring Q10) and much younger water from the other springs. The slow groundwater runoff RG2 of 29 mm/a corresponds to an average discharge of 4.2 l/s. The analysed springs contribute with varying intensities to this value: • spring Q10 0.36 l/s with 650 years mean residence time • spring Q21 0.50 l/s with 11 years mean residence time • spring Q9 0.17 l/s with 8 years mean residence time

20 Objectives of hydrological research in forested catchments

• spring Q6 0.79 l/s with 9 years mean residence time • other springs 2.38 l/s (no isotope measurements)

Figure 1.15 Alteration of the tritium activity during the soil passage at a measuring profile in the Wernersbach catchment

Figure 1.16 Dating of the mean residence time using an exponential regression model – brook water at the catchment outlet

21 Forest hydrology – results of research in Germany and Russia

The rhyolithe spring Q10 is a cleft spring which shows a very constant yield and does not respond to precipitation events. Water temperature, electrical conductivity as well as inorganic substances show very low fluctuations. These facts indicate that the spring water has a very long residence time in the catchment. This thesis is supported by the tritium analyses which yielded a mean residence time of 650 years (Figure 1.17, left). However, this estimate is only an approximation since tritium is not the optimum tracer for the dating of such long residence times. With a high probability, the residence time is greater than 100 years. Presently, other environmental tracers are being evaluated with regard to their suitability for the more precise dating of long residence times. The contact spring Q21, which yields water ponded in the sandstone above the comparatively impermeable rhyolithe bedrock, shows much higher tritium concentrations. The estimated residence time of the spring water is about 10 years (Figure 1.17, right).

Figure 1.17 Dating of the mean residence time using an exponential regression model – left: rhyolithe spring Q10, right: sandstone spring Q21

Subsequent to dating the residence time of the slow groundwater component, the residence time for the total indirect runoff, i.e. fast subsurface runoff, fast groundwater runoff and slow groundwater runoff, was estimated using 18O measurements of the brook water. The analysis of 20 flood events of varying intensities (cf. example in Figure 1.18) showed that only 14% of the total runoff during the flood events was contributed by direct runoff, i.e. 86% of the runoff in Wernersbach stemmed from indirect runoff components. The mean residence time of the total runoff in the Wernersbach catchment was estimated at 5.2 years. These results were also proven by the hydrograph separation with DIFGA (Table 1.4). Summarising, the average annual water balance, with a mean precipitation sum of P = 867 mm/a, can be separated into a superficial direct runoff RD1 of 34 mm/a, a fast subsurface runoff RD2 of 101 mm/a and a groundwater runoff RG of 120 mm/a (with RG1 = 91 mm/a and RG2 = 29 mm/a). The evapotranspiration, as remaining term of the water balance, amounts to 612 mm/a (Table 1.4).

22 Objectives of hydrological research in forested catchments

Figure 1.18 Separation of the direct runoff of Wernersbach using the environmental isotope 18 O for the flood event from 12 April to 13 April 1994 (Qmax = 1356 l/s with recurrence interval = 5 yrs); yellow: direct runoff proportion = 12%, red: indirect 18 16 o runoff proportion = 88%, black: ratio O/ O in the precipitation in /oo, expressed as deviation from the international standard V-SMOW, green: ratio 18 16 o O/ O in the brook discharge in /oo, expressed as deviation from the international standard V-SMOW

Table 1.4 Results of the hydrograph separation for Wernersbach with DIFGA (years 1968 to 1994) and comparative results from the isotope analyses

runoff in percentage of recession constant mean residence time runoff component mm/a total runoff in % (DIFGA) in d (isotope anal.) in yrs direct runoff RD1 34 14 CD1 = 0 0 subsurface runoff: 221 86 fast subsurface runoff RD2 101 40 CD2 = 10 <0.5 fast groundwater runoff RG1 91 35 CG1 = 18 <2 slow groundwater runoff RG2 29 11 CG2 = 300 38

1.2 Chemical deposition and seepage water quality in forests

On its way through the atmosphere, water absorbs various dissolved and undissolved substances and transports them over large distances [Worch, 1997]. Thus, the atmosphere is a large source of highly dispersed substances and therefore responsible for the element input into ecosystems. Substances are mainly washed out from the atmosphere through rain and

23 Forest hydrology – results of research in Germany and Russia snow (wet deposition) or fog and smog (moist deposition). Dry deposition is the deposition of dust-bound particles and gases.

Precipitation permanently contains traces of O2, CO2 and atmospheric nitrogen. Moreover, precipitation contains remarkable amounts of neutral salts originating from sea spray and/or calcium, magnesia and potassium compounds originating mainly from soil dust. Thus, precipitation is an important source in the element budgets of forest ecosystems. The composition and concentration of the deposited substances directly depend on the substance composition in the atmosphere, the cloud elevation, the wind direction as well as the type of precipitation [Alekin, 1962]. The element budget of forest ecosystems, i.e. the input from the atmosphere and the uptake by the vegetation and the storage in the soil, is more or less stable under natural conditions. Diverse filter and buffer mechanisms exist, which mitigate the disturbances caused by varying natural and, specifically, climatic boundary conditions. Thus, forest ecosystems tend to provide stable and favourable living conditions for well-adapted biological populations. The management and yield prognosis in forests is based on the knowledge of a stable equilibrium between site conditions and biological populations. However, anthropogenic acid and nitrogen deposition has disturbed this equilibrium and substantially changed the "chemical climate" of forest ecosystems in Central Europe. This fast alteration is not part of our "historic knowledge" about the behaviour of forests and therefore adequate diagnostic tools in ecosystem research have been hardly developed. Furthermore, management practices which are suitable to stabilise the forest ecosystems and to counteract those developments still need to be developed and tested. Deposition on forests is higher than deposition on open land because aerosols and particles are accumulated, as dry deposition, on the large intrinsic surface of the crown layer [Ulrich, 1983; Hug et al., 2005; Staelens, 2006]. In comparison with broadleaved stands, coniferous stands show higher deposition rates, especially during the dormancy period in winter [de Schrijver, 2007; Staelens, 2006]. Most components of deposition interact with the crown tissue. Basic cations, predominantly potassium and calcium, are largely leached from the crown area. This is attributed to acid buffering and thus normally results in lower proton activity in throughfall water as compared to open-land deposition. Also, the direct uptake of plant nutrients, and especially nitrogen compounds like ammonium, in the crown area is a quite common observation. In the Solling ecosystem study, Eilers et al. [1992] calculated the uptake rate for total nitrogen to be 9 to 13 kg a-1 ha-1. A practical approach to assess the true deposition rate is the so-called crown budget model, which was developed by Ulrich [1994]. The crown budget model uses an accumulation factor for Na, which is assumed not to interact with the plant tissue, for assessing the accumulation of the other deposition components by multiplying the open-land bulk deposition with this accumulation factor. This procedure relies on the precondition that the precipitation contains a measurable amount of Na, which can be a problem in regions with very continental climate where Na concentrations in the precipitation reach the analytical detection boundary. Furthermore, Draaijers et al. [1996] and Staelens [2006] pointed out that the crown budget model tends to underestimate the real deposition because Na is bound to comparatively large aerosols and is therefore deposited faster than elements which are bound to smaller particles. An alternative to the crown budget model was proposed by Draaijers et al. [1996] in the form of the EDACS (European Deposition maps of Acidifying Components on a Small scale) [van Pul et al., 1995] regression model. In EDACS, dry deposition is calculated using individual deposition velocities, which were experimentally determined, for the various chemical

24 Objectives of hydrological research in forested catchments compounds. Similar to the crown budget model, EDACS assumes that the dry deposition is completely washed off from the tree canopy by precipitation and reaches the forest floor with the throughfall. Uncertainties in the model predictions are primarily caused by the model parameter "roughness length", which is used as a proportional factor for the turbulent air flux above the crowns and the related deposition velocity. This parameter cannot be measured directly and must be approximated from information on tree species composition, stand structure and stand height. Figure 1.19 shows the relative distribution of acid and nitrogen depositions over Europe modelled with EDACS model [Draaijers et al., 1996]. According to the model results, deposition intensities are highest in Central Europe and decrease to non-hazardous amounts towards the periphery of Europe [Mueller-Edzards et al., 1997]. In Central Europe, impacts of depositions on forest ecosystems are to be expected, especially in regions where the deposition load has been surpassing the buffering and uptake capacity of forest ecosystems over a long time.

The acidifying effect of deposition on the soil and the hydrosphere is mainly caused by SO4 and NO3, which are the mobile anions of strong mineral acids. During the deposition history of the last 20 to 30 years, the composition of the deposited substances changed substantially. The discussion on forest dieback in the 1980s resulted in an effective clean-air policy which regulated by law the purification of exhaust air of large power plants. This policy led to a large reduction of SO4 emissions. However, the nitrogen deposition still remains at a high level. About half of the nitrogen deposition originates from individual traffic (high temperature combustion of fuel in cars, thus oxidizing N2 to NO3) and from stock breeding in 1 agriculture (emission of NH3, which is deposited mainly in liquid form as NH4 ). Figure 1.20 shows the development of the total acid and nitrogen depositions in Baden- Württemberg between 1987 and 2000. In the mid-1980s, total acid deposition amounted to -1 -1 about 1 to 2 kmolc ha a . In the central plains and in the western Black Forest, which are both under the influence of industrial urbanization, total acid deposition was dominated by direct proton deposition. In the eastern plains of Baden-Württemberg, extensive stock breeding and agriculture leads to an acid deposition which is clearly dominated by ammonium. In all regions, the total acid depositions decreased by about 50% between 1987 and 2000. In contrast, the N deposition remained at a high level in all observed regions.

1.2.1 Soil acidification

Frequently, the seepage water from forest ecosystems is subject to indirect strain caused by the ongoing acidification of the soils. In Central Europe, the typical soil development started at the end of the last glacial period, around 10,000 to 15,000 years ago. In those comparatively young soils, acidification should have remained, apart from singularities, in a range of pH values between 6 and 5. On this pH level, carbon acid is active, and base saturations (percentage of neutral cations stored at the soil exchanger surfaces) should lie above 40% [Ulrich, 1995]. Under natural conditions, soil acidification is mainly caused by

1 In forest ecosystems, this slightly basic compound acts as an acid because it is taken up by plants and assimilated as amino-N (NH2). Thus, plants have to set free two protons per NH4 molecule, which provokes an acidification pulse directly in the rhizosphere [Ulrich, 1994].

25 Forest hydrology – results of research in Germany and Russia

Figure 1.19 Mean total acid and total nitrogen deposition between 1985 and 1995 in Europe [Draaijers et al., 1996] 26 Objectives of hydrological research in forested catchments

Figure 1.20 Total acid deposition (through protons and NH4) and SO4 deposition between 1987 and 2000 (left) and total nitrogen deposition (right) in three landscapes of Baden-Württemberg organic acids. This acidification process (podsolisation) is normally restricted to the upper 30 to 50 cm of the soil because organic acids typically polymerise below this depth. Under the influence of stronger acids like sulphate or nitrate, acidification intensities can reach pH values substantially below 5 and furthermore can propagate into deeper soil horizons. As a consequence, cation acids like iron, manganese, aluminium or heavy metal cations are mobilised and leach from the soil zone [Bihl, 2004]. Wolff and Riek [1998] observed that, although acid deposition was already decreasing by this time, the water quality, with respect to pH, N, SO4 and Al, in the German low mountain ranges generally did not show a positive development. The ongoing acidification leads to an obvious leakage of alkaline cations (K, Mg, Na), which affects root growth and activity and eventually derogates the filter functions of the soil and the plants [Jordi, 2005]. Thereby, the natural nutrient cycle becomes decoupled and, due to the retreat of the roots from the acidified and base-depleted mineral soil, the tree nutrition is more or less restricted to the current decomposition of the litter, which is also limited due to the acidification [Buberl et al., 1994]. This disturbance of the soil-plant interactions causes an increased element leakage and relocation of substances

27 Forest hydrology – results of research in Germany and Russia into deeper soil layers, which constitutes a serious threat to the seepage water and the groundwater. There is some discussion in the scientific community about whether the acidification intensities measured in soil chemical surveys are caused by natural acidification processes or by anthropogenic acid depositions. A comparison between actual and "historical" pH data can provide some insight into the acidification dynamics of the past decades. Already in the 1920s, Frank [1927] systematically measured the pH of various soil substrates in the Black Forest for various geological conditions. Because he described the sampling regions and his measuring methods very carefully, Buberl et al. [1994] were able to carry out comparable measurements (Figure 1.21). The frequency distributions in Figure 1.21 show that between 1927 and 1992 the range of observed pH values narrowed in all analysed soil substrates. Furthermore, a sharp pH decrease of about 1 to 2 pH units was observed on the Gneiss, Moraine Loam and Triassic Sandstone substrates. Only on the poor Granite sites were the ranges of pH values in 1927 and 1992 comparable. This can be explained by the fact that on the coarse, base-poor granite sites podsolisation processes led to a depletion of the basepool already prior to the massive anthropogenic immission influences whereas on all other sites, this acidification process started only in recent decades under the influence of acid immissions. The pH decrease in this comparably short period is by order of magnitude faster than the observable natural acidification trend in moderate humid climate zones.

Figure 1.21 Comparison of pH values (relative frequency distributions) measured in 1927 by Frank and in 1992 in the soil chemical survey (BZE) in top soils of the Black Forest on different bedrocks [according to Buberl et al., 1994]

28 Objectives of hydrological research in forested catchments

1.2.2 Base saturation

The ability of soils to neutralise acids is linked to their pool of basic compounds. Exchangeable basic cations are the pool of basidity which can be most easily mobilised and can contribute to neutralisation reactions. Therefore, the base saturation2 is a good indicator of the base/acid status of the soil. The depth gradients of base saturations are an integrating measure of the summarised effects of the individual acidification history of a forest site [von Wilpert, 1996]. Figure 1.22 shows the individual and averaged depth gradients of base saturations at 101 carbonate-free soil profiles in coniferous stands in Baden-Württemberg [von Wilpert, 2002].

Figure 1.22 Base saturations of 101 carbonate-free soil profiles in Baden-Württemberg; dark lines = averages, bars = ± standard deviation [von Wilpert, 2002]

Only 15% of the profiles show a depth development of the base saturation which would be expected as the result of a natural soil acidification. At about 15%, the neutral cation depletion already surpassed the rooting zones. About 70% of the sites show an almost total depletion of the exchangeable pool of neutral cations, down to the interface between soil and bedrock. The large proportion of deeply acidified soil profiles is a strong indicator for the impact of anthropogenic acid deposition. This input of very mobile anions of strong mineral acids forwards the acidification front throughout the total depth of the soil layer into the aquifers. At these sites, the soils have lost almost all acid-buffering capacity.

2 Percentage of neutral cations in the total amount of cations which are adsorbed at the exchanger surfaces in the soil.

29 Forest hydrology – results of research in Germany and Russia

1.2.3 Nitrogen saturation

Nitrogen depositions affect forest ecosystems in two ways. On the one hand, nitrogen acts in most cases as an acidifying agent. On the other hand, it is a macro-nutrient for plants. Forest vegetation is well-adapted to poor nitrogen supply because in natural ecosystems nitrogen availability is limited due to the high energy demand of nitrogen assimilation. Anthropogenic nitrogen inputs lead to a substantial increase of nitrogen availability. This causes an increase in growth of almost all plant species and a tendency towards nutrient imbalances. If nitrogen saturation surpasses the uptake capacity of forest ecosystems, NO3 can leach from the rooting zone. The deposition threshold above which NO3 leakage occurs is not very sharp and largely depends on the individual ecosystem characteristics. In most Central European case studies, -1 -1 NO3 output occurs above a total nitrogen input of between 10 and 25 kg ha a [Aber et al., 1989; Dise et al., 1998]. An indicator for an increasing nitrogen saturation are increasing nitrogen contents in the humus layer. Hildebrand [1994] measured C/N ratios and compared them with C/N ratios measured by Evers et al. [1968] about 30 years ago. Figure 1.23 shows the frequency distributions of the C/N ratios in the humus layer observed in the two measuring campaigns. Between 1968 and 1992, the C/N ratios decreased by about 10 to 15 and the frequency distribution markedly shifted to the left.

Figure 1.23 Absolute frequencies of C/N ratios in the humus layers of spruce stands in Baden-Württemberg in 1992 compared to 30-year-old data [from Hildebrand, 1994]

A further indicator of an increasing nitrogen saturation is the increasing NO3 content in the seepage water from forest soils [von Wilpert et al., 2000]. During the last two to three decades, an increasing trend in NO3 concentrations was observed in many springs in the Black Forest. Figure 1.24 shows a frequency distribution for linear trends of NO3 concentration for the years 1950 to 1999 of 55 springs and groundwater wells in the Black Forest. This example shows that the nitrate problem is no longer restricted to agriculture, but increasingly arises in forested areas. In order to protect the groundwater and drinking water quality in the long run, the stabilisation of the forest soil systems is one of the most urgent needs [Bihl, 2004].

30 Objectives of hydrological research in forested catchments

Figure 1.24 Frequency distributions of trends in groundwater NO3 concentrations in forest areas of the Black Forest region. Data from the groundwater survey of the State Environmental Agency Baden-Württemberg (LUBW) for the years 1950 to 1999 [von Wilpert and Zirlewagen, 2004]

1.2.4 Element budgets and their disturbances

The chemical composition of the soil solid phase stands in a well-defined relation to mean element concentrations in the soil liquid phase. Thus, the observations of element concentrations and element fluxes in the soil liquid phase can serve as a sensitive indicator for ongoing changes in element budgets of forest ecosystems. Short-term element fluxes can be observed using a high spatial and temporal resolution of measurements. For the EU Level II plots, element fluxes were calculated by multiplying element concentrations of soil-water samples, collected bi-weekly by suction cups, with calculated water fluxes [von Wilpert and Zirlewagen, 2007]. A description of the modelling concepts is given in the Conventwald case study (sections 2.2.5 and 2.2.6). Mean annual element budgets were derived from the modelled element fluxes. These element budgets indicate local sources and sinks along the vertical flow path and thereby represent the actual ecosystem status and its driving forces (e.g. deposition input, mineral weathering). Furthermore, key processes of the site development become perceptible. First results of this attempt are presented in Figure 1.25, where sites with different deposition regimes are compared. The flux densities are related to the intensity and the composition of the depositions. In Rotenfels, which received in the recent past very high deposition amounts and is still the -1 -1 site with the highest acid load (total acid deposition = 1.7 kmolc ha a , nitrogen deposition = 16.4 kg ha-1 a-1), the leakage from the rooting zone is mainly driven by organic anions and nitrate, which are accompanied by aluminium and protons. The available Mg and Ca sources are reduced to marginal remainders. The illuvial organic horizon (Bhs) of the podsol dissolves and moves downwards due to the high acidity strength in the whole solum. -1 -1 In Altensteig, the site with the lowest deposition load (total acid deposition = 0.7 kmolc ha a , nitrogen deposition = 9.0 kg ha-1 a-1), the element leakage from the rooting zone is clearly

31 Forest hydrology – results of research in Germany and Russia

Figure 1.25 Mean annual element flux densities at the interfaces between ecosystem input (OP: open field precipitation and Thr: throughfall) and the seepage water below the rooting zone for the years 1996 to 2006 (left and centre) and 1994 to 2006 (right). Rotenfels: Podsol with high water permeability and low buffer capacity; Altensteig: seep-retarded Gleyic Cambisol with moderate buffer capacity; Ochsenhausen: Gleyic Luvisol with seep-retarded subsoil, high buffer capacity

lower than in Rotenfels. It is mostly driven by the anions SO4 and NO3, which are accompanied by Al and comparatively high fluxes of Ca and Mg. In Altensteig, the mineral soil is a slight source of SO4, especially in the upper 40 cm. Since in this depth also organic anions are mobilised, SO4 is likely to be mobilised from organic binding forms and thus can be thought to originate mainly from an internal sulphur cycle [Prietzel, 1996]. The transport of organic anions is limited to the upper soil layer (< 30 cm), which is typical for the natural soil-forming process of podsolisation. In Ochsenhausen, the element flux is characterised by an excessive nitrogen availability (total -1 -1 -1 -1 acid deposition = 1.5 kmolc ha a , nitrogen deposition = 32.0 kg ha a ), leading to high NH4 fluxes at the soil surface. NH4 is mainly taken up by roots in exchange with protons, resulting in an equivalent increase of protons in the soil solution. The anions of the seepage water are mainly SO4, organic anions and NO3, accompanied by an extremely high leakage of Mg and Ca. The complete rooting zone is a strong source of SO4; the SO4 flux increases from the upper soil to the output interface by the factor 2 to 3. This indicates a re-mobilisation of formerly stored sulphur, which originated mainly from deposition and which is mobilised when sulphur deposition decreases [Alewell et al., 2000]. Although a remarkable Al translocation occurs along the soil passage, Al leakage from the rooting zone is low, compared to the heavier acidified site Rotenfels. From Figure 1.25, key processes of soil acidification and site development can be identified: • In Rotenfels, acid deposition caused high amounts of mobile acidity in the soil solution, which mobilised short-chained organic polyanions from the sesquioxid enriched horizon (Bhs) and thereby propagated podsolisation towards the subsoil where it would not have

32 Objectives of hydrological research in forested catchments

occurred under natural conditions. Since the exchangeable Mg pool is mostly exhausted, deficiencies in forest nutrition are to be expected in the long run. Acid inputs are no longer buffered and are passed on to the lower soil layers both as Al and protons. • Both Altensteig and Ochsenhausen represent an earlier phase of soil acidification, which is characterised by a high leakage of Ca and Mg. • Altensteig already shows a considerable output of acidity below the rooting zone. • Presently, at Ochsenhausen, about 95% of the incoming acid amounts are still buffered in the soil. However, the chemical quality of seepage water is threatened by high fluxes of organic anions, which can serve as a "carrier" for heavy metals like Mn or Pb. The acidification front propagates in the direction of aquifers in the form of the mobile anions of the strong acids NO3 and SO4. • The observed re-mobilisation of sulphur in Ochsenhausen clearly indicates the after-effect of a formerly higher sulphur deposition.

1.2.5 Exceedance of critical loads

The impact of the acid and nitrogen loads on ecosystem functions can be quantified based on the concept of tolerance thresholds or critical loads. Critical loads are defined as the sum of acid and nitrogen inputs which will be buffered or taken up by an ecosystem without a long- term deterioration of essential ecosystem functions [Nilsson, 1986]. In Baden-Württemberg, critical loads were modelled for 300 sampling points using the Steady State Mass Balance (SSMB) model. SSMB assumes a steady state between acidifying/N- eutrophying processes and processes which counteract acidification/N-eutrophication [Becker, 2000; UBA, 1996]. It includes soil-water/solid-phase equilibria, mineral weathering, nitrification and nutrient uptake [Sverdrup and de Vries, 1994]. Mineral weathering is the main process to "recharge" the buffering capacity of the soil after its depletion by buffer reactions. Nitrification releases protons and thereby intensifies soil acidification. Nutrient uptake reduces the availability of buffering neutral cations in the soil. From the difference between the actual total deposition of sulphur and nitrogen and the critical loads modelled with SSMB, critical load exceedances can be calculated. Emission- based deposition values for sulphur and nitrogen, for the 300 sampling points and for the years 1987 and 1997, were extracted from the Europe-wide reference values modelled with the EDACS model in a modified version of Gauger et al. [1997]. Figure 1.26 displays the modelled exceedances of critical loads for all analysed sampling points. From Figure 1.26, a large reduction of critical load exceedances between 1987 and 1997 can be seen. In 1987 critical loads were exceeded at all sampling points, with a mean exceedance -1 -1 value of 2.04 kmolc ha a . In 1997, at about 88% of the sampling points, the deposition load -1 -1 was above the critical loads and the mean of the exceedances was about 0.1 kmolc ha a . In 1987 56% of the critical load exceedances were due to SO4 depositions whereas in 1997, at 89% of the sampling points, SO4 did not contribute to critical load exceedances at all. The large reduction of critical load exceedances demonstrates the success of an air purification policy which predominantly reduced emissions of sulphur oxide. The remaining problem is the rather diffuse nitrogen emissions.

33 Forest hydrology – results of research in Germany and Russia

1987

1997

Figure 1.26 Exceedances of the critical loads of forest ecosystems of total nitrogen and sulphur deposition for the years 1987 (above) and 1997 (below) at 300 sampling points in Baden-Württemberg. The circle size is proportional to the amount of the total critical load exceedances (dark yellow sectors = nitrogen, light blue sectors = sulphur, red dots = no exceedance of critical loads).

34 Objectives of hydrological research in forested catchments

1.2.6 Effect of deposition on seepage and groundwater quality

The observations in environmental monitoring systems and ecosystem case studies support the hypothesis that industrial air pollution has caused a new "chemical climate", which triggers disturbances in the functionality of forest soils and ecosystems [von Wilpert, 2002; von Wilpert and Zirlewagen, 2001]. In recent decades a tremendous soil acidification and a simultaneous depletion of neutral cations has occurred. New pedogenetic processes like the re-dissolution of Bhs horizons take place and cause severe side effects, e.g. metal and DOC leakage to the groundwater. Furthermore, an increasing and severe threat to the water quality in forest areas arises from the ongoing nitrogen saturation, which results in an increasing NO3 concentration in the surface water and in the groundwater. Although the contamination with NO3 and pesticides is still at a comparatively low level in forests (e.g. Gäth and Frede, 1991), the reaction products of soil acidification, and especially the increasing NO3 concentrations, are an increasing problem in forest seepage water. Von Wilpert and Zirlewagen [2004] used multiple linear regression analyses in order to identify the causes for the increasing NO3 concentrations in the groundwater below forested areas in the Black Forest. As a target variable, mean NO3 concentrations (data from the 1990s) in springs and groundwater wells throughout Baden-Württemberg were used. As potential predictors in the regression model, the soil data, pH, C and N content and base cation pools at the exchanger surfaces in the mineral soil, were used. Depositions, assessed on an EMEP grid according to Gauger et al., [1997], critical loads for sulphur and nitrogen, and critical load exceedances, calculated with SSMB by Sverdrup and de Vries [1994], were also used as potential predictors in the regression model. With this data set, a multiple linear regression model was derived which explains 52% of the variance of NO3 concentrations in the groundwater [von Wilpert and Zirlewagen, 2004]. The critical load exceedance for nitrogen, C stocks in the mineral soil (0 to 30 cm), annual precipitation (which can be seen as a surrogate for the plot altitude or the air temperature), the C/N gradient between the humus layer and the uppermost mineral soil (0 to 5 cm), Ca stock in 0 to 5 cm and pH (KCl) in 10 to 30 cm were significant predictors in the regression model. The critical load exceedance for N and the C stock in the upper mineral soil showed positive regression coefficients, all other predictors negative ones. This supports the interpretation that elevated nitrogen depositions as well as high organic material content in the upper soil pose an elevated risk for NO3 leakage. High pH and high Ca availability in the mineral soil provide favourable site conditions for soil-biological activity and thus enhance a stable nitrogen storage. High C/N ratios in the mineral soil reduce the risk of nitrate leaching. According to the regression model, higher precipitation heights result in lower NO3 concentrations, which could be primarily explained by a dilution effect due to higher seepage rates and larger groundwater flow. -1 The average modelled NO3 concentrations (Figure 1.27, left) are in the range of 3 to 11 mg l . The model residuals (Figure 1.27, right) lie in the range of ± 20% of the observed values. On the eastern slopes of the Black Forest, NO3 depositions are very low and the regression model tends to over-estimations. The new risks for ecosystem functions, caused by anthropogenic impacts, are a challenge for forest managers because they have to decide on implementing strategies for counteracting the deposition effects, e.g. with liming programmes or the adaption of silvicultural strategies. They are also a challenge for politicians, who have to promote an effective and problem- adapted air purification policy. Finally, they are a challenge for ecosystem researchers because it is up to them to provide reliable facts for politicians and forest managers. Only then

35 Forest hydrology – results of research in Germany and Russia

Figure 1.27 Model predictions for NO3 concentrations in the groundwater (left) and absolute residuals between observed and predicted NO3 concentrations (right) in the Black Forest (Southwest Germany). For residuals, dark circles indicate the observed values to be higher than the predicted ones, light circles the opposite. Groundwater data from LUBW Karlsruhe [from von Wilpert and Zirlewagen, 2004] can they realise alternative forest practices, which tend to be costly and are often objected to by various interest groups in society.

1.3 Silvicultural management of water quality

As discussed in the previous section, element deposition negatively affects both the soil chemical status and the quality of the seepage water and the groundwater. It is very unlikely that deposition rates fall below the critical value for ecosystem integrity in the near future [Zirlewagen and von Wilpert, 2002]. Therefore, the endangered soil functions must by all means be supported by adapted forest management options, e.g. tree species selection, type of thinning, type of forest regeneration. However, the evaluation of the effectiveness of the various possible management options is rather difficult due to the complex interactions between deposition, site/forest properties and water/element fluxes. The chemical composition of the soil water and the seepage water is influenced not only by the buffer capacity of the soil. In addition, chemical transformation processes during the soil passage as well as the specific soil-physical and soil-hydraulic properties play an important role. Ecosystem studies try to evaluate the relationship between forest status, soil status and water quality. In the following, selected results of various ecosystem studies are presented for various water-preserving forest management strategies.

36 Objectives of hydrological research in forested catchments

1.3.1 Tree species selection for controlling seepage water quality

The amount of the deposited substances depends not only on the direct input through precipitation. It depends also on the ability of the trees to comb the air with their needles and leaves, from which the deposited substances are transported to the forest floor through throughfall and stemflow. Numerous studies, e.g. Einsele [986], Körner [1996], Hegg et al. [2004b], Zirlewagen and von Wilpert [2002], have shown that element concentrations below spruce are much higher than below beech stands (Figure 1.28). This results from the evergreen condition of the spruces and the greater surface of the spruce needles, which are able to comb out more substances from the air, leading to larger element concentrations in the throughfall.

Figure 1.28 Total atmogen acid entry in the Conventwald catchment (average values from 1992 to 1998). Parting line: comparison of forest stands with open land deposition [Zirlewagen and von Wilpert, 2002]

In general, forest ecosystems are able to buffer most of the incoming deposition, depending on the tree species/age composition and the general forest structure. In many ecosystem studies, the site conditions of spruce stands were significantly more affected by deposition than those of beech stands or mixed forests [e.g. Matzner, 1983; Bredemaier, 1987; von Wilpert et al., 2000; Rothe et al., 2002a, 2002b; de Schrijver et al., 2004]. Apart from the tree-type specific deposition with the throughfall, the internal biological processes within the stands play an important role in the matter fluxes of an ecosystem. Spruces not only introduce more substances due to their crown and needle architecture. They also bind less nutrients in their biomass. This can be mainly attributed to their comparatively shallow rooting system. Depending on the buffer capacity of the specific soil, normally much higher element concentrations leak from the rooting zone of spruces and consequently reach the groundwater [Zirlewagen and von Wilpert, 2002]. On eutrophic soils, Mellert et al. [2005b] found a slight tendency of higher nitrate concentrations below deciduous as compared to coniferous stands (Figure 1.29). However, apart from the vegetation condition in a catchments, the physical properties of the soil significantly influence the chemical composition of the seepage water. Irrespective of the vegetation conditions, seepage water and groundwater recharge from clayey and sandy soils as well as soils with a high skeleton content typically show low nitrogen concentration

37 Forest hydrology – results of research in Germany and Russia

-1 Figure 1.29 Pie charts of NO3 concentration (mgl ) in the seepage water, classified according to the soil substrate and the tree species [from Mellert et al., 2005b]

[Mellert et al., 2005a]. Clayey soils have low nitrate concentrations in the seepage water mainly because of their generally low hydraulic conductivity, which hinders an intense leakage and avails denitrification processes because the soil gets easily saturated [Davidson et al., 2003]. Chalky and carbonate-rich soils typically have a low depth development and a high hydraulic conductivity, which increases the risk of nitrate leaching. The influence of the stand structure, i.e. the composition of various tree species and tree ages, is discussed in detail in the Conventwald case study (section 2.2).

1.3.2 Thinning strategies for controlling seepage water quality

If the ecosystem is heavily disturbed, for example as a consequence of a clear-cut or a deforestation through windthrow or vermin infestation, a drastic change both in the water balance and the element fluxes can be observed. Here, the main influencing factor is the extensive loss of the active rooting zone, which leads to a drastic increase of the element leakage. However, Zirlewagen and von Wilpert [2002] observed a fast reduction of the

38 Objectives of hydrological research in forested catchments leakage peaks following a clear-cut. This could be, at least partially, explained by the fast- growing ground vegetation, which developed due to the very good accessibility of light, water and nutrients in the clear-cut area and which subsequently bound large amounts of NO3 and other nutrients. A second explanation of the steep decrease of the NO3 peaks could be the limited pool of mineralisable organic material. In contrast, in areas with windthrow and vermin infestation, which usually cover only comparatively small areas, the fast development of an alternative vegetation is hindered because of the less suitable light conditions in small gaps. The absence of a new vegetation and the remaining crown material and dead roots lead to an enhanced mineralisation and element leakage. Russow et al. [2004] observed an increased nitrogen uptake by the roots with higher soil water saturation. With an increasing soil water content, NO3 is increasingly mobilised and is then available for plant uptake. On the other hand, NO3 leaching increases during extreme rainfall events or during snowmelt and therefore the NO3 leaching shows a distinct annual periodicity [Providoli, 2005]. Providoli [2005] found that mosses are especially efficient in nitrogen storage and release it again if their storage capacity for nitrate is reached. The thickness of the active humus layer is a further influencing factor for the reduction of the substance concentrations in the soil water. Many of the substances which are dissolved in the precipitation water are adsorbed to the humus and clay particles or are biologically transformed. In conclusion, the humus conditions, the understorey vegetation and the natural regeneration in disturbed stands play a dominant role for the stabilisation of ecosystems. The leakage of hazardous substances, like NO3, Al, DOC, heavy metals, etc., therefore depends not only on the tree species composition but also on the spatial distribution of the vegetation, its age and vitality. The Conventwald case study, which is described in detail in section 2.2, gives an overview of the impact of various thinning strategies, e.g. clear-cuts, "femel"3 gaps with or without prior ground vegetation and removal of single trees, on the element cycles in forested catchments. In the Große Ohe case study (section 2.1), the element leakage following a severe vermin infestation is analysed.

1.3.3 Forest liming for enhancing seepage water quality

The adaptation of silvicultural management options is not sufficient for the compensation of chemical deposition and soil acidification, and therefore well-directed measures are required in order to protect the filter and buffer functions of the soil. Over large areas in Germany, forest liming campaigns are carried out in order to buffer acid input and to secure seepage water and groundwater quality [von Wilpert and Schäffer, 2000]. Forest liming is carried out with the aim to stabilise the current pH of the soil or even to bring the pH back to its former level. The application of carbonates on the forest floor through the forest liming raises the mineralisation rate of litter and humus. This, on the one hand, has the desired effect of increasing the pH level in the soil. However, on the other hand, it implicates the risk of an initial increase in the NO3 release, which can lead to the leakage of basic cations from the root zone [Bihl, 2004]. In order to evaluate these risks and to avoid them during the liming campaigns, it is essential, for the realisation of such campaigns, to take into account the

3 Irregular shelterwood (femel) system

39 Forest hydrology – results of research in Germany and Russia specific soil and substrate conditions as well as the vegetation characteristics. This necessitates a careful and detailed analysis of the area which is going to be limed [von Wilpert and Schäffer, 2000]. If carried out properly, the liming of forests can (i) neutralise pollution from air deposition, (ii) support the biological cycles, (iii) support the soil's storage capacity for nutrients, C, N, and alkaline cations, (iv) restore and secure the soil filter function and (v) vitalise the forest vegetation and thereby reduce its susceptibility to abiotic damages as well as to vermin infestations. Altogether, this contributes to a preventive protection of the seepage water [Bihl, 2004]. The influence of forest liming on the soil chemical status and the drinking water quality from forested catchments is discussed in detail in the Kleine Kinzig case study in section 2.3.

40 Selected case studies in German low mountain ranges

2 Selected case studies of forest-hydrological research in German low mountain ranges

2.1 Große Ohe: impact of bark beetle infestation on the water and matter budget of a forested catchment

2.1.1 Abstract

Disastrous flood or drought events are primary effects of the climatic change on the water budget and are currently in the focus of the public interest. Apart from these publicly acknowledged changes, secondary effects increasingly affect the water balance and economics. An example is the extensive dieback of spruce forests as a result of a bark beetle infestation. Since 1995, the bark beetle population has been growing enormously in the Bavarian Forest National Park. On the mountain ridges (> 1150 m a.s.l.), more than 90% of the spruce trees were killed due to bark beetle (Ips typographus L.) infestation. This rapid change in the vegetation cover led to dramatic hydrological and biogeochemical changes both on a plot and catchment scale. Discharge and NO3 loads of the surface water and groundwater strongly increased. Presently, the vegetation succession seems to balance out this situation and NO3 loads are decreasing to their former level. The aim of this study is to investigate the impact of the bark beetle infestation in the Bavarian Forest National Park on water quantity and quality in six differently infested catchments. The case study presented focuses on the Große Ohe basin (Taferlruck gauge) and its sub-catchments Markungsgraben and Forellenbach. One main requirement of a basin-related description of water and matter dynamics is the availability of information about flow paths, runoff components, transit times, basin turnover and source reservoirs of water. The hydrological and hydrochemical impacts of the tree dieback are investigated in an ongoing intensive monitoring programme which started in 1994 [Nationalpark Bayrischer Wald et al., 1999]. Based on the hydrological and chemical data of the long-term monitoring programme, tracer experiments and distributed hydrological simulations, a new approach to the problem was developed which combines a set of comprehensive analysis methods: • runoff component analysis technique DIFGA, statistical analyses and tracer hydrological methods coupled with hydrochemical analyses of 14 chemical components in order to (i) quantify the impact of the bark beetle infestation on the water balance and on the flood

41 Forest hydrology – results of research in Germany and Russia

formation, and (ii) identify the source reservoirs and retention times of the various runoff components (section 2.1.4) • distributed deterministic simulations with an eco-hydrological river basin model including process knowledge and model parameters obtained in the first step (section 2.1.5). The project was funded by the Bavarian Government within the "High-Tech-Offensive Bayern" (HTO 33-7, 2002 to 2005) and the Federal Environmental Agency of Germany (Fkz. 351 01 012/01). Klöcking et al. [2005] gives a comprehensive report about the research project.

2.1.2 Research area

The Bavarian Forest National Park (242 km²) is, together with the adjacent Sumava National Park in the Czech Republic, the largest protected forested area in central Europe. According to the principles of the Bavarian Forest National Park – leaving nature to evolve naturally – no countermeasures were undertaken against the bark beetle infestation in the south-eastern part of the National Park (Rachel-Lusen area), except for a border zone to prevent further

Figure 2.1 The Große Ohe catchment in the central part of the Bavarian Forest National Park

42 Selected case studies in German low mountain ranges spreading into unprotected, managed forest stands. In the core zone, the dead trees were left standing to decay naturally without any disturbance. Consequently, the dead spruce stands reached 90% of the area in 2004 [Bayerisches Landesamt für Wasserwirtschaft et al., 2004]. The Große Ohe catchment (19 km²), a head catchment of the River Danube, is located in the central part of the National Park and ranges from 770 m a.s.l. at Taferlruck gauge to 1453 m a.s.l. at Großer Rachel peak (Figure 2.1). Within this catchment, two sub-catchments (Markungsgraben, Forellenbach) are monitored (Figure 2.1). The climate is characterised by high annual precipitation (1670 mm/yr) with a high snow proportion (30 to 40%) and a low annual mean air temperature (5.5°C). Prior to the severe tree dieback, the catchment was almost completely covered by forest, with deciduous trees (mainly European beech) on 28% and conifers (mainly Norway spruce) on 70% of the area. In the catchments, soils of the silicate series, predominantly acid cambisols, dominate. The soils are developed up to depths of 60 to 100 cm from periglacial solifluction layers. The periglacial layers consist of a sandy- loamy, 0.3 to 1.5-metre-thick, loose upper layer with a generally high skeleton fraction and an underlying layer which is solidified cement-like and up to 5 m wide [Beudert and Breit, 2004]. The main characteristics of the Große Ohe catchment and its sub-catchments Markungsgraben und Forellenbach are summarised in Table 2.1.

Table 2.1 Environmental characteristics of the Große Ohe basin and its sub-catchments Markungsgraben and Forellenbach

Große Ohe Markungsgraben Forellenbach discharge gauge Taferlruck Rachel-Diensthütte Schachtenau drainage area in km² 19.1 1.1 0.7 mean elevation in m a.s.l. 982 (770-1435) 1128 (890-1355) 894 (787-1293) mean slope in ° 11.1 16.1 8.4 basin length in km 4.8 1.7 2.8 channels length in km 47.5 1.7 2.2 annual air temperature in °C 5.5 5.3 6.2 annual precipitation in mm/yr 1670 1800 1630 mean discharge in m3/s 0.605 0.046 0.022 runoff coefficient (1989-2004) 0.61 0.73 0.62 (since 1991) coniferous/deciduous trees in % 71/29 84/16 69/31

2.1.3 Monitoring programme

Already in the 1970s, hydrological investigations were launched in the Große Ohe catchment by a research association of the Bavarian Forest National Park Administration (NPV), the Bavarian State Institute of Forestry (LWF), the Bavarian State Office for Environment (LfU) und the Technical University Munich. The basic programme for monitoring precipitation, water storage in snow cover, stream discharge and weather observations were complemented by temporary special investigations, which are documented in the scientific series "Wasserhaushalt und Stoffbilanzen im naturnahen Einzugsgebiet der Großen Ohe" (ISSN

43 Forest hydrology – results of research in Germany and Russia

0937-0056). Since 1987, the groundwater in the Markungsgraben catchment is monitored with respect to groundwater protection from element input through deposition and seepage water [Bayerisches Landesamt für Wasserwirtschaft, 2004]. Since 1990, the adjacent Forellenbach catchment is part of the "International Cooperative Programme on Integrated Monitoring of Air Pollution Effects on Ecosystems (ICP IM)" within the framework of the UN/ECE Convention on Long-Range Transboundary Air Pollution (CLRTAP) [Beudert and Breit, 2004]. The purpose of this programme is to document the state of ecosystems and changes caused by anthropogenic impacts, such as atmospheric pollutants and climate change [Finnish Environment Institute, 2004]. The Federal Environmental Agency (UBA) and the NPV have carried out the monitoring project comprising the measurement of:

• meteorological parameters and pollutants in ambient air (SO2, NOx, ozone) • water and element cycling on stand (beech/spruce) and catchment scale • vitality and growth of single trees, forest stands and understorey vegetation • populations of birds and brown trout. On the lower slopes of the Forellenbach area, intensive monitoring plots were established for beech (B1, 100 years) and spruce (F1, 110 years). On the two plots, the cycling of water and solutes (precipitation, soil water, groundwater) and of biomass and organic-bound components (stem growth, foliage, litter and litter decomposition) are monitored. Additionally, soil water contents are measured at beech plot B1, at five depths (10, 30, 55, 85 and 115 cm) in five pits with TDR (time domain reflectometry) probes. Furthermore, the groundwater level is recorded half-hourly in a nearby well (GW 221) by a pressure transducer. Stream discharge is measured at the Schachtenau gauge and meteorological data (temperature, humidity, insolation, wind) at the Schachtenau tower (51 m above ground). By 2002, in 84% of the Markungsgraben catchment and in 39% of the Forellenbach catchment (and Große Ohe catchment in total), spruce was killed by bark beetles (Figure 2.2). When dead spruce stands had reached about 20% of the catchment areas, distinct changes became apparent: the runoff coefficient of Forellenbach increased from about 0.6 (1992 to 1998) to 0.7 (1999 to 2004) and the runoff coefficient of Markungsgraben from 0.7 (1992 to 1996) to more than 0.8. On the monitoring plots in the Markungsgraben catchment and the Forellenbach catchment, bark beetle infestation and tree dieback induced drastic changes in seepage water chemistry. From June 1996 to autumn 1997, the entire spruce stand on plot F1 (Forellenbach catchment) was killed by bark beetles. NO3 concentrations in seepage water, which were well below 10 mg/l under living trees, increased to about 200 mg/l and 130 mg/l at a depth of 40 cm and 100 cm respectively (Figure 2.3). In winter 1998/1999, NO3 concentration in 100 cm depth had already exceeded its maximum and it reached normal values in the summer 2001. NO3 concentration in 40 cm depth already reached a normal level in autumn 2000. Additionally, pH values and ion concentrations in seepage water reached previous levels, suggesting that the reserves of readily degradable organic nitrogen in fresh litter (green needles, fine roots) and in unstable soil organic matter had been exhausted by this time. The cycling of matter on this site has presumably returned to a new balance between decomposers and the rapidly growing mixed stand of former third-layer beech and numerous young spruce plants.

Measured NO3 concentrations were generally much lower in the groundwater than in the soil water [Bayerisches Landesamt für Wasserwirtschaft et al., 2004]. This can be explained by the integration of soil water from both unaffected spruce stands and beech stands in the groundwater. Furthermore, NO3 concentrations might have been reduced by biogeochemical

44 Selected case studies in German low mountain ranges

Figure 2.2 Development of the deadwood areas in the Forellenbach and Markungsgraben catchments

Figure 2.3 NO3 concentrations in seepage water (40 and 100 cm) at spruce plot F1 in the Forellenbach area [Beudert and Breit, 2004] processes along the flow paths through the soil to the groundwater. Despite the extensive tree damage, deeper groundwater, sampled in two wells in the Markungsgraben catchment, showed very low NO3 concentrations of less than 10 mg/l. Here, parallel flow paths on compacted solifluction layers prevent most of the NO3 from entering the fractured aquifer system. In contrast, shallow groundwater, sampled in a spring in the Markungsgraben catchment (1010 m a.s.l.) and a well in the Forellenbach catchment (GW 221, 820 m a.s.l.), showed increased NO3 concentrations of more than 25 mg/l.

45 Forest hydrology – results of research in Germany and Russia

2.1.4 Process analysis

2.1.4.1 Methods

Runoff separation with DIFGA The runoff separation method DIFGA [Schwarze, 1985; Schwarze et al., 1989, 1991, 2004a] allows the partitioning of a measured time series of discharge at a catchment outlet into various runoff components, according to their source area and mean residence time in the catchment. As input data, DIFGA uses long time series of daily discharge, precipitation and air temperature. The long-term observed system input P (precipitation, snowmelt water) and the output Q (discharge) are analysed with an inverse description of the precipitation-runoff process. DIFGA is based on the assumption that the runoff formation and the concentration process in a catchment can be described by the parallel connection of linear reservoirs. It allows the determination of up to four runoff components, each corresponding to a linear reservoir with a specific storage constant or mean transit time. The following components can be calculated: • Direct runoff RD1, QD1 • Interflow RD2, QD2 • Fast groundwater runoff RG1, QG1 • Slow groundwater runoff RG2, QG2 "R" describes inflows into the catchment storage (runoff formation). "Q" indicates the outflow from the storage into the drainage system (runoff concentration). Furthermore, the evapotranspiration can be determined as residual of the water balance equation. The principle is illustrated in Figure 2.4. In addition, DIFGA also calculates actual and mean monthly water balances, which allows the verification of the separation results. P - RG2 - RG1 - RD = (ΔW + ETR) (2.1)

QG2 + QG1 + QD = Qobserved (2.2) (ΔW + ETR) is a residual term which includes the real evapotranspiration ETR and the soil moisture change ΔW.

Tracer hydrological analyses Using high resolution measurements of the stable environmental isotope oxygen-18 (18O), the runoff portion originating from directly effluent precipitation water (event water) and the part coming from effluent pre-event water was estimated for several flood events in the Große Ohe catchment and in the Markungsgraben and Forellenbach sub-catchments. The mean transit time of the slow groundwater runoff component RG2 was calculated by the tritium method (long-dated 3H measurements in low flow periods since 1990 in five catchments). In addition, the mean transit time of the total runoff was estimated from long-dated measurements of 18O.

Hydrochemical analyses The concentrations of the isotopes were simultaneously measured with the concentrations of DOC, total N, NH4-N, NO3, K, Na, Ca, Mg, Al, Fe, Mn, Si, Cl and SO4. These element concentrations are especially significant for the determination of source areas and the identification of the flow paths during a flood. Element concentrations were measured in the open-land precipitation, in the deposition in intact and in dead spruce stands, in the soil water

46 Selected case studies in German low mountain ranges

Figure 2.4 Scheme of the DIFGA method: concept of different runoff components and storages and runoff separation

(depths 0, 40, 70, 100 cm) and in the groundwater. Additionally, measurements of the soil moisture and the matric potential (five depths) and the groundwater level were included in the analyses. Figure 2.5 illustrates the combined application of the previously described methods.

Statistical analyses In the three catchments studied, the massive bark beetle infestation caused a drastic and very rapid change of the stand structure. In particular, the large percentage of newly originated deadwood areas has a wide influence on the precipitation runoff process in the affected catchments. Analyses of inhomogeneities in the hydrological data allow conclusions about the chronological trend and the magnitude of this influence on the water balance. Among other things, double sum analyses between precipitation and the components of the water balance were performed. These rely on the assumption that open-land precipitation is not influenced by the bark beetle infestation and hence does not show a trend.

2.1.4.2 Results As an example, water balances were estimated with DIFGA for the hydrological years 1992 (before significant tree dieback) and 1998 (tree dieback on 15% of the Forellenbach catchment, 76% of the Markungsgraben catchment, 18% of the Große Ohe catchment) (Table 2.2). The precipitation sum differs only marginally between the two years. The balance equations calculate the balance of the runoff formation, i.e. the inflow into the reservoirs (cf.

47 Forest hydrology – results of research in Germany and Russia

analysis of origin • dependent on event, depth measurements analysis of water constituents of total 18O • soil moisture, piezometric head

average duration percentage of dwell direct runoff average duration of dwell total runoff 18O-analysis separation of flood hydrographs DIFGA average duration inverse percentage analysis of runoff of dwell components calculation indirect runoff

3H measurements average duration dry weather runoff of dwell groundwater percentage

Figure 2.5 Combination of the runoff component analysis DIFGA with tracer hydrological and hydrochemical analyses left side of Figure 2.4 or Equation 2.1). In 1992, the balances show no particularities. The comparatively low evaporation rate in the Markungsgraben catchment can be attributed to the mountain ridge position of this area. Between 1992 and 1998, the hydrological conditions in the Markungsgraben catchment changed markedly; the evaporation averaged only 16% compared to the value of 1992. In the Forellenbach catchment and the Große Ohe catchment, the relation of evaporation to precipitation decreased only marginally. In the Markungsgraben catchment, the portion of direct runoff RD nearly doubled between 1992 and 1998, whilst RD increased only slightly in the Forellenbach and Große Ohe catchments. The groundwater runoff component RG1, which is descended from periglacial solifluction layers (compare discussion about measurements of chemical parameters below), increased only slightly. Because of the large annual precipitation sum, the generally low thickness of the periglacial layers (maximum: a few metres) and the underlying impermeable bedrock layer, the periglacial layers are mostly water-saturated, even if this area is covered by a vital and well- transpiring forest stand. Therefore, of all runoff components and in all catchments, RG1 varied least between 1992 and 1998. From 1992 to 1998, the slow groundwater component RG2 increased only slightly in the Forellenbach and Große Ohe catchments, whereas it doubled in the Markungsgraben catchment. RG2 primarily feeds the fracture reservoir of the underlying granite and gneiss bedrock. Because of its episodic occurrence and its strong dependence on both precipitation dynamics and soil moisture regime, the RG2 component varies largely from year to year. The observed changes in the partitioning of the total runoff demonstrate the influence of forest development and dynamics on the runoff regime of the catchments studied. In

48 Selected case studies in German low mountain ranges particular, the intensification of floods in terms of their runoff volume as well as in the increase of the maximum discharge yield is remarkable (Table 2.1).

Table 2.2 Comparison of the actual water balance of the Forellenbach, Markungsgraben and Große Ohe catchments in 1992 and 1998

Subsequent to the water balance analyses, the data series of the observed discharge Q, the precipitation P and the resultant runoff components and evaporation (calculated with DIFGA) were tested with respect to their homogeneity using double sum analyses (DSA). The open- land precipitation, which is free of a trend and hence not affected by the bark beetle infestation, was used as the reference series in the double sum analyses. DSAs were calculated between the precipitation and each water balance component. All runoff components (Q, QD, QG2, QG1) as well as the real evapotranspiration (ETR) of the catchments Markungsgraben, Forellenbach and Große Ohe show significant trends. As an example, Figure 2.6 shows the DSAs for the Markungsgraben catchment. All DSAs exhibit a significant inhomogeneity. The dieback of the spruce apparently caused changes in the soil water balance and the runoff formation. The influence of the bark beetle infestation is marked by three phases: • Phase 1: uninfluenced (reference data) until 1996 with dead-wood areas < 20% • Phase 2: shock, fast dieback of spruces over large areas (several years) with dead-wood areas increasing up to 80% • Phase 3: alternative vegetation starting to develop in 2001; only slow increase of deadwood areas up to 85%. The effects of the bark beetle infestation on the water balance can be demonstrated as follows: • Phase 2: - ETR decreases to 39% and R increases to 135% of the reference data.

49 Forest hydrology – results of research in Germany and Russia

Figure 2.6 Double sum analyses – above: cumulative sums of ΣP vs ΣQ and ΣP vs ΣETR; below: cumulative sums of ΣP vs ΣRD, ΣP vs ΣRG1, ΣP vs ΣRG2, ΣP vs ΣETR. Results for the Markungsgraben catchment from 1 November 1988 to 31 October 2003.

- The runoff accelerates due to an over-proportional increase of the RD component to 162% of the reference data. - RG1 increases to 125%; RG2 increases to 132% of the reference data. • Phase 3: - ETR recovers to 78% and R decreases to 112% of the reference data.

50 Selected case studies in German low mountain ranges

- However, runoff acceleration persists and RD further increases to 70%! - The sum of the groundwater components (RG1 + RG2) recovers to its normal level. However, RG1 decreases to 74% whereas RG2 increases to 136%. It must be noted that measurements within the third phase, which started in 2000, are still rather sparse and further observations are necessary in order to verify the present findings. Presently, the observations suggest that the vegetation in the catchments develops to a natural forest ecosystem by a succession that starts naturally. Tracer hydrological analyses based on 18O concentrations were carried out during flood events in all three catchments studied and combined with hydrochemical measurements in order to identify source areas of the different water components contributing to the flood discharge. Furthermore, the tritium method was used to estimate the mean transit time of the groundwater component RG2. The quality of the groundwater resource changes because of the matter inflow which comes from deadwood areas. The tritium method is able to evaluate the recreation tendency of this resource, which is based on a succession that is beginning and a decrease of matter inflow. In order to calculate the mean residence time of the RG2 component, the tritium input function cin(t) has to be estimated. The starting point is the tritium concentration in the precipitation cp(t), which has been measured since 1953. During the passage of the precipitation through the soil zone, the tritium concentration is modified due to storage processes. In order to transfer the tritium concentration of the precipitation to a concentration in the groundwater, the transfer of the precipitation to the groundwater through the soil zone must be modelled by soil water budget models. The soil water flow below the soil zone (deep seepage) is then considered as groundwater recharge. For the analyses, the water balance model AKWA-M [Münch, 1994] was applied in combination with the isotope transport model ISOFLOW [Heidenreich, 1997]. With this approach, tritium concentrations can only be modelled for times when groundwater recharge occurs. Thus, the calculated time series of the tritium concentration in the groundwater generally shows gaps. As an example, the input function for Markungsgraben (Figure 2.7) shows that the tritium concentration in the groundwater at the time of the "bomb peak" is much lower than in the precipitation due to the attenuating effect of the soil zone and the relatively low groundwater recharge volume in the dry years 1962 and 1963. This modification of the precipitation concentration must be taken into account when establishing the tritium input function in order to correctly calculate the residence time of the groundwater from the tritium time series. Subsequent to the estimation of all input functions, the residence time of the groundwater was estimated using an inverse method implemented in MULTIS (Richter et al. 1993; Richter and Szymczak, 1995). Assuming an exponential model (EM), a mean residence time Tm of eight years was calculated for the slow groundwater component RG2 in the Markungsgraben catchment (Figure 2.8). Figure 2.9 shows the estimated residence times for various catchments in the Bavarian Forest (Tm 6.5 to 13.5 a) [Schwarze, 2005a; Klöcking et al., 2005] and compares them to results of similar studies in the German Ore mountains [Schwarze et al., 2004a]. In the catchments of the Ore mountains, the mean residence time of the groundwater runoff Tm significantly depends on the mean annual precipitation sum and the geological conditions. An increasing precipitation leads to higher groundwater recharge rates and thus to a higher exchange rate in the groundwater storage. This, in turn, reduces the mean residence time of the water in the groundwater storage. This principal relation is additionally influenced by the geological conditions in the catchments. The estimated residence times for the catchments in the

51 Forest hydrology – results of research in Germany and Russia

Figure 2.7 Comparison of the tritium concentration in TU (tritium unit) in the precipitation and in the groundwater recharge for the period around the "bomb peak"; data from the Markungsgraben catchment.

Bavarian Forest principally fit the established regression functions although all Tm values are slightly higher than would be expected from the regression curve (Figure 2.9). This can possibly be explained by the different structure of the periglacial layers in the two regions studied. The influence of the periglacial layers on the mean residence time of the groundwater is the subject of further investigations. The analyses of the groundwater residence times for more than 50 catchments did not indicate a significant dependence on the land use. However, it can be expected that, due to the much larger groundwater recharge rate in dead wood areas, the residence time in these areas is smaller than in areas with intact forest stands. This thesis is supported by the significantly smaller residence time in the highly damaged Markungsgraben catchment when compared with the adjacent, but much less affected, Forellenbach catchment as well as the overall Große Ohe catchment. Subsequent to the analyses of the groundwater runoff component RG2, the portion of the direct runoff RD and the interflow component (or fast groundwater runoff) RG1 were estimated by measurements of the environmental isotope 18O. As an example, the results of the isotope analysis are given for the precipitation event of 22 September 2004 10:00 a.m. to 23 September 2004 5:00 a.m. (P=70.6 mm during 35 h, two intensity maxima) which completely covered the Große Ohe catchment and induced a double-peaked discharge hydrograph (HQ(T=3 a)) (Figure 2.10). The 18O concentration in the precipitation varied only very slightly. For the first peak it was -6.87 δ18O in ‰ of VSMOW, for the second peak -7.19‰. Similarly, the concentration in the groundwater remained constant at -11.25‰ at the "Schachtenebene" groundwater gauge and -10.83‰ at "Forellenbuchet". Prior to the event, the concentration of the brook water was -10.73‰ in the immediate vicinity of the

52 Selected case studies in German low mountain ranges

Figure 2.8 Time series of the tritium concentrations (in tritium units TU) in the slow groundwater runoff QG2 (estimated with MULTIS, using the exponential model EM) and measured tritium concentrations in the catchment runoff groundwater measurements. Thus, it can be assumed that prior to the precipitation on 22 September 2004 the discharge was primarily fed by groundwater. The isotope concentration in the flood wave results from the mixing of precipitation event water (direct runoff) and pre-event water which already existed in the catchment (indirect runoff). Consequently, the 18O concentration in the surface water increased to values between -8.1‰ in the peak discharge. The water sampling was continued during the flood recession until the base level had been approximately reached. Summarising, the analysis of the environmental isotopes allows the following conclusions on the effects of the bark beetle infestation on flood generation to be reached (Figure 2.11): • The runoff volume in the extremely damaged Markungsgraben catchment is 2.6 times higher than in the less affected Forellenbach catchment. • The peak flow in the Markungsgraben is 2.2 times higher than in the Forellenbach. Further measurements will show if the doubling of the peak discharge can be generalised. • However, the partitioning into direct and indirect runoff components is comparable in the two catchments: - Event water constitutes only 10% of the flood wave; for the total catchment of the Große Ohe it amounts even to only 8%.

53 Forest hydrology – results of research in Germany and Russia

30 mica-slate catchments located in the Erzgebirge mountains gneiss

25 granite

catchments located in the Bavarian Forest National Park

15 Große Ohe gneiss Forellenbach

10 mean transittime TMa in Markungsgraben

5 granite

0 700 900 1100 1300 1500 1700 1900 2100 mean anual precipitation P in mm/a

Figure 2.9 Mean groundwater residence time of catchments in the Bavarian Forest and the Ore mountains as a function of the geological conditions and the mean annual precipitation sum

Figure 2.10 Partitioning of direct runoff water and pre-event water; flood of 23 to 25 September 2004 in Forellenbach

54 Selected case studies in German low mountain ranges

- Pre-event water constitutes about 90% of the total runoff during the flood. • The water balances calculated with DIFGA indicate an increase of the runoff component RD (flood discharge = sum of event and pre-event water during the flood) from 100% (first phase) to 170% (average during the third phase) in Markungsgraben.

Figure 2.11 Comparison between direct runoff (above) and indirect runoff (below) of Markungsgraben, Forellenbach and Große Ohe; flood of 23 to 28 September 2004

55 Forest hydrology – results of research in Germany and Russia

The large fraction of pre-event water in the flood hydrograph provokes the following question: "Where does this water come from and how is it mobilised by the precipitation event?". Simultaneous measurements of deposition, soil moisture and seepage water chemistry in the intensely instrumented measuring sites F1 and B1 in the Forellenbach catchment and the groundwater levels (see Figure 2.1 for sampling locations) can answer this question. Firstly, the course of the matric potential and the groundwater level was analysed. Figure 2.12 (top) shows that the groundwater level decreased until 15 hours after the second discharge peak and reached its maximum only about 11 days later. This suggests that indirect runoff formation by an increased exfiltration of groundwater to the drainage system can be excluded in the investigated catchment. This is contrary to case studies in the German Hartz mountains [Herrmann et al., 2007] which found groundwater exfiltration to be a major runoff-forming process. The conclusions drawn are supported by the observed development of the matric potentials (Figure 2.12, top); during the first discharge peak, the matric potential in 15 cm depth rapidly decreased to 0 hPa, whereas the matric potential in 85 cm depth kept increasing until about four hours after the first peak. Hence, mainly pre-event water from the topsoil generated the first flood wave. Similarly, the second wave is firstly generated from the saturated upper soil layers. However, about five hours after the beginning of the flood hydrograph (or three hours after the precipitation maximum) the matric potentials began to decline in the deeper soil depths and the upper 85 cm of the soil were gradually involved in the formation of the flood peak. Both the course of the matric potential and the groundwater levels as well as the 18O analysis allow the conclusion that, in the observed flood waves, the major runoff volume is contributed by pre-event water which generates from saturated and partly saturated soil water. Accordingly, the indirect runoff is mostly hypodermic runoff (interflow). The dominating role of interflow in the flood wave formation is also confirmed by the development of the NO3 concentration in the brook water (Figure 2.12, centre). Prior to the flood event, the NO3 concentration in the brook water (8.8 mg/l) was slightly below the groundwater concentration (9.1 mg/l). The concentration in the flood-causing precipitation was 1 to 2 mg/l in the open-land precipitation and 2 to 3 mg/l in the throughfall. In the soil water the NO3 concentration increases with depth from values between 0.5 and 0.7 mg/l in the upper soil to 2.3 mg/l in the subsoil above the periglacial layer. During the first discharge peak the NO3 concentration in the brook decreases to 6 mg/l. Since the direct runoff from directly effluent precipitation contains comparatively large NO3 concentrations, the decreasing NO3 concentration can only be explained by the incorporation of indirect runoff from the soil water of the upper soil layers, which is almost NO3-free. During the second discharge peak, the concentration in the brook water decreases again, but this time only to 7 mg/l. This indicates that now water from the deeper soil depths (having higher NO3 concentrations) is also mobilised, and hence the dilution of NO3 is poor compared to the first flood wave.

An interesting feature is the elongated third decrease of the NO3 concentrations in the brook water, which starts on 24 September 2004 at 05:00 a.m. The soil moisture measurements show that the deep seepage water, which contains comparably low NO3 concentrations, reaches the solidified zone of the periglacial layer (at a depth of about 100 cm) on 24 September 2004 between 0:00 and 4:00 a.m. The retaining effect of this zone induces a lateral runoff (interflow) shortly afterwards, which leads to a decrease of the NO3 concentration in the brook water to 6.5 mg/l. In areas without periglacial layers (according to Beudert and Breit [2004] above altitudes of 1050 m a.s.l.) the seepage water directly reaches

56 Selected case studies in German low mountain ranges

Figure 2.12 Development of the matric potential and groundwater level (top), NO3 concentration (centre) and DOC (bottom) of Forellenbach, flood of 23 to the fracture 28aquifer September of the 2004 underlying bedrock. There, it causes the beginning of the 57 Forest hydrology – results of research in Germany and Russia groundwater rise shortly after the beginning of the deep lateral runoff from those areas in the catchment which are covered by periglacial layers. Therefore two different source areas exist for interflow and groundwater runoff. If the soil water percolation reaches a solidified zone of a periglacial layer, interflow is generated (= fast groundwater runoff RG1 in the DIFGA model). If the percolation reaches the fractured solid rock aquifer in areas without periglacial layers, groundwater recharge is generated (RG2 in the DIFGA model). The combined analyses of the concentration of NO3 (and other chemical components which are not discussed here) in the brook water, groundwater and soil water leads to the conclusion that the indirect runoff, which was estimated by the isotope analyses, generates itself primarily from the soil water and not from the groundwater. The main explanation for these findings are the effects of the periglacial layers on the percolation. Figure 2.12 (top) displays the development of the concentration of dissolved organic carbon (DOC). In the investigated catchments, DOC originates primarily from the throughfall of intact spruce populations and from the very thick litter layer in bark beetle infested areas. The DOC concentration in the soil water decreases with depth. During the event, DOC increased rapidly in the brook water, but did not change in the soil water and the groundwater. The DOC development corresponds well with the direct runoff hydrograph determined from the isotope measurements.

2.1.5 Modelling

2.1.5.1 The river basin model ArcEGMO-PSCN is a GIS-based, multi-scale modelling system for the spatially distributed simulation of hydrological sub-processes in river catchments. In addition to the usual model approaches for describing the lateral surface and subsurface water flows at river basin scales [Becker et al., 2002; Pfützner, 2003], it contains complex growth models for forest and agricultural areas and a detailed soil model (water, heat, carbon/nitrogen budget). The required climatic driving variables are air temperature, precipitation, humidity and global radiation in daily resolution. The catchment is discretised into hydrotopes, which are characterised by their land use, topography and soil profile, and are associated with a fixed relative position within the modelled catchment. In each of the specified hydrotopes, vegetation dynamics are simulated for various land uses. The forest stand development was simulated using the forest growth model FORESEE (4C) [Lasch et al., 2005]. Non-forested areas (paths, etc.) were simulated with a general static model (only water budget without C/N dynamics). The modelling of the soil processes took into account the soil profile with horizontal layers down to the C horizon. For sites influenced by groundwater, temporarily saturated layers and the current depth of the groundwater surface were also considered. A detailed description of the PSCN module is given in Klöcking et al. [2005] and at http://pscn.arcegmo.de. The following input data were used: Forest Site Map, ATKIS land-use map 1:25.000, Digital Elevation Model 1:50.000, daily meteorological data from the Deutscher Wetterdienst (German Weather Service) and the observational network in the catchments (air temperature, precipitation, humidity, global radiation). The stand development was initialised using forest inventory data from 1991 (200x200 m grid). The spreading of the forest dieback due to bark beetle infestation was monitored with aerial photographs, which were taken every year in early summer.

58 Selected case studies in German low mountain ranges

2.1.5.2 Model validation The model was validated in two steps. Firstly, the results of the various process simulations were checked on the basis of plot measurements. Secondly, the overall performance of the hydrological simulation was verified by comparing the simulated with the observed discharges at the three gauging stations in the catchment. Data series suitable for the verification of the water, snow and vegetation sub-models of ArcEGMO-PSCN were available for the beech stand B1 and the spruce stand F1 in the Forellenbach catchment. Additionally, the simulated tree number and timber volume was compared with the observed data at the inventory points of the forest inventory in 2002 (not shown here). As shown in Figure 2.13 for the beech stand B1, the simulated growth was successfully tested against measured parameters.

Figure 2.13 Comparison of simulated (lines) and measured (symbols) growth parameters for the beech stand B1; unit of wood volume V: Vfm = m³ solid volume over bark

The simulated water contents in the different soil layers of the B1 plot generally fit the measured values (Figure 2.14). The simulated water fluxes below the rooting zone (145 cm) were compared with groundwater table variations, which were recorded in the nearby well on the same plot. As shown in Figure 2.15, the course of the modelled deep seepage (groundwater recharge) closely follows the groundwater dynamics, indicating a realistic description of the ecosystem's vertical hydrological processes in this model. The conformance between simulated and observed brook discharges is satisfactory for all gauging stations (Nash/Sutcliff efficiency between 0.64 and 0.7). On average, the model results show that surface (direct) and subsurface (hypodermic) runoff are of minor importance, contributing 2% and 25% to the total runoff (Figure 2.16). Groundwater discharge was estimated to contribute 73% of total runoff. This result is supported by the hydrochemical hydrograph separation [Haag, 1997], which gave groundwater discharges of about 80% of total runoff. Although the runoff separation with ArcEGMO-PSCN seems to be correct, the comparison of the observed discharge with the simulated values suggests a further separation of the groundwater component, in dependency of the generation areas and their specific geo-hydraulic characteristics [Becker et al., 2002]. However, the subsurface flow characteristics of the area are not sufficiently known, emphasizing the need for further specific investigations. The use of coupled water budget/groundwater flow models and additional process analysis tools is necessary to elucidate the origin of fast-reacting

59 Forest hydrology – results of research in Germany and Russia

Figure 2.14 Comparison of simulated (black line) against measured water contents (dotted lines) at 55 cm depth at beech stand B1

Figure 2.15 Comparison of simulated groundwater recharge and measured groundwater table variations at beech stand B1 subsurface flow paths and the dimension of retention areas. This includes a sophisticated hydrogeological survey and the continuation of the tracer monitoring.

2.1.5.3 Scenario simulations Model runs with (reference) and without spruce dieback (scenario) provided an insight into the influence of bark beetles on the water cycle of the research area. Both reference and scenario model runs were based on the same driving forces (weather) and boundary conditions. The simulation experiment was tested for the intensive monitoring plots B1 and F1 before starting the scenario simulations at catchment scale. These tests confirm that the simulated impacts of the bark beetle attack differ in accordance to the stand mixing and the amount of damage. Consistently, a decrease of evapotranspiration and an increase of the discharges (seepage, interflow, surface runoff) are calculated for both plots. The changes at the beech/spruce stand B1 are very small. The beech trees react immediately with increasing growth and transpiration efforts to the loss of competition by adjoining spruce. In contrast, the

60 Selected case studies in German low mountain ranges

Figure 2.16 Comparison of modelled runoff components and measured discharge (dotted line) at the Forellenbach gauge Schachtenau discharge of the spruce stand F1 is enlarged up to 65 mm/month. This phenomenon has been also simulated at catchment scale. The first consequence of the beginning dieback of the spruce stands is an immediate decrease of the interception evaporation (Figure 2.17). However, this does not influence the water budget since other evaporation components increase simultaneously.

Figure 2.17 Dead spruce stands (% area) and bark beetle-induced changes in the water cycle (mm/a) of the Forellenbach area

Not until the share of dead spruce stands had reached about 20% of the total area (1999), did interception and transpiration losses decrease by 60 to 80 mm/a. In contrast, evaporation from soils, snow and waterlogged depressions increased, as did groundwater recharge and runoff (~60 mm/a). In addition, increasing amounts of stand precipitation and insolation in dead spruce stands led to earlier snowmelt floods; on average the discharge peak from snowmelt occurs about nine days earlier in the scenario than in the reference simulation. Since 2003, the

61 Forest hydrology – results of research in Germany and Russia water cycle in the scenario seems to have stabilised at a new level as a result of the succession that has begun. Simulated N losses from dead spruce stands into aquifers began to increase simultaneously with the changes in the water dynamics (Figure 2.18). Simultaneously with the onset of NO3 leaching in the Markungsgraben area, the concentrations in the shallow groundwater in the Forellenbach area (GW 221, 820 m a.s.l.) increased as well, although the forest dieback began later and was less extensive than in the Markungsgraben area. This supports the findings from the hydrological modelling, showing that there are fast-reacting lateral flow paths in these areas, which have not yet been characterised but have to be considered in future modelling of water and element cycling.

Figure 2.18 NO3 concentrations in runoff water (discharge weighted means) and in groundwater (medians) [Bayerisches Landesamt für Wasserwirtschaft et al., 2004; Beudert and Breit, 2004]

2.1.5.4 Conclusions Bark beetle infestation and the subsequent dieback of spruce stands on large areas affected the water and N cycling in the investigated catchments. Significant changes were observed after the dead spruce stands exceeded about 20% of the total catchment area. Scenario simulations with ArcEGMO-PSCN proved that these changes were caused by the bark beetle infestation. Nevertheless, species composition and site characteristics strongly influenced the extent to which the components of the water cycle and N fluxes changed in the different parts of the Große Ohe catchment. The flood protection potential of the catchment decreased as a consequence of reduced evapotranspiration and, correspondingly, a generally more saturated soil water storage. However, it has to be noted that the role of forests for flood protection is primarily caused by the high infiltration and water-holding capacity of forest soils, which are barely affected by bark beetle calamities, rather than by the stand characteristics. Furthermore, the protection effects of forests are always limited in the case of extreme precipitation events. Nevertheless,

62 Selected case studies in German low mountain ranges an increase in the flood protection potential of the investigated catchments can be expected due to the vegetation succession that has already begun. Among other investigations in the Große Ohe catchment, the long-term integrated monitoring programme in the Forellenbach catchment offers a solid basis for the parameterisation, verification and improvement of complex deterministic river basin models, such as Arc- EGMO. This model type will play an increasing role in estimating the effects of global change on regional water and element cycling.

2.2 Conventwald: silvicultural management of seepage water quality

The Conventwald ecosystem study is an ongoing research project which started in 1991 and by now comprises more than 15 years of uninterrupted observations of the water and element cycles in a forested area including a preserved natural mixed forest and various silviculturally managed stands. The field study, carried out in a nutrient-rich and climatically favourable region in the South Black Forest, mainly aims at assessing the deterioration of the formerly excellent nutrient status – and, consequently, the risk of debasing tree growth – caused by anthropogenic deposition. In the study area, various silvicultural management options, e.g. tree type/age composition and harvesting methods, are tested and evaluated with respect to their ability to counteract a probable soil chemical drift.

2.2.1 Research focus

Central Europe is a highly industrialised and densely populated area. The deposition rates of acids and nitrogen, originating from air pollution, have exceeded the buffering and uptake capacity of forest soils for decades and thus endanger the sustainability of ecosystem functions in Central Europe [Nilsson, 1986]. With the exception of heavily industrialized regions, the effect of these air pollutants is not an acute damage of forest trees, but rather a chronic shift in site quality. A criterion for ecosystem sustainability is the quasi steady state of their energetic status and element budgets, i.e. element cycles should be closed and net ecosystem output of elements should be low. During the last decades, the input of strong acids and nitrogen compounds resulted in a fast acidification process and consequently increased element leaching from forest soils. The comparison between actual and archived soil samples shows an extremely rapid decrease in soil pH which by no means can be caused exclusively by the natural soil development in humid climate zones. In South Sweden, Hallbäcken and Tamm [1986] found a decrease of about one pH unit in the surface soils of forest ecosystems since the 1920s. Ulrich [1987] found the same in northern Germany. In southwest Germany, a pH decrease of one to two units between 1927 and 1992 was found by von Wilpert and Hildebrand [1994] in non-chalky soils. The first objective of the Conventwald study was to answer the question if, and to what extent, variations in silvicultural techniques can counteract the ecosystem disturbances caused by depositions. Foresters can modify stand settings, e.g. by planting site-specific broad-leafed trees instead of conifer monocultures, by changing the stand structure from even-aged and mono-layered stands to a highly developed spatial structure or by changing from clear-cuts to a clear-cut free harvesting system. In the Conventwald, the way is examined in which various silvicultural techniques influence element-leaching from the root zone and thus influence the

63 Forest hydrology – results of research in Germany and Russia long-term site sustainability in terms of the dynamics of the plant-available, exchangeable ion stock as well as the chemical quality of the groundwater and the surface water. A second focus of this field study is on the problem that hydrologic processes and parameters can show considerable spatial variability [e.g. Shah et al., 1996; Bárdossy and Lehmann, 1998; Merz and Bárdossy, 1999; Netto et al., 1999; Dekker et al., 1999]. In forest ecosystems, the spatial distribution and the dynamics of soil-water flow and solute transport are influenced by the structure of the free crowns and the root zone as well as the composition of tree species [e.g. Kimmins, 1973; Kreutzer, 1985; Koch and Matzner, 1993]. Nonetheless, when designing the spatial setup of field measuring campaigns, this structural information is very typically ignored. The heterogeneity (in space and in time) of intensively mixed-species stands can be taken into account by identifying representative structural units and by measuring water and solute flux separately in each of those units [von Wilpert, 1994]. In the Conventwald study, it is proved whether stratifying the catchment in relevant structure units and measuring the influence of the structures caused by crown architecture and root distribution on water and ion fluxes helps in understanding, simulating and predicting the output of the various structure units as well as the overall catchment response.

2.2.2 Site characteristics

The Conventwald research area is located in the Black Forest, near Freiburg (SW Germany), in a transitional zone between sub-mountainous and mountainous altitudes at 700 to 860 m above sea level. The area of interest includes the 9.6 ha Conventwald catchment as well as various bordering stands (Figure 2.19). The stands in the catchment consist of approximately 160 year-old4 beech (Fagus silvatica), silver fir (Abies alba) and spruce trees (Picea abies). Silvicultural use in this area stopped in 1950 and since then the stands have developed to a natural forest community. The stands in the catchment display a pronounced spatial structure through the interspersion of closed areas and more open regeneration areas. Other experimental plots were located outside the catchment, in stands with similar slope direction and the same site conditions as found within the catchment. These plots were in a strip-shaped clear-cut, a spruce pole-sized stand, a beech pole-sized stand and a 90-year old spruce stand. The clear-cut was created in 1989. The spruce was planted around 1960 after a clear-cut; the beech comes from natural regeneration. The old spruce stand was planted on a previous forest meadow. The topography is dominated by S to SSW-oriented slopes with an inclination of about 20°. The mean annual precipitation is 1400 mm and the mean air temperature 6.6°C. The bedrock is a dark, fine-grained paragneiss, which consists primarily of plagioclase and quartz, with varying amounts of biotite and orthoclase. The parent material was periglacially loosened and converted to a cambisol with a deep soil profile (Table 2.3). The soil is moderately acidic with high buffer reserves in the deeper horizons (Table 2.4). The pH (KCl) level ranges from 3.2 to 3.5 in the upper 10 cm of the mineral soil and rises to values between 4 and 5 in 120-140 cm depth. The cation exchange capacity (CEC) in the A- -1 -1 horizon is high with values between 100 and 170 µmolcg and decreases to 20-30 µmolcg in the subsoil. The base saturation (BS) ranges from 5 to 40%.

4 All stand ages refer to the beginning of the investigations in 1991.

64 Selected case studies in German low mountain ranges

Figure 2.19 Location of the Conventwald ecosystem research area

Table 2.3 Soil profile description (location near measuring tower, cf. Figure 2.19)

Horizon Depth Description

L/Of +4-0 cm leaf cover Ah 0-10 cm very dark brownish gray (10YR3.2); moderately sandy loam, stone fraction 15 Vol%; very fine (< 2 mm) crumb structure, aggregation type open to asymmetrical; large amount of visible pores; bulk density 0.90 g/cm³; high humus content; high root content; corrugated transition to next horizon. AhBv 10-20 cm drab (10YR5.4); middle sandy loam, stone fraction 25 Vol%; fine (2-5 mm) crumb structure, aggregation type open to asymmetrical; large amount of visible pores; bulk density 1.00 g/cm³, high humus content; high root content; corrugated transition to next horizon. Bv 20-80 cm tawny (10YR5.8); moderately sandy loam, stone fraction 35 Vol%; middle-sized (5-20 mm) sub-prismatic structure decomposable into fine (2-5 mm) crumb structure; large amount of visible pores; bulk density 1.30 g/cm³; low humus content; moderate root content; corrugated transition to next horizon. II Bv 80-120 cm light tawny (10YR6.6); heavily sandy loam, stone fraction 65 Vol%; middle-sized (5-20 mm) sub-prismatic structure decomposable into fine (2-5 mm) crumb structure; moderate amount of visible pores; bulk density 1.40 g/cm³; low root content; linear transition to next horizon. BvCv 120-180 cm dunnish (10YR6.3); moderately sandy loam; moderately rocky, highly gravely, stone fraction 85 Vol%; fine (2-5 mm) crumb structure; moderate amount of visible pores; bulk density 1.50 g/cm³; very low root content.

65 Forest hydrology – results of research in Germany and Russia

The fine earth consists of a sandy loam with a coarse stone fraction of 30 to 60 Vol% (Table 2.5). The soils typically have a high porosity and an extremely high water conductivity ks. The field capacity is very low with values around 55 to 100 mm/m. Despite the low storage capacity of plant-usable water, the probability of drought stress is low, since precipitation is fairly constant throughout the year [Atlantic climate, von Wilpert et al., 1996a].

Table 2.4 Chemical characteristics of a typical soil profile in the Conventwald (location near measuring tower, cf. Figure 2.19). Samples taken in 1993.

Depth pH Corg N S CEC exchangeable cations in mmolc/kg BS t C/N eff cm (KCl) % mg/g mg/g mmolc /kg H Al Ca Mg K Na % L +4-2 Of +2-0 0.51 Ah 0-10 3.36 6.8 4.4 15.5 0.38 129.4 5.36 106.7 5.9 2.48 2.23 0.53 8.6 AhBv 10-20 3.27 4.1 2.9 14.1 0.29 98.3 1.67 90.4 1.4 1.1 1.16 0.52 4.2 Bv 20-80 3.97 1.9 1.5 12.6 0.32 66.4 0.27 63.3 0.7 0.46 0.79 0.34 3.3 IIBv 80-120 4.15 0.4 0.4 9.6 0.13 29.9 0.11 28.7 0.3 0.14 0.37 0.23 3.2 BvCv 120-140 4.11 0.3 0.3 10.0 0.07 25.8 0.24 24.4 0.3 0.16 0.38 0.14 3.8

Table 2.5 Physical characteristics of a typical soil profile in the Conventwald (location near measuring tower, cf. Figure 2.19). Samples taken in 1993.

Texture (% of humus bulk pore Depth Stones k Vol% water at pF Horizon free fine earth) s density volume cm % cm/s 3 sand silt clay g/cm % 0.6 1.8 2.5 4.2 L +4-2 Of +2-0 Ah 0-10 15 40.4 33.7 26.0 1.52 0.9 67.5 40.1 28.7 24.7 14.8 AhBv 10-20 25 46.5 36.9 16.6 1.52 1.0 67.4 44.3 33.7 29.7 17.0 Bv 20-80 35 46.6 34.8 18.5 0.71 1.3 59.5 41.9 32.9 28.9 21.3 IIBv 80-120 65 57.1 30.6 12.3 1.4 47.3 34.3 29.3 26.2 14.8 BvCv 120-140 85 1.5

The natural boundary conditions allow sufficient access to water and nutrients. Hence, it can be assumed that the essential bio-geo-ecological cycles are intact, apart from a decoupling of nutrient cycles and food chains, which leads to nutrient loss and leaching of bases, especially in the upper soil horizons.

66 Selected case studies in German low mountain ranges

2.2.3 Instrumentation – configuration of the stratified approach

All installations to measure ion concentrations in the throughfall or in the soil solution and all instruments for the parameterisation of water transport models were concentrated on 2-4 m²- wide "microplots". The locations of these microplots were chosen taking consideration of the different stand structures and tree species, and took into account the spatially varying density of the crown layer [von Wilpert, 1994; von Wilpert and Mies, 1995; von Wilpert et al., 1996a]. Table 2.6 gives an overview of the differing structural units of the stands which were identified in the catchment. The stand structure was considered to be on two spatial scales. On the scale of metastructures (i.e. first-order structures), a distinction was made between mature and closed stands (97%) on the one hand, and treefall gaps and regeneration areas (3%) on the other. Within the closed stand area, for the major tree species, the mesostructure (i.e. second- order structures) was stratified according to the density of the crown layer, namely into crown centres, crown peripheries and crown gaps. Crown centres, which cover about two thirds of the stand area, are the dominant mesostructure.

Table 2.6 Proportional area of the various structural types identified in the Conventwald catchment

Metastructures: Regeneration areas 1% Treefall gaps5 2% Closed stand areas Fagus sylvatica 54% Abies alba 39% Picea Abies 4% others <1% Mesostructures in the closed stand Fagus sylvatica: stemflow6 6% areas: crown centre without stemflow 39% crown periphery 7% crown gap7 2% Abies alba: crown centre 19% crown periphery 14% crown gap7 6% Picea abies: crown centre 1% crown periphery 2% crown gap7 1%

The various metastructures as well as the distribution of the tree species were identified by aerial photos [Münch, 1995] (Figure 2.20). The mesostructures were identified by measuring the density of the crown layer from the ground [von Wilpert and Mies, 1995]. It must be emphasized that in this case study the spatial variability of throughfall or soil chemistry and water flow was not considered as the effect of distance to the stem basis, but as the effect of measured crown density. A stem-basis-related method as used, for example, by Beier et al. [1993], Koch and Matzner [1993], Seiler and Matzner [1995], Manderscheid and Matzner

5 Canopy opening larger than one projected crown area 6 The calculation of the beech stemflow infiltration area is based on the assumption that the stemflow infiltrates 80 cm around the beech stem [Wittig and Nette, 1983]. 7 Canopy opening smaller than one projected crown area

67 Forest hydrology – results of research in Germany and Russia

Figure 2.20 Metastructures identified in the Conventwald. The different tree species and various gap types in the closed stands are highlighted in different colours. Spots between the coloured polygons show the beech crowns in the original air photograph.

[1995] requires circular crowns. In the site in question, which is characterised by very heterogeneous crown structures and different tree species as well as a systematically larger downhill extension of crowns, a consideration of stem-related gradients would thus be of little value. Crown gaps were defined as canopy openings smaller than one projected crown area, treefall gaps as canopy openings larger than one projected crown area. In contrast to treefall gaps, which have high biogeochemical dynamics in consequence of decay and growth processes, crown gaps are part of the mature stand areas with more stable (steady state) ecosystem characteristics [von Wilpert et al., 1996a; 1996b]. The ecosystem quality of treefall gaps (e.g. the photosynthetic active radiation) and the ecosystem processes such as water fluxes and nutrient cycling vary depending on gap size, form and age [Parsons et al., 1994; Coates and Burton, 1996]. Therefore, three different gaps are investigated in the Conventwald: a naturally originated treefall gap (developed in 1989 with a size of 0.2 ha) and two gaps which were introduced in the mixed stand and in the beech pole stand after the start of the measurements in order to observe the immediate effect of their formation on the element fluxes. Altogether, 22 different structure units were distinguished (Table 2.7). In each structure unit, one or more microplots were equipped with bulk rain samplers, tensiometers and suction cups. Open-land precipitation above the canopy (climate tower 27 m) and throughfall below the canopy is collected in precipitation bulk samplers (200 cm² surface area), which are

68 Selected case studies in German low mountain ranges constantly open. The stemflow of the beech stand is gathered with cuffs (polyurethane foam) and measured with a tipping gauge. At the climate tower, all meteorologically relevant data – precipitation height (tipping gauge), air temperature, relative air humidity, global radiation and wind velocity – are measured. Additionally, air temperature and humidity are measured within the stands at 20 cm and 3 m above ground in order to estimate the stand climate. The soil temperature is measured at 5 and 30 cm depth as an explicatory variable for nitrification and mineralization processes in the soil.

Table 2.7 Strata of stand structure in which separate measurements and calculations were made and number of structure-related microplots

Metastructure Mesostructure No. of microplots mixed stand (160yr) beech (2 trees) crown centre, crown periphery, crown opening, 8 stemflow area silver fir (2 trees) crown centre, crown periphery, crown opening 6 spruce (2 trees) crown centre, crown periphery, crown opening 6 gaps 4 regeneration area 1 beech pole stand (40yr) (2 trees) crown centre, stemflow area, crown opening 6 spruce pole stand (40yr) spruce (2 trees) crown centre, crown opening 4 gap 2 old spruce stand (90yr) (2 trees) crown centre, crown periphery, crown opening 6 clear-cut 1

Seepage water is extracted by suction cups below the humus layer, in the rooting zone at soil depths of 15, 30, 60 and 120 cm and in the transition zone between soil and bedrock debris at 180 cm depth. The soil solution is obtained through vacuum pumping in a quasi continual mode (at 200 to 400 hPa for 4 to 6 hours per day) with porous nylon-membrane filter cups (pore diameter = 0.45 μm). Element concentrations in water are determined by IC technique for anions, by ICP technique for cations and by photometry for NH4, P and DOC. The pH values are measured by a glass electrode. HCO3 is determined periodically by titration and at other times derived from the pH value by a regression equation. The charge density of organic anions is calculated as the remainder of the charge balance. CEC is determined by percolation with a 1N NH4Cl exchange solution. All of the analytical methods refer to the standard German work guide for soil analyses [Ministry of Food, Agriculture and Forestry, 1990]. Matric potential of the soil is measured at the same soil depths as the element concentrations through a combination of 120 automatically registering tensiometers and 400 penetration tensiometers. At two plots, the volumetric soil water content is measured with a TDR measuring device. The combination of measurements with high spatial but low time resolution (400 "low cost" tensiometers) and timely representative high resolution measurements (with registering tensiometers) at selected locations allows the recognition of stable spatial patterns in the flux dynamics as well as the observation of highly dynamic transport processes, e.g. following thunderstorms.

69 Forest hydrology – results of research in Germany and Russia

At the catchment's outlet, the outflow is measured by a triangle-shaped weir (60° Thomson Weir). Precipitation, seepage and brook water are sampled and analysed bi-weekly. Meteorological sensors and automatically registering tensiometers as well as the gauge at the catchment outlet measure at ten-minute intervals and values are stored as one-hour averages/sums.

2.2.4 Measurements – results and discussions

2.2.4.1 Precipitation and deposition load The different stand structures show a great variation in deposition rates, due to varying crown densities and crown architectures. Figure 2.21 exemplarily shows the variation of acid and nitrogen deposition between the main silvicultural management types investigated. Generally, deposition rates under the canopy are always higher than open-land deposition. It is highest in the younger, very dense spruce stands and is significantly lower in the beech-dominated stands. The nitrogen input in the beech-dominated stands was 14 to 17 kg ha-1a-1, a value which Nilsson [1986] judged to be within or near the range of nitrogen that can be incorporated by fast-growing stands. In both the spruce stands, the nitrogen input of around 25 kg ha-1a-1 is far above this critical load. The element deposition varies not only between tree types, but depends also on the density of the crown layer. Figure 2.22 shows the pH values in throughfall below beech and spruce in the mixed stand in the catchment. The pH in the throughfall is 0.5 to 1 units higher than in the open-land precipitation. This can be explained by the buffering in the crowns; Ca is leached from the plant tissue and forms Ca salts on the leaf surfaces, which can buffer protons in acid precipitation water [Fink, 1991]. The pH in the throughfall water depends on the mass balance between the crown buffer capacity and the accumulation of acid compounds on the leaf surfaces. The comparably small pH differences between the various tree species and crown

Figure 2.21 Total acid (left) and nitrogen (right) deposition (throughfall data) in various stands (annual loads averaged over the years 1991 to 1994)

70 Selected case studies in German low mountain ranges

Figure 2.22 Boxplots of pH values (daily data for the years 2000 to 2006) for open-land deposition and throughfall below spruce and beech in the mixed beech/silver-fir stand structures can be explained by different relations between crown buffering and accumulation of acids. In comparison with spruce, beech displays a tendency to higher pH values. This results above all from the comparatively lower (over the year) crown surface of beeches and, hence, lower acid accumulation. Furthermore, the Ca content of beech leaves is higher than that of spruce needles and, therefore, the buffer capacity of beech leaves is higher. Beech stemflow areas and spruce crown centres display slightly lower pH values than the other structure units of the same tree species. This can be explained by a more extensive contact with the crown surface and the subsequently more complete washing-off of acids from the leaves.

2.2.4.2 Element concentrations in the soil matrix, the seepage and the catchment outlet The chemical characteristics of the soil solid phase show distinct differences between the investigated stand varieties. The beech pole area has a pH value that is 0.2 to 0.5 units higher than that of the other varieties. The base saturation shows the same tendencies as the pH values [von Wilpert et al., 1996a]. Notable differences are also found in the amount of sulphur storage in the soil matrix (Figure 2.23). Depositional influences are assumed to be the major factor in the sulphur storage amounts, since sulphur is only found in trace amounts in the parent material. The temporarily stored deposition sulphur can lead to long-term stress in the seepage, even after the sulphur input has been reduced to air purity standards. The most visible differences between the investigated stand varieties are found in the substance concentrations of the seepage water. As an example, Figure 2.24 displays average depth profiles of measured concentrations of Al, Mg and SO4 and the pH in the soil solution (suction cup measurements) under beech in the mixed stand, the clear-cut, the beech pole stand and the spruce pole stand. Depth profiles of the two mobile anions of strong mineral acids are complementary to each other. NO3 shows highest values at the soil surface and

71 Forest hydrology – results of research in Germany and Russia

Figure 2.23 Depth profiles of sulphur amounts in the stand varieties of the research area, placed into a soil profile typical for the study area

decreasing values with depth; SO4 shows a clear maximum in the subsoil. Al shows similar depth profiles as NO3, whereas Mg is obviously more linked to SO4. Both anions, which are the carriers in the ion flux through the soil, originate from deposition. SO4 is stored in precipitates [Alewell et al., 2000] and can be dissolved and infiltrate into the soil long after the SO4 deposition with rainfall has ended. For both NO3 and SO4, concentrations are lower under beech than under spruce. This difference is comparably small for SO4 and very high for NO3. The very low NO3 concentrations under beech cannot fully be explained by differences in the NO3 deposition since total N input is only 40% lower in beech than in spruce (Figure 2.21). Additionally, NO3 concentration in beech stands is reduced by a more effective NO3 uptake. The totalised concentration of mobile anions is much lower in beech than in spruce, resulting in equivalently lower cation concentrations. In each forest ecosystem, disturbances of the stand structure such as the appearance of gaps, clear-cuts, or the death of single trees may cause leakage in the element cycles. In the Conventwald, elevated NO3 concentrations were the predominant changes observed after such disturbances. Figure 2.25 shows the development of the NO3 concentration in the seepage water following the clear-cut in 1989. Until 1991, ground vegetation was absent in the clear- cut area and NO3 concentrations at 180 cm depth exceeded the EC (European Community) -1 -1 threshold value for drinking water (50 mg l or 806 µmolcl ). In 1991 nitrophilic ground vegetation began to develop and in the following years NO3 concentrations dropped to one

72 Selected case studies in German low mountain ranges

Figure 2.24 Depth profiles of average element concentrations (years 2000 to 2006) in the soil water sampled with suction cups for beech in the mixed stand (solid lines), clear- cut (dashed lines), beech pole stand (dash-dotted lines) and spruce pole stand (dotted lines)

1400

1200

1000

800

600 NO3 [µmol/l] NO3

400

200

0 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 Clear-Cutting Natural Regeneration

Figure 2.25 NO3 concentrations in the seepage water at 180 cm below a clear-cut and a 10 to 20-year-old regeneration area

third and lower. Comparing those concentrations with the NO3 concentrations below a 10 to 20-year-old closed beech regeneration area shows that even 15 years after the clear-cut NO3 concentrations in the clear-cut vary largely, indicating a yet unstable system state, and are generally higher.

73 Forest hydrology – results of research in Germany and Russia

A similar development in the element leaching was observed in the spruce pole stand. Here, heavy NO3 leaching occurred in the subsoil at 180 cm depth after the cutting of two small trees (representing a thinning gap). The NO3 leaching was accompanied by a decrease of the pH value from about 6.5 to 5.5 (Figure 2.26). This decrease of pH can be explained by proton production through nitrification. The disturbance of the element cycle continues even seven years after the removal of the trees and resilience is not yet visible. NO3 concentrations were low during the drought in 2003, which led to an overshooting nitrification in the following two years.

Figure 2.26 NO3 concentrations (solid line) and pH levels (dotted line) in the seepage water at 180 cm depth following the cutting of two small trees in the spruce pole stand

Both examples, the clear-cut as well as the cutting of two small trees in the spruce pole stand, caused heavy NO3 leaching which is still far above the "normal" level. The long-lasting shift of the N budget after comparatively slight disturbances of the stand structure is typical for ecosystem stages that are near N saturation and then show a tendency to chaotic, non- predictable system behaviour [Aber et al., 1989]. According to Zysset et al. [1996], an indication of a drift of the soil chemical site quality is given by decreasing base cation/Al ratios (BC/Al ratios) in the soil solution. Al ions hinder the uptake of basic cations by roots and, therefore, the BC/Al ratio can indicate acidification stress. Sverdrup and Warfvinge [1993] stated that BC/Al ratios below 1 can cause growth reductions even under field conditions. Figure 2.27 shows the development of the BC/Al ratio in the two spruce stands since the start of the observations. In the old spruce stand, the BC/Al ratios are very low at the beginning of the observations and then increase and fluctuate greatly. At the end of the observation period, the values again approach the threshold value of a potentially phytotoxic BC/Al ratio. The largely varying BC/Al ratios, especially in the old

74 Selected case studies in German low mountain ranges

Figure 2.27 Relationship between base cations (BC) and Al in the soil solution in 60 cm depth under the spruce pole stand: a) crown gap, b) crown centre and the old spruce stand, c) crown gap, d) crown centre. The solid lines at BC/Al = 1 molc/molc indicate the threshold value of potentially phytotoxic BC/Al ratio.

75 Forest hydrology – results of research in Germany and Russia spruce stand, can possibly be explained by mobilisation of base cations from mineralisation through decay of organic material. The concentration in the brook water at the catchment outlet is an integrative response to the element dynamics at the different stand structures of the catchment. Figure 2.28 displays time series of some of the measured chemical parameters in the brook water at the catchment outlet. Besides typical seasonal variations, which are due to nutrient uptake by the trees, the disturbance in the element cycle, caused by the extremely dry summer of 2003, is remarkable. Whilst in summer 2003, the sum of cations and anions sharply decreased (Figure 2.28c), it increased when the catchment became gradually re-saturated in the following winter/spring. Of importance is the sharp peak in NO3 output from the catchment which is accompanied by a comparably large Ca loss. Since 2003, both the cation sum and the electrical conductivity of the brook water are elevated and only slowly approximate their former levels.

Figure 2.28 Time series (daily data) of chemical parameters in the catchment outlet

2.2.4.3 Soil hydrological dynamics The soil water dynamics are mainly governed by (i) the climatic driving variables, (ii) the soil-hydraulic characteristics (retention and conductivity) and (iii) the soil-water uptake by the trees. The continuous tensiometer readings in the various microplots (cf. Figure 2.34) were analysed with respect to the spatial and temporal correlation between the different stand types

76 Selected case studies in German low mountain ranges and crown situations. As an example, Figure 2.29 shows the correlations between tensiometers in similar depths but in different stand/crown situations. All microplots investigated show very similar correlation patterns; the correlation of the tensions between

Figure 2.29 Correlations of tensions measured in similar depths in the different microplots different microplots is highest in the rooting zone (measuring depths 30 and 60 cm) and decreases with depth. Furthermore, the correlation in the uppermost measuring depth (15 cm) is lower than those in the rooting zone. From these correlation patterns, it can be concluded that • the meteorological conditions (specifically, precipitation/throughfall) vary largely between the various stand/crown structures and hence, the tensions in 10 cm depth, which immediately reflect the dynamics of the precipitation, are comparatively low-correlated; • the root water uptake has a large levelling effect on the soil water tensions and hence, the tensions in the root water zone show high correlations between the various stand/crown structures; • the dynamics of the tensions below the rooting zone depend on the soil properties and specifically on the water flux immediately after rain events, which in turn is mainly governed by the hydraulic conductivity of the soil. Hence, the low correlations between the tensions in the lower measuring depth (120 and 180 cm) indicate highly variable conductivities between the investigated stand/crown structures. With the tensiometers, soil water tensions can be measured only up to the air entry value of the porous ceramic cup, which is around 600 hPa. For observing soil water dynamics in the drier range, FDR (frequency domain reflectometry) probes were installed in the "femel"8 gap

8 Irregular shelterwood (femel) system

77 Forest hydrology – results of research in Germany and Russia and the spruce pole stand. In the time series displayed in Figure 2.30, the extreme desiccation of the soil during the unusually dry summer of 2003 is remarkable. In the spruce stand, the soil water fell below the wilting point even in the lower soil depths, which led to a die-off of fine roots in the entire soil profile. The "femel" gap desiccated comparatively less and recovered almost completely in the following years. By contrast, the spruce stand has not yet reached former water content values. This is firstly due to the hysteresis effect of the retention and conductivity characteristics and secondly the soil structure might have changed in consequence of the extreme desiccation (e.g. through the forming of desiccation cracks, which lower the soil water retention capacity). The pronounced hysteresis of the soil-hydraulic properties manifest itself in the extremely fast desiccation in summer 2003 and the very prolonged re-saturation period, which has not ended yet.

Figure 2.30 FDR measurements in the spruce stand and a "femel" gap

2.2.5 Modelling water flow

The water flow in the unsaturated zone was simulated using numerical transport models based on the Richards equation for 1D water flow (WHNSIM) [Huwe and Totsche, 1995] and 2D water flow (Hydrus2D) [Rassam et al., 2006], which has the general form: dθ ∂h = ∇[]k(h) ⋅ ∇h + U ()h (2.3) dh ∂t where θ = the volumetric water content h = the pressure potential in head units t = the time k = the hydraulic conductivity.

78 Selected case studies in German low mountain ranges

The source/sink term U accounts for the water uptake by roots, which is a function of the pressure head in the rooting zone. The 1D model was set up separately for each of the tensiometer measuring profiles. For the 2D model, the slope transects were defined including the topography from the 1m DGM. The slope transects were chosen such that they included all microplots related to one metastructure measuring point (cf. Table 2.7). As an example, the transect shown in Figure 2.31 (mixed stand, spruce tree) covers the measuring points at the crown centre, the crown periphery and the crown gap of the observed spruce tree in the mixed stand. Thus, the model can be calibrated simultaneously on the tensiometer readings of the different crown strata. The main differences in the model parameters of the different slope transects were, apart from the morphology and the location of the tensiometer observation points, the root distribution, the maximum transpiration capacity and the interception capacity, all depending both on tree species and crown density.

Figure 2.31 Example slope transect in Hydrus-2D showing the boundary conditions defined and the zoning of the model domain according to the crown density (green lines). Different colours indicate areas with different root densities.

2.2.5.1 Model boundary conditions The upper model boundary was described by the atmospheric conditions, the potential water flux resulting from open-land precipitation, interception and potential evapotranspiration. Interception was calculated from measured precipitation, throughfall and stemflow. For this, weekly measured throughfall and beech stemflow data were dissolved into daily values using daily precipitation values as reference. The potential evapotranspiration was calculated with the Penman-Monteith equation. The solar radiation, which is measured horizontally, was corrected according to the inclination of the modelled sites.

79 Forest hydrology – results of research in Germany and Russia

The lower model boundary was always defined as free drainage. For the 2D model, the upslope boundary was a zero-flux condition and the down-slope boundary was described as a seepage face (cf. Figure 2.31).

2.2.5.2 Soil hydraulic characteristics The essential point for modelling water fluxes based on the Richards equation is to have knowledge of the functional relationship between soil water content θ, matric potential ψm and hydraulic conductivity k. The soils in the Conventwald are characterised by extremely high skeleton fractions which vary highly in space. In comparison, the hydraulic properties of the soil matrix are relatively homogeneous. Various soil surveys were conducted in order to quantify both the hydraulic properties of the soil matrix and the impact of the soil skeleton on these properties. Various laboratory approaches were applied for estimating the hydraulic properties of the soil matrix. Field measurements of soil water contents and matric potentials were combined with experiments on soil columns in the laboratory in order to quantify the hydraulic effect of the soil skeleton at plot scale. In order to quantify the retention characteristic θ(ψ) of the soil matrix, 100 ml soil sample rings were taken at four soil profiles (five depths, three samples per depth and profile) and the retention curves were measured in the laboratory in a pressure plate apparatus. For the description of the retention curve in the numerical simulations, various forms of the retention curves, either discrete θ-ψ-value pairs or the parametric model of van Genuchten [1980], were applied. The unsaturated hydraulic conductivity characteristic k(θ) was derived from the retention curve using two different pore models, Millington and Quirk [1960] in a modification of Jackson [1972] and Mualem [1976]. For both pore models used, the relative conductivity was scaled with the saturated hydraulic conductivity ks. The ks values were measured at 100 ml sample rings. Due to the small sample size, measurements at 100 ml sample rings hardly capture the effect of the soil skeleton on the soil hydraulic characteristics and thus primarily observe the hydraulic properties of the soil matrix. For both the hydraulic conductivity and the water retention curve, the various samples taken in the catchment revealed similar values, which varied within rather narrow value ranges only. For the subsequent water flow simulations, it was therefore assumed that any soil-hydraulic variability in the investigated area is mainly caused by the highly heterogeneous soil skeleton fractions. In order to characterise the spatial variability of the soil skeleton conditions, soil samples were taken in four depths and at 54 randomly chosen locations within the catchment. The expected values of the skeleton content are similar in all measuring depths: E15cm(x) = 61 Vol.%, E30cm(x) = 55 Vol.%, E60cm(x) = 54 Vol.%, E120cm(x) = 61 Vol.%. However, the variability decreases with depth: σ15cm(x) = 11 Vol.%, σ30cm(x) = 10 Vol.%, σ60cm(x) = 9 Vol.%, σ120cm(x) = 7 Vol.%. The correlations between the skeleton contents of the different soil depths are small (correlations between depths 15 cm, 30 cm und 60 cm 0.4 to 0.6, correlations with depth 120 cm <0.3). Contrary to the upper soil depths, the skeleton contents of the lower soil depths (60 cm, 120 cm) show a distinct spatial structure (Figure 2.32). The typical correlation length of the skeleton contents was calculated from the fitted analytical variance functions and equals on average 18 m for all soil depths.

80 Selected case studies in German low mountain ranges

Figure 2.32 Empirical variances of the soil skeleton content and fitted variance functions

In laboratory experiments, the influence of the soil skeleton fraction on the hydraulic conductivity was analysed. For this, the natural soil structure was reproduced in experimental columns where skeleton sampled in the catchment was stratified into a sandy-silty quartz substrate according to the natural alignment in the field. The skeleton content in the soil columns was varied systematically within the value range observed in the catchment. The saturated hydraulic conductivity was measured at the prepared quartz substrates. Not unexpectedly, ks values decrease with increasing skeleton content. A reduction function was fitted to the measurements and was subsequently used to reduce the ks values measured at the (quasi skeleton-free) sample rings according to the observed skeleton content in the field where the sample was taken (Figure 2.33).

2.2.5.3 Model calibration The 1D model WHNSIM was calibrated firstly for the hydrological years 1992 to 1996 and over the years, at times, the model accuracy was validated and improved as necessary by prolonging the calibration period with recent data. During the calibration, the parameters of the root water uptake function, i.e. the maximum possible root water uptake and the vertical root distribution9, were varied in order to fit the observed tensions in the different measuring depths (Figure 2.34). The parameters of the calibrated 1D model WHNSIM were then used to parameterise the 2D model Hydrus-2D.

9 Root counts (data not shown) were used to define the possible value range for the variation of the relevant parameters during the model calibration.

81 Forest hydrology – results of research in Germany and Russia

Figure 2.33 Left graph: Measured relative saturated conductivities relating to the measured saturated conductivity of skeleton-free quartz substrates and fitted reduction function. Right graph: Example for the reduction of the ks values measured at 100 ml soil sample rings (dashed line) and reduced ks values according to the observed soil skeleton content at the soil profile (solid line).

In addition to model calibration with matric potentials, the discharge in the catchment outlet was simulated by summarising the simulated seepage fluxes (180 cm depth) in the various microplots, weighted by the surface percentage of the corresponding structure unit. Even though a 1D model cannot explicitly describe the transport process in the transition between the location of the simulated soil profile and the catchment outlet (runoff concentration), this comparison provides at least a general assessment of the model performance. With a few exceptions, the simulated and measured brook discharges are in good agreement, and the model adequately represents the drainage dynamics (Figure 2.35).

2.2.5.4 Results The results of the water fluxes simulated with WHNSIM indicate a dependency on the crown structure, down to depths of 120 to 180 cm. The water flux at a soil depth of 180 cm is strongly related to the crown density and the magnitude of the throughfall (Figure 2.36). Differences are especially pronounced between crown openings with high throughfall (treefall gap, regeneration area, crown gaps) on the one side, and the beech stemflow areas and the more closed structures (crown centres without stemflow and crown peripheries) on the other. According to Dunnett's test, simulated water fluxes below crown openings and stemflow areas were significantly higher than in crown centres. Measured stemflow and throughfall showed similar, although less significant, patterns. The separate input path of the beech stemflow, which is concentrated in a small area around the stem centre, led to water flow rates that at 180 cm depth were still 30% greater than the precipitation (Figure 2.36).

82 Selected case studies in German low mountain ranges

Figure 2.34 Calibration of WHNSIM using the continuous tensiometer readings in the different soil depths; here, model fit for the microplot "mixed beech stand, crown centre".

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Figure 2.35 Measured and simulated daily discharge, Conventwald catchment, Black Forest, Germany, 1 November 1998 to 31 October 2002. (Simulated data: mean of the fluxes in the structural units at 180 cm depth weighted with their surface percentage in the catchment)

At the beech stemflow area, the calculation of fluxes is based on the assumption that the stemflow infiltrates 80 cm around the beech stem (input = stemflow + crown-centre throughfall). At the other structural variants, the input was measured as throughfall. These results agree with measurements at catchment scale, where a thinning of stand density resulted in an increase of the annual water yield [e.g. Bosch and Hewlett, 1982; Swank and Johnson, 1993; Sahin and Hall, 1996; Hornbeck et al., 1997]. A reduction in forest cover generally means less transpiration, reduced losses to canopy interception and increased streamflow. Patches of high crown density are usually patches of high interception and low throughfall, with the consequence of low infiltration and low soil-water flux rates. In the crown centres, the fine root density and hence water uptake are generally larger than under the crown periphery [von Wilpert et al., 1996b]. Due to these overlaying effects, the differences between crown centres and crown openings appear more clearly in the simulated soil water fluxes than in throughfall and stemflow data (Figure 2.36). In the next step, the calibrated models were used for assessing the impact of the different tree composition and harvesting strategies on the water quantity and, as a consequence, element leaching from the root zone. In order to derive conclusions for silviculturally relevant plot sizes the observations and model results obtained at a measuring point need to be transferred to the catchment scale. For this transfer, it is essential to take into account the effects of the spatial variability in the model. The spatial variability in the catchment is firstly due to the spatially heterogeneous tree composition and related root/crown densities, and secondly to the spatially variable soil properties (retention and hydraulic conductivity). The spatial distribution of the tree species and crown/root densities is well known from the different root and canopy measurements and can be explicitly described in the models. However, the quantification of the impact of the variability of the soil hydraulic properties on

84 Selected case studies in German low mountain ranges

Figure 2.36 Simulated water fluxes at 180 cm soil depth and measured stemflow and/or throughfall for various microplots in the Conventwald catchment (1 November 1991 to 31 October 1996). Values are averages over the model period. Error lines are the standard error of the daily mean. The dotted vertical line indicates mean precipitation. Stars indicate a significant difference between two microplots according to Dunnett's test (α = 5%). the water transport at catchment scale is hampered by the uncertainties connected with the spatial distribution of the soil skeleton. Using Hydrus-2D simulations it was investigated to what extent the present uncertainty in the soil skeleton description influences the simulated water flux along the model slopes. This gives an indication as to how far the effects of the soil variability overlies the effect of the stand structure on the water fluxes in the catchment. Secondly, it reveals whether an improved spatial assessment of the soil skeleton properties is necessary in order to correctly quantify the seepage rate and element leaching under different stand structures. The water flux was simulated for all transects defined and for selected rain events (four rain events: two in summer, two in winter, and from those, two with moist preconditions and two with dry preconditions). The variability of the soil hydraulic properties within a transect was described by means of scaling factors which scale the saturated hydraulic conductivity in relation to a reference conductivity value (Miller and Miller similitude). The reference value

85 Forest hydrology – results of research in Germany and Russia was dependent on the soil layer and derived from laboratory measurements. At each computational node of the model grid, the scaling factor was calculated as follows: • quantifying the skeleton content at the different measuring depths in the field

• calculating the corresponding reduction factor for ks (Figure 2.33) • transforming the reduction factors (decadal logarithm); calculating the expected values and variances of the logarithmic reduction factors for the different soil depths • calculating the spatial correlations of the skeleton contents; calculating the maximum correlation lengths of the scale factors

• calculating the log-normally distributed scaling factors for ks using the scaling function in Hydrus-2D under consideration of the estimated spatial correlations. From the log-normal distribution of the scaling factors, 100 realisations of the spatial distributions of the scaling factors within the model transect were drawn. For each realisation, the soil water transport following the selected rain events was simulated. The model results were analysed with respect to the seepage rates below the root zone. Figure 2.37 shows the expectations and confidence intervals of the seepage rates of the two model zones, crown centre and crown gap for the stand structure example "mixed stand, spruce tree". It can be seen that the seepage rates below different crown density situations are significantly different, even if the uncertainties in quantifying the hydraulic soil properties are taken into account. The simulations at the slope transects showed that a realistic characterisation of the spatial distribution of the soil skeleton content is possible by using scaling factors for ks. Further investigations are aimed at identifying the impact of other skeleton properties, e.g. form and alignment of the skeleton, on the seepage dynamics. Finally, the investigations aim at designing a model concept which allows for an adequate consideration of the skeleton conditions in the simulations of the soil water and matter transport at the catchment scale.

Figure 2.37 Modelled seepage rates in a soil depth of 180 cm for a precipitation event from 14 to 17 July 2001 for the stand structures "mixed stand, spruce tree", density strata crown centre and crown gap (solid lines = expectations, dash-dotted lines = 95 % confidence intervals)

86 Selected case studies in German low mountain ranges

2.2.6 Modelling ion fluxes

2.2.6.1 Model setup The ion fluxes were calculated by multiplying the ion concentrations in the soil water (weekly suction cup measurements) with the simulated water fluxes in the respective soil depths (daily values). Data related to the catchment were composed by summarizing the simulated fluxes in the different structural units and evaluating these fluxes with the surface percentage of the referred strata within the catchment (cf. Table 2.6).

2.2.6.2 Spatial effects on ion fluxes In each structural unit (microplot), the flux density throughout the entire soil profile was summarised by compiling the individual fluxes related to crown density strata. In Figure 2.38, flux rates averaged over the hydrological years 1992 to 2006 are shown for extremely different stand situations. The flux of (the conservative element) chloride is more or less equal for all soil depths and stands, which indicates the validity of the flux calculations. The flux rates in the two neighbouring pole stands differ markedly. Prior to the afforestation 50 years ago, the stands were similar at both sites. Hence, the observed differences in the element cycling are caused by the different stand characteristics during the previous 50 years. In the beech stand, the flux rates are more or less constant throughout the root zone. Below the root zone, a higher pH (>5) leads to a completely different chemical regime with high hydrogen-carbonate activity. Furthermore, possible lateral flow from the neighbouring spruce pole stand to the beech pole stand can account for the elevated element fluxes below the root zone. In the root zone (0 to 60 cm depth) of the spruce pole stand, the high ion flux is mainly caused by extremely high NO3 and SO4 fluxes, two to three times higher than in the beech pole stand, resulting in over-proportionally high Mg and Al fluxes. In the root zone of the spruce stand, about one third of the anion transport are organic anions. Below 60 cm, they amount to only 10 to 15%. In the clear-cut, the element flux is about as high throughout the entire soil profile as in the upper soil of the spruce stand. Again, this is mainly caused by very high NO3 and SO4 fluxes. SO4 shows a clear net mobilisation in the mineral soil between 20 cm and 80 cm depth. In the clear-cut area, high transport rates were also found for Ca, Mg, Al and H. Another example of the structural effects on ion fluxes is presented in Figure 2.39. The simulated NO3 fluxes of the 12 investigated structural units at 180 cm soil depth are compared to the measured NO3 input by throughfall and stemflow.

In contrast to water flux, NO3 flux in the soil depends less on infiltration rates, but rather on uptake by roots and local N mineralisation (Figure 2.39). The variability in deposition may be explained by variability of dry deposition and canopy exchange processes, which are related to the crown density [Ibrom, 1993; von Wilpert et al., 1996a] or, for circular crowns, to the distance from the tree trunk [Beier et al., 1993; Seiler and Matzner, 1995]. As was shown by Manderscheid and Matzner [1995], the solution chemistry in forest soils generally has a large spatial heterogeneity, which varies depending on the spatial variability of deposition, soil solid chemistry, weathering and microbiological processes. On the catchment scale, Matzner et al. [1998] found a very high spatial variation in NO3 flux in soil solution at 100 cm depth (Norway spruce stand) that was not related to differences in throughfall. The spatial patterns of the soil chemistry were found to correspond to the results of Koch and Matzner [1993] for beech and spruce stands in the Solling ecosystem study.

87 Forest hydrology – results of research in Germany and Russia

a) Beech pole stand (40 yr) b) Spruce pole stand (40 yr)

Figure 2.38 Element flux densities through the soil down to the interface between soil and bedrock (180 cm) for the whole stand areas in the beech pole stand (a), the spruce pole stand (b) and the clear-cut (c), averaged values for hydrological years 1992 to 2006. The graph contains the whole flow path from open-land precipitation (OL) over throughfall (TH) to the seepage water output at 180 cm soil depth.

At 180 cm soil depth, NO3 shows significantly higher flux rates below crown openings than below crown centres and crown peripheries. NO3 fluxes at 180 cm depth below the areas of high crown density are significantly lower than the fluxes resulting from the input by throughfall. This can be attributed to higher N uptake rates in the more intensively rooted crown centres. At the crown gaps of beeches, the NO3 fluxes at 180 cm depth and the NO3 input rates from throughfall are similar. Processes of mineralisation and nitrification open the nutrient cycle when ground vegetation is absent [Gundersen and Bashkin, 1993]. This is clearly evident in the extremely high NO3 seepage of the treefall gap. Although treefall gaps have a proportional area of only 2% in the catchment and hence contribute only with a minor

88 Selected case studies in German low mountain ranges

Figure 2.39 Simulated NO3 water fluxes at 180 cm soil depth and measured NO3 in stemflow and/or throughfall for various microplots in the Conventwald catchment (1 November 1991 to 31 October 1996). Values are averages over the model period. Error lines are the standard error of the daily mean. The dotted vertical line indicates mean precipitation. Stars indicate a significant difference between two microplots according to Dunnett's test (α = 5%).

part to the total NO3 flux (cf. Table 2.6), the load at the catchment outlet is almost exclusively driven by the NO3 dynamics of the gaps. Without the existence of treefall gaps, the NO3 output of the catchment would therefore be within the low range of natural ecosystems [Raison and Stottlemeyer, 1991].

Significant NO3 leaching was found for all experimental stands. In the beech-dominated stands, this was mainly restricted to the non-growing season. The total N input rate of about 15 kg ha-1a-1 in beech stands marks the critical load above which N saturation is expected, which consequently leads to an increased N leaching [Aber et al., 1989]. In the spruce stands,

89 Forest hydrology – results of research in Germany and Russia

-1 -1 with a nitrogen input rate of about 25 kg ha a , the threshold above which NO3 leaching occurred in all previous European case studies [Dise et al., 1998] was overridden.

2.2.7 Discussion

2.2.7.1 Site drift Observations since 1991 certify that even silicate-rich areas with a good base saturation (from the parent material) can have a severe loss of typical soil functions, due to the reduction of exchangeable base storage capacity through deposition input.

2.2.7.2 Spatial variability of water and matter fluxes Results show that precipitation throughfall and element deposition are dependent on crown density. The soil-water suctions as well as the soil-water fluxes and the matter transport also show a large spatial variability. The spatial distribution of precipitation throughfall in forest ecosystems is determined by crown interception, which decreases significantly in treefall gaps and crown gaps. The spatial variability of water fluxes is governed by the throughfall distribution as well as the hydraulic soil characteristics (water conductivity and retention capacity, stoniness) and the spatial variability of soil-water uptake/root densities.

2.2.7.3 Forest management options to improve seepage water quality In this study, the deposition of acids and nitrogen were significantly different for different tree species and stand structures. In beech-dominated stands, the deposition load was about 45 to 85% lower than in spruce stands. For future silvicultural guidelines, it can be stated that changing from spruce stands to beech stands has a high potential for the reduction of further depositions and thus the acidification of forest soils. However, even in the beech-dominated stands of this study, symptoms of a nitrogen surplus are evident and soil acidification is in progress. Nonetheless, a change from spruce to beech will significantly reduce the deposition load on forest soils and will certainly slow down the acidification rate and enhance the uptake capacity of the ecosystem for nitrogen. It should be pointed out that the various silvicultural methods for forest conversion are no panacea; in the long run, there is no alternative to the reduction of nitrogen and acid compounds brought about by a policy of air purification! It can be expected that especially nitrogen emissions will not be reduced in the near future as their sources are mainly diffuse. For this reason, silvicultural policy must react to the actual deposition situation and combine all suitable techniques to enhance the medium-term storage capacity of forest ecosystems for nitrogen and to counteract the chemical and biological damages caused by acid depositions. Stable and more or less closed biogeochemical matter cycles are a precondition for site sustainability and guarantee high drinking water quality under forest soils. The study showed that especially in spruce stands the filter and buffering function for drinking water is seriously endangered under the influence of current depositions and the after-effects of former depositions. The stock of inorganic and organic nitrogen is already close to saturation and hence each disturbance of the stand structure leads to an immediate NO3 mobilisation and, thereby, an acute hazard for the drinking water quality. The results show that, under the present immission regime, generating even very small canopy openings leads to high NO3 fluxes, albeit on a much lower level than caused by large clear-cuts. It is therefore strongly

90 Selected case studies in German low mountain ranges recommended that common silvicultural practice focus on gap-oriented natural regeneration (preferentially in stands with an already existing understorey) and completely turn away from the tradition of clear-cuts.

2.3 Kleine Kinzig: forest liming to enhance the water quality in the catchment of a drinking-water reservoir

2.3.1 Research focus

The current soil-chemical modification induced by atmospheric nitrogen and acid deposition, discussed in detail in the case study "Conventwald", can be compensated by forest liming. In the "Kleine Kinzig" case study the extent to which forest liming can increase the buffer capacity of forest soils and thus reduce the leaching of hazardous elements to the groundwater and surface water is studied. For this, two sub-catchments of the "Kleine Kinzig" drinking- water reservoir, one completely limed and the other one only partly limed, have been compared with each other with respect to their soil chemical status and the element loads of soil water, groundwater (perched groundwater and springs) and surface water (brooks). In order to quantify the impact of the soil buffer on the groundwater and brook-water chemistry in the two catchments, it is essential to understand the flow paths from precipitation through infiltration and transfer to the brook.

2.3.2 Site characteristics

The sites studied are sub-catchments of the "Kleine Kinzig" drinking-water reservoir in the Northern Black Forest, about 5 km southwest of Freudenstadt (Figure 2.40). The Huttenbächle catchment has an area of 3.92 km² and an average altitude of 780 m a.s.l. (600 to 810 m a.s.l.). The average slope inclination amounts to 6.9°, and the catchment is south to southwest exposed. The Teufelsbächle catchment has an area of 2.16 km², with an average altitude of 790 m a.s.l. (600 to 840 m a.s.l.). The average slope inclination is higher than in the Huttenbächle catchment with 10.4°. The catchment is southeast exposed. The climatic conditions in both catchments are comparable. The average yearly precipitation amounts to 1700 mm, with about one third falling during the vegetation period from May to October. The annual average temperature is about 7.9°C in the lower catchment parts and 6.6°C in the upper parts. The runoff yield is almost identical for the two catchments with 33 l s-1 km-1 in Huttenbächle and 31 l s-1 km-1 in Teufelsbächle. The two catchments are almost completely covered by forest. The tree composition in the two catchments is similar with 52% spruce, 37% silver fir, 9% beech and 2% larch in the Huttenbächle catchment, and 52% spruce, 45% silver-fir and 3% beech in the Teufelsbächle catchment. The geological conditions in the two catchments are comparable. Apart from very few alluvial and glacial deposits, Triassic Sandstone (Buntsandstein) is the dominating geological formation (Figure 2.41). It is formed mainly from quartz sand with variable, but always low, contents of mica, dolomite and clay layers. Triassic Sandstone is characterised by coarse weathering material and hence a large amount of fractures. The mineral content is dominated

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Figure 2.40 "Kleine Kinzig" drinking-water reservoir and the investigated sub-catchments "Huttenbächle" and "Teufelsbächle" by quartz, and the content of basic cations is generally low. Thus, Triassic Sandstone is both very conductive for water and badly buffers any chemical element input. Therefore, atmogen acidification can easily reach the deeper subsoil and the bedrock and influences the groundwater relatively fast. Water circulation and water retention in the Buntsandstein are primarily bound to the joint system. The Bausandstein and the Lower Conglomerate have a higher joint ratio than the Upper Conglomerate. The lower joint ratio in the Lower Conglomerate and the existence of clay layers at the border between Bausandstein and Lower Conglomerate result in an important spring horizon in this zone (Figure 2.42). Impermeable layers also exist in the Lower Buntsandstein, leading to springs above it. In some areas, the weathered sandstone below the periglacial layer also has a retaining effect; here, at the foot slope of the periglacial debris, "flat" springs occur. The groundwater flow in the catchments studied can be roughly described as follows: the water ponded on the impermeable layers drains according to the inclination of the sandstone formations to the southeast and discharges as contact springs primarily on the western slopes of the valleys. These springs typically have relatively constant yields and are influenced by the chemical properties of the underlying bedrock. In contrast, the springs on the eastern side of the valleys, which are much rarer, are "overflow" springs, which reflect the chemical characteristics of the fast draining interflow and temporarily occurring perched groundwater in the periglacial layer [Sawatzki, 1994]. Triassic Sandstone weathers to sandy or sandy-loamy substrates, from which podzols or podzolic cambisols formed on the hill slopes and stagnic soils formed on the badly drained plateaus.

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Figure 2.41 Geological formations in the Huttenbächle and Teufelsbächle catchments

Figure 2.42 Groundwater conditions in the Buntsandstein formations: 1 = water-conducting layers; 2 = saturation spring; 3 = contact spring; 4 = spring in the periglacial layer; so = Upper Buntsandstein; smc2 = Upper Conglomerate; sm = Bausandstein; smc1 = Lower Conglomerate; su = Lower Buntsandstein; ro = Oberrotliegendes; G = Granite [from Sawatzki, 1994] 93 Forest hydrology – results of research in Germany and Russia

Liming campaigns were carried out in the two catchments in order to compensate the soil acidification resulting from atmospheric deposits (Figure 2.43). The liming in the Huttenbächle catchment started in 1982, and by 1991 the larger part of the catchment was limed. The liming continued in the remaining parts until 1995. In 2003, the liming was repeated over the entire catchment (3 t/ha dolomite). The Teufelsbächle catchment was limed only once (between 1987 and 1991) and only part of the catchment was covered.

Figure 2.43 Limed areas in the two catchments with years and dosage of liming

2.3.3 Instrumentation and sampling

The sampling scheme and the instrumentation in the two catchments aimed at measuring the quantity and chemical composition of the water in all relevant compartments along the flow path from precipitation to the catchment outlet. Measurements were taken in the soil water, the perched groundwater, the major springs and at various sections of the brooks.

94 Selected case studies in German low mountain ranges

Additionally, soil samples were taken in order to quantify the chemical properties of the soil matrix.

2.3.3.1 Climate and deposition Precipitation and air temperature were measured in the catchments. The distance from the climate station to the Teufelsbächle gauge is about 2.5 km and to the Huttenbächle gauge about 1.5 km. In addition, meteorological data of the Deutsche Wetterdienst, stations Wolfach (19 km away from the catchments studied), Freudenstadt (3 km), Lützenhardt (14 km) and Baiersbronn (12 km) were used. Deposition data were obtained from the state-wide deposition network, stations Freudenstadt (3 km) and Baiersbronn/Stöckerkopf (10 km).

2.3.3.2 Surface water, perched groundwater and soil water Water samples were taken bi-weekly at various sampling points along the brooks as well as from the main springs (Figure 2.44). Perched groundwater was also sampled bi-weekly, along a transect of 23 observation boreholes (20 m spacing) in each catchment. Additionally, the water level in the boreholes was measured at 10-minute intervals. In the lowest borehole of the two transects, the pH value and electrical conductivity were recorded at one-hour intervals. In both catchments, suction cups (20 and 80 cm depth) were installed at the lower and the upper ends of the transects, and soil water samples were taken bi-weekly. In this way, the element concentrations in the soil water (sampled in the suction cups) could be compared with those in the perched groundwater (sampled in the observation holes).

HB_B08 TB_B99 ! TB_Q01 ! # TB_Z04 " ! TB_B04 HB_B07 HB_B06 TB_Z03 EEE ! " EEE EE ! TB_B03 ! EE slope water transect EE EE TB_Z22" E TB_Z02 " ! TB_B02 " TB_B01 TB_Z01 ! HB_Q06 ! TB_B00 ! # HB_B05 ! HB_B04 HB_Q05 # ! HB_B03

HB_Q04 EEEEEEEEEE # EEEE EEEE EEEEE slope water transect HB_B02 ! HB_Q03 HB_Q02# # # HB_Q01 HB_B01! HB_Z01 HB_Q00 # " ! HB_B00

Figure 2.44 Sampling locations in the Huttenbächle and Teufelsbächle catchments (B = primary brooks, Z = secondary brooks, Q = springs) 95 Forest hydrology – results of research in Germany and Russia

In addition, the water contained in the very fine pores, which is less mobile, was sampled every two weeks. For this, soil samples were taken with an auger (80 cm drilling depth). From the soil samples, so-called D-solutions (desorption solutions) were obtained by imposing a pressure of 1600 hPa on the soil samples [Blattner et al., 2000]. The samples were then analysed using capillary electrophoresis, which allows the analysis of very small sample volumes only (up to 160 µl). The D-solution characterises the water fraction which is less influenced by root water uptake and thus is in relative equilibrium with the mineral supply from the soil matrix and the weathering material. All water samples were analysed with respect to their concentration of Al, Ca, K, Mg, Mn, Na, Si, Cl, SO4, DOC, NO3, NH4, total N and P. At the two catchment outlets, the water level and the electrical conductivity were registered in 30-minute or 1-minute (for high flow events) intervals.

2.3.3.3 Soil chemistry Soil profiles were dug in both catchments, according to a 200-metre-wide grid (98 soil profiles in the Huttenbächle catchment and 55 soil profiles in the Teufelsbächle catchment). Disturbed soil samples were taken from the humus layer (separately for the Of and Oh horizon) and from the mineral soil (0-10, 10-30, 30-60 and 60-90 cm depth). Additionally, soil samples (20 and 80 cm depth) were taken along the slope transect of the groundwater observation holes, cf. Figure 2.44). All soil samples were analysed with respect to their concentration of Al, Ca, Fe, K, Mg, Mn, Na, Cl, SO4, C, DOC, NO3, NH4 and total N.

2.3.3.4 Electrical Resistivity Tomography Geoelectrical surveys were undertaken in collaboration with the Institute of Meteorology and Climate Research of Karlsruhe, Germany. They aimed at estimating • the position of the upper boundary of the intact bedrock • the distribution of rocks in the weathering zone • the spatial distribution of the soil water content on three different days. The Electrical Resistivity Tomography (ERT) technique is based on electrical resistivity differences between different subsurface materials. In homogeneous material, low resistivities can be associated with high water saturation and high resistivities with low water saturation. In DC (direct current) resistivity surveys, electrical current is injected into the ground via two current electrodes. The resistance of the ground is then determined by measuring the electric potential between two other electrodes and dividing by the current. By multiplying the resistance with a geometric factor depending on the distance between the electrodes and choosing different electrode spacing and locations, the so-called apparent electrical resistivity is determined on a 2-dimensional grid. By using a tomographic inversion scheme (RES2DINV) these apparent resistivities can be inverted to yield a 2-dimensional specific resistivity model of the ground. By modifying the distance between the electrodes, the depth of investigation is also modified [Hauck, 2002; Hauck and Scheuermann, 2005]. For monitoring purposes, the ERT measurements are repeated at certain time intervals using a permanently installed electrode array. The permanent electrode array effectively filters resistivity variations due to variable electrode contacts or geological background variations. By this, temporal resistivity changes can be related to changes in the subsurface water content

96 Selected case studies in German low mountain ranges on a 2-dimensional grid. A detailed description of the experimental principles and analysis methods is given in Hauck and Scheuermann [2005]. For the ERT surveys in the Huttenbächle catchment, 423 electrodes were inserted parallel to the slope water transect (Figure 2.44). The electrodes reached down to a depth of 1 m into the ground. Three measuring campaigns were carried out in June 2005 (moderately dry moisture conditions), October 2005 (dry conditions at the end of the vegetation period) and April 2006 (wet conditions after snowmelt). A distance between the electrodes of 10 m was chosen to display the boundary between weathered material and the underlying intact bedrock. Smaller distances (1 m and 5 m) were used for estimating the spatial distribution of rocks in the weathering zone. In order to measure the soil moisture distribution in the soil zone, the distance between the electrodes amounted to 1 m.

2.3.4 Results

All results are discussed with respect to differences between the two catchments and their possible explanations, which are mainly • differences in the natural conditions (e.g. differences in geology and soil properties) • differences in the intensity of the forest liming campaigns.

2.3.4.1 Runoff generation and matter transport The measured discharges and element concentrations at the gauging stations are an integrative measure of the dynamics of water flow and element transport in the catchments. The double sum curve of the runoff yield (Figure 2.45a) suggests that around December 1989 forest thinning in the Huttenbächle catchment significantly altered the runoff formation in the catchment. Due to a decreased crown density, interception loss and transpiration decreased and consequently discharge increased. The effects of the forest thinning are also visible in the double sum curves of DOC and Al (Figures 2.45c,d). After the forest thinning, crown material left on the forest floor as well as dead roots are mineralised. This leads to an increased DOC concentration in the seepage water and consequently in the brook water. The similar trend in the Al concentrations can be explained by the strong binding of Al on to organic material. The double sum curve of Si (Figure 2.45b) gives information about the groundwater flow regime in the two catchments. The silica concentration in the brook water immediately reflects the contact time of the groundwater with the bedrock material. A larger residence time of the groundwater would lead to a larger ion exchange with or dissolution of bedrock material and hence larger Si concentrations in the brook water. Since the double sum curve of Si lies exactly on the 1:1 line, we can conclude that the groundwater flow mechanisms in both catchments are very similar and, furthermore, did not change during the observation period. Hence, the changes observed in the Huttenbächle discharge must have been caused by a perturbance of near-surface runoff components (direct runoff or interflow). A hydrograph separation was carried out using the model DIFGA [Schwarze, 1985; Schwarze et al., 1989, 1991, 2004a]. DIFGA separates the discharge into three runoff components of different flow velocities, i.e. direct runoff, QD, fast groundwater runoff, QG1, slow groundwater runoff, QG2, and relates them to reservoirs with different residence times, i.e. direct runoff storage, RD, fast groundwater storage, RG1, slow groundwater storage, RG2. The residual term in the mass balance is attributed to the evapotranspiration ETR. A

97 Forest hydrology – results of research in Germany and Russia

Figure 2.45 Double sum curves (Huttenbächle vs. Teufelsbächle) of a) runoff yield, b) Si, c) DOC and d) Al; data from January 1989 to May 2006 description of the methodology behind DIFGA is given in section 2.1.4. The hydrograph separation was based on daily streamflow and precipitation data, which were available from 1 Nov 1983 to 31 Oct 2006 for the Huttenbächle brook and from 1 Nov 1990 to 31 Oct 2006 for the Teufelsbächle brook. Table 2.8 summarises the proportions of the different runoff components and storage terms of the two analysed brooks. Generally, the two catchments show a very similar partitioning of the various runoff components. In winter, the slow groundwater storages are filled (RG2 > QG2 for the winter half year). In both catchments, the fast runoff components (RG1 and RD) contribute with about 85% to the total catchment runoff in winter. In summer, the slow groundwater component is emptied (RG2 < QG2). The proportion of the direct runoff is much smaller in summer than in winter, which goes along with an increasing proportion of the slow groundwater runoff. Throughout the year, the fast groundwater runoff is the dominating component. The fast groundwater runoff is mainly generated in the highly conductive periglacial layers and the weathering zone, whereas the slow groundwater component stems from the deep groundwater in the comparatively less conductive intact bedrock. The generally high RG1 proportions indicate the importance of the periglacial layer for the runoff formation in the catchments and therefore for the transport of the liming product from the soil zone to the brook. Apart from the observations at the catchment outlets, a more detailed investigation of the runoff-producing mechanism was possible at the slope transects of the two catchments

98 Selected case studies in German low mountain ranges studied. For all three measuring dates, the ERT surveys in the Huttenbächle catchment display a distinct difference between the resistivities of the soil zone and periglacial layer (high resistivities) and the underlying bedrock and periglacial layer (low resistivities) (Figure 2.46). According to the geological map, the contact between Bausandstein and Upper Conglomerate outcrops around electrode no. 290 with an inclination of about one degree in an eastward direction. Downslope of electrode no. 290, the underlying bedrock is overlaid by a periglacial layer.

Table 2.8 Runoff components of the Huttenbächle and Teufelsbächle brooks, determined from the hydrograph separation with DIFGA

P RG2 RG1 RD Q QG2 QG1 QD ETR winter half year mm/6 months 1173 139 439 154 678 94 430 154 440 % 100 12 38 13 100 14 63 23 37 summer half year mm/6 months 850 45 150 32 282 91 159 32 623 % 100 5 18 4 100 33 56 11 73

Huttenbächle Huttenbächle complete year mm/a 2023 184 590 186 960 185 589 186 1063 % 100 9 29 9 100 19 61 20 53 winter half year mm/6 months 1278 135 438 145 707 117 445 145 560 % 100 11 34 11 100 17 63 20 44 summer half year mm/6 months 893 87 135 32 271 111 128 32 639 % 100 10 15 3 100 41 47 12 72

Teufelsbächle Teufelsbächle complete year mm/a 2172 222 573 176 978 228 573 177 1199 % 100 10 27 8 100 23 59 18 55

Comparing the three different measuring days, it can be seen that the resistivity panels vary with time (Figure 2.46). This can be explained by both the precipitation conditions as well as the transpiration conditions prior to the ERT surveys. Furthermore, the geoelectrical surveys along the slope transect indicate largely heterogeneous flow conditions due to the boulder-like weathering of the sandstone and the resulting very high stone content in the soil layer (Figure 2.47). The comparison between the resistivities measured in October 2005 with those measured in June 2005 gives a detailed picture of the root zone and the structure of the underlying periglacial layer (Figure 2.48). Again, this figure displays the high spatial heterogeneity of the periglacial layer, which is characterised by large boulders and loose material side by side. Up to a depth of about 1.3 m, the saturation decreased remarkably during the vegetation period from June to October (i.e. increase of resistivities). Below the rooting zone, the saturation is higher in October than in June. This can be interpreted as an indication for a two-phase flow system. In times of high water saturation, the soil water contributes over-proportionately to the runoff formation, and fast-flowing interflow waves are generated. In times of drier soil-

99 Forest hydrology – results of research in Germany and Russia

Figure 2.46 Resistivity measurements with a 10 m electrode spacing water conditions (especially during the vegetation period), the runoff is mainly formed from deeper, slower interflow layers and groundwater. From Figure 2.48, a distinct difference in the soil depth development can be seen between the flatter parts of the transect (electrode numbers 0 to 80) and the steeper slopes. The flatter parts generally showed lower resistivities, and from the change from June to October a deeper root development in these areas can be identified. The larger depletion of the soil water storage during the vegetation period and their larger re-filling in the winter half-year might indicate that in the flatter parts of the transect a slower interflow pulse in larger depths of the periglacial layer is generated. This interflow formation is interrupted in the vegetation period due to the depletion of the soil water storage. The pronounced discontinuities in the electrical resistivities and the water saturation in the region of the electrodes numbers 288 to 304, 128 to 144 and 417 to 430 can be explained by the influence of forest tracks on the ERT measurements.

2.3.4.2 Soil chemical status Soil samples were taken according to a 200-metre-wide sampling grid and analysed with respect to the concentrations of the major elements. Table 2.9 gives the average concentrations of Al, Ca, Mg and K for all soil samples in each of the catchments. At the near-surface soil depth (0 to 10 cm), element concentrations are significantly different between the two catchments. The higher Ca concentration in the 0-10 cm samples of the Huttenbächle catchment might be the result of the liming campaigns. However, the Al

100 Selected case studies in German low mountain ranges

Figure 2.47 Heterogeneous material distribution displayed in the geoelectrical surveys concentrations are also higher there. This can be explained by the significantly higher cation exchange capacity (CEC) in the Huttenbächle. This example illustrates that a comparison of the soil chemical status of the two catchments on the basis of single elements is not meaningful, since the absolute element concentrations are directly dependent on the CEC of the soil. An integrative measure of the acidification in the catchments is the base saturation (BS), which is the percentage of the CEC which is actually occupied by bases. Therefore, the subsequent analyses of the soil chemical status focus on explaining the spatial distribution of the BS, and the impact of the forest liming on it, rather than on the concentration of single elements, such as Ca. In the 60-90 cm samples, BS is significantly higher in the more intensely limed catchment (Huttenbächle), which is the expected effect of the forest liming. In 0-10 cm depth, BS is not

101 Forest hydrology – results of research in Germany and Russia increased in the Huttenbächle catchment when compared to the Teufelsbächle catchment. Again, the CEC gives an explanation; the CEC in the Huttenbächle catchment is significantly higher than in the Teufelsbächle catchment. Since more Ca is needed to increase the BS to a certain level when the CEC is higher, it is harder to increase the BS in the Huttenbächle catchment than in the Teufelsbächle catchment with the same liming dosage.

Table 2.9 Average values of Al, Ca, Mg, K, CEC and BS at 0 to 10 cm and 60 to 90 cm soil depth. Significance of differences between the two catchments was tested using the non-parametric Wilcoxon rank sum test (exceedance probabilities: * ≤ 5%, ** ≤ 1 % and *** ≤ 0.1 %, n.s. > 5 %).

Al Ca Mg K CEC BS

µmolc/g µmolc/g µmolc/g µmolc/g µmolc/g % HB 0 to 10 cm 56.74 13.61 6.67 0.83 95.46 21.61 TB 0 to 10 cm 36.02 5.59 3.26 0.54 60.63 19.25 Significance of difference *** *** *** *** *** n.s. HB 60 to 90 cm 29.06 0.91 0.38 0.79 33.70 7.05 TB 60 to 90 cm 29.89 0.52 0.31 0.34 34.65 3.93 Significance of difference n.s. n.s. ** *** n.s. ***

Figure 2.48 Resistivities measured with 1 m electrode spacing and the percentage change of the resistivities between the two measuring days

102 Selected case studies in German low mountain ranges

The next comparison between the two catchments tries to explain the spatial distribution of BS in the two catchments rather than comparing mean values only. With this, we hoped to better extract the impact of the forest liming from other influencing factors on BS since the applied liming dosages and the timing of the liming campaigns varied largely in space (Figure 2.43). Figure 2.49 shows the base saturations derived from the measured element concentrations at the regular 200-metre sampling grid. Information on the base saturation allows conclusions on the nutrition status of the soil for trees. In mineral soils, base saturations above 40..70 % are optimal for plant growth [Ulrich, 1995]. Despite the intensive forest liming, the values both in the Huttenbächle catchment and the Teufelsbächle catchment are far below this optimum level. Apart from the liming, other catchment properties, such as the chemical properties of the underlying bedrock or morphological parameters, have an effect on the BS. In order to quantify the impact of the forest liming and to distinguish the most influencing factors, empirical regression models were derived using multiple regression analysis. The regression analysis aimed at predicting the spatial distribution of the base saturation in the two catchments. As predictors, only parameters which were in logical coherence with the base saturation were considered [Zirlewagen and von Wilpert, 2004]. For the measured base saturations and the possible predictors, various statistical tests were carried out in order to prove the applicability of linear regression models and the necessity to transform predictors. The regression models were adapted and optimised using statistical criteria: index of co-linearity, index of tolerance, root mean square error, model R², partial R² of the model predictors. Conventional geostatistical methods (variogram analyses) were used to prove the stochastic independence and distribution of the model residuals [Zirlewagen et al., 2007; Zirlewagen and von Wilpert, 2004; Zirlewagen, 2003]. Table 2.10 shows, for the two catchments and the two measuring depths, the predictors included in the regression models using a step-wise selection algorithm. For both the measurements and the model residuals, the geo-statistical analyses did not display anisotropic structures. This is mainly caused by the high influence of the forest liming on the base saturation, which does not show spatial autocorrelation and which evens out any spatial correlation of other predictors (e.g. geomorphologic parameters). Therefore, regression models based on data kriging were not applicable to our dataset. The regression models for the Teufelsbächle catchment show relatively high R² values (cf. Figure 2.50) which decrease with soil depth. This decrease can be explained by the decreasing influence of the forest liming on the base saturation in the subsoil. The most influencing factor on the predicted base saturation is the time span since the last liming campaign. Soil profiles which had been recently limed (not more than 12 years ago) show distinctively elevated values. Liming effects were significant up to a depth of 60 cm. If soil-typological attributes were included, the regression model could be significantly improved. However, exact soil descriptions are not available in the form of universally accessible maps and therefore, such regression models cannot be transferred to other catchments for which detailed soil descriptions are not available. Therefore, the regression models presented are exclusively based on mapped parameters as model predictors. Topographic parameters dominate the regression models especially in the lower soil depths. Higher BS values are modelled for foot slope positions but also for top positions. Whilst higher BS values in foot slope positions are conform with the slope catena concept (downslope transport of base cations), the higher BS values at the top positions may be attributed to the fact that the liming concentration is higher in flat slopes than in steep slopes with the same dosage applied. At all depths, the topographic wetness index (TWI) shows significant positive correlations with the base saturation, thereby indicating an effect of lateral flow processes on the transport of basic cations.

103 Forest hydrology – results of research in Germany and Russia

Table 2.10 Predictors included in the regression models and their partial R2 for the two measuring depths in the Huttenbächle and Teufelsbächle catchments (M = metric value, N=nominal value). Liming parameters are highlighted in grey.

Huttenbächle Teufelsbächle 0 to 10 cm 60 to 90 cm 0 to 10 cm 60 to 90 cm partial partial partial partial predictor predictor predictor predictor R² R² R² R² in vicinity of 0.094 in vicinity of 0.296 years since last 0.304 TWI (smoothed) 0.223 helicopter landing helicopter landing liming campaign (M) place (yes/no) place (yes/no) (M) last liming at 0.080 slope inclination 0.135 landform = open 0.110 slope position = 0.123 most 15 yrs ago (M) slopes, ridges or upper slope (N) (N) mountain tops (N) landform = 0.072 percentage of 0.055 slope position = 0.058 vertical curvature 0.076 mountain tops or needle trees upper slope (N) (M) ridges (N) (others than spruce) (M) northing 0.053 erosion index 0.033 TOPEX (100 m 0.056 position index 0.056 coordinate (M) SLF (M) distance) (M) (50m distance) (M) at least two 0.035 landform = 0.027 last liming 0.044 TWI (smoothed, 0.038 liming campaigns mountain tops or campaign at most mean filter) (M) (yes/no) ridges (N) 12 yrs ago (N) TOPEX (25 m 0.031 years since last 0.022 erosion index 0.037 percentage of 0.028 distance) (M) liming campaign SLF (mean filter) stagnosol (M) (M) (M) TOPEX (50 m 0.026 TWI (M) 0.019 percentage of 0.029 distance) (M) stagnosol (M) vertical distance 0.026 TOPEX (50 m 0.017 TWI (smoothed, 0.0259 from flow path distance) (M) mean filter) (M) (M) erosion index 0.040 northing 0.014 SLF (M) coordinate (M)

In the Huttenbächle model, the most influencing factor on the base saturation was the position of the helicopter landing place. At this place, significantly higher liming doses reached the soil surface and hence extraordinarily high base saturations were found in the immediate vicinity. Generally, the variance in the BS values displayed by the regression model is lower for the Huttenbächle catchment than for the Teufelsbächle catchment (model R2 0.45 to 0.62, cf. Figure 2.50). This can be explained by the more homogenous liming of the Huttenbächle catchment, which led to a more homogeneous distribution of the base saturation. In the subsoil, the influence of liming is generally lower. Here, the model R2 of the Huttenbächle catchment and the Teufelsbächle catchment converge. As in the Teufelsbächle catchment, the most significant liming parameter is the time since the last liming campaign. The topographic predictors (TOPEX, SLF) and their positive correlation with the base saturations is consistent with the slope catena concept. The slope inclination is negatively correlated with the base

104 Selected case studies in German low mountain ranges

Figure 2.49 Base saturation in the Huttenbächle catchment – a) 0 to 10 cm depth, b) 60 to 90 cm depth – and in the Teufelsbächle catchment – c) 0 to 10 cm depth, d) 60 to 90 cm depth. The coloured surfaces display the predictions of the regression models, the green squares indicate the range of the measured values. saturation, i.e. the steeper the slope, the lower the base saturation. This may be caused by (i) heavier erosion in steeper slopes (and, hence, export of base cations), (ii) steeper slopes receiving less carbonate when applied from the air, and (iii) advanced decomposition of

105 Forest hydrology – results of research in Germany and Russia

Figure 2.50 Base saturation predicted from multiple regression models vs measured values organic material due to more intensive solar radiation of the slope (negative effect on the base saturation). The regression models for the base saturation give an unbiased estimate of the exchangeable base pool in the soil matrix. From the soil matrix concentrations, the average base concentrations in the soil solution can be approximated by linking the BS of the soil matrix with the BS of the soil solution using empirical distribution coefficients (e.g. Gapon coefficient). Eventually, the spatial distribution of the BS in the soil solution can indicate the role of various hydrotopes in the catchment for the brook chemistry and therefore the effectiveness of forest liming in different areas (hydrotopes) of the catchments on the brook acidification.

2.3.4.3 Element concentrations in the perched groundwater The water samples taken in the transect boreholes represent the relatively fast-flowing perched groundwater. Due to the high conductivity of the periglacial layer and its high boulder content, the observed water levels vary largely in time and also between the boreholes. This variance of the flow dynamics is also reflected in the measured element concentrations.

106 Selected case studies in German low mountain ranges

Figure 2.51 shows boxplots of the Ca concentrations measured along the slope transects in the Huttenbächle and Teufelsbächle catchments.

Figure 2.51 Ca concentration in the perched groundwater along the slope transect in a) Huttenbächle catchment and b) Teufelsbächle catchment. Observation boreholes go upslope from left to right.

In the Huttenbächle catchment, a significant effect of the forest liming can be seen (Figure 2.51, top). The area of the lower boreholes (HB_P01 to HB_P13) was limed once, in 1988. The higher slope positions (HB_P16 to HB_P22) were limed twice, in 1982 and 1993 (cf. Figure 2.43). There, comparatively high Ca concentrations were observed. According to the regionalisation of the soil chemistry (cf. section 2.3.4.2 and Table 2.10), this can be interpreted as an immediate liming effect. Furthermore, the upper boreholes are situated in flat slope positions, which, again in conformance with the regionalisation of the soil chemistry, leads to higher Ca concentrations. Generally, slow deep infiltration hinders the leakage of basidity in the flat areas, whereas basidity is quickly leached out along the steeper slopes due

107 Forest hydrology – results of research in Germany and Russia to higher flow velocities. In the borehole right above the brook (HB_P01), a deviation from this general trend is observed. Here, the Ca concentrations immediately reflect the concentration of the brook water (cf. Figure 2.54). The surrounding area of the Teufelsbächle transect is completely unaffected by the forest liming (cf. Figure 2.43). Consequently, the observed Ca concentrations are generally very low and vary only slightly.

2.3.4.4 Element concentrations in the springs The element concentrations in the springs and the secondary brooks reflect both the geological conditions in the aquifer as well as a possible liming influence. The influence of the liming on the spring concentrations depends on the proportion of seepage water in the spring water. Rather than the mineralogical conditions above the spring horizon, the intensity of the flow passage through the periglacial till seems to have the largest impact on the Ca content of the springs. This is plausible since all sandstone formations in the two catchments have a very low mineral Ca content and hence the bedrock weathering hardly contributes to the Ca concentration in the springs. However, in times when the water table reaches into the upper depths of the periglacial zone (winter and early spring), the groundwater (poor in Ca) mixes with soil water and deep seepage (rich in Ca). This consequently leads to a dilution of the seepage water. During the vegetation period the groundwater level decreases and, consequently, the seepage water is less diluted and the Ca concentrations in the spring increase. Although the general annual course is similar for all springs – increasing values during the vegetation period and a decrease through winter – the amplitude of this variation varies considerably between the springs (Figure 2.52). The springs showing the highest Ca concentrations discharge either in swampy saturation zones (spring type no. 2 in Figure 2.42, e.g. HB_Q_01, TB_Z_22) or have long flow passages through the periglacial layer (spring type no. 4 in Figure 2.42, e.g. HB_Z_01, TB_Z_01) rather than being intermediate direct contact springs above an impermeable formation. The lowest Ca values are found in contact springs which directly discharge from the bedrock without passing through the periglacial layer (spring type no. 3 in Figure 2.42, e.g. HB_Q_02, HB_Q_03, TB_Z_02, TB_Z_03, TB_Q_01).

2.3.4.5 Element concentrations in the brooks The measured element concentrations at the gauge stations are an integrative measure for the element budget and cycling in the investigated catchments. At the catchment outlets, the two catchments differ significantly in their element load (Table 2.11).

Table 2.11 Mean values for selected chemical parameters at the Huttenbächle and Teufelsbächle gauges. Significance of differences between the two catchments was tested using the non-parametric Wilcoxon rank sum test (exceedance probabilities: * ≤ 5%, ** ≤ 1 % and *** ≤ 0.1 %, n.s. > 5 %).

pH Ca K Mg Al Si Mn Na Cl SO4 DOC HB 6.450 3.660 0.980 1.180 0.016 2.030 0.002 0.750 1.350 3.670 0.986 TB 6.380 2.470 0.950 0.760 0.017 2.020 0.017 0.700 1.210 4.480 0.635 significance n.s. *** ** *** n.s. n.s. *** *** *** *** n.s.

108 Selected case studies in German low mountain ranges

The development of the element concentrations along the brook can be linked to different attributes of the catchment (e.g. geological properties, morphological effects). The two brooks clearly differ in their acidification parameters. Figure 2.53 displays the alkalinity (or Acid Neutralising Capacity) at different sections of the brooks. Alkalinity is related to bicarbonate and organic anion activity and therefore to the acidification degree of the water. Whilst all along the Huttenbächle, the alkalinity is in the positive range, values along the Teufelsbächle tend to the negative range in some extreme circumstances (downstream) or even yearly (upstream).

Figure 2.52 Ca concentrations in the springs and the secondary brooks in the Huttenbächle catchment (top) and the Teufelsbächle catchment (bottom)

109 Forest hydrology – results of research in Germany and Russia

Figure 2.53 Alkalinity of the brook waters for various sampling locations on the Huttenbächle and the Teufelsbächle (HB=Huttenbächle, TB=Teufelsbächle, 00=gauge; higher indices are for upstream sampling locations, cf. Figure 2.44).

Since the two catchments are similar in deposition of base cations, location, altitude, geological characteristics and discharge yield, it is very likely that the liming conditioned the differing chemical composition of the brook water. This is not only indicated by the results of the soil chemical analyses (section 2.3.4.2), but can also be seen in the Ca concentrations along the brooks (Figure 2.54). All along the brook, Ca concentrations in the Huttenbächle are significantly higher than in the Teufelsbächle.

2.3.4.6 Soil water chemistry The chemical composition of the soil water was continuously measured with suction cup samples. In addition, desorption solution (D-solution) samples were obtained at the lower suction cup plot in the Huttenbächle catchment every two weeks. In comparison to all other samples (suction cups, perched groundwater, spring water and brook water) D-solution values show distinctly different patterns (Figure 2.55). The various sampling methods differ with regard to the pore space from which the water is sampled as well as the spatial integration of the sampled water. The samples analysed by D-solution have the smallest sample size (down to 160 µl) and extract water from the very fine pores (matric potential of 1600 hPa) [Blattner et al., 2000]. This water is highly bound to the soil solid phase. Among all the samplings, the D-solution has the most complete equilibrium between the liquid and the solid phase. Suction cups sample water from an already larger soil volume and reach comparatively larger pores (matric potential of 320 hPa), which should show the influence of plant uptake, at least with respect to nutrients. The perched groundwater boreholes collect free-flowing water from the soil zone and the upper part of the periglacial layer. The water collected at the springs has a still larger collecting area and is a mixture of groundwater and lateral runoff in the soil zone

110 Selected case studies in German low mountain ranges

Figure 2.54 Ca concentration along the Huttenbächle and the Teufelsbächle flow path (B=primary brooks, Z=secondary brooks, Q=springs, cf. Figure 2.44) and the periglacial layer. Finally, the water samples at the catchment outlet are an integrative measure of all water components and the whole catchment. The relations of the element concentrations among the different sample types show two different general patterns (Figure 2.55). The conservative elements Cl and Na display much higher concentrations in the D-solution than in all other water samples. Since the exchange with the soil and plant uptake is negligible for these elements, this pattern indicates the existence of at least two main pore systems with poor interactions between each other. A plausible explanation for the extraordinarily high concentrations of Na and Cl in the D-solution and low values in all other samples is that the weathering of the primary rock material mobilises Cl and Na from salt and/or silicate-binding forms. A pre-condition for this is that in the primary rock material Na-containing feldspars and at least traces of salt are available. This is supported by geological descriptions and chemical analyses of the Buntsandstein in this region [Geyer and Gwinner, 1991]. In the immobile water of the very small soil pores, the influence of weathering on the Na and Cl concentrations is most intensely developed whereas in all other water samples Na and Cl concentrations are diluted by low concentration precipitation water. In the case of Cl, the very high concentration in the D-solution can be partly interpreted as an after-effect of formerly high Cl deposition originating from combustion of Cl-containing polymers (e.g. PVC). During the last decade, Cl deposition was reduced to about 20 to 30% of the amount before [Hug et al., 2005]. The second general pattern in the relations of the element concentrations among the sample types applies for the nutrients NO3 and Ca. Both NO3 and Ca display the lowest concentrations

111 Forest hydrology – results of research in Germany and Russia

Figure 2.55 Concentrations of Cl, Na, NO3 and Ca in the soil water (D-solution and suction cup samples at the down-slope suction cup plot (80 cm depth), perched groundwater (HB_P03), spring (HB_Q_04) and brook (HB_B_00) water in the Huttenbächle catchment in the macro-pores, sampled in the suction cups. Obviously, this finding represents a local sink for these elements due to plant uptake. The concentration found in the free-draining water (1 to 3 mg/l) possibly indicates a quasi-equilibrium state between minerals or organic material of the soil solid phase and the concentrations of Ca and NO3. This equilibrium is realised in small soil pores with immobile water, where no direct impact of roots exists, or in free-draining water, in the case that a sufficient contact time with the solid phase is given (spring and brook measurements). The disproportionately high Ca values in the spring and in the brook could be interpreted as the effect of liming which slowly propagates down slope as a “wave of basidity”. The nutrient concentrations measured in the perched groundwater are intermediate but cannot be significantly distinguished from suction cup water, possibly due to the comparably close vicinity of the sample borehole to the suction cup plot. Summarising, the patterns of element concentrations in the water fraction of varying mobility and interfaces to other system compartments provide evidence for distinct hypotheses on how different contact surfaces and contact times modify the chemical composition of the runoff water. The hypothesised effect of liming fits without contradiction into the observed

112 Selected case studies in German low mountain ranges concentration relations. The fact that the lowest Ca concentrations were found in the soil solution near the soil surface (suction cups) can be sufficiently explained by plant uptake.

2.3.5 Discussion

Various studies have shown that liming can compensate the pollution from acid-air deposition. Furthermore, liming can enhance biological cycles (e.g. litter mineralisation), the nutrient storage capacity of soils as well as their filter capacity. Liming therefore constitutes an effective management option for vitalising the forest vegetation.

2.3.5.1 Observed liming effects The results of the Kleine Kinzig study show an effect of the liming campaigns on the soil solid and liquid phase as well as on the water quality of the brooks. The soil-chemical surveys (section 2.3.4.2) revealed that liming has the highest influence on the upper soil layer (0- 10 cm depth), where the highest base and Ca saturations were measured. The high spatial variability of the measured base saturations shows that liming affects the soil chemistry very locally. Furthermore, the regionalisation of the base saturation values (section 2.3.4.2) shows that the influence of the liming campaigns lasts approximately 12 years, which can be seen as an approximate residence time of the Ca applied during a liming campaign. The observations in the soil solid phase are supported by the measurements taken in the soil liquid phase (section 2.3.4.6). Ca concentrations were higher in 20 cm soil depth than in 80 cm depth, which coincides well with the measurements of the soil solid phase. Accordingly, higher Al concentrations were measured below the root zone at 80 cm depth than in 20 cm depth. Generally, Al concentrations in the soil water were significantly higher in the less limed Teufelsbächle catchment. Significant differences in the element concentrations were also observed in the brooks of the two investigated catchments. As expected, higher Ca concentrations were measured along the Huttenbächle, where liming was more intense. Similar results were found when regarding the alkalinity as a direct measure of the acidification status. All along the Huttenbächle, the alkalinity is positive whereas along the Teufelsbächle it frequently reaches negative values. The catchment topography was found to be a major influencing factor on the effectiveness of the liming. Generally, a higher impact of liming is visible on flat slopes due to slower flow velocities of the soil water. Fast lateral flow in steeper slopes leads to a comparatively fast leaching of the liming product from the soil zone.

2.3.5.2 Perspective The Kinzig case study shows that forest liming has a remarkable effect on the soil acidification and consequently on the groundwater and surface water quality. Looking at the dramatic acidification degree of the forest soils in many parts of Europe, it is strongly recommended to continue and to extend programmes for forest liming. However, with regard to the costs involved, forest liming will always be limited with respect to its practicable areal extent and therefore cannot be the one and only forest management option for enhancing the water quality. Other management options which are able to counteract a further soil acidification, e.g. tree species composition and harvesting strategies, must accompany any planned liming programme. As an example, forests need to be developed and managed towards a closed nitrogen cycle in order to reduce acid peaks in the leakage water from the

113 Forest hydrology – results of research in Germany and Russia soil zone. Adequate silvicultural options and their impacts on the acid leakage have been discussed in the Conventwald case study (section 2.2). Presently, the Kleine Kinzig case study has been extended with the aim of understanding better the importance of the periglacial layer on the leakage of the liming product to the catchment outlet. Establishing an isotope-measuring network will lead to a better understanding of the hydrological characteristics and would make it possible to better interpret the measured element concentrations in the springs and brooks.

114 References

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Schwarze, R. (2002) Analyse des Abflusskomponenten- und Verweilzeitregimes des Wernersbaches. In: Bernhofer, Ch. (Ed.) (2002) Exkursions- und Praktikumsführer Tharandter Wald. Tharandter Klimaprotokolle Bd. 6, Eigenverlag TU Dresden, ISBN 3-86005-313-2, 149-164. Schwarze, R. et al. (2004a) Regionalspezifische Untersuchungen zum Wasser- und Stofffluss im Festgesteinsbereich. In: Becker, A. and Lahmer, W. (Eds.) Wasser- und Nährstoffhaushalt im Elbegebiet und Möglichkeiten zur Stoffeintragsminderung – Konzepte für die nachhaltige Entwicklung einer Flusslandschaft. Weißenseeverlag, Berlin, 183-216. Schwarze, R. (2004b) Berechnung der Veränderung des Wasserhaushaltes infolge einer sich ändernden Landnutzung oder Landbedeckung. In: Becker, A. and Lahmer, W. (Eds.) Wasser- und Nährstoffhaushalt im Elbegebiet und Möglichkeiten zur Stoffeintragsminderung – Konzepte für die nachhaltige Entwicklung einer Flusslandschaft. Weißenseeverlag, Berlin, 217-224. Schwarze, R. (2005a) Tracerhydrologische Analyse der Verweildauern der Boden- und Grundwässer gekoppelt mit einer Durchflussganglinienanalyse in den Einzugsgebieten der Großen Ohe und der Trinkwassertalsperre Frauenau im Nationalpark Bayerischer Wald. Forschungsbericht Mai 2005: Freistaat Bayern HTO - Projekt 33-7: Wasser- und Stoffhaushalt einer sich verändernden Naturlandschaft im Bayerischen Wald. Eigenverlag TU Dresden Inst. f. Hydrologie und Meteorologie. 49 pp. Schwarze, R. (2005b) Abflussbildung in Mittelgebirgseinzugsgebieten mit Festgesteins- aquiferen. In: Axel Bronster (Herausgeber): Abflussbildung – Prozessbeschreibung und Fallbeispiele. Forum für Hydrologie und Wasserwirtschaft Hennef, DWA, 13.05, 89-96, ISBN 3-937758-91-7. Schwarze, R., Grünewald, U., Becker, A. and Fröhlich, W. (1989) Computer-aided analysis of flow recessions and coupled basin water balance investigations. In: IAHS Publ. No. 187, Wallingford UK, 75-84. Schwarze, R., Herrmann, A., Münch, A., Grünewald, U. and Schöniger, M. (1991) Rechnergestützte Analyse von Abflusskomponenten und Verweilzeiten in kleinen Einzugsgebieten. Acta Hydrophys. Berlin, 35(2), 143-184. Schwarze, R., Herrmann, A. and Mendel, O. (1994) Regionalization of runoff components for Central European basins. IAHS Publ. no. 221, Wallingford, 1994, 493-502. Schwarze, R., Hebert, D. and Opherden, K. (1995) On the residence time of runoff from small catchment areas in the Erzgebirge region. In: Isotopis Environment Health Stud. 1995, 31, 15-28. Schwarze, R., Dröge, W. and Opherden, K. (1999) Regional analysis and modelling of groundwater runoff components from catchments in hard rock areas. IAHS Publ. No. 254, 221-232. Seiler, J. and Matzner, E. (1995) Spatial variability of throughfall chemistry and selected soil properties as influenced by stem distance in a mature Norway Spruce (Picea abies Karst) stand. Plant and Soil 176, 139-147. Shah, S.M.S., O'Conell, P.E. and Hosking, J.R.M. (1996) Modelling the effects of spatial variability in rainfall on catchment response, 2. Experiments with distributed and lumped models. J. Hydrol. 175, 89-111. Staelens, J. (2006) Spatio-temporal patterns of throughfall water and ion deposition under a dominant beech tree (Fagus sylvatica L.) in relationship to canopy structure. PhD thesis. Gent University. Fac. Bio-Ingenieurwetenschappen, 169 pp.

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Sverdrup, H. and de Vries, W. (1994) Calculating Critical Loads for acidity with the Simple Mass Balance Method. Water, Air, and Soil Pollution, 72, 143-162. Sverdrup, H. and Warfvinge, P. (1993) The effect of soil acidification on the growth of trees, grass, herbs as expressed by the (Ca+Mg+K)/Al ratio. Rep. in Ecology and Environmental Engineering 2. Lund, Lund Univ., Dept. of Chem. Eng. II, 42 pp. Swank, W.T. and Johnson, C.E. (1993) Small Catchment Research in the Evaluation and Development of Forest Management Practices. In: Moldan, B. and Cerny, J. (Eds.), Biogeochemistry of Small Catchments. John Wiley & Sons, Chichester, 383-408. UBA (1996) Manual on methodologies and criteria for mapping Critical Levels & Loads and geographical areas where they are exceeded. UBA Texte 71/96. UBA (2000) Quantifizierung des Einflusses der Landnutzung und -bedeckung auf den Hochwasserabfluss in Flussgebieten, Kapitel 2: Hochwasserentstehung. Uhlenbrook, S. and Leibundgut, C. (1997) Investigation of preferential flow in the unsaturated zone using artificial tracers. Proceedings of 7th Intl Symposium on Water Tracing, Portorose, Slovenia.

Ulrich, B. (1983) Interaction of forest canopies with atmospheric constituents: SO2, alkali and earth alkali cations and chloride. In: Ulrich, B. and Pankrath, J. (Eds.): Effects of Accumulation of Air Pollutants in Forest Ecosystems. Reidel Publ. Co. Dordrecht, pp. 35-45. Ulrich, B. (1987) Stabilität, Elastizität und Resilienz von Waldökosystemen unter dem Einfluss sauere Deposition. Forstarchiv 58(6), 232-239. Ulrich, B. (1994) Nutrient and acid-base budget of central European forest ecosystems – effects of acid rain on forest processes. In: Wiley-Liss, Process hierarchy in forest ecosystems: an integrative ecosystem theory. New York, 1-50. Ulrich, B. (1995) Der ökologische Bodenzustand – seine Veränderung in der Nacheiszeit, Ansprüche der Baumarten. Forstarchiv 66, 117-127. van Genuchten, M.Th. (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892-898. van Pul, W.A.J., Potma, C.J.M., van Leeuwen, E.P., Draaijers, G.P.J. and Erismann, J.W. (1995) EDACS: European Deposition maps of Acidifying Components on a Small scale. Model Description and Preliminary Results. National Institute of Public Health and the Environment (RIVM). Bilthoven. Report No. 722401005. {Download: http://www.rivm.nl/bibliotheek/rapporten/722401005.pdf}. von Wilpert, K. (1994) Bedeutung der makroskaligen Bestandesstruktur für Wasser- und Stofftransport durch Waldökosysteme. Tagungsberichte der Arbeitsgruppe Ökologie, Deutsche Region der Internationalen Biometrischen Gesellschaft, 5, 53-66. von Wilpert, K. (1996) Aus der BZE abgeleitete Indizien einer bodenchemischen Drift in Baden-Württemberg. Mitteilungen der Deutschen Bodenkundlichen Gesellschaft, 79, 189-192. von Wilpert, K. (2002) Soil acidification and nitrogen saturation – a new challenge for ecosystem research and forest management. In: Schrijver, A., Kint, V. and Lust, N. (Eds.), Comparison of ecosystem functioning and biogeochemical cycles in temperate forests in Southern Chile and Flanders. Workshop proceedings, Ghent University, Belgium, 17-19 September 2001, 23-34. von Wilpert, K. and Hildebrand, E.E. (1994) Stoffeintrag und Waldernährung in Fichtenbeständen Baden-Württembergs. Forst u. Holz 49, 629-632.

122 References von Wilpert, K., Kohler, M. and Zirlewagen, D. (1996a) Die Differenzierung des Stoffhaushalts von Waldökosystemen durch waldbauliche Behandlung auf einem Gneisstandort des Mittleren Schwarzwalds. Mitt. der Forst. Vers. u. Forschungsanst. Baden-Württemberg 197, 94 pp. von Wilpert, K., Kohler, M. and Zirlewagen, D. (1996b) The structure of crown layers and root systems as key variables for element cycling and nutrient uptake. IUFRO Conference on Effects of Environmental Factors on Tree and Stand Growth, Berggießhübel, Dresden, 23-27 Sept. 1996, 290-297. von Wilpert, K. and Mies, E. (1995) The influence of stand structure and tree species on mineral cycling. In: Nilsson, L.D., Hüttl, R.F., Johansson, U.T. and Mathy, P. (Eds.), European Commission, Ecosystem research report 21, 267-276. von Wilpert, K. and Schäffer, J. (2000) Bodenschutzkalkung im Wald. Merkblätter FVA Bad.-Württbg., 50, 21 pp. von Wilpert, K. and Zirlewagen, D. (2001) Bodenversauerung und Entwicklung der Wasserqualität im bewaldeten Einzugsgebiet der Conventwald Fallstudie. Freiburger Forstliche Forschung, 33, 123-137. von Wilpert, K. and Zirlewagen, D. (2004) Forestry Management Options for Water Preservation. In: Andersson, F., Birot, Y. and Päivinen, R. (Eds.), Towards the Sustainable Use of Europe's Forests – Forest Ecosystem and Landscape Research: Scientific Challenges and Opportunities. EFI-Proceedings, 49, 189-197. von Wilpert, K. and Zirlewagen, D. (2007) Forestry Management Options to maintain Sustainability – Element Budgets at Level II Sites in South-West Germany. Schriften aus der forstlichen Fakultät der Univ. Göttingen und der Nordwestdeutschen Forstlichen Versuchsanstalt, 142, 170-179. von Wilpert, K., Zirlewagen, D. and Kohler, M. (2000) To what extent can silviculture enhance sustainability of forest sites under the immission regime in Central Europe? Water, Air and Soil Pollution 122, 105-120. Waldenmeyer, G. (2003) Abflussbildung und Regionalisierung in einem forstlich genutzten Einzugsgebiet (Dürreychtal, Nordschwarzwald). Uni Karlsruhe/Karlsruher Schriften Band 20, 195 pp. Wetzel, M. (2005) Modellierung der Hangwasserdynamik eines Untersuchungsgebietes im Nationalpark Hainich mit dem Modell HYDRUS 2D. Diplomarbeit an der Friedrich- Schiller-Universität Jena, Chemisch-Geowissenschaftliche Fakultät, Institut für Geografie, unveröffentlicht. Wittig, R. and Nette, H. (1983) Sind Säurezeiger im Stammfußbereich der Buche Indikatoren für immisionsbelastete Kalk-Buchenwälder? AFZ: 1232-1237. Witzig, J., Badoux, A., Hegg, C. and Lüscher, P. (2004) Waldwirkung und Hochwasserschutz – eine standörtlich differenzierte Betrachtung. Forum für Wissen - Forschungsanstalt WSL - Wald, Schnee und Landschaft (Schweiz). Wolff, B. and Riek, W. (1998) Chemischer Waldbodenzustand in Deutschland, Ergebnisse der Bodenanalysen im Rahmen der BZE. Allgemeine Forstzeitschrift 10. Worch, E. (1997) Wasser und Wasserinhaltsstoffe, eine Einführung in die Hydrochemie. Teubner, B.G. Verlagsgesellschaft, Stuttgart, Leipzig, 205 pp. Zirlewagen, D. (2003) Regionalisierung bodenchemischer Eigenschaften in topographisch stark gegliederten Waldlandschaften. Schriftenreihe Freiburger Forstliche Forschung, Bd. 19, 154 pp.

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Zirlewagen, D. and von Wilpert, K. (2002) Was hat Waldbau mit Trinkwasservorsorge zu tun? Schriftenreihe Freiburger Forstliche Forschung, 18, 309-319. Zirlewagen, D. and von Wilpert, K. (2004) Using model scenarios to predict and evaluate forest management impacts on soil base saturation at landscape level. European Journal of Forest Research 123/4, 269-282. Zirlewagen, D., Raben, G. und Weise, M. (2007) Zoning of forest health conditions based on a set of soil, topographic and vegetation parameters. Forest Ecology and Management 248, 43-55. Zysset, M., Brunner, I., Frey, B. and Blaser, P. (1996) Response of European chestnut to varying Ca/Al ratios. J. Environ. Qual. 25, 702-708.

124 List of figures

List of figures

1.1 Water balance of a forest ecosystem [from Hegg et al., 2004b] …………… 4 1.2 Canopy drip and stemflow from a spruce and a beech tree [from Hegg et al., 2004b] ………………………………………………………………….. 5 1.3 Effect of forests on the runoff formation [from Hegg et al., 2004a] ………. 6 1.4 Proportions of the various runoff components (RD = direct runoff, RG1 = fast baseflow, RG2 = slow baseflow) and the evapotranspiration in relation to the annual precipitation sum; DIFGA application for the Lange Bramke catchment, 1948 to 1998 …………………………………………………… 7 1.5 Trends in the ratios of total runoff, the various runoff components and the evapotranspiration in relation to the precipitation; DIFGA results for the Lange Bramke catchment …………………...……………………………… 8 1.6 Annual development of runoff coefficients for different runoff components from DIFGA applications for forested and agricultural catchments in the Váh region of the Slovakian highlands (Rybarik, Lesny) and the German Ore Mountains (Neunzehnhain, Saidenbach) ...……………………….…… 10 1.7 Development of forest damage from 1960 to 1994 in the Natzschung catchment (gauge Rothenthal) [from Fichtner, 1995; modified] ...………… 12 1.8 Cumulative vitality of the vegetation in the Natzschung catchment weighted by the area proportion of the respective land-use class ………….. 13 1.9 Water balance components of the Natzschung catchment for the period from 1961 to 1992, calculated with a) runoff separation method DIFGA, b) AKWA-M scenario "with land-use dynamics", c) AKWA-M scenario "without forest dieback" ……………………………………………………. 14 1.10 Comparison of water balance terms calculated with DIFGA from the observed discharge and simulation results of the AKWA-M scenario 0 (present land use) ...………………………………………………………… 16 1.11 Tritium concentration in the precipitation, Freiberg station [from Hebert, 1990, modified]. The unit T.U. (tritium unit) is equivalent to 1 T.U. = 1 3H atom per 1018 1H atoms. …………………………………………………… 18 1.12 Tritium concentrations in the Wernersbach brook for low-flow periods (selection for 1990 to 1997) ...……………………………………………… 19 1.13 Input and output concentrations of 18O for the Wernersbach catchment. The 18O content is not measured in terms of an absolute concentration, but rather as a deviation from a defined standard concentration. ………………. 19 1.14 Tritium input into the groundwater in the Wernersbach catchment as compared to the tritium concentrations in the precipitation at the Freiberg station ………………………………………………………………………. 20

125 Forest hydrology – results of research in Germany and Russia

1.15 Alteration of the tritium activity during the soil passage at a measuring profile in the Wernersbach catchment ……………………………………... 21 1.16 Dating of the mean residence time using an exponential regression model – brook water at the catchment outlet ………………………………………... 21 1.17 Dating of the mean residence time using an exponential regression model – left: rhyolithe spring Q10, right: sandstone spring Q21 …………………… 22 1.18 Separation of the direct runoff of Wernersbach using the environmental 18 isotope O for the flood event from 12 April to 13 April 1994 (Qmax = 1356 l/s with recurrence interval = 5 yrs); yellow: direct runoff proportion = 12%, red: indirect runoff proportion = 88%, black: ratio 18O/16O in the o precipitation in /oo, expressed as deviation from the international standard 18 16 o V-SMOW, green: ratio O/ O in the brook discharge in /oo, expressed as deviation from the international standard V-SMOW ………………………. 23 1.19 Mean total acid and total nitrogen deposition between 1985 and 1995 in Europe [Draaijers et al., 1996] ……………………………………………... 26

1.20 Total acid deposition (through protons and NH4) and SO4 deposition between 1987 and 2000 (left) and total nitrogen deposition (right) in three landscapes of Baden-Württemberg ………………………………………… 27 1.21 Comparison of pH values (relative frequency distributions) measured in 1927 by Frank and in 1992 in the soil chemical survey (BZE) in top soils of the Black Forest on different bedrocks [according to Buberl et al., 1994] 28 1.22 Base saturations of 101 carbonate-free soil profiles in Baden-Württemberg; dark lines = averages, bars = ± standard deviation [von Wilpert, 2002] …... 29 1.23 Absolute frequencies of C/N ratios in the humus layers of spruce stands in Baden-Württemberg in 1992 compared to 30-year-old data [from Hildebrand, 1994] ………………………………………………………….. 30

1.24 Frequency distributions of trends in groundwater NO3 concentrations in forest areas of the Black Forest region. Data from the groundwater survey of the State Environmental Agency Baden-Württemberg (LUBW) for the years 1950 to 1999 [von Wilpert and Zirlewagen, 2004] ………………….. 31 1.25 Mean annual element flux densities at the interfaces between ecosystem input (OP: open field precipitation and Thr: throughfall) and the seepage water below the rooting zone for the years 1996 to 2006 (left and centre) and 1994 to 2006 (right). Rotenfels: Podsol with high water permeability and low buffer capacity; Altensteig: seep-retarded Gleyic Cambisol with moderate buffer capacity; Ochsenhausen: Gleyic Luvisol with seep- retarded subsoil, high buffer capacity ……………………………………… 32 1.26 Exceedances of the critical loads of forest ecosystems of total nitrogen and sulphur deposition for the years 1987 (above) and 1997 (below) at 300 sampling points in Baden-Württemberg. The circle size is proportional to the amount of the total critical load exceedances (dark yellow sectors = nitrogen, light blue sectors = sulphur, red dots = no exceedance of critical loads). ………………………………………………………………………. 34

126 List of figures

1.27 Model predictions for NO3 concentrations in the groundwater (left) and absolute residuals between observed and predicted NO3 concentrations (right) in the Black Forest (Southwest Germany). For residuals, dark circles indicate the observed values to be higher than the predicted ones, light circles the opposite. Groundwater data from LUBW Karlsruhe [from von Wilpert and Zirlewagen, 2004] …………………………………………….. 36 1.28 Total atmogen acid entry in the Conventwald catchment (average values from 1992 to 1998). Parting line: comparison of forest stands with open land deposition [Zirlewagen and von Wilpert, 2002] ……………………… 37 -1 1.29 Pie charts of NO3 concentration (mgl ) in the seepage water, classified according to the soil substrate and the tree species [from Mellert et al., 2005b] ……………………………………………………………………… 38

2.1 The Große Ohe catchment in the central part of the Bavarian Forest National Park ……………………………….……………………………… 42 2.2 Development of the deadwood areas in the Forellenbach and Markungsgraben catchments ……………….………………………………. 45

2.3 NO3 concentrations in seepage water (40 and 100 cm) at spruce plot F1 in the Forellenbach area [Beudert and Breit, 2004] ...………………………… 45 2.4 Scheme of the DIFGA method: concept of different runoff components and storages and runoff separation …………………...………..……………….. 47 2.5 Combination of the runoff component analysis DIFGA with tracer hydrological and hydrochemical analyses ………………………...……….. 48 2.6 Double sum analyses – above: cumulative sums of ΣP vs. ΣQ and ΣP vs. ΣETR; below: cumulative sums of ΣP vs. ΣRD, ΣP vs. ΣRG1, ΣP vs. ΣRG2, ΣP vs ΣETR. Results for the Markungsgraben catchment from 1 November 1988 to 31 October 2003 ……………………………...... ……. 50 2.7 Comparison of the tritium concentration in TU (tritium unit) in the precipitation and in the groundwater recharge for the period around the "bomb peak"; data from the Markungsgraben catchment …..……..……….. 52 2.8 Time series of the tritium concentrations (in tritium units TU) in the slow groundwater runoff QG2 (estimated with MULTIS, using the exponential model EM) and measured tritium concentrations in the catchment runoff .... 53 2.9 Mean groundwater residence time of catchments in the Bavarian Forest and the Ore mountains as a function of the geological conditions and the mean annual precipitation sum...... ………………………………………………. 54 2.10 Partitioning of direct runoff water and pre-event water; flood of 23 to 25 September 2004 in Forellenbach ………………………..………………. 54 2.11 Comparison between direct runoff (above) and indirect runoff (below) of Markungsgraben, Forellenbach and Große Ohe; flood of 23 to 28 September 2004 ………………………………………….……………... 55

2.12 Development of the matric potential and groundwater level (top), NO3 concentration (centre) and DOC (bottom) of Forellenbach, flood of 23 to 28 September 2004 ………………………………………………….……... 57

127 Forest hydrology – results of research in Germany and Russia

2.13 Comparison of simulated (lines) and measured (symbols) growth parameters for the beech stand B1; unit of wood volume V: Vfm = m³ solid volume over bark …………………………………………………………... 59 2.14 Comparison of simulated (black line) against measured water contents (dotted lines) at 55 cm depth at beech stand B1 ………….………………... 60 2.15 Comparison of simulated groundwater recharge and measured groundwater table variations at beech stand B1 …………………………………….……. 60 2.16 Comparison of modelled runoff components and measured discharge (dotted line) at the Forellenbach gauge Schachtenau …...….………………. 61 2.17 Dead spruce stands (% area) and bark beetle-induced changes in the water cycle (mm/a) of the Forellenbach area …………………………..…………. 61

2.18 NO3 concentrations in runoff water (discharge weighted means) and in groundwater (medians) [Bayerisches Landesamt für Wasserwirtschaft et al., 2004; Beudert and Breit, 2004] ………………………………...…..…... 62 2.19 Location of the Conventwald ecosystem research area …………….……… 65 2.20 Metastructures identified in the Conventwald. The different tree species and various gap types in the closed stands are highlighted in different colours. Spots between the coloured polygons show the beech crowns in the original air photograph. ………………………………………………… 68 2.21 Total acid (left) and nitrogen (right) deposition (throughfall data) in various stands (annual loads averaged over the years 1991 to 1994) ……………..... 70 2.22 Boxplots of pH values (daily data for the years 2000 to 2006) for open-land deposition and throughfall below spruce and beech in the mixed beech/ silver-fir stand ……..……………………………………………………….. 71 2.23 Depth profiles of sulphur amounts in the stand varieties of the research area, placed into a soil profile typical for the study area ...………………… 72 2.24 Depth profiles of average element concentrations (years 2000 to 2006) in the soil water sampled with suction cups for beech in the mixed stand (solid lines), clear-cut (dashed lines), beech pole stand (dash-dotted lines) and spruce pole stand (dotted lines) .……………………………………….. 73

2.25 NO3 concentrations in the seepage water at 180 cm below a clear-cut and a 10 to 20-year-old regeneration area ………………………………..………. 73

2.26 NO3 concentrations (solid line) and pH levels (dotted line) in the seepage water at 180 cm depth following the cutting of two small trees in the spruce pole stand …………………………………………………………………... 74 2.27 Relationship between base cations (BC) and Al in the soil solution in 60 cm depth under the spruce pole stand: a) crown gap, b) crown centre and the old spruce stand, c) crown gap, d) crown centre. The solid lines at BC/Al = 1 molc/molc indicate the threshold value of potentially phytotoxic BC/Al ratio. ....……………………………………………………………… 75 2.28 Time series (daily data) of chemical parameters in the catchment outlet ….. 76 2.29 Correlations of tensions measured in similar depths in the different microplots ...... ……………………………………………………………… 77 2.30 FDR measurements in the spruce stand and a "femel" gap ………………... 78

128 List of figures

2.31 Example slope transect in Hydrus-2D showing the boundary conditions defined and the zoning of the model domain according to the crown density (green lines). Different colours indicate areas with different root densities. 79 2.32 Empirical variances of the soil skeleton content and fitted variance functions ……………………………………………………………………. 81 2.33 Left graph: Measured relative saturated conductivities relating to the measured saturated conductivity of skeleton-free quartz substrates and fitted reduction function. Right graph: example for the reduction of the ks values measured at 100 ml soil sample rings (dashed line) and reduced ks values according to the observed soil skeleton content at the soil profile (solid line). …………………………………………………………………. 82 2.34 Calibration of WHNSIM using the continuous tensiometer readings in the different soil depths; here, model fit for the microplot "mixed beech stand, crown centre". ……………………………………………………………… 83 2.35 Measured and simulated daily discharge, Conventwald catchment, Black Forest, Germany, 1 November 1998 to 31 October 2002. (Simulated data: mean of the fluxes in the structural units at 180 cm depth weighted with their surface percentage in the catchment) …………………………………. 84 2.36 Simulated water fluxes at 180 cm soil depth and measured stemflow and/or throughfall for various microplots in the Conventwald catchment (1 November 1991 to 31 October 1996). Values are averages over the model period. Error lines are the standard error of the daily mean. The dotted vertical line indicates mean precipitation. Stars indicate a significant difference between two microplots according to Dunnett's test (α = 5%). … 85 2.37 Modelled seepage rates in a soil depth of 180 cm for a precipitation event from 14 to 17 July 2001 for the stand situations "mixed stand, spruce tree", density strata crown centre and crown gap (solid lines = expectations, dash-dotted lines = 95 % confidence intervals) ………………..……...…… 86 2.38 Element flux densities through the soil down to the interface between soil and bedrock (180 cm) for the whole stand areas in the beech pole stand (a), the spruce pole stand (b) and the clear-cut (c), averaged values for hydrological years 1992 to 2006. The graph contains the whole flow path from open-land precipitation (OL) over throughfall (TH) to the seepage water output at 180 cm soil depth. …………….…………………………… 88

2.39 Simulated NO3 water fluxes at 180 cm soil depth and measured NO3 in stemflow and/or throughfall for various microplots in the Conventwald catchment (1 November 1991 to 31 October 1996). Values are averages over the model period. Error lines are the standard error of the daily mean. The dotted vertical line indicates mean precipitation. Stars indicate a significant difference between two microplots according to Dunnett's test (α = 5%). …………………………………………………………………… 89 2.40 "Kleine Kinzig" drinking-water reservoir and the investigated sub- catchments "Huttenbächle" and "Teufelsbächle" …..………………………. 92 2.41 Geological formations in the Huttenbächle and Teufelsbächle catchments .. 93

129 Forest hydrology – results of research in Germany and Russia

2.42 Groundwater conditions in the Buntsandstein formations: 1 = water- conducting layers; 2 = saturation spring; 3 = contact spring; 4 = spring in the periglacial layer; so = Upper Buntsandstein; smc2 = Upper Conglomerate; sm = Bausandstein; smc1 = Lower Conglomerate; su = Lower Buntsandstein; ro = Oberrotliegendes; G = Granite [from Sawatzki, 1994] ……………………………………………………………………..… 93 2.43 Limed areas in the two catchments with years and dosage of liming …….... 94 2.44 Sampling locations in the Huttenbächle and Teufelsbächle catchments (B = primary brooks, Z = secondary brooks, Q = springs)………………..... 95 2.45 Double sum curves (Huttenbächle vs. Teufelsbächle) of a) runoff yield, b) Si, c) DOC and d) Al; data from January 1989 to May 2006 …….……... 98 2.46 Resistivity measurements with a 10 m electrode spacing ……………….…. 100 2.47 Heterogeneous material distribution displayed in the geoelectrical surveys . 101 2.48 Resistivities measured with 1 m electrode spacing and the percentage change of the resistivities between the two measuring days ……………….. 102 2.49 Base saturation in the Huttenbächle catchment – a) 0 to 10 cm depth, b) 60 to 90 cm depth – and in the Teufelsbächle catchment – c) 0 to 10 cm depth, d) 60 to 90 cm depth ……………………………………………………….. 105 2.50 Base saturation predicted from multiple regression models vs measured values …………………………………….………………………………… 106 2.51 Ca concentration in the perched groundwater along the slope transect in a) Huttenbächle catchment and b) Teufelsbächle catchment ………….……… 107 2.52 Ca concentrations in the springs and the secondary brooks in the Huttenbächle catchment (top) and the Teufelsbächle catchment (bottom) … 109 2.53 Alkalinity of the brook waters for various sampling locations on the Huttenbächle and the Teufelsbächle ……………………………………….. 110 2.54 Ca concentration along the Huttenbächle and the Teufelsbächle flow path .. 111

2.55 Concentrations of Cl, Na, NO3 and Ca in the soil water (D-solution and suction cup samples at the down-slope suction cup plot (80 cm depth), perched groundwater (HB_P03), spring (HB_Q_04) and brook (HB_B_00) water in the Huttenbächle catchment ………………………………………. 112

130 List of tables

List of tables

1.1 Main characteristics of Saxonian (Saidenbach, Neunzehnhain) and Slovakian research catchments (Rybarik, Lesny) ...………………………... 9 1.2 Overview of the land-use scenarios applied in the AKWA-M simulations ... 15 1.3 Water balance components as calculated with DIFGA and with the various AKWA-M scenarios and their statistical measures (x = arithmetic average, s = standard deviation, cv = variation coefficient = s/x) …………………… 16 1.4 Results of the hydrograph separation for Wernersbach with DIFGA (years 1968 to 1994) and comparative results from the isotope analyses ………… 23

2.1 Environmental characteristics of the Große Ohe basin and its sub- catchments Markungsgraben and Forellenbach ……...…………………….. 43 2.2 Comparison of the actual water balance of the Forellenbach, Markungsgraben and Große Ohe catchments in 1992 and 1998 .………….. 49 2.3 Soil profile description (location near measuring tower, cf. Figure 2.19) ..... 65 2.4 Chemical characteristics of a typical soil profile in the Conventwald (location near measuring tower, cf. Figure 2.19). Samples taken in 1993. … 66 2.5 Physical characteristics of a typical soil profile in the Conventwald (location near measuring tower, cf. Figure 2.19). Samples taken in 1993. .... 66 2.6 Proportional area of the various structural types identified in the Conventwald catchment ..…………………………………………...……… 67 2.7 Strata of stand structure in which separate measurements and calculations were made and number of structure-related microplots ..…………………... 69 2.8 Runoff components of the Huttenbächle and Teufelsbächle brooks, determined from the hydrograph separation with DIFGA …………………. 99 2.9 Average values of Al, Ca, Mg, K, CEC and BS at 0 to 10 cm and 60 to 90 cm soil depth. Significance of differences between the two catchments was tested using the non-parametric Wilcoxon rank sum test (exceedance probabilities: * ≤ 5%, ** ≤ 1 % and *** ≤ 0.1 %, n.s. > 5 %). …………….. 102 2.10 Predictors included in the regression models and their partial R2 for the two measuring depths in the Huttenbächle and Teufelsbächle catchments …….. 104 2.11 Mean values for selected chemical parameters at the Huttenbächle and Teufelsbächle gauges. Significance of differences between the two catchments was tested using the non-parametric Wilcoxon rank sum test. ... 108

131

Part II

Results of research in Russia

Physiographic description

Experimental studies of the hydrological role of forest have been carried out in the central part of the Valday Hills, in the northern part of the mixed forest sub-zone. Here are the sites of the Valday Branch of the State Hydrological Institute for observing the hydro-meteorological elements. The sites are situated in river catchments which form part of the Lake Il'men drainage basin and most of them are located close to the town of Valday in the Novgorod region. The size of the study area is about 10 000 km². The Valday Hills territory is a hilly moraine landscape, characterised by hills of different elevations and forms alternating with wetland hollows. The moraine relief was formed during the Veps stage of the Valday glaciation. In several areas the hills are replaced with ridges and slightly waved plains and, in some places, with peat bogs and lakes. The catchments are distinct. The central and southern parts of the territory have an elevation of about 200-225 m a.s.l., the highest places of the Hills being about 300 m. On both of sides of the ridge of the Hills, the terrain declines significantly. To the west there is a sharp decline of up to about 50 m. The terrain to the east declines gradually to 150-175 m a.s.l. The geological composition of the region is characterised by deposits of the Upper Devonian, Lower Carboniferous and Quaternary ages. The Quaternary deposits are mainly silt loam with boulders and streaks of sand and gravel. Soils have mostly been formed on sands, sandy loam, silt loam and peat deposits. Sandy deposits (the fluvio-glacial fields, kames and eskers) are observed to be uneven and occupy on average about 10% of the area, the sandy loam and silty loam about 75% and the peat up to 15%. The soils are podzolic in their genesis. Vegetation cover is mostly fir and deciduous forests with areas of pine forests on sandy soils. The forests occupy up to 75% of the area. The basic species are fir, birch, pine, alder and aspen. Bushes (about 20%) are widespread. The climate of the region is slightly continental, sufficiently humid. The global solar radiation on a horizontal surface is, for a year, 3352 MJ/m² with the following seasonal distribution: spring (April – May) 27%, summer (June – August) 48%, autumn (September – November) 2% and winter (December – March) 13%. The highest solar radiation flux is in June (599 MJ/m²), slightly less in May and July. The lowest heat flux on the land surface is in December (28 MJ/m²) and January (50 MJ/m²). The radiation budget annual rate of the active surface is 1206 MJ/m²; that is 36% of the global solar radiation income. The period with a positive radiation budget lasts from March to October. The highest values are observed in May to July (272–311 MJ/m²), the lowest in December to January (-35 MJ/m²). The total value of the budget is 113 MJ/m². The mean annual air temperature at the Valday weather station for the observation period 1901, 1902 and 1924–2000 is 3.6°C. The warmest month is July with a monthly mean air

135 Forest hydrology – results of research in Germany and Russia temperature of 16.8°C; the coldest is January with -9.3°C. The date at which the mean daily temperature passes over 0°C is 3 November. Cloudy weather is usual for the Valday Hills area; annually there are 186 cloudy days (51%). Mean cloudiness for a year is 7/8. Only 18% of the days are sunny (cloudiness 0 – 2/8). The mean annual wind speed, at a height of 11 m, is 3.4 m/sec. Westerly and south-westerly winds predominate during most of the year. The percentage of calm wind days is only 9%. The study area is characterised by superfluous moistening. According to V.S. Golubev's data [1962], the mean annual precipitation at the Valday meteorological station amounts to 780 mm. The mean annual number of days with precipitation is 207, of which 113 days are in the warm season. Maximum precipitation is observed in June and July (83 mm). There are several years when the monthly rates can differ significantly from the annual mean. Precipitation is mainly liquid (70% of the annual mean). A constant snow cover is usually observed from the last third of November to the second half of April. The deepest snow is observed at the end of February and is, on average, up to 50 cm on an open field and 53 cm in the forest. During the cold period of the year snow melt often occurs and this significantly affects the structure of the snow cover. Freezing of the soil begins in October/November and at the end of winter its depth reaches, on average, 37 cm in the field and 17 cm in the forest with considerable variability within the drainage basins. Full thawing of the soil is usually observed at the end of April. The hydrographical network of the region is well developed and represented by ravines, brooks, rivers, lakes and wetlands. The brook and river valleys are usually deep. The thalwegs of the ravines are very distinct. The drainage network density (except for ravines) is, on average, 0.80 km/km². The area occupied by lakes is 2–3%, by wetlands up to 20% and by peat bogs 5%. Wet hollows occur widely. Lowland swamps mainly occur in river flood plains.

136 Map of the study area

Novgorod M a t s s ta M

a a

h v Nis a o y l ala M o

Lake Ilmen’

va olo Kh

P 2 ol om 3 et’

t’ e Dvorerets Valday 1 m lo o P Berezaika

P ola

a Pol

Volga

Map of the study area 1 = Taezhny, 2 = Usadievsky, 3 = Siniaya Gnilka

137

Forest and precipitation

1 Forest and precipitation

1.1 General

Precipitation is the main source of river runoff formation and water resources renewal in catchment basins. Numerous studies have been made of the impact of forest on precipitation. This is a first- priority issue in the study of the hydrological effect of forest. The history of research on this topic is more than a hundred years old. An almost complete list of references on this subject is given in Leyton and Rodda [1970], Rakhmanov [1981] and Fedorov and Marunich [1985]. There is no unanimous opinion in literature: some scientists assume that forest, if compared with treeless areas, stimulates higher precipitation, whereas other scientists assume that there is no reason to expect more precipitation over forest. These contrary views were historically held because of the lack of reliable technical facilities and methods for precipitation measurements, underestimation of the effects of some meteorological factors on which the amount of the measured precipitation depends and methodological problems during precipitation measurements (e.g. uncertainty about locations for the installation of precipitation gauges in forests). Therefore, even today, the problem of correct precipitation assessment is the focus of attention for scientists and practitioners. Great emphasis has been placed on the solution of this problem in Russia within the system of the hydrometeorological services, i.e. at the Main Geophysical Observatory (MGO) and the State Hydrological Institute (SHI). To make multi-purpose studies of this problem a special experimental plot for precipitation measurements was created in Valday. This experimental plot was recently named the "Russian centre for intercomparison of different facilities for precipitation measurements".

1.2 Determination of the quantity of precipitation on the surface of the catchment

During hydrometeorological studies, including those related to the study of the hydrological effects of forest, it is very important to determine the quantity of precipitation correctly. Meanwhile, the quantity of precipitation measured in any precipitation gauge is usually less than the amount of precipitation that has fallen on the surface of the catchment basin. This is explained by systematic errors related to the designs of precipitation gauges and meteorological factors. The reasons for systematic errors are as follows: • loss of precipitation through the wetting of the inner walls of the receiving vessel

139 Forest hydrology – results of research in Germany and Russia

• loss of precipitation collected in the vessel due to evaporation from the precipitation gauge • decrease (or increase) of the amount of precipitation due to the effect of wind. The actual amount of precipitation is estimated by introducing corrections to the total measured precipitation. Precipitation measurements on the experimental plot in Valday (Valday Branch of the SHI) were made in different rain gauges installed on open ground and among bushes. Major difficulties arise during the quantitative assessment of solid and mixed precipitation. Numerous studies made on the plot show that the actual amount of solid, mixed and liquid precipitation can be measured quite reliably by the usual precipitation gauges installed among bushes or on an open site if surrounded by a double fence. Moreover, the receiving surface of the precipitation gauge should be level with the top of the bushes (2 m above the ground). It should be noted that a precipitation gauge surrounded by a double fence was also installed in the Usadievsky field experimental catchment 6 km from the experimental plot for precipitation measurements. These experiments prove that a valid amount of solid precipitation can be obtained from the measurements of the water equivalent of snow on a plot with deciduous forest or among bushes (if snow drift was not observed) during periods without thaws when the soil is frozen. This conclusion agrees with the results of solid precipitation measurements in a small field catchment. The results of these experiments were the basis for "Instructions for the calculation of corrections to measured precipitation" published in 1969. These instructions describe the methodology for the correction of the measured liquid, solid and mixed precipitation at meteorological stations, also for past observation periods. Later, these instructions were updated and appeared in Meteorological Instructions No. 89 [1974].

1.3 Methodology for precipitation measurements in the forest

Precipitation above the forest can be measured either at the level of tree crowns or in forest glades. As precipitation measurements above the forest are difficult, these measurements are usually made in glades. These glades are usually considered as reference sites for the installation of precipitation gauges. However, the accuracy of precipitation measurements made in glades is not clear. Investigations made in Valday show that the amount of precipitation measured in precipitation gauges installed in glades, especially solid precipitation, differs from the amount of actual precipitation that has fallen over the whole forest area. The quantity of the measured precipitation depends on the size of the glade. This is explained on the one hand by the so- called aerodynamic effect and on the other hand by the screening effect of the tree stand (the effect of the forest "wall") on the trajectory of raindrops and the motion of snowflakes. The essence of the aerodynamic effect on the process of precipitation in the lower air layer is in the fact that wind velocity is subject to change above the glade. This causes more precipitation above the glade. To estimate the aerodynamic effect experimentally, two series of observations were made at low and medium wind velocities above the glade surrounded by thick low bushes. The bushes were on average 3.2 m high. The glade was round and 7.2 m in diameter. Wind velocity was measured by 32 manual anemometers. The anemometers were

140 Forest and precipitation attached to masts at different elevations of 1.0 m to 5.0 m. These measurements demonstrated the effect of the glade on the wind velocity distribution above the glade. The screening effect of the tree stand in the glade causes less precipitation to accumulate in the glade. The effect of this factor is most evident during precipitation accompanied by high wind velocities. Thus, aerodynamic and screening effects produce opposing results on precipitation storage in the glade, i.e. the aerodynamic effect stimulates more precipitation storage while the screening effect stimulates less precipitation storage. Proceeding from general theoretical ideas, it follows that in smaller glades the aerodynamic effect on precipitation is minimal. Simultaneously, the screening effect of the tree stand ("wall") is maximal. As the glade diameter increases, so does the relative importance of the aerodynamic effect. There may be, however, glades of a size where the aerodynamic and screening effects offset each other. In this case precipitation in the glade over a long-term period (a month) would be equal on average to the total precipitation on the forest area. Such glades are termed "indicatory" in this report.

1.4 Liquid and solid precipitation on forest glades

To estimate the actual amount of precipitation on the forest area it should be measured by precipitation gauges installed on indicatory glades. A special study was made to determine the size of such glades. Liquid precipitation was measured using Tretjakov rain gauges installed in glades of different sizes. Measurements were made above the forest at various wind velocities. The experimental glades were spaced at no more than 1 km apart. The results of measurements indicated that on average during a 12-year period (1960-1971) the total precipitation from May to October was practically the same as that in glades with 20° to 52° shading. Deviations in the readings did not exceed 3%. A slightly greater deviation was observed during other individual years. If the shading of the glade was about 75%, the quantity of precipitation was 15% less than that in glades with 20-52° shading. Therefore, it was necessary to establish in which glades precipitation caught by the rain gauges was closest to the actual precipitation. This was possible when the specific features of precipitation caught in the glades depending on wind velocity were known. For this purpose the data of measurements in the glades were compared with the data obtained above the forest and with the data of measurements in the field catchment; here the screening effect of the tree stand was important. A comparison was made between the precipitation data obtained in glades with 20°, 31° and 46° shading and the data obtained above the forest (measured by two rain gauges installed on a tower in the forest) at an elevation of 44 m above the ground (18 m higher than the average tree height). Wind velocity in the glades was measured by manual anemometers 2 m above the ground. Wind velocity data show that even at high wind velocities above the forest, the wind velocity 2 m above the ground (i.e. the level of the receiving surface of the rain gauge) does not usually exceed 1.0 m/s if the glade has 30-50° shading and it does not exceed 2.2 m/s if the glade has 20° shading. Above the forest, where wind velocity is much higher than in the glades, the amount of measured precipitation is much lower.

141 Forest hydrology – results of research in Germany and Russia

To estimate the effect of wind velocity on the readings of rain gauges above the forest, deviations of precipitation from that measured in the glades were calculated. Let us conventionally assume the amount of precipitation in glades to be the actual precipitation without taking the size of the glade into account:

Pgl − Pt ΔPt = 100 = f (U ) (1.1) Pgl where Pgl = precipitation measured in the glade Pt = precipitation measured on the tower (above the forest) U = wind velocity on the tower. Deviations were calculated for particular rainfalls. The values obtained were averaged according to wind velocity gradation. Wind velocity was assumed to be mean for each rainfall. Altogether seventy to one hundred rainfalls were measured (Table 1.1).

Table 1.1 Deviations of precipitation measured above the forest, in per cent of precipitation measured in glades (ΔP), depending on wind velocity above the forest

Glade number Wind velocity No. 24 (α=31°) No. 19 (α=46°) No. 21 (α=20°) (m/s) ΔP No. of rainfalls ΔP No. of rainfalls ΔP No. of rainfalls 1-2 5.3 12 6.4 5 4.7 10 2-3 8.2 37 8.0 23 10.6 23 3-4 8.2 28 9.0 23 12.5 26 4-5 12.9 13 12.0 12 15.7 15 5-6 12.6 8 16.2 7 17.6 5 6-7 10.0 3 11.0 3 25.0 3

Table 1.1 contains deviations of precipitation measured in glades of different sizes depending on wind velocity above the forest. These values were used to plot graphs (Figure 1.1) for glades with 20° shading (α = 20°) (glade no. 21), for glades with 31° shading (α = 31°) (glade no. 24) and with 46° shading (α = 46°) (glade no.19). In Figure 1.1 a graph plotted on the basis of the data given by Golubev [1962] for an open area is given for comparison. As evident from Figure 1.1, underestimation of precipitation above the forest per unit of wind velocity is different: maximum underestimated precipitation (3.5% per 1 m/s of wind velocity change) is observed if the data from the glade with 20° shading are used; this underestimation is less for glades with 31° and 46° shading. In these two cases underestimation of precipitation is similar to that in the field and equals less than 3% per 1 m/s of wind velocity change. Thus, it follows that the most valid quantity of precipitation is caught in glades of such a size for which a dependence of rainfall underestimation on wind velocity is similar to that in the field. In such cases the aerodynamic effect explained by the influence of the forest glade on the deposition of liquid precipitation in the glade would be minimum. This condition is satisfied if the rain gauges are installed in the glade with about 30-45° shading.

142 Forest and precipitation

Figure 1.1 Dependence of underestimation of precipitation (∆P%) measured by Tretjakov rain gauge upon wind velocity (U m/s) on an open area and on the glades of different sizes 1. Rain gauge installed on an open site; 2. Rain gauge no. 24; 3. Rain gauge no. 19; 4. Rain gauge no. 21

Systematic measurements of solid precipitation in the forest are made with the Tretjakov precipitation gauge and with snow courses. According to investigations made by Kuzmin [1960], solid precipitation measurements in forested areas should be made on plots covered with deciduous trees because snow in such forests is not caught in the crowns and is not redistributed by wind. To determine the possibilities of correct measurements of solid precipitation in a coniferous forest in winter, special simultaneous measurements of precipitation were made in glades in a coniferous forest and on an indicatory plot in a deciduous forest. The indicatory plot was covered with birch trees with a forest density of 0.5. Precipitation was measured with the Tretjakov precipitation gauge together with snow courses under the forest canopy and in the glade. Similar simultaneous measurements were made in glades in the coniferous forest. Data of these measurements are given in Table 1.2 and show that solid precipitation is greater in the glades in coniferous forest with 20° to 45° shading than on the indicatory plot. Taken as a whole, this overestimation was as follows: 10 to 37% for snow course surveys and 4 to 30% for data obtained with the precipitation gauge. A similar snow exceedance was observed in the glade in the deciduous forest.

143 Forest hydrology – results of research in Germany and Russia

Table 1.2 Amount of solid precipitation (mm) in deciduous forest and in the glades based on the data of snow course surveys and snow measurements using the Tretjakov precipitation gauge in late winter

Deviation of snow storage in Glades in coniferous Deciduous Glade in the glades from snow storage Method of forest deciduous forest Year in deciduous forest (%) measurements (indicatory forest plot) α=22° No.21 No.26 No.19 No.6 No.21 No.26 No.19 α=20° α=22° α=46° α=22° α=20° α=22° α=46° snow survey 107 124 120 - 114 16 12 - 6 1966 -67 precipitation gauge 116 129 129 133 116 11 11 15 0 snow survey 110 136 124 134 108 24 13 22 -2 1967 -68 precipitation gauge 116 134 137 - 130 15 18 - 12 snow survey 122 144 152 156 132 18 24 28 8 1986 -89 precipitation gauge 136 157 156 155 152 16 15 14 12

A difference in snow storage observed during snow course surveys in the glades in coniferous and deciduous forests for the whole observation period is 6% on average, which is within the limits of the accuracy of water equivalent of snow pack determination. The results obtained with precipitation gauges differ on average by 3% only. The conclusion reached agrees well with the results of snow surveys (snow courses) made in the VB of the SHI in some other catchments (the Polomet', Malaya Nisha, Kholova and Mayata rivers). The exceedance of the water equivalent of snow pack in forest glades with 8 to 46% shading equals 17% on average when compared with that in the deciduous forest. However, observations of snow accumulation in the glades with more than 55° shading showed an underestimation of the amount of snow, not an overestimation (Table 1.3). For example, in the glades with α = 75-80° the amount of snow storage was similar to that under the canopy of the coniferous forest. This is clearly seen in Figures 1.2 and 1.3. Dots on the graphs correspond to a difference between snow storage in the glades with different shading rates and snow storage on indicatory plots. A similar dependence was obtained on the basis of measurements with precipitation gauges (Table 1.4). Moreover, the maximum positive difference in snow storage in glades and on the indicatory plot is observed in the glade with 37° shading. In glades with higher shading this difference tends to decrease and at α = 58° is ΔP = 0; in glades with α ≥ 58° the amount of precipitation is underestimated, i.e. ΔP is negative. Finally, at the maximum shading rate when the glade diameter is so small that the crowns almost close the glade, the amount of precipitation is similar to that under the canopy of the conifers. The value of ΔP would correspond in this case to the amount of intercepted solid precipitation which is proved by the observations. Thus, a correct account of solid precipitation in the forest can also be made with precipitation gauges installed in forest glades with about 55-60° shading.

144 Forest and precipitation

Table 1.3 Solid precipitation (mm) measured in glades in a coniferous forest and on an indicatory plot (in a deciduous forest) between 26 November 1973 and 31 January 1974 (snow survey data)

Woodland in Valday Forestry Woodland in Edrovsky Forestry

On Glade No. and α° On Glade No. and α° indicatory No.10 No.17 No.19 No.20 No.21 No.27 indicatory No.8 No.9 No.10 plot 37° 52° 46° 75° 20° 50° plot 53° 80° 65° P 102 134 114 121 77 126 - 91 104 75 85 ΔP +32 +12 +19 -25 +24 10 - +13 -16 -6

Note: P is precipitation measured with the Tretjakov precipitation gauge (mm); ΔP is the difference in snow storage in the glades and on the indicatory plot in a deciduous forest.

Figure 1.2 Difference between snow storage (∆P%) on indicatory plots, on glades and on felled areas depending on the shading rate (angle, α°) in the centres of the glades

Mixed precipitation (in April and November) and solid precipitation (from December to March) in the forested catchment was estimated by two methods. Mixed precipitation was accepted as the total precipitation measured on the indicatory plot taking account of corrections only for the wetting of the gauge and for evaporation from the bucket; solid precipitation was estimated as the amount of precipitation measured among bushes on the experimental plot for precipitation measurements. The total precipitation difference for a long-term period resulting from the use of the above two methods equalled only 5 mm. On average, solid and mixed precipitation for a long-term period (1955-2000) equalled 315 mm.

145 Forest hydrology – results of research in Germany and Russia

As a result, the total annual precipitation for the warm and cold seasons in the Taezhny small experimental catchment equalled: P = 471 + 315 = 786 mm.

Figure 1.3 Difference between solid precipitation (∆P%) measured by Tretjakov precipitation gauges from 16 November 1973 to 31 January 1974, on indicatory plots and on glades depending on the shading rates (angle, α°) in the centres of the glades

1.5 Interception of precipitation by the forest canopy, penetration of precipitation under the canopy

Interception of precipitation by plants is an important process in the course of precipitation from the atmosphere to the underlying surface. Intercepted precipitation is very important for the water balance of forested areas. Therefore, interception of precipitation by the canopy is of particular importance for the hydrological studies of the forest. Forest plants cause precipitation distribution to a great extent. Some precipitation is intercepted by the crowns, some precipitation appears under the canopy as throughfall where it is partly intercepted by the top cover; a certain amount of rain water flows down the stems. This process is considered on the one hand as a process which explains a certain retardation of runoff formation and on the other hand it causes precipitation loss for evaporation of the intercepted moisture from the surfaces of the plant cover. Thus, the study of this problem is of practical importance both for the discovery of the effect of forest plants on the redistribution of precipitation and for a quantitative assessment of the evaporation from the forest. The first significant investigations on precipitation redistribution by forest plants were made early in the 20th century by E. Ebermayer, N. Hamberg, E. Goppe, et al. The basic results of these investigations were generalised by Morozov [1959]. The results of numerous later studies were improved and modified relative to local conditions [Kittredge, 1951; Molchanov, 1960].

146 Forest and precipitation

The most complete historical review of the studies on this problem can be found in Leyton and Rodda [1970], Rakhmanov [1981] and Fedorov and Marunich [1985].

Table 1.4 Solid precipitation measured with precipitation gauges in glades in a coniferous forest and on the indicatory plot in a deciduous forest, and the difference between these measurements

On In glades in coniferous forest Characteristics indicatory plot No. 21 No. 24 No. 19 No. 17 No. 20 February – March 1968

P (mm) 76 96 90 100 93 60 ΔP′ (%) - 20 14 24 17 -16 ΔP″ (%) - 26 18 32 22 -22 December 1968 – March 1969 P (mm) 119 129 142 134 132 93 ΔP′ (%) - 20 23 15 15 -26 ΔP″ (%) - 17 19 13 13 -22 December 1969 – March 1970 P (mm) 138 146 - 158 148 98 ΔP′ (%) - 8 - 20 10 -40 ΔP″ (%) - 6 - 14 7 -29 December 1970 – March 1971

P (mm) 164 191 198 180 167 136 ΔP′ (%) - 27 34 16 3 -28 ΔP″ (%) - 16 21 10 2 -17 Mean 16 (19) 17 10 23

Note: P (mm) is precipitation measured with the Tretjakov precipitation gauge; ΔP′, ΔP″ (%) indicate the difference between snow measurements in glades in coniferous forest and on the indicatory plot in deciduous forest.

The intercepted part of precipitation (P1) is estimated as the difference between the precipitation depth above the forest canopy (P) and the mean precipitation measured under the canopy (P2), as well as the stemflow (P3):

P1 = P – P2 – P3 (1.2) As the amount of stemflow on coniferous trees (spruce, pine) is only about 1.0-1.5% of the precipitation depth (if recalculated per area), the amount of intercepted precipitation is estimated without taking this component into account:

P1 = P – P2 (1.3) The mechanism of precipitation interception by forest plants as well as stemflow was first investigated in detail by R. Horton. A formula was offered to calculate the interception of precipitation. Linsley et al. [1962] modified their formula. These problems were described in

147 Forest hydrology – results of research in Germany and Russia detail in Penman [1968] and Merriam [1960] amongst others. The formula proposed by Merriam for the calculation of intercepted precipitation depending on the precipitation depth is as follows:

P S Ld = S(1− e ) + KP (1.4) where Ld = total loss for interception of precipitation as water depth (layer) on the surfaces of plants S = maximum layer of intercepted precipitation (capacity of interception) K = constant part of loss for evaporation and absorption of moisture. Later, some theoretical studies were made, e.g. Hilmy [1957], Kharitonov [1977], Mendel et al. [1993]. Rainfall characteristics (duration, intensity, amount of precipitation and type of rain), characteristics of forest plants (species, density, age), meteorological situation (air temperature, deficit of air humidity, wind velocity) and vegetation phase of the plants are the main factors which determine the interception of precipitation by the canopy. To determine the intercepted amount of precipitation on forest experimental plots in the VB of SHI, precipitation under the canopy was measured over several years. For this purpose Tretjakov rain gauges were installed on some plots 2 m above the ground; on other plots 10 to 15 receiving vessels (buckets) were placed on the ground. One rain gauge was installed near each plot in the glade. Precipitation gauges on all plots were spaced at an equal distance of 10 or 20 m. Besides, snow course surveys were made every month. Table 1.5 contains the characteristics of plots where long-term measurements of precipitation under the forest canopy were made.

Table 1.5 Characteristics of the plots where precipitation measurements under the canopy were made

Name of plot Characteristics

Water balance plot no. 3 10 spruce trees; age – about 90 years; density – 0.7; the second layer is not characterised. Water balance plot no. 4 10 spruce trees; age – 35 years; density – 0.9 Elovy small experimental catchment 10 spruce trees; age – about 90 years; density – 0.4 Plot at Dobyvalovo 10 pines, 1 spruce; age – about 90 years; density – 0.7 Runoff plot no. 15 5 spruce trees, 4 pines, 1 birch + willows; age – 23-40 years; density – 0.8 Runoff plot no. 16 3 birches, 2 aspens + willows; age – 23-40 years; density – 0.7 Sinyaya Glinka catchment 6 spruce trees, 4 pines; age – 15-40 years; density – 0.8

The results of measurements under the canopy were used to estimate the accuracy of measurements. For example, on the basis of measurements by 15 precipitation gauges the

148 Forest and precipitation accuracy of monthly precipitation was equal to 7-8%. Errors of measurements during individual rainfalls at precipitation depths of 3 mm, 12.5 mm and 35 mm equalled 35, 17 and 10%, respectively. To analyse precipitation data under the canopy and the amount of intercepted precipitation, the data from individual rainfalls and from total monthly precipitation were used. To study the effect of rainfall characteristics on the amount of interception, pluviograph data were also used. Figure 1.4 shows a graph of the relationship between the amount of interception and amount of precipitation, plotted from observations made on the forest water balance plot no. 3 and the forest runoff plot no. 15.

Figure 1.4 Relationship between the amount of liquid precipitation intercepted by the canopy (ΔP) and total precipitation on the glade (P)

Here cases of interception are shown during rainfalls of less than 24 mm. Rainfalls of up to 64 mm were used for the analysis. These data show that the increase of precipitation leads to an increase of intercepted precipitation up to a certain limit and then it becomes constant. For example, on water balance plot no. 3 the extreme interception is 3.5 mm and it occurs during a total precipitation of about 11 mm; on runoff plot no. 15 (density: 0.8; age: 23 years) it is 3.5 mm and it was observed during precipitation of about 8 mm. On water balance plot no. 4 this value equals 3.4 mm during a precipitation of 12 mm. Data in literature also show that rain depth, when the extreme interception of precipitation is observed, is different for plants with different characteristics. A rise in precipitation leads to a lower amount of intercepted precipitation. It is evident from Table 1.6 where mean interception values are given in % of the total precipitation according to rain gradation. These data have been obtained from the analysis of 1127 rainfalls on water balance plot no. 3 and 426 rainfalls on water balance plot no. 4. It is evident from Table 1.6 that for rainfalls up to 1 mm the interception of precipitation attains 66 and 69% respectively on the two study plots; for rainfalls exceeding 20 mm about 20% of precipitation is intercepted by the tree crowns. Consequently, the relative interception of precipitation by the tree crowns greatly depends on rainfall depth.

149 Forest hydrology – results of research in Germany and Russia

Table 1.6 Interception of liquid precipitation (%) by spruce trees for the warm season (April – October) depending on the depth of rainfall

Rainfall depth, mm Plot 0-1 1-5 5-10 10-18 20 Water balance plot no.3 66 51 41 27 20 Water balance plot no.4 69 49 33 21 21

Rainfall intensity also affects the amount of intercepted precipitation. For example, if rainfall depth exceeds 5-10 mm, higher rainfall intensity leads to less interception. Precipitation data under the canopy of the coniferous forest make it possible to discover the effects on the interception of liquid precipitation. For example, during simultaneous observations (1957-1968) the mean intercepted precipitation in April – October on water balance plot no. 3 equalled 32% for a plant density of 0.7; in the Elovy small experimental catchment (forest density: 0.4) interception equalled 28% of the total precipitation. The comparison of the mean seasonal interception of precipitation by conifers of different ages during the same period showed a small difference. For example, a forest stand of 80-90 years old on water balance plot no. 3 intercepted on average 32% of liquid precipitation; a forest stand of 30-40 years intercepted 27%. Compared with spruce forest, pine forest intercepts less liquid precipitation. According to observation data in Valday, wood sorrel pine forest (about 80-90 years old, plant density: 0.7) intercepts 18-25% of precipitation. Interception in a deciduous forest is less. A great deal of observation data has been collected in different forests with different tree species and ages. Annex 1.1 shows the results of long-term observations in some experimental areas in Valday. Similar ratios of intercepted precipitation for different tree species were obtained at the Istrinsky base station in the Moscow region [Voronkov, 1973]. These results are given in Table 1.7.

Table 1.7 Interception of precipitation by different tree species at Istrinsky base station

Deciduous species Mixed species Spruce Period Precipitation mm % mm % mm % Liquid precipitation 1962-1975 387 69 18 70 18 155 43 Total liquid and solid precipitation during a year 1962-1975 616 79 13 109 18 235 39

On the basis of long-term observation data on the interception of precipitation by the forest canopy as well as its value under the canopy, some authors have developed appropriate formulas for the calculation of these values. Such formulas can be found in Kittredge [1951],

150 Forest and precipitation

Delfs [1958], Zinke [1967] and Rakhmanov [1981] amongst others. Most of these formulas, however, have been developed for local conditions.

1.6 Determination of liquid precipitation under the forest canopy and quantity of intercepted precipitation

Experimental data on precipitation measurements under the forest canopy and determination of the quantity of intercepted precipitation by tree crowns have been used for different generalisations and theoretical studies for many years. These are the works of Hilmy [1957], Rutter [1967], Leonard [1967], Leyton and Rodda [1970], Kharitonov [1977], Osukh [1993], Mendel [1993], and others. Hilmy's formula is widely used in Russia and is as follows:

P D* 1 P3 D* F(P) = (1− A)A{A (1− ) − (1− )2} (1.5) P* D 3 (P*)2 D where A = forest density D*/D = parameter characterising the part of crown space with needles of the tree which is determined visually as a typical tree P* = is precipitation depth at which precipitation completely penetrates through the crown. This formula is valid at P ≤ P*; for cases of P ≥ P* precipitation under the forest canopy is calculated by the simplest formula:

F(P) = P – P0 (1.6) where P0 is precipitation intercepted by the tree crowns in a state of complete saturation. Its value is estimated experimentally or the following formula is applied for its calculation: 2 P0 = AP* – AP*{(1–D*/D) – 1/3(1–D*/D) } (1.7) To get a reliable accuracy of the above formula, precipitation was calculated under the forest canopy during individual rainfalls on water balance plot no. 3 during the warm seasons of 1962-1965 and on runoff plot no. 15 during the period 1968-1970. Calculations for plot no. 3 were based on 330 rainfalls, for plot no. 15 on 62 rainfalls. Moreover, the following design parameters were used: A = 0.7, D*/D = 0.25 and P* = 11 mm for the water balance plot; A = 0.9, D*/D = 0.25 and P* = 8 mm for plot no. 15. The calculated precipitation under the forest canopy was compared with the observed values (Table 1.8). The comparison showed conformity between the calculated and observed precipitation under the forest canopy. It is evident from Table 1.8 that in 52% of the cases deviations do not exceed 10%; in 80% of the cases they do not exceed 20%. Deviation distribution, however, is asymmetric and a systematic underestimation of the calculated values takes place compared with the observed ones. It is probably explained by the fact that the structure of the formula takes into account precipitation redistribution by the crowns only during the vertical fall of the raindrops. In reality, interception is observed most often during the sloping fall of the raindrops due to wind.

151 Forest hydrology – results of research in Germany and Russia

Thus, the precipitation layer under the forest canopy during particular rainfalls and the quantity of precipitation intercepted by the crowns can be estimated with a sufficient degree of accuracy with the Hilmy formula.

Table 1.8 Frequency of deviations between total precipitation calculated by the Hilmy formula and precipitation measured under the forest canopy during individual rainfalls

Occurrence of deviations Frequency of deviations, % positive negative Interval of % of the % of the deviation number total number total (%) of number of number positive negative total cases of cases of cases cases 0-10 94 39 32 13 39 13 52 11-20 47 19 20 8 58 22 80 21-50 24 10 17 7 68 29 97 51-100 2 1 4 2 69 31 100

1.7 Stemflow

To ascertain the correct account of precipitation under the canopy and the part of precipitation intercepted by the crowns requires knowledge of the stemflow. Special vessels with receiving troughs fixed to a tree stem are applied to measure the flow down the stems. Different designs of these vessels are available. The stemflow mainly depends on the species of tree and on meteorological factors. A low flow is observed in a coniferous forest; in a deciduous forest it is greater. Under average conditions stemflow does not exceed 1% for spruce, while for beech it attains 16% of the total precipitation in the forest. In Valday observations of stemflow on 70-to-80-year-old trees were made from June to October in 1955-1962 (three spruces, three pines, two birches and two aspens); three spruces were observed from 1987 to 2001. The portion of stemflow equalled (for a long-term period) 0.5% for spruce, 1.5% for pine and about 4% for birch and aspen. It follows that for the assessment of interception by conifers (spruce in particular) stemflow can be neglected, whereas in deciduous forests it should be taken into account. In the forest different tree species intercept different amounts of solid precipitation. The total precipitation intercepted by deciduous trees does not exceed 2 or 3% of the precipitation. But the conifers (spruce in particular) intercept much more precipitation; with greater forest density the intercepted precipitation attains 20% and more on average.

Intercepted solid precipitation (P1) is calculated as the difference between the total precipitation depth within the forest area (PT) and the mean total precipitation measured under the forest canopy (P2T):

152 Forest and precipitation

P1T = PT – P2T (1.8) According to the data of numerous solid precipitation measurements the intercepted precipitation in a coniferous forest of about 80-85 years old was calculated in the small Taezhny experimental catchment. The amount of precipitation on the forest area was estimated using a precipitation gauge installed on a plot in a deciduous forest; the amount of precipitation under the canopy was accepted as the mean value from the observations made on two water balance plots: in the small Elovy catchment and on water balance plot no. 3. Interception of solid precipitation during individual winter seasons varied from 24 mm to 35 mm; the mean value for 4 years equalled 29 mm. On average for the long-term period 1951-1969 precipitation intercepted by the coniferous forest in the Taezhny experimental catchment also equalled 29 mm or 18% of the total precipitation on the forest during the same period. Annex 1.1 gives the mean long-term monthly liquid and solid precipitation for several experimental areas in Valday which penetrated under the canopy and calculated values of the intercepted precipitation. These data show that total monthly precipitation intercepted by the spruce forest (water balance plot no. 3) varied from 12% to 35% with a mean annual interception of 27%. The mean intercepted liquid precipitation in a spruce forest (water balance plot no. 3 and the small Elovy experimental catchment) from April to October was about 30%. The solid precipitation intercepted from November to March was about 18%. Precipitation (both liquid and solid) intercepted by pine forest and by deciduous forest is usually less.

1.8 Estimation of precipitation in a field catchment

Precipitation measurements in the Usadievsky catchment were made using a standard Tretjakov precipitation gauge. In 1989 an additional precipitation gauge was installed there surrounded by a double fence. All kinds of corrections were introduced to the measurement results. Correction for the wetting of the vessel with liquid and mixed precipitation was +0.2 mm, with solid precipitation +0.1 mm for each measurement of precipitation layer of 0.05 mm and deeper. The results of the measurements were also corrected for wind effect using a standard precipitation gauge. The readings of the gauge surrounded by the double fence were corrected only for the wetting of the vessel and for the evaporation from the vessel. Correction for evaporation from May to October equalled 5 mm on average (May – October 1989-1995). It is evident from Table 1.9 that the mean total corrected precipitation in the catchment and on the experimental plot for precipitation measurements for the warm season is insignificant (only 4 mm). A correct estimation of the solid precipitation observed in a field catchment is more complicated. The problem has been quite inadequately investigated by scientists because it is difficult to select a reference plot. According to Kuzmin [1960], a flat open plot in a treeless territory of about 1.0 km² in size was taken as such a reference plot; there is an equilibrium between the amount of snow taken off by wind and the amount of snow fallen from an air flux. These are the so-called areas with a zero balance of snow transfer. But it is not always possible to select such plots.

153 Forest hydrology – results of research in Germany and Russia

Table 1.9 Quantity of the measured and corrected precipitation (mm) using standard precipitation gauges in the Usadievsky catchment and on the experimental plot for precipitation measurements (May – October 1989-1995)

Experimental plot for precipitation Usadievsky catchment measurements measured (mm) corrected (mm) measured (mm) corrected (mm) 433 448 416 452

According to Golubev [1962], these requirements are also satisfied in small areas (about 200x200 m) covered with bushes and located in a treeless territory, such as the bushy plot on the experimental plot for precipitation measurements in the VB of the SHI. Investigations show that precipitation measurements and snow courses (surveys) in winters without thaws make it possible to measure solid precipitation almost accurately. Special investigations made at the VB of the SHI in the Sorot' river basin (A = 2700 km²) on 13 bushy plots proved this conclusion. For a quantitative assessment of solid precipitation in the Usadievsky field catchment (a conventional reference plot) a bushy plot (plot no. 2) was also selected. This plot contains a watershed. Here, as well as in the catchment, solid precipitation was measured by using a standard precipitation gauge and with a snow course. Measurements were made in the winters of 1966-1970 (series 1) and 1977-1980 (series 2). Table 1.10 shows the results of the solid precipitation measurements (snow storage) for particular seasons in the catchment and on the plots with zero balance of snow transfer (among bushes) taking account of evaporation from snow. Evaporation was estimated using the formula of Kuzmin [1960].

Table 1.10 Quantity of solid precipitation (mm) in the catchment and on the plots with zero balance of snow transfer

Precipitation taking Evaporation from Snow storage (mm) account of snow (mm) Year Period evaporation (mm) Field Bushy Field Bushy Field Bushy catchment plot catchment plot catchment plot Series 1 1966-1967 27/XI-31/I 84 94 9 5 93 99 1968-1969 31/XII-30/III 59 71 20 10 79 81 1969-1970 31/XII-28/II 92 107 10 5 102 112 Series 2 1977-1978 30/XII-31/III 57 64 5.3 3 96 98 1978-1979 27/XI-1/III 112 120 1.2 6 124 126 1979-1980 31/XII-21/I 93 95 5 3 98 98

154 Forest and precipitation

Comparative measurements in the Usadievsky catchment and on the experimental plot for precipitation measurements (series 1) show that the difference between precipitation data on these plots (taking account of evaporation from snow) is not great. The small difference in the total precipitation is also characteristic of the measurements during series 2. It varies between 0 and 9% during six winters, with a mean value of less than 5%. It should be noted that during those years about the same difference in the water equivalent of snow was observed in a deciduous forest (on the indicatory plot in the Taezhny experimental catchment) as compared with the field catchment (Table 1.11).

Table 1.11 Quantity of solid precipitation (mm) on the indicatory plot in the forest and in the field catchment

Precipitation taking Evaporation from Snow storage (mm) account of snow (mm) Years Period evaporation (mm) forest field forest field forest field 1966-1967 27/XI-31/I 91 84 5 9 96 93 1968-1969 31/XII-20/III 72 59 11 20 83 79 1969-1970 31/XII-28/II 109 92 5 10 114 102

Note: Precipitation data were measured for periods without thaws.

The last two columns in Table 1.11 show the amount of precipitation in the forest and in the field. It is evident from these data that the difference between the total precipitation in the forest and in the field varied between 3 and 11% in the years of observation. The results of the measurements made on the experimental plot during April and November have been accepted for the quantitative assessment of total mixed precipitation. This value equals 109 mm for the period 1948-2001. Taken as a whole, the total precipitation in the field catchment for the cold season (November – April) was 260 mm. On average the total precipitation layer in the Usadievsky catchment during the period 1948- 2001 (taking account of the correction for wind effect: 37 mm) is

Pyears = 446 + 260 + 37 = 743 mm Experimental measurements of precipitation in the Valday region have been made in many other catchments. Long-term observations have been made in the Usadievsky open (treeless) catchment, on the experimental plot for precipitation measurements and in the Polomet' river basin.

1.9 The impact of forest on precipitation

Studies on the impact of forest on precipitation were widely carried out in Russia during the 1950s and 1960s and continued later on, too. With the use of data from meteorological stations studies were undertaken to investigate the dependence of the quantity of precipitation on forested areas. These and other studies resulted in the following conclusion: there is a clear

155 Forest hydrology – results of research in Germany and Russia relationship between annual precipitation and forested areas in all the regions studied. The mean increment of annual precipitation in forested areas has been accepted to be equal to 10% when compared to treeless areas [Rakhmanov, 1981; Fedorov and Marunich, 1985]. This impact has been explained in the greater values of the dynamic roughness of the forest surface (one or two orders higher than in treeless areas). High values of dynamic roughness result in a more intensive turbulent exchange between heat and moisture from the underlying surface to the atmosphere [Dubov et al., 1978], which leads to intensified air-mass precipitation forming above the land surface. It should be noted that the observation series used to reach this conclusion were usually short, no longer than 10 years. Fundamental studies of this problem were made by the SHI in Valday; at present continuous observation series are longer than 50 years on most research objects. For the assessment of the impact of forest on precipitation, additional experimental data on precipitation for a long-term period obtained from experimental catchments in Valday, from some water balance stations and from a network of specialised stations installed in different regions of European Russia were used. To characterise the impact of forest on precipitation, a coefficient has been accepted which is the ratio between precipitation above the forest and treeless areas:

∑ Pforest K forest = ∑ Pfield It can be conventionally termed as "coefficient of the impact of forest on precipitation". It is usually calculated for a year or for particular periods.

Temporal and spatial changing of the factor Kforest is primarily defined by the radiative energy resources and humidity. To illustrate this, Figure 1.5 shows the relationship between this factor and the radiative index of dryness, drawn from the data of the VB of the SHI. Similar graphs are also typical for other locations with a wide range of the factor with respective values of the radiative index.

Figure 1.5 Ratio of annual precipitation "forest to open land" (Kforest) dependent on the radiative index of dryness (JV-IX)

156 Forest and precipitation

Table 1.12 contains the results of Kforest determination from the data on liquid precipitation measured on the network of coupled (forest and field) catchments located in particular regions in the forest zone of European Russia.

Table 1.12 Coefficient of the impact of forest (Kforest) on precipitation on the basis of data from coupled (forest and field) catchments (for total precipitation from May to October)

River Drainage Forest Type of Observation Precipitation K basin Stream area forest area (%) surface period (mm)* % (region) (km²) Vologda Ershovka 5.8 94 spruce 1974-83 437 1.11 (Vologda Lapach 3.6 10 arable 1974-83 389 region) land Vetluga Krasnitsa 3.6 86 pine 1969-83 383 1.08 (Nizhny Mokrusha 4.5 2 arable 1969-83 357 Novgorod land region) Kliazma Savornia 3.7 32 arable 1973-83 371 1.07 (Ivanovo land region) Lukh 2.7 14 spruce 1971-83 Viatka Mezhnik 2.5 79 pine 1973-83 466 1.10 (Kirov Kliuchi 2.0 10 arable 1973-83 422 region) land

Note: * indicates data on precipitation taking account of the correction for the wetting of the vessel and correction for wind effect.

Table 1.12 has been prepared on the basis of precipitation data published in "Materials of hydrometeorological observations on field and forest coupled catchments" (1969, vol. 12). Moreover, data have been used only from those precipitation stations that are spaced no further apart that 7 km. These data show that this coefficient ranges from 1.07 to 1.11 irrespective of the tree species. This is probably explained by the fact that the dynamic roughness, the main factor determining the impact of forest on precipitation, differs slightly for different types of plants.

Similar values of the Kforest coefficient are presented in Table 1.13. These coefficients have been derived from the results of long-term precipitation measurements in Valday and at water balance stations installed in various physiographic zones. Here Kforest is based on the precipitation data for the warm season, cold season and for the whole year. It is evident from Table 1.13 that the highest values of the coefficient are characteristic of the forest-steppe zone (Kforest = 1.13) and the lowest values are observed in the zone of coniferous and mixed forests. While evaluating data it became evident that there was a low value of this coefficient (Kforest = 1.04) at the Podmoskovnaya water balance station. This is probably explained by the effect of Moscow, as a large city, on the hydrometeorology of the environment. A possible urban effect is discussed in Oke [1977].

157 Forest hydrology – results of research in Germany and Russia -

1.07 1.04 1.08 1.09 1.11 1.13 t Year stations fores K

1.09 1.08 1.08 1.06 V-X 1-07 1-.06 - - -

809 756 675 650 651 605 471 677 593 686 515 Year ) mm ( ------478 452 414 391 351 321 363 340 Precipitation V-X

balance stations and permanent tion period Observa- 1955-2000 1955-2000 1955-2000 1950-2000 1950-2000 1963-1978 1963-1978 1946-1949 1946-1949 1961-1985 1961-1985 1974-1981 1974-1981 1949-1980 1949-1980 field surface Type of meadow pine forest arable land fallow land fallow land fallow land fallow land arable land, mixed forest mixed forest mixed forest spruce forest, deciduous forest deciduous forest deciduous forest deciduous forest 2 7 % 40 53 93 10 44 100 100 100 field field field field area Forest based on data from Valday, water based on data from Valday,

km 0.45 0.36 0.45 0.36 12.0 3.5 2.57 area 920 1010 Drainage Catchment ct of forest on precipitation Taezhny Usadievsky Mesvedka river meteorological plot, field Istra river meteorological plot Kirzhach river Vytebet river Nygr river Iznan brook Rakovka brook Dolgy brook in forest catchments Station Table 1.13 Table Coefficient of the impa Valday Branch of the SHI (zone of Valday Branch of the SHI (zone coniferous forests) water balance Podmoskovnaya station (mixed forest zone) Permanent forest station. Istrinsky base station (mixed forest zone) Permanent forest station “Prokudin bor” (mixed forest zone) water balanceBolkhovskaya station (broad-leaved forest zone) water balanceBolkhovskaya station (broad-leaved forest zone) Nizhnedevitskaya water balance station (forest-steppe zone)

158 Forest and precipitation

Mean long-term precipitation in the forest and field catchments in Valday for the warm and cold seasons and for the whole year (1948-2001) is as follows: Forest catchment (Taezhny):

ΣPI-XII = 471V-X+ 316XI-IV = 787 mm (with correction for wetting) ΔE = 7 + 4 = 11 mm ΔH = 9 + 9 = 18 mm

ΣPI-XII = 477 + 331 = 816 mm (taking account of all corrections)

Field catchment (Usadievsky):

ΣPI-XII = 444 + 263 = 707 mm (with correction for wetting) ΔE = 6 + 4 = 10 mm ΔU* = 10 + 37 = 47 mm

ΣPI-XII = 452 + 304 = 764 mm (taking account of corrections)

Pforest 816 Coefficient of the impact of forest on precipitation: K forest = = =1.07 Pfield 764

Note: * indicates correction for wind effect (ΔU) of precipitation gauge readings of total precipitation, estimated for the field catchment only; ΔH is a correction.

On average, the ratio of the total precipitation for the whole year (for forest and for field) for the Valday region is as follows: liquid precipitation 60%, solid and mixed precipitation 40%. As evident from the analysis of comparative data on precipitation measured by individual precipitation gauges, the positive impact of forest on precipitation is observed everywhere in forest and forest-steppe zones. For the assessment of the impact of forest on precipitation it is necessary to mention field studies on weather radar calibration carried out on two adjacent plots equipped with precipitation gauges. This experiment was carried out by the SHI and by the Valday laboratory in 1965 in an area of the Novgorod region. Each experimental plot was 100 km² in area. One plot was 87% forested, the other 17% forested. A specific feature of the experiment was the high density of precipitation gauges. One hundred precipitation gauges and twenty-five pluviographs were evenly distributed on each plot. According to observation data for the period June – September 1965 the number of days with rainfalls on these experimental plots was 88 and 99 respectively. During the warm season the number of rainfalls on the forested area was 12% more than on the field area. The difference in the total precipitation for that period was 8%. Despite the short observation period, the results of that experiment also show a positive impact of forest on liquid precipitation. The results of determining the ratio between the quantity of precipitation on forest and on the field for particular years are given in Annex 2. These results have been obtained on the basis of data of precipitation measurements in adjacent pairs of experimental catchments in Valday over the last 50 years. Figure 1.6 shows data of the total annual precipitation measured at the Valday meteorological station from 1926 to 2001. During that period a stable trend towards an increase in the total annual precipitation has been observed, from mean values of about

159 Forest hydrology – results of research in Germany and Russia

600 mm/year during the 1930s to 800 mm/year during the 1990s. Three periods may be selected here: the first period is up to 1950 when some stability is observed in the precipitation values (about 600 mm/year); the second period is a period of significant rise in total annual precipitation from 1950 to 1990 (600 mm/year to 800 mm/year) and the third period has continued since 1990 (mean annual precipitation about 800 mm/year).

Figure 1.6 Total annual precipitation (mm) (at the Valday meteorological station), 1926-2001

Similar data have been observed in the Usadievsky field catchment (Figure 1.7).

Figure 1.7 Total annual precipitation (mm) in the Usadievsky field catchment, 1948-2001

160 Forest and precipitation

Quite different data are available for the Taezhny forest catchment (Figure 1.8). Here, since 1948 the mean annual precipitation of about 800 mm has been stable. The change in the total annual precipitation in the field and forest catchments determines the type of change in the difference between precipitation in the forest and field catchments (Figure 1.9). During the 1950s annual precipitation on forest areas was about 100 mm higher than on field areas (i.e. 14% on average per year) whereas this difference disappeared by 1990. During the last 10 years the amount of precipitation in the forest catchments has been about the same as in the field catchments. Moreover, the contribution to the difference between solid precipitation in the forest and field catchments in winter is approximately equal to the contribution of liquid precipitation (Figure 1.10). The difference between solid precipitation in forest and field catchments is more stable. During the 1950s solid precipitation on forest was about 70 mm higher than on fields. During the 1990s, however, the amounts of liquid and solid precipitation in the Usadievsky and Taezhny catchments were about the same. The annual distribution of the ratio between the mean monthly precipitation in the forest and field catchments was calculated for two different periods. The first period lasted from 1948 to 1992 when the difference in precipitation was significant, about 150 mm at the beginning of the period and less than 50 mm at the end of the period; the second period began in 1993 and is still continuing with approximately the same amount of precipitation on forest and on field.

Figure 1.8 Total annual precipitation (mm) in Taezhny forest catchment area, 1948-2001

Considering the differences between solid and liquid precipitation in the Taezhny and Usadievsky catchments (Figure 1.10), it should be noted that on average the curves are of the same type. A slightly more stable curve is observed for the difference in solid precipitation. During the last 10 years the amount of solid and liquid precipitation on forest and on fields has been about the same. In western Europe, where precipitation always depends on cyclonic circulation, scientists have not discovered any evident impact of forest on precipitation [Leyton & Rodda, 1970; Kittredge, 1951].

161 Forest hydrology – results of research in Germany and Russia

An increase in the mean annual air temperature has been observed from both the Valday data and from the data of the water balance stations (see Table 1.14). Unfortunately, for some of the stations there are no data available for the last 10-15 years.

Figure 1.9 Differences (mm) between the total annual precipitation in the Taezhny and Usadievsky catchments

Figure 1.10 Differences (mm) between the total precipitation in Taezhny and Usadie for the warm (1) and cold (2) seasons

162 Forest and precipitation

Table 1.14 Changes of the mean annual air temperature (ToC) and precipitation (P, mm) over decades from data of Valday Branch of the SHI and Water Balance Stations (WBS)

Pribaltijskaja Podmoskovnaja Bolkhovskaja Niznedevitskaja VB of the SHI Decade WBS WBS WBS WBS ToC P (mm) ToC P (mm) ToC P (mm) ToC P (mm) ToC P (mm) 1950-1959 3.3 672 4.4 3.8 583 1960-1969 3.4 650 4.2 680 3.8 580 4.7 516 5.8 521 1970-1979 3.8 673 4.6 697 4.2 631 5.1 587 6.0 557 1980-1989 3.9 733 4.7 760 6.4 582 1990-1999 4.6 745

Two reasons for this transformation in the difference between precipitation in forest and field catchments can probably be explained. The first reason is that the forest itself has changed over the last 50 years. At present, it is an over-mature spruce forest of 100-120 years; its density is 0.5 now instead of 0.7 as it was 50 years ago. The second reason is probably explained by climate change. The forest should affect the process of formation and fall of air- mass precipitation. A change in the type of atmospheric circulation, i.e. predominance of precipitation, connected with a cyclonic circulation over the year, is probably the most significant cause of smoothing precipitation in the forest and field catchments in Valday. This situation has probably been observed elsewhere, too. Climate change, namely a mean air temperature rise, has been observed in the north-west and central regions of European Russia and this is typical of the whole globe. The mean air temperature rise has been observed at the Valday meteorological station (Figure 1.11) and the total annual precipitation/precipitation difference on field and forest at the Podmoskovnaya water balance station (Figures 1.12 – 1.14); these observation data are used

Figure 1.11 Change in mean annual air temperature 1926-2000 (°C) (at the Valday meteorological station)

163 Forest hydrology – results of research in Germany and Russia in this report. Unfortunately, observation data from these stations are not available for the last 10-15 years.

Figure 1.12 Total annual precipitation (mm) 1950-1981 for the field (row 1) and for the forest (row 2) (at the Podmoskovnaya water balance station)

Figure 1.13 Differences in annual precipitation (mm) on forest and field at the Podmoskovnaya water balance station

164 Forest and precipitation

Figure 1.14 Differences (mm) in precipitation between forest and field over the year (at the Podmoskovnaya water balance station)

165

Evaporation from forest

2 Evaporation from forest

2.1 General

Evaporation is one of the water and heat balance components of the Earth’s surface. Therefore, evaporation has always been the centre of attention for scientists. A history of the research of evaporation from land has been described in detail in Budyko [1956], Penman [1968], etc. Studies of the development of methods for the computation of evaporation from land surface have greatly affected the development of experimental and theoretical evaporation studies in forests. In general, these studies have been based on water and heat balance methods, turbulent diffusion, soil evaporimeters and lysimeters of different designs, as well as the use of some versions of the biophysical method. Experience of the use of some experimental instruments and methods for evaporation measurements and computations in the forests in different countries was first generalised in the international guide for research and practice "Representative and experimental basins" edited by C. Tuebes and V.A. Ouryvaev [1971]. In the late 1960s, recommendations on the computation of evaporation from land, including forested areas, were developed and put into practice in Russia on the basis of the analysis of theoretical methods and checking of different empirical schemes, as well as using experimental methods. Such work was also done in other countries. Sufficient knowledge of the studies of evaporation from forests can be obtained from papers presented at various international symposia: on forest hydrology (New York, 1965), on the impact of forest on the environment (Moscow, 1970), on man's influence on the hydrological regime (Helsinki, 1980), on studies of the hydrological regime in experimental catchments (1989), as well as at some conferences of the International Association of Hydrological Sciences (IAHS). Average evaporation from forested areas, just like evaporation from any other surface, can be estimated for a long-term period and for particular years, months and shorter periods. Mean annual evaporation from forested catchments is most often estimated on the basis of the water balance equation. This method, being the simplest one, is widely applied in many countries. If data on the radiation balance and precipitation is available, mean annual evaporation can be estimated quite reliably with the equation of M.I. Budyko [1956].

167 Forest hydrology – results of research in Germany and Russia

It should be noted that the use of different methods for the estimation of evaporation from a forest is associated with many difficulties because of the specific features of forest plants when compared to the top cover in the field. Therefore, even nowadays, quite incompatible data on evaporation from forest are available in hydrology. At present, a great number of methods for the computation of evaporation for short periods have been developed. Most fundamental methods are those based on a joint solution of equations of the turbulent diffusion, and on heat and water balances. Design schemes by Budyko [1956], Penman [1968] and others are most widely spread. Many of these methods are based on the principles of the turbulent diffusion theory and on the theory of the heat balance of the underlying surface:

LE = aLρD1()e0 − ez

H = cρD2 ()t0 − tz (2.1) R = LE + H + B Here L = heat of water vapour formation E = evaporation a = 0.622/ N = coefficient of transition from specific humidity (g/g) to water vapour pressure e in the air at elevations 0 and Z from the surface

D1 and D2 = integral coefficients of the turbulent exchange of heat and moisture, respectively ρ = air density

t0 and tz = air temperatures at 0 and Z elevations H = turbulent heat exchange with the atmosphere B = heat exchange with the soil. The methods most often applied to compute evaporation are briefly discussed below.

Water balance method When this method is applied, evaporation from a forested catchment is estimated as a residual term of the water balance equation, which in general can be presented as follows: E = P − R ± ΔU (2.2) Where P = precipitation R = total runoff ΔU = water storage change in the catchment. This equation may be presented in a complete way as follows:

E1 + E2 + E3 = P1 + P2 + P3 + ()R1′ − R1 + (R2′ − R2 )+ (V1 −V2 )+ (W1 −W2 )(+ U1 −U 2 ) (2.3) where:

P1 = precipitation throughfall

P2 = precipitation intercepted by the canopy

P3 = stemflow

E1 = evaporation under the canopy

168 Evaporation from forest

E2 = evaporation of precipitation intercepted by tree crowns

E3 = transpiration of the stand

R1′and R1 = surface water inflow and outflow, respectively

R2′ and R2 = ground water inflow and outflow

V1 and V2 = water storage accumulated on the catchment surface at the beginning and at the end of the period

W1 and W2 = water storage in soils and subsoils in the unsaturated zone at the beginning and at the end of the period

U1 and U 2 = water storage at the beginning and at the end of the period. This method has been applied to study evaporation from different geotopes and to assess the effects of the most important hydrometeorological factors and human activities on the process. The direct use of the water balance equation in its complete form was not always possible. Therefore, it is reasonable to apply this equation to a large well-gauged catchment when runoff from this catchment is completely drained by the river, its surface and subsurface boundaries are coincident and the value of water exchange between the adjacent catchments can be neglected. This method can also be applied quite successfully to calculate evaporation from very small catchments or water balance plots. According to the plant species, these plots should be indicatory for the forest study area. Water balance plots no. 3 and no. 4 and the small experimental plot Elovy in the Taezhny experimental catchment have been taken as indicatory plots for this report. The assessment of changes in subsurface (ground) water storage is very difficult with water balance computations. It has been computed from a change in the water table ( ΔH gr ) for the specified time interval and water yield coefficient ( μ ) for the layer where water level change was fixed:

ΔU gr = ΔH gr μ (2.4)

The value of μ was based on the data obtained for experimental catchment in Valday.

During the last 30 years the heat balance method has been widely applied to determine evapotranspiration from forests.

Heat balance method In accordance with the well-known heat balance equation, the following equation can be applied to calculate evaporation: 1 E = ()R − H − B (2.5) L where R = radiation balance LE = heat loss for evaporation H = turbulent heat exchange between the active surface layer and the atmosphere B = heat flux in the active surface layer L = specific heat of phase transitions.

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The data of heat balance measurements used in this report were obtained from a tower erected in the forest. The amount of heat flux in the active soil layer consists of a change in the amount of heat in the surface part of the phytomass ( Ba ) and heat flux through the soil surface ( Bs ):

B = Ba + Bs (2.6) Heat accumulated in the tree stand is calculated with the following equation by Y.L. Rauner [1972]: Δt B = C Z (2.7) st W D τ where

CW = volumetric heat capacity of the wood at specified temperature and humidity

Z D = reduced layer of the phytomass where heat exchange occurs. In a spruce stand around the tower where the total wood volume equals about 430 m³/ha, the value of Z D equals 4.3 cm. Heat exchange in the soil is estimated with the well-known approximate equation: C B = S (2.8) s τ where C = volumetric heat capacity of soil

τ = duration of time interval (in min.) for which heat flux Bs is estimated S = a value characterising the temperature change in the upper layer.

The turbulent heat flux is calculated from the observation data on the gradients of air temperature and humidity above the forest, radiation balance and calculated heat exchange in the active layer: R − B H = (2.9) Δe 1+1.56 Δt A detailed methodology for calculating evaporation and heat exchange with the use of the heat balance equation is described in Rauner [1972]. In the case of low values of radiation balance and gradients of air temperature and humidity, heat exchange is calculated with the turbulent diffusion equation: ∂t H = ρC K (2.10) ρ ∂z where ρ = air density

Cρ = specific heat capacity K = turbulent exchange coefficient.

170 Evaporation from forest

Considering the difficulties in determining the turbulent exchange coefficient, turbulent fluxes can be calculated with the use of the integral coefficient of exchange in Budyko's equation [1956]:

H = ρCρ D()t0 − t (2.11) where D is integral characteristic of exchange slightly dependant on elevation. The use of this parameter for calculations is discussed in Budyko [1956]. It should be noted that gradient measurements should be made above a forest at elevations of approximately 1.3 times the mean height of the tree stand; this is based on the results of measurements of vertical profiles of turbulent fluxes in the forest and is described in Dubov et al. [1978].

Complex method The calculation of evaporation by the complex method is based on the following equation by M.I. Budyko [1956]:

W1 +W2 W1 +W2 E = E0 if ≤ Wmin (2.12) 2Wmin 2 W +W E = E if 1 2 ≥ W (2.13) 0 2 min where

W1 and W2 = water storage in soil layer 1 m deep at the beginning and end of each month respectively

Wmin = minimum water-bearing capacity on the experimental forest plot of the Valday hydrological laboratory; it equals 290 mm. On the basis of data on the total potential evapotranspiration for a month and water storage, evaporation for particular months in the warm season was calculated. These data are given in Table 2.1 together with the data on evaporation calculated by the water and heat balance methods. As evident from Table 2.1, evaporation for monthly intervals obtained by the water and heat balance methods as well as by the complex method agree satisfactorily. The mean deviation of evaporation values calculated by the water balance method from the values obtained by the heat balance method was 15%; this deviation equalled 19% when the complex method was applied. A rather big difference is observed during the autumn months, equalling 17% and 30%. This is explained by the fact that in autumn evaporation is not high and errors in its calculation by the water balance method tend to increase. Besides, the accuracy of the heat balance method is lower because of low values of the radiation balance in the autumn months. In September the relative error for the water balance method equals 17%.

Method of turbulent diffusion To calculate heat and water exchange between the underlying surface and the atmosphere on the basis of measurements of different parameters of the lower atmospheric layer, a number of methods have been developed which are based on the equation of the hydromechanical properties of the boundary layer. From equations of movement, equations of heat and water vapour transport in the atmosphere, when instantaneous values of wind velocity components

171 Forest hydrology – results of research in Germany and Russia

U , W and V , temperature T and humidity q are substituted by the sums of their mean values and deviations

U = U +U′, W = W +W′ , T = T + T′, q = q + q′ (2.14)

Table 2.1 Evaporation (mm) from forest, calculated by 1) the heat balance method, 2) the water balance method and 3) the complex method

Evaporation for a month (mm) Year Method VI VII VIII IX 1 107 103 77 49 1971 2 115 84 70 37 3 97 80 77 32 1 95 90 90 39 1972 2 98 90 70 49 3 - 116 105 - 1 109 119 87 - 1973 2 109 97 74 - 3 94 84 - - 1 91 118 102 77 1974 3 - 91 159 74 1 - 109 73 49 1975 2 - 106 90 60 3 - 84 64 - and when these equations are averaged, the following expressions are obtained for heat and water vapour fluxes and for the quantity of movements: ∂Q P = ρC T′W′ − ρC χ (2.15) ρ ρ ρ ∂Z ∂q E = ρq′W′ − ρχ (2.16) D ∂Z ∂U τ = −ρU′W′ + ρν (2.17) ∂Z where

χ ρ and χ D = coefficients of molecular heat conductivity ν = kinematic viscosity coefficient. Molecular terms in the second expressions of the right-hand side of the equations are much less than the first expressions describing the turbulent transfer; therefore they may be neglected. Computation methods resulting directly from these ratios are sometimes called direct methods. In this case, if we want to calculate the fluxes, the values of U′, W′ , T′ and

172 Evaporation from forest

q′ should be continuously measured by low-inertia equipment. The use of this method is hindered by complicated equipment intended for the measurements of temperature and humidity pulsations and by a great amount of data to be processed, especially during computations of fluxes for long time intervals. Therefore, the so-called gradient methods are more widely applied; these methods are based on the assumption that turbulent and molecular combinations are similar. In this case equations for turbulent fluxes are written as follows: ∂T P = ρC K (2.18) ρ ∂Z ∂q E = ρK (2.19) ∂Z ∂U τ = −ρK (2.20) ∂Z To estimate the values of fluxes, it is necessary to know not only the values of gradients but also the turbulent exchange coefficient K . Many equations have been derived for K within the framework of the semi-empirical turbulence theory. The most widely used equation, however, has been derived from the heat balance equation: R − B K = (2.21) ⎛ ∂T U ∂q ⎞ ρC ⎜ + ⎟ ρ ⎜ ⎟ ⎝ ∂Z Cρ ∂Z ⎠ In Russia a similarity theory is widely applied in atmospheric turbulence studies; according to this theory gradients can be presented as follows:

∗ U ⎡ ⎛ Z2 ⎞ ⎛ Z1 ⎞⎤ U ()Z2 −U ()Z1 = ⎢ fU ⎜ ⎟ − fU ⎜ ⎟⎥ (2.22) K ⎣ ⎝ L ⎠ ⎝ L ⎠⎦

∗ ⎡ ⎛ Z2 ⎞ ⎛ Z1 ⎞⎤ T ()Z2 − T ()Z1 = T ⎢ fT ⎜ ⎟ − fT ⎜ ⎟⎥ ⎣ ⎝ L ⎠ ⎝ L ⎠⎦ (2.23)

∗ ⎡ ⎛ Z2 ⎞ ⎛ Z1 ⎞⎤ q()Z2 − q ()Z1 = q ⎢ fq ⎜ ⎟ − fq ⎜ ⎟⎥ (2.24) ⎣ ⎝ L ⎠ ⎝ L ⎠⎦

⎛ Z ⎞ where: f ⎜ ⎟ terms are some universal functions requiring experimental determinations. ⎝ L ⎠ Here E q∗ = − ρK ∗ U P T ∗ = − ρC K ∗ ρ U U 2 L = βK 2T ∗

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g β = T g = free-fall acceleration K = Karman's constant U ∗ = dynamic velocity of friction. If we know universal functions and the gradients are measured, it is possible to estimate turbulent fluxes. These methods were tested in Valday. Table 2.2 shows the results of evaporation computations for individual 10-day periods in the summers of 1974 and 1975.

Table 2.2 Evaporation (mm) from a spruce forest calculated by the heat balance method (HB) and by the method based on the use of similarity theory (ST) for 10-day periods

Months

Me- V VI VII VIII IX Year thods 10-day period I II III I II III I II III I II III I II III 1974 HB - - - 35.7 24.0 30.6 42.0 38.1 38.3 41.7 29.1 30.7 18.0 17.7 40.8 ST - - - 36.0 27.0 - - - - - 30.0 29.4 15.2 16.1 - 1975 HB 33.8 40.8 28.1 - - - 31.2 44.7 33.0 40.5 15.6 18.0 10.0 12.3 22.8 ST 37.5 38.6 32.9 - - 44.4 38.2 39.4 36.0 40.2 15.6 17.4 12.2 12.0 25.2

Data obtained from the comparison of different methods for the computation of evaporation from forest make it possible to conclude that the following methods may be recommended for the computation of monthly values: heat balance method, water balance method, complex method and method of nomograms. To calculate evaporation for shorter intervals (10 days, a day or hours) it is possible to apply the pulsation method or the method based on the similarity theory.

Penman's method for the calculation of evaporation Penman suggested two variants to calculate evapotranspiration. These variants are based on the use of the turbulent diffusion theory and heat balance of the underlying surface. If the plant coverage is complete, heat loss for soil heating is small. Therefore, this loss is neglected and the heat balance equation is applied: R = LE + P (2.25)

According to Penman, evaporation from the plant cover which replaces the soil completely and which is provided by the accessible moisture is equal to potential transpiration. It was also assumed that turbulent transfer of heat and water vapour fluxes follow the same laws. Using Magnus's well-known ratio between the saturation pressure and the temperature of the evaporating surface and the above assumptions, the following equation was derived for the estimation of heat losses for evaporation after some transformations of the heat balance equation:

174 Evaporation from forest

Δ R + E γ A E = (2.26) Δ + X γ

Here

EA = drying capacity of the atmosphere, i.e. the value which takes account of the effect of wind velocity and humidity deficit in the air Δ = a value which depends on the temperature and equals a tangent of the angle of the tangential slope to a curve expressing a dependence between the saturation pressure and air temperature at the point corresponding to the mean air temperature γ = psychrometric constant equalling 0.49 Δ = a dimensionless value on which the role of R and E in the equation depends γ A X = a dimensionless parameter equalling 1.

According to Penman, when the plant cover is green, the equation may be used to calculate the potential transpiration ET . All energy terms ( R , EA , E ) are expressed in millimetres of the water layer evaporating during appropriate losses of energy (heat).

The drying capacity of the atmosphere EA (mm/day) is derived from the following equation:

EA = 0.35()0.5 + 537U (eA − ed ) (2.27) where U = wind velocity (m/s) 2 m above the ground eA = saturation pressure at mean air temperature ed = mean water vapour pressure (mm) obtained during standard meteorological observations. Table 2.3 shows the results of calculations of monthly and seasonal evapotranspiration from a forest using Penman's method. Calculations have been based on the data of meteorological and heat balance observations from a tower installed on the experimental forest plot of the Valday branch of the State Hydrological Institute (VB of the SHI). Measurements of air temperature, air humidity and wind velocity at an elevation of 37 m were taken at a mean height of the stand of 28-29 m. The radiation balance was measured at an elevation of 42 m. The basic data for the calculation of evaporation have been obtained by averaging the mean daily meteorological components and radiation characteristics. For the sake of comparison Table 2.3 also shows the mean evaporation derived by the heat and water balance methods and by the heat-water balance method. Moreover, the heat-water balance method has been used as a 'conventional reference' method. As is evident from the above data, there are seasonal variations in the evapotranspiration with maximum monthly values in June – July. If compared with the heat-water balance method, a difference in the evapotranspiration in May – September does not exceed 15%. As to the difference in monthly values, it is more evident in particular months. This is probably explained by insufficient accuracy of basic data measurements and it is typical of each method for the calculation of evaporation from a forest.

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Table 2.3 Evapotranspiration (mm) from a mature spruce forest calculated by different methods (data from the experimental plot of the VB of the SHI)

V-IX V-IX Year Method V VI VII VIII IX (mm) (%) 1975 1 73 98 103 64 49 384 100 2 98 88 106 66 29 387 101 3 - - 109 73 49 - - 4 85 85 127 94 75 426 109 1976 1 98 76 99 69 31 373 100 2 82 75 104 66 39 366 98 3 - - 85 66 50 - - 4 103 83 91 111 39 427 115 1977 1 88 96 89 57 30 360 100 2 83 114 106 68 43 414 113 3 - 113 104 - - - - 4 87 94 77 93 52 408 110 1978 1 89 88 86 77 43 367 100 2 95 97 100 57 17 366 - 3 - 93 87 50 22 - - 4 90 71 106 69 53 389 102 1979 1 90 101 103 67 40 401 100 2 121 107 90 85 30 433 107 3 - 129 116 96 40 - - 4 102 86 112 82 49 431 107

Note: 1 – heat-water balance method; 2 – Penman's method; 3 – heat balance method; 4 – water balance method

Heat-water balance method The heat-water balance method is an improvement of M.I. Budyko's complex method. Later, this method was improved by Andrejanov and Babkin [1974]. Moreover, an equation of transition from potential evapotranspiration E0 to the appropriate value of evaporation E was made more accurate. The structure of the equation for the computation of evaporation using the heat-water balance (HWB) method is as follows:

M M E = KP + ()E0 − KP if p 1 (2.28) M min M min

M E = E0 if ≥ 1 (2.29) M min where E = evaporation

E0 = potential evapotranspiration

176 Evaporation from forest

P = precipitation during the design time interval M = mean productive soil moisture content in the upper soil 1 m deep for the specified time interval

M min = minimum productive water-bearing capacity K = coefficient indicating a portion of precipitation (from the total amount of precipitation during the design time interval) lost for evaporation from the basin surface (from soil and plants).

Numerically, the value of K is derived from the following equation: N −n E P + n 0 ∑ I N K = 0 (2.30) P where N = number of days during the design period (a month, etc.)

E0 / N = mean daily potential evapotranspiration during the design time interval

n = number of days during this interval with precipitation PI exceeding mean daily

potential evapotranspiration ( E0 / n ) during its fall

∑ PI = total precipitation during the days when precipitation is less than the daily

potential evapotranspiration ( PI p E0 / N ) mean for this interval.

In equation 2.28 the term KP indicates evaporation of some portion of precipitation directly ⎛ M ⎞ from the soil surface and from the plants, whereas the term ⎜ E − KP ⎟ is evaporation due ⎜ 0 ⎟ ⎝ M N ⎠

M − M w to soil water storage or ; M w is water storage corresponding to the level of plants M min − M w wilting.

The transformed equation is as follows:

M − M ⎛ M − M ⎞ w ⎜ w ⎟ ′ ′ E = E0 + KP⎜1− ⎟ = E + ΔE (2.31) M min − M w ⎝ M min − M w ⎠ Here E′ = evaporation due to change in soil water storage ΔE′ = a positive addition. Numerically, it equals the amount of precipitation intercepted by the green mass of plants, i.e. ΔE′ = ΔP .

It will be shown below that this structure of the equation is applicable for the estimation of evapotranspiration both from the forest and from the field, using appropriate parameters of potential evapotranspiration and moisture content in soils and subsoils. Moreover, two methods are applied to compute evaporation depending on the range of water storage in the soil.

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The first method is used for the periods with water storages above critical values (Wcr ); evaporation here is estimated using equations 2.28 or 2.29. Conventionally, this variant may be termed Method I.

The second method is applied for the periods with water storages below critical values (Wcr ). In this case evaporation is computed taking account of the ratio between the mean water storage in the soil in the catchment and water storage corresponding to the least (minimum) water-bearing capacity using the following equation:

M ⎛ M ⎞ ⎜ ⎟ E = E0 + KP⎜1− ⎟ if M p M min (2.32) M min ⎝ M min ⎠ This method of evaporation computation is called Method II. The necessity of application of the first or second method to compute evaporation for individual months or 10-day intervals depends on the rate of moisture content in soils and subsoils. Moreover, in the zone of sufficient moistening (except some dry months and 10-day periods) Method I is usually applied; for the zone of insufficient moistening where water storage in soils is often observed below the level of Wmin Method II is used. Later, Method II was modified by introducing the parameter of critical soil moisture content instead of the parameter of the least (minimum) water-bearing capacity (see Chapter 3). In connection with the studies of the hydrological impact of forest, it is very important to select the proper method for estimating evaporation from forested basins. It is explained on the one hand by the necessity to apply a more accurate method for the computation of evaporation from the forest and on the other hand by the available basic data to compute evaporation from some particular plot. In fact, most of the available methods can be applied to compute evaporation during warm seasons. As to evaporation in the cold period, these values are estimated from the potential evapotranspiration value calculated by Kuzmin (1960). The analysis of the existing methods for the computation of evaporation shows that the heat- water balance method (HWB) is very satisfactory for the above requirements. It contains a minimum number of parameters involved; these parameters are estimated from the data obtained from the hydrometeorological network and from agrometeorological stations. The general principles of using this method for the computation of evaporation is applicable both for the forested areas and for the open areas. The difference is in numeric parameters of potential evapotranspiration and in soil moisture content included into the design formulas.

2.2 Potential evapotranspiration

Potential evapotranspiration is the most important parameter for the determination of evaporation from land. Potential evapotranspiration is the maximum possible evaporation from a sufficiently moistened surface under specified meteorological conditions. Under natural conditions evaporation measurements are associated with some difficulties but some design methods have been developed. The most well-known methods usually applied in Russia have been developed by Budyko [1956], Andrejanov and Babkin [1974], Penman [1968], Monteith [1965], amongst others. Most of these methods are based on the use of the

178 Evaporation from forest heat balance method. To estimate potential evapotranspiration, two methods can be applied depending on the data available. The first method is based on the use of data of actinometric measurements and partially on the tabular values of some parameters. The second method is based on the use of graphs of relations between mean long-term potential evapotranspiration for particular months and a conventional deficit of air humidity. Both methods are used in this report to compute potential evapotranspiration.

Let us consider a procedure to compute E0 by the first method. To this end, M.I. Budyko [1956] offered the following equation resulting from Dalton's equation:

E0 = ρD()qs − q (2.33) where ρ = 1.293⋅10−3 g/cm³, air density D = integral coefficient of turbulent exchange, in cm/s

qs = specific humidity of the saturated air at the temperature of the underlying surface, in g/g q = actual specific humidity, in g/g.

Taking into account that ρ = 0.623e/ P , where e is water vapour pressure and P is atmospheric pressure (760 mm), q = 0.82e g/g. If the expression for q is substituted into equation 2.33 as well as ρ = 1.293⋅10−3 g/cm, and if it is reduced to a layer of potential evapotranspiration in mm for a month (2.63⋅106 s), it becomes:

E0 = 27.8D()es − e (2.34) where E0 is in mm/month.

M.I. Budyko recommends taking the mean value of D = 0.63 cm/s for the computation of potential evapotranspiration. With this value of D equation 2.33 becomes:

E0 = 17.5()es − e (2.35) In equation 2.35 e is the actual water vapour pressure from the observation data. The value of es , i.e. the pressure of the saturated air and the corresponding temperature of the underlying surface θ w under the available plant cover, cannot be measured directly and should be derived, as proposed by Budyko [1956], from a joint solution of the heat balance equation for the underlying surface and Magnus's equation. Table 2.4 shows a case study for the computation of potential evapotranspiration during the months of the warm season 1971-1979. The second method of potential evapotranspiration estimation from the so-called conventional deficit of air humidity is based on the use of data on maximum water vapour pressure depending on the air temperature. This method was developed by Budyko and Zubenok

[1961] and presented as graphs of mean monthly potential evapotranspiration E0 versus the conventional deficit of air humidity d . Such a graph for the zone of coniferous forests is given as a case study in Figure 2.1.

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Table 2.4 Monthly values of potential evapotranspiration (mm) from a forest computed by 1) the heat balance method and 2) the graphs of relations between mean monthly potential evapotranspiration and conventional deficit of air humidity

Method Years V VI VII VIII IX 1 1971 111 95 117 98 38 2 106 112 117 91 42 1 1972 86 159 172 112 39 2 81 132 137 106 48 1 1973 93 124 147 112 35 2 83 128 145 97 37 1 1975 103 102 125 74 - 2 103 113 137 86 - 1 1976 - 105 133 91 32 2 90 94 112 76 49 1 1977 94 135 105 84 - 2 91 117 117 75 - 1 1978 82 128 125 71 32 2 98 125 120 79 37 1 1979 110 151 72 101 38 2 109 126 106 81 43

Successive design stages during potential evapotranspiration computation are as follows:

• an appropriate value of water vapour pressure saturation e1 is taken from a table of monthly air temperatures;

• the conventional deficit of air humidity d (mb) (d = e1 − e) is computed from the

difference between the water vapour pressure saturation e1 and the observed absolute air humidity e (mb); • the obtained values of the conventional deficit of air humidity are used to estimate monthly potential evapotranspiration. It should be noted that the above method was used to estimate potential evapotranspiration from treeless areas. It can be used, however, in the forest, too. In this case, potential evapotranspiration is estimated through a coefficient of transition of potential evapotranspiration from treeless areas to potential evapotranspiration from a forest. Transition coefficients were estimated for several years from the data of the VB of the SHI on evapotranspiration from the Taezhny small experimental catchment (spruce forest) and the Usadievsky small experimental catchment (field) (Table 2.5). Table 2.5 also contains mean monthly values of this coefficient estimated from the data of the Podmoskovnaya water balance station (birch forest and treeless plots).

These data show that the transition coefficient value K0 is not constant during the vegetation period and depends on the type of tree species.

180 Evaporation from forest

Figure 2.1 E0 = f ()d for the zone of coniferous forests on a monthly basis

Table 2.5 Transition coefficient values (K0 )

Months Tree species V VI VII VIII IX Spruce 1.00 1.04 1.09 1.12 1.11 Birch 0.90 0.98 1.07 1.05 1.00

Thus, potential evapotranspiration from forested areas (and from field catchments) for long- term observation periods was estimated taking account of the transition coefficient. In general, the second method is rather simple in use to compute potential evapotranspiration from the forest. Table 2.6 shows comparative results of computations of monthly potential evapotranspiration from the Taezhny forested experimental catchment obtained by two methods, i.e. on the basis of actinometric data and with the use of graphs of potential evapotranspiration versus a

181 Forest hydrology – results of research in Germany and Russia conventional air humidity. The difference in potential evapotranspiration values is less than 20% except in some particular cases.

Table 2.6 Monthly potential evapotranspiration (mm) from the forest computed by the heat balance method (1) and calculated by the graphs of mean monthly potential evapotranspiration versus a conventional deficit of air humidity (2)

Method Years V VI VII VIII IX 1 1971 111 95 117 98 38 2 94 112 117 91 42 1 1972 86 159 172 112 39 2 81 128 137 104 46 1 1973 93 124 147 112 35 2 83 128 145 97 37 1 1974 103 102 125 74 2 103 113 137 96 1 1975 126 105 133 91 32 2 80 92 112 76 49

Investigating relations between mean evaporation and meteorological conditions, M.I. Budyko offered the following function: E ⎛ R ⎞ = f ⎜ ⎟ (2.36) P ⎝ LP ⎠ where P = precipitation R = radiation balance E = evaporation L = latent heat of phase transitions. Parameter R / LP is the radiation index of aridity characterising the rate of moistening of the terrain. It is the most important physiographic parameter. Here, to characterise space-time variability of this parameter, its values from 48 actinometric stations mostly distributed within the forest zone of Russia were analysed (Annex 2.1). For this, data from the stations with long-term observation series on radiation balance were used. These data for May-September show that the radiation index of aridity for a long-term period varies from 1.2 to 2.4 over area and its variation is of a zonal nature increasing from north to south. The variation coefficient of this parameter for such sites (e.g. Voejkovo and Valday) equal 0.21 and 0.24 respectively. In this report, this parameter has been used for the analysis of evapotranspiration and its components, productivity of the plants phytomass and some runoff characteristics.

182 Evaporation from forest

2.3 Evaporation from a field catchment

Much emphasis has been laid in Valday on the problems of evaporation from different surfaces. Experiments have been made in the plots for measuring evaporation from water surfaces and from land, in an agricultural water balance plot, in individual areas of the Polomet' river basin, in the Usadievsky field experimental catchment and with the large hydraulic evaporimeter. A great number of hydrometeorological and agrometeorological observations have been made. The latter were used as a basis to study daily and seasonal variations in evaporation, to discover the main factors explaining these variations, to develop and test instruments and to solve some methodological problems. To study specific features of the evaporation regime in forested areas, it was necessary to have similar data for the field. To estimate evaporation from the field, well-known methods were applied, i.e. the gravimetric method, water and heat balance methods and the turbulent diffusion method. When the gravimetric method was used, the following evaporimeters were applied: the GGI-500-50 evaporimeter, the GGI-500-100 evaporimeter, GGI-B-1000 swamp evaporimeters, evaporimeters with surface areas of 0.3 m² and 1.0 m², weighted lysimeters 0.2 m² in area and 1.0 to 2.5 m high; a small hydraulic evaporimeter (0.2 m² in area, 1.5 m high) and a large hydraulic evaporimeter (5.0 m² in area and 2.0 m high). Experimental results of evaporation measurements in Valday by different methods were published in Fedorov and Marunich [1985].

2.3.1 Evaporation measurements from catchment surfaces

Evaporation from different catchment surfaces is calculated on the basis of data on evaporation from various kinds of farmland and agricultural fields. If such data are available, evaporation ( E ) for the design time interval (month, season) averaged over area is calculated with the following equation:

N E = ∑ Ei fi = f1F1 + f2F2 + ... + f N FN (2.37) i−1 where

Ei = total evaporation (mm) measured in the study plots

fi = size of areas or plots, as parts of the total drainage area.

When evaporation is estimated from the catchment, it is necessary to determine: (a) the extreme distance for distribution of data obtained at any observation site (b) the size of farmlands which are rather small and can be neglected (c) the required number of observation sites to obtain rather accurate evaporation data. These problems are discussed in detail in Kuzmin [1960]. Item (a) is solved experimentally on the basis of evaporation measurements at different points within the catchment, of the autocorrelation function and by plotting a graph of correlation versus a distance between the points of measurements. The problem in item (b) on a sufficiently small farmland area where evaporation measurements may be neglected is solved by the following equation:

183 Forest hydrology – results of research in Germany and Russia

⎛ Ei ⎞ γ = fi ⎜1− ⎟100% (2.38) ⎝ E ⎠ where E = actual evaporation mean weighted over the drainage area

Ei and fi = evaporation and a sub-area occupied by the i -th plot γ = a computation error in per cent of E resulting from the use of equation 2.37,

explained by a substitution of Ei by E during calculations. If this error is quite admissible, it is not necessary to measure evaporation from the i -th plot.

Table 2.7 demonstrates at what values of fi and modulus coefficients Ei / E taking account of the accurate evaporation (measured) from the particular plot it is possible to neglect the γ value. For example, if modulus coefficient variations in the forest zone are assumed to be within the range of 0.7-1.3 and if γ = + 5%, then, as is evident from Table 2.7, plot fi , which is less than 0.2, can be assumed to be rather small.

Table 2.7 Values of γ (%) depending on the modulus coefficient of evaporation from the th i plot ( Ei / E ) and its percentage of the area occupied by this plot ( fi ), according to P.P. Kuzmin [1960]

Area occupied by fi (%) Ei / E 0.01 0.1 0.2 0.3 0.4 0.5 0.6 0.8 0.10 1 9 18 27 0.20 1 8 16 24 0.30 1 7 14 21 28 0.40 1 6 12 18 24 30 0.50 0 5 10 15 10 25 30 0.60 0 4 8 12 16 20 24 32 0.70 0 3 6 9 12 15 18 24 0.80 0 2 4 6 8 10 12 0.90 0 1 2 3 4 5 6 8 1.00 0 0 0 0 0 0 0 0 1.10 0 -1 -2 -3 -4 -5 -6 -8 1.20 0 -2 -4 -3 -8 -10 -12 -16 1.30 0 -3 -6 -9 -12 -15 -18 -24 1.40 0 -4 -8 -12 -16 -20 -24 -32 1.60 -1 -6 -12 -18 -24 -30 -36 1.80 -1 -8 -16 -24 -32 2.00 -1 -10 -20 -30 3.00 -2 -20 -40 4.00 -3 -30 5.00 -4 6.00 -5 7.00 -6

184 Evaporation from forest

For an approximate computation of the required number of observation sites within the catchment, at the specified accuracy, it is possible to use the approximate equation of Kuzmin [1960]: E lg min E n = max (2.39) 100 + P lg 100 − P where

Emin and Emax indicate plots in the catchment with minimum and maximum evaporation respectively P = the admissible error (%) of evaporation measurements from the plots.

When soil evaporimeters are applied, providing evaporation measurements with an accuracy of about 10% (in taiga forest), it is not reasonable to specify γ as being less than 10%. It should be noted that more accurate theoretical schemes are available for observation sites distribution within the catchment. These schemes are based on the use of structural and correlation functions. In simple cases, however, Kuzmin's scheme can be applied.

2.3.2 Estimation of evaporation from the Usadievsky small experimental catchment using the method with soil evaporimeters

This method is widely applied to study the water balance of a catchment. Evaporation in this case is often calculated without sufficient validity (with a random number of observation sites and without taking account of evaporation accuracy). Sometimes the results of evaporation measurements even at one point are used, irrespective of the size of the catchment. This approach to estimating evaporation from the catchment is not valid. Basic statements on the use of the soil evaporimeter method have been made in a case study in the Usadievsky field experimental catchment where special measurements were made early in the 1970s. In 1970 different uses of farmland were distributed over the catchment as follows: winter rye occupied 32% of the catchment, fallow land 18%, meadow 31% and others 19% (including swamps 16%, forest and bushes 3%). GGI-500-50, GGI-500-100 and GGI-B-1000 evaporimeters were installed to measure evaporation. Evaporimeters were distributed over the catchment taking account of agricultural plots and specific relief. The following number of observation points were organised: six points in a dry meadow, one point in a wet meadow, one point in a swamp and six points in the plot with winter rye. Each point was equipped with a set of two evaporimeters. Concurrently with the evaporation measurements, soil moisture content, green plant mass, meteorological elements and relative potential evapotranspiration were also measured. The relative potential evapotranspiration is of particular importance when evaporation change is estimated depending on relief, wind effect and other factors. The green plant mass in the meadow was measured before haymaking, in the plot with winter rye and in evaporimeters. Samples of vegetation were taken at equal distances by means of a square 50x50 cm frame. Altogether 50 samples were taken from the catchment.

185 Forest hydrology – results of research in Germany and Russia

Meteorological observations were made in a meteorological plot in the centre of the catchment. Small weather shelters 0.5 m above the ground surface were installed for relative potential evapotranspiration measurements. These shelters were set up in different geotopes. Twelve shelters were erected in the catchment. To estimate the rate of representativeness of the observation data on evaporation obtained in soil evaporimeters, it was necessary to discover to what extent the conditions of water balance formation in the monoliths placed in the soil evaporimeters corresponded to the mean conditions in the catchment. If these conditions differed, it was necessary to discover how these differences affected evaporation. The following criteria were used to clarify this problem: • the ratio of soil moisture content in the evaporimeters to soil moisture content at the sites where monoliths were taken and placed in the evaporimeter • the ratio of the phytomass quantity in the evaporimeters to the phytomass quantity in the catchment • the ratio of mean water storage at the points where evaporimeters are installed to mean water storage in the catchment. For example, the assessment of representativeness of the points of evaporation measurements in the catchment was made by a comparison of the mean soil moisture content in the top soil 0.5 m deep at these points and the mean soil moisture content in the catchment; this comparison was based on the data of serial measurements at 44 points. Table 2.8 shows soil moisture content resulting from measurements taken on 2 July and 29 July 1970. As is evident from Table 2.8, the mean soil moisture content at the points where evaporimeters were installed agrees well with the mean soil moisture content in the catchment.

Table 2.8 Soil moisture content (mm) in the 0 to 50 cm soil layer in the Usadievsky small experimental catchment

Site of soil moisture content measurements 2 July 29 July

At 11 points where evaporimeters are installed 77 100 At 44 points distributed over the catchment 79 100

To estimate the accuracy of the evaporation measurements in the catchment it was first necessary to estimate the random and systematic errors of the gravimetric method of evaporation measurement. As a case study, Table 2.9 shows the calculated mean-root-square error of evaporation measurements by two evaporimeters (No. 1 and No. 2) in a meadow. The relative value of this error for fifteen cases of measurements does not exceed 11.8%. A similar computation of the absolute and relative errors was made from the readings of other pairs of evaporimeters. Moreover, the values of the absolute errors in 50% of cases for some pairs of evaporimeters in the meadow did not exceed 1.3 mm and the values of relative errors did not exceed 6.5%; these errors in the plot with winter rye equalled 0.9 and 5.3% respectively. The analysis of 10-day evaporation values at individual points shows a significant evaporation variability within the catchment. The difference in evaporation at two adjacent points is much greater than the errors of evaporation measurement at one point. The mean-root-square deviation of evaporation measured at individual points, if compared with mean evaporation

186 Evaporation from forest for particular sections of farmland for the periods between weighing, equalled 3.8 mm in May – July and 1-2 mm in August – September.

Table 2.9 Mean-root-square error of evaporation measurements by two evaporimeters

No. of the Data of Evaporation (mm) Absolute Relative interval measurement error error E1 E2 mean 1 1/VI – 11/VI 50 55 52.5 2.5 4.8 2 11/VI – 19/VI 15 19 17.0 2.0 11.8 3 10/VI – 29/VI 41 43 42.0 1.0 2.4 4 29/VI – 9/VII 32 34 33.0 1.0 3.0 5 9/VII – 17/VII 11 12 11.5 0.5 4.4 6 17/VII – 29/VII 30 29 29.5 0.5 1.7 7 29/VII – 11/VIII 31 29 30.0 1.0 3.3 8 11/VIII – 18/VIII 18 20 19.0 1.0 5.3 9 18/VIII – 27/VIII 17 17 17.0 0.0 0.0 10 27/VIII – 9/IX 21 24 22.5 1.5 6.7 11 9/IX – 18/IX 10 12 11.0 1.0 9.1 12 18/IX – 29/IX 4 4 4.0 0.0 0.0 13 29/IX – 7/X 2 1 1.5 0.5 3.3 14 7/X – 15/X 2 5 3.5 1.5 4.3 15 15/X – 27/X 2 4 3.0 1.0 3.3

The data in Table 2.9 show that the mean-root-square deviation of total monthly evaporation by evaporimeters installed at different points in the catchment equalled 3.5-10.5 mm for the meadow and 0.8-7.5 mm for the winter rye. The mean-root-square deviation for the winter rye is somewhat less than that for the meadow. This is explained by a greater homogeneity of areas sown with rye than occupied by meadow plants in the catchment. The difference in evaporation in individual areas of the catchment is explained by heterogeneous factors on which evaporation depends (meteorology, soils and plants). It is rather difficult to separate the effect of each factor on evaporation because the effects of different factors are interrelated in a particular catchment; for example, the effect of meteorological factors on evaporation greatly depends on topography and slope exposure. In turn, the exposure is closely connected with soil moistening. The same may be said about the importance of the green plant mass, the amount of which is directly related to the type of relief and, consequently, to soil moistening, etc. Evaporation from the surface of the Usadievsky small experimental catchment has been calculated on the basis of data obtained during evaporation measurements from different kinds of farmland. As a case study, Table 2.10 shows monthly evaporation from different kinds of farmland as mean values obtained from several evaporimeters. The mean evaporation from the catchment was calculated for particular months as mean weighted value taking account of the portion of catchment occupied by different kinds of farmland. The seasonal evapotranspiration from the catchment during the whole observation period in 1970 equalled 400 mm.

187 Forest hydrology – results of research in Germany and Russia

Table 2.10 Monthly evaporation (mm) from different kinds of farmland as mean values based on observations by a group (cluster) of evaporimeters

Evaporation, mm Period meadow winter rye swamp catchment 16-30/IV 22 26 (22) 23 V 70 88 (70) 76 VI 104 84 121 100 VII 81 100 74 87 VIII 69 48 71 62 IX 40 39 42 39 X 12 16 14 13 16/IV-27/X 398 401 414 400

Noting that 10-day values of evaporation from some types of farmland differ more than the error of the method, the required number of evaporimeters to be installed was calculated separately for each type of farmland, the area of which was no less than 20% of the catchment. The relative error was assumed to be 10%. According to computations, six sets of evaporimeters are required to determine the total 10- day evaporation from the Usadievsky experimental catchment with a difficult geomorphology with a 10% accuracy; four sets of evaporimeters were sufficient to determine total monthly evaporation. In other catchments where underlying surfaces are more homogeneous (when individual areas of farmland are less than 15-20%) and the relief is slightly rough the required number of evaporimeters may be two or three times less. To estimate evaporation from a field catchment, however, it is not always possible or reasonable to use the evaporimeter method, during dry vegetation periods in particular. Besides, when this method is used (e.g. with GGI-500-50 evaporimeters), soil monoliths should be replaced rather often. Therefore, hydrometeorological methods are applied for a quantitative assessment of evaporation from a catchment. The heat-water balance method has been applied in this report. The results of the computation of monthly evaporation for 1955-1999 are given in Annex 2.2-2.3.

2.4 Calculation of evaporation from the Taezhny forest experimental catchment

The heat-water balance method has been accepted as the main method to calculate evaporation both from the field and from the forest. For example, humidity parameters for the Taezhny forest catchment have been estimated from the analysis of experimental data for a long-term period and are as follows: the least (minimum) water-bearing capacity equals 290 mm, soil moisture content corresponding to the wilting coefficient equals 140 mm and critical soil moisture content in the top layer of soil 1 m deep equals 215 mm.

188 Evaporation from forest

For a forest, the potential evapotranspiration parameter can be calculated by two methods, (1) by means of monthly transition coefficients ( K0 ); and (2) experimentally with the so-called coefficient of external diffusion obtained on the basis of data of heat balance measurements in the forest. In this report the first method has been applied to estimate potential evapotranspiration. It is based on the determination of the ratios between monthly evaporation from the forest and field catchments by the water balance method, using data of long-term water balance measurements:

Fforest = K0E field

Proceeding from the statement that evaporation is directly proportional to potential evapotranspiration and if data on monthly evaporation from the forest and from the field are available, the transition coefficient for potential evapotranspiration was estimated. On the basis of monthly transition coefficients for potential evapotranspiration, evapotranspiration from the forest for the warm season (May – September) was calculated. Evaporation for the cold season, as well as for October, was accepted to be equal to potential evapotranspiration. Table 2.11 contains evaporation data from the forest for certain periods from 1955 to 1999. The study period corresponds to two classes of tree stand age: 1955-1974 (class III) and 1975- 1999 (class IV). The analysis of evaporation from the forest during the above periods was made taking account of the effect of the main factors on which evaporation depends, i.e. hydrometeorological, pedological, hydrogeological and biophysical factors. Hydrometeorological factors are subject to great variations while the other factors are more conservative. Figure 2.2 demonstrates a type of general change in evapotranspiration for a long-term period under the impact of these factors. Higher values were observed when the aridity index varied from 1.3 to 2.0. This corresponds to a soil moisture content above the critical value. During extremely dry vegetation periods (at J ≥ 2.0 ), as observed in 1959, 1970, 1972, 1992 and 1996, evaporation from the forest was much lower (see Table 2.11). In general, evapotranspiration for particular years depends on the radiation balance and soil moisture content. These results of computations refer to evaporation measurements from a large spruce forest. If a quantitative assessment of evaporation from restricted forest areas and other characteristics is to be made, the effect of forest age, species, type and forested area should be taken into account. The effect of forest age on evaporation from the forest is quite evident from Figure 2.3. Figure 2.3 was plotted on the basis of observation data at the VB of the SHI for years with about the same water availability. Experimental data obtained from a plot with a young forest and from a plot with a spruce forest with a stand of a different age were used. Figure 2.3 clearly demonstrates characteristic periods of evaporation changes from a forest; these changes correspond to particular stages of forest plants evolution, i.e. period of intensive development, period of climax and period of gradual ageing. Moreover, the first period is characterised by an intensive evaporation increase up to the climax phase in the stand evolution. This stage is observed at the age of 35-50 years old. Later, evapotranspiration gradually decreases. This is evident from a comparison of evapotranspiration and its components of age classes III and IV (Table 2.12). Evapotranspiration from the Taezhny experimental catchment decreased by 14 mm on average for the 20-year period. This decrease is due to a lower transpiration of trees.

189 Forest hydrology – results of research in Germany and Russia

Table 2.11 Evapotranspiration (E) and potential evapotranspiration (E0) from the forest during the warm season and during the year (Taezhny small experimental catchment)

May – September January – December Year JV −IX E (mm) E0 (mm) E / E0 E (mm) E0 (mm) E / E0 1955 390 467 0.84 454 525 0.86 1.59 1956 389 430 0.90 430 458 0.94 1.28 1957 399 405 0.99 478 470 1.02 1.29 1958 412 414 1.00 485 481 1.01 1.42 1959 405 514 0.79 488 585 0.83 2.09 1960 390 479 0.81 465 557 0.83 1.65 1961 410 443 0.93 492 512 0.96 1.15 1962 401 401 1.00 483 457 1.06 0.95 1963 409 516 0.79 466 557 0.84 1.79 1964 397 495 0.80 461 546 0.84 1.83

Age class III 1965 391 456 0.86 465 519 0.90 1.66 1966 405 495 0.82 474 549 0.86 1.57 1967 404 479 0.84 489 551 0.89 1.59 1968 405 485 0.84 479 546 0.88 1.50 1969 382 458 0.83 456 519 0.88 1.60 1970 371 489 0.76 440 544 0.81 2.25 1971 378 471 0.80 450 528 0.85 1.68 1972 371 506 0.73 449 574 0.78 2.32 1973 388 504 0.76 467 569 0.82 1.41 1974 391 456 0.86 484 560 0.86 1.40 1975 387 528 0.73 472 559 0.84 1.62 1976 373 421 0.89 446 469 0.95 1.51 1977 360 440 0.82 436 503 0.87 1.10 1978 383 419 0.91 462 508 0.91 1.49 1979 404 486 0.83 461 527 0.87 1.65 1980 354 425 0.83 429 496 0.86 1.30 1981 372 465 0.80 442 518 0.85 1.17 1982 362 491 0.74 450 517 0.87 1.32 1983 394 460 0.86 462 515 0.90 1.22 1984 355 436 0.81 432 500 0.86 1.23 1985 394 429 0.92 468 493 0.95 1.47 1986 385 453 0.85 470 524 0.90 1.22 1987 390 404 0.97 483 475 1.02 1.17 1988 381 478 0.80 463 549 0.84 1.17

Age class IV 1989 391 475 0.82 470 538 0.87 1.36 1990 348 384 0.91 446 463 0.96 0.93 1991 394 422 0.93 473 491 0.96 1.36 1992 379 542 0.70 496 570 0.87 2.52 1993 363 470 0.77 492 549 0.90 1.36 1994 396 490 0.81 489 576 0.85 1.76 1995 389 495 0.79 474 567 0.84 1.65 1996 357 494 0.72 436 572 0.76 2.27 1997 373 495 0.75 441 540 0.82 2.25 1998 413 413 1.00 493 1.09 1999 385 489 0.79 465

190 Evaporation from forest

Figure 2.2 Dependence of evapotranspiration (mm) from the forest (E3) on soil moisture content (J) during May – September

Figure 2.3 Ratio of evapotranspiration to potential evapotranspiration (E/Eo) versus forest age (years)

The surrounding landscape affects evaporation from the forested area. The effect of this factor tends to increase at the transition from the forest zone to forest-steppes, where forested areas are usually small. For example, according to Rauner [1972], if forests occupy areas of 10 km², 100 km² and 1000 km², evaporation from the forest tends to be higher by 16%, 7% and 2% respectively. The effect of this factor explains high evaporation values from the forest as compared with treeless areas obtained in this zone by different scientists.

191 Forest hydrology – results of research in Germany and Russia

Table 2.12 Mean evapotranspiration and its components (mm) for age classes III and IV of a spruce forest during May – September

Evaporation Evaporation Age Transpiration Period Evapotranspiration under the of intercepted class of trees canopy precipitation 1955-1974 III 394 122 108 166 1975-1994 IV 380 115 115 151

2.5 Structure of evapotranspiration from the forest

The structure of evapotranspiration from the forest ( E ) is a relationship between its components, i.e. evaporation under the canopy ( E1 ), evaporation of precipitation intercepted by plants ( E2 ) and transpiration from trees ( E3 ); it is presented as follows:

E = E1 + E2 + E3 (2.40)

Depending on the characteristics of the stand (species, age, density and type), relations between different evaporation components are different. Besides, these relations differ in time which is most important during the warm and cold seasons. For instance, during the warm season evaporation under the canopy consists of evapotranspiration from the soil cover, consisting, in turn, of evaporation from the soil under the canopy of plants ( E1′ ), evaporation of precipitation intercepted by plants ( E2′ ) and transpiration from plants and small bushes

( E3′ ):

E′ = E1′ + E2′ + E3′ (2.41)

During the cold season E′ is evaporation from the snow pack under the forest canopy. As shown below, evaporation of precipitation intercepted by tree crowns and transpiration differ greatly during the warm and cold seasons. It should be noted that the structure of evapotranspiration from the forest has been studied rather poorly. The necessity to study this problem is associated with the solution of some scientific and practical problems. These are: • change in the evaporation structure under the impact of human activity • studies of relations between transpiration of forest plants and their productivity • selection of the most rational procedures for forestry projects. Unlike those of many other authors, this report gives research results on evapotranspiration and its structure for a long-term period of tree stand evolution because the changes observed during this evolution affect the value of runoff from a catchment. Different methods can be used for a quantitative assessment of evapotranspiration components. The results of the analysis of individual components as well as the possibilities of their measurements and computation for particular periods are given below.

192 Evaporation from forest

2.5.1 Evaporation under the forest canopy

Evaporation from the soil and from the top cover, including low bushes, makes up a considerable portion of evapotranspiration from the forest. Evaporimeter and lysimeter methods are used to estimate evaporation under the forest canopy. Different evaporimeters have been designed in different countries at different times. They are briefly described in some publications, in particular in the international guide Representative and Experimental Basins edited by C. Tuebes and V.A. Ouryvaev [1971]. Without touching on the history of the application of this method, let us note that these observations were initiated in Valday in 1937; systematic measurements have been made since 1952. Evaporimeters 500 cm² and 1000 cm² in area and 50 cm high, swamp evaporimeters 1000 cm² in area and 50 cm high, as well as large and small hydraulic evaporimeters with single trees have been used. Small and large hydraulic evaporimeters have been installed in a small forest glade. Evaporimeters under the forest canopy were distributed among predominant plant species according to a geobotanic map of the catchment. Species of grass and sedge, wood sorrel, bilberries, ferns, raspberry bushes, white peat moss as well as forest litter were among them. Evaporation was measured once every five days by periodic weighing of the evaporimeters. The results of measurements on certain plots have been published in the yearbooks of the Valday Branch of the SHI. Data on evaporation from individual types of top soil and the available geobotanic map of the catchment made it possible to calculate the mean weighted evaporation under the forest canopy in the whole catchment. The results show that the highest evaporation from all plant species occurs in July, during the most intensive vegetation period. Moreover, the maximum evaporation is from species of grass and sedge. On average, the long-term mean evaporation from June to September equals 150 mm; the same evaporation is observed from plots with ferns; slightly less evaporation (by 9%) occurs from raspberry bushes and it is twice as low from plots with bilberries. Evaporation from wood sorrel is the least compared with other plant species. Evaporation from forest litter is about four times less than evapotranspiration from soil on which grass and sedge grow. For example, during observations in 1958 evaporation for May and June equalled 50 mm and 192 mm respectively. Evaporation from the surface of swampy areas covered with white peat moss and pines (quality class IV, density 0.4) during the period June – September 1959-1967 turned out to be 1.5 times lower than that from areas with grass and sedge. Long-term evaporation under the canopy of the forest from May to September varies from 103 to 145 mm with a mean value of 121 mm which is 30% of the evapotranspiration value from the forest catchment. Maximum values are observed during warm seasons with optimal moistening; minimum values are observed during rainy seasons when dull weather prevails. As observation results show, similar evaporation values under the forest canopy are characteristic of the subsequent decades (1965-1974, 1985-1994) and equal 30-32% of evapotranspiration.

193 Forest hydrology – results of research in Germany and Russia

Evaporation under the forest canopy depends on the same factors as evapotranspiration does, primarily on the ratio between radiation balance and precipitation. This is clearly seen in the graph of evaporation under the forest canopy versus moistening parameter (Figure 2.4).

Figure 2.4 Dependence, E1 = f ()JV −IX , of evapotranspiration (mm) under the forest canopy (∑E1) on the moistening parameter (JV-IX)

As is evident from the long-term data analysis, there is a stable relationship between evaporation under the forest canopy E1 and potential evapotranspiration E0 from a forested area for the vegetation period May – September dependent on the moistening parameter J (see Figure 2.5).

Figure 2.5 The E1/E0 =f(JV-IX) ratio for the Taezhny small experimental catchment

The so-called coefficient of potential evapotranspiration of the forest canopy is the most stable: K1 = E1 / E0( forest) . It characterises ratios between mean weighted evaporation under the forest canopy in the catchment ( E1 ) and potential evapotranspiration from the forested area. Table 2.13 gives the values of this coefficient for particular months as well as its mean values for 1955-1964. Table 2.14 shows these values for the 20-year period. Moreover, its mean

194 Evaporation from forest

value K1 equals 0.26. The value of K1 depends on the condition of the top cover and soil moisture content. It is stable for the study age class and forest density. According to observations in the experimental catchment, the seasonal values of K1 are as follows:

• for a mature spruce forest, K1 = 0.26

• for a spruce forest with available forest litter only and a forest density of 1.0, K1 = 0.09- 0.11

• for a spruce forest with grass stand and a forest density of 0.6-0.8, K1 = 0.24-0.30.

Similar values of K1 were observed at the Podmoskovnaya water balance station, for a birch forest in particular. The mean long-term value of K1 in the period 1971-1982 equalled 0.31 (Table 2.14). It is evident that if potential evapotranspiration from the forest is known, this coefficient can be used to estimate mean weighted evaporation under the forest canopy for the vegetation period if direct measurements are not available. In this case two different situations can be considered. The first situation is the most typical of the forest zone, i.e. when soil moisture content is quite sufficient and water storage in the top layer 1 m deep equals or is above the critical level. In this case evaporation under the forest canopy can be estimated from potential evapotranspiration through the mean value of the ratio E1 / E2 = 0.26. The second situation is characteristic of dry seasons when water storage in soils and subsoils is below the critical level. Moreover, the value of K1 varies from 0.25 to 0.29 depending on the moistening parameter. In this case the graph of relations K1 = f (JV −IX ) is used to estimate

K1 (Figure 2.5).

2.5.2 Evaporation of liquid precipitation intercepted by forest plants

Evaporation of liquid precipitation from the forest plants includes evaporation of precipitation intercepted by the green mass of trees and grass. It primarily depends on meteorological factors and on the characteristics of the stand. Besides, the evaporation rate differs at different levels. At the first approximation, it can be assumed that it would be proportional to the intensity of turbulence in the lower atmospheric layer at different levels of the inter-crown space. For example, according to the data of VB of the SHI in a spruce forest it is possible to assume on the basis of turbulence intensity that the evaporation of raindrops from the crown tops is four times more intensive than that at the lower boundary of the forest canopy. Evaporation of that part of precipitation which is intercepted by the tree crowns by its absolute value corresponds to the total amount of precipitation intercepted by the forest canopy. As noted in Chapter 1, the amount of precipitation intercepted during some rainfalls can be estimated by measurement data and by computation.

195 Forest hydrology – results of research in Germany and Russia

Table 2.13 Evaporation (mm) under the forest canopy ( E1 ), potential evapotranspiration

(mm) above the forest ( E0 ) and K1 = E1 / E0

Taezhny small experimental catchment

Year V VI VII VIII IX V-IX 1955 18 25 31 24 12 110 E1

E0 66 111 123 106 64 470

K1 0.27 0.23 0.25 0.23 0.19 0.23 1956 19 32 25 18 14 108 E1

E0 85 131 104 63 47 430

K1 0.22 0.25 0.24 0.29 0.30 0.25 1957 19 25 31 20 17 112 E1

E0 70 102 120 74 36 402

K1 0.27 0.25 0.26 0.27 0.47 0.28 1958 19 33 36 21 10 117 E1

E0 83 107 112 71 41 414

K1 0.23 0.31 0.32 0.29 0.24 0.28 1959 25 30 39 35 11 145 E1

E0 96 122 146 114 36 514

K1 0.26 0.25 0.27 0.31 0.31 0.28 1960 21 30 33 24 12 120 E1

E0 101 122 131 88 37 479

K1 0.21 0.25 0.25 0.27 0.32 0.25 1961 21 28 26 23 13 111 E1

E0 86 129 109 77 42 443

K1 0.24 0.22 0.24 0.30 0.31 0.25 1962 20 28 26 20 11 103 E1

E0 74 104 111 75 39 403

K1 0.27 0.27 0.23 0.27 0.28 0.26 1963 27 28 40 28 22 145 E1

E0 117 109 131 97 62 516

K1 0.23 0.26 0.30 0.27 0.35 0.28 1964 24 33 40 27 12 136 E1

E0 86 131 135 94 47 495

K1 0.27 0.25 0.30 0.29 0.25 0.27 Mean 21 29 33 24 13 121 E1

E0 87 117 122 86 45 457

K1 0.25 0.25 0.27 0.28 0.30 0.26

196 Evaporation from forest

0.26 0.26 Mean Mean ean 0.31 0.31 0.24 0.24 1.28 1974 ) for May- V-IX 0.24 0.24 1.41 1973 J 0.27 0.27 1.60 1982 0.29 0.29 2.32 1972 0.23 0.23 1.68 1971 0.30 0.30 3.25 1981 0.28 0.28 2.25 1970 0.26 0.26 1.60 1969 0.31 0.31 1.10 1980 0.27 0.27 1.82 1968 ) and radiation aridity index ( 1 K 0.30 0.30 1.51 1978 0.27 0.27 1.59 1967 0.22 0.22 1.41 1966 0.32 0.32 1.73 1977 0.25 0.25 1.66 1965 on of the forest ( 0.28 0.28 1.83 1964 0.32 0.32 1.49 1976 1. Taezhny catchment 0.28 0.28 2.02 1963 0.25 0.25 0.95 1962 0.28 0.28 2.61 1975 0.25 0.25 1.15 al evapotranspirati 1961 0.25 0.25 1.62 1960 2. Forest plot at the Podmoskovnaya water balance station 0.34 0.34 2.97 1973 0.28 0.28 2.09 1959 0.28 0.28 1.42 1958 0.28 0.28 3.18 1972

r 0.28 0.28 1.29 1957 tembe p 0.25 0.25 1.28 1956 0.37 0.37 2.77 1971 Se 0.24 0.24 1.59 1955

1 1 V-iX K V-iX K J J Year Year

2.14 Table Coefficient of parts potenti

197 Forest hydrology – results of research in Germany and Russia

Evaporation of the intercepted precipitation is a complicated matter. It is explained by the following. First, when a rainfall starts, the wetting of needles and leaves is accompanied by some moisture absorption; this is most typical of rainfalls after a dry period. Second, during the evaporation of intercepted precipitation the transpiration rate tends to be considerably lower. When the rain is over, evaporation equals potential evapotranspiration of the surfaces of the plants. For a long-term period this process can be characterised by a ratio between heat and moisture, i.e. moistening parameter ( J ). This is clearly seen in the graph E2 /E0 = f (J ) in Figure 2.6.

Figure 2.6 Evaporation ratio of intercepted precipitation to potential evapotranspiration (E2/E0) under the forest canopy in the Taezhny small experimental catchment versus the moistening parameter (J) (May – September)

This graph has been plotted on the basis of data on potential evapotranspiration from the forest and the results of measurements of precipitation intercepted by tree crowns during the period May – September ( E2 ) and is presented as a diminishing function:

−γJ E2 = E0e (2.42) where γ is coefficient of proportionality characterising the rate of decrease of evaporation of intercepted precipitation; it is a ratio between mean water storage (W ) during the state of moistening below the critical level and the critical water storage Wcr characteristic of the particular catchment, i.e.

γ = W /Wcr (2.43)

Relative to the Taezhny experimental catchment Wcr = 215 mm. Then, if W = Wcr , γ = 1.0 ; if

W is less than Wcr , the value of γ varies from 1.0 to 0.75.

198 Evaporation from forest

In this equation the effect of the characteristics on evaporation is taken into account indirectly through the moistening parameter. The above equation is used to estimate evaporation of that portion of precipitation which has been intercepted by the forest canopy during the period May – September, including evaporation of precipitation intercepted by grass. As evident from observations in the Taezhny experimental catchment, the amount of precipitation intercepted by the grass stand equals less than 10% of the amount intercepted by the crowns. Table 2.15 shows the results of computations of evaporation of the intercepted precipitation from May to September 1960-1985. It also gives the total precipitation estimated from the measurements and difference in %. The maximum deviation does not exceed ± 20%.

Table 2.15 Results of the computation of evaporation of intercepted precipitation ( E2 ) during the period May – September

Year E0 (mm) J α E2comp. (mm) E2obs. (mm) ΔE2 (%) 1960 479 1.65 1.00 88 83 6 1961 443 1.15 1.00 142 143 -1 1962 401 0.95 1.00 154 157 -2 1963 516 2.02 0.93 98 94 4 1964 495 1.93 0.85 99 93 6 1965 456 1.66 1.00 91 107 -15 1966 495 1.47 0.93 114 114 0 1967 479 1.59 0.93 96 110 -13 1968 485 1.82 0.82 106 99 7 1969 458 1.60 0.95 103 103 0 1970 489 2.25 0.76 88 82 7 1971 471 1.68 1.00 90 114 -20 1972 506 2.32 0.79 82 73 12 1973 504 1.41 1.00 121 103 17 1974 456 1.28 0.93 129 117 10 1975 528 1.60 0.85 107 89 20 1976 421 1.42 1.00 101 107 -6 1977 440 1.10 1.00 145 128 13 1978 449 1.56 0.95 117 135 -13 1979 486 1.65 0.88 114 108 6 1980 425 1.30 1.00 115 117 -2 1981 465 1.17 1.00 144 130 -11 1982 491 1.32 1.00 119 121 -2 1983 460 1.22 0.93 143 115 1984 436 1.23 1.00 126 111 -14 1985 429 1.47 1.00 98 105 -7

199 Forest hydrology – results of research in Germany and Russia

Thus, seasonal evaporation of intercepted precipitation can be calculated. The above equation can also be applied for the calculation of the evaporation of intercepted precipitation during individual months. Moreover, the moistening parameter is accepted as a mean value for the current and previous months in increasing order since the first month of the vegetation period.

2.5.3 Water loss for transpiration

Forest greatly affects water loss for transpiration. Transpiration by forest plants is a significant amount of the total water loss for evaporation from forested areas and it is the most important component of the land water balance. Transpiration is evaporation of water by plants or the process of water escaping as liquid from the soil through the plants (leaves, needles, etc.) to the atmosphere as vapour. Being one of the processes of plant life, transpiration is closely related to plant evolution and productivity. Thus, transpiration is a complicated occurrence and depends on numerous factors, i.e. plant species, age, meteorological situation and soil moisture content. Transpiration has been investigated for many decades. Transpiration by trees is being studied by many scientists in different countries. Experimental studies of transpiration by plant cover (including trees) have been made in the past. The principles of the transpiration theory are available in many publications (e.g. Molchanov [1960]). On the basis of the information available about transpiration the following equation is used for the analysis of water exchange between the leaf surface and the atmosphere:

ET = ρD0 ()qn − q2 (2.44) Here

ET = transpiration ρ = air density

D0 = coefficient of exchange rate between the leaf surface and the atmosphere

qn and q2 = specific air humidity at the leaf surface and in the air. It has been established that, if soil moistening is sufficient, transpiration of the closed plant cover is equal to potential evapotranspiration calculated from meteorological data, i.e.

ET = E0 . This is valid for the condition of soil moistening of W ≥ Wcr .

If soil moistening is inadequate when W is less than Wcr , transpiration depends on potential evapotranspiration, soil moisture content, the coefficient of exchange rate between the leaf surface and the air, as well as on air temperature. Moreover, dependence of transpiration on potential evapotranspiration and soil moisture content is clearly demonstrated in the following equation:

WE0 ET = ψ ()b3,W (2.45) Wic + ξE0

200 Evaporation from forest

where Wic is initial critical moisture content. Function ψ (b3,W ) is estimated from an empirical dependence. Many scientists have made similar studies of transpiration, in particular with regard to the modelling of agricultural crops productivity. Studies of this problem have recently been carried out in connection with the modelling of forest plants increment. To study the transpiration of forest plants, different methods are applied: the gravimetric method and its variants; the water balance method; the indirect method based on a calculation of transpiration from the difference between evapotranspiration and its components; the biophysical method; methods based on the turbulent diffusion theory and energy balance of the forest, including the methods of Penman and Monteith-Penman. There are other methods, too, but application of these methods is limited by laboratory conditions. Some of these methods have been used at the VB of the SHI. The results of calculating monthly transpiration are discussed below. The present report shows the results of transpiration studies mainly on forest areas and partly with individual trees.

The gravimetric method has been widely applied in many countries to study transpiration. Two variants of this method are used in practice. The first variant comprises estimation of transpiration by considering the weight of branches measured on a special balance immediately after cutting. In literature this is known as the method of fast weighing or the thermo-weighing method. It is based on the assumption that during the first few minutes after cutting the cut branches and leaves continue to transpire at the previous rate. The change in the weight of the cut branches and leaves is estimated after 3 minutes (sometimes after 5-8 minutes) of exposure. Such experiments are made several times a day during every month of the vegetative period. A change in the weight of the leaves determines the transpiration rate and it is expressed as a value of water loss (in mg) per unit of raw weight of leaves during one hour. Thus, the transpiration rate is established for the typical weather conditions of the day during the whole vegetative period; moreover, the duration of daylight is taken into account. When this method is applied, it is necessary to know the number of hours of transpiration during the whole vegetative period and the weight of fresh leaves per unit of forest area. The total water loss for transpiration is derived from the following equation: N E = ()J M + i m (2.46) T F ⋅106 ∑∑k k k k where N = the number of transpiration hours during the design period

Jk , ik = transpiration rates of tree stand and soil cover, mg/g per hour

M k , mk = the green mass of tree stand and soil cover, g/ha F = a plot area in the forest, ha. The green mass storage and transpiration intensity of its individual components, soil cover included, comprise the biological factor in this equation. The number of transpiration hours during the design period is often taken as the factor of heat energy resources.

201 Forest hydrology – results of research in Germany and Russia

This parameter depends on the transpiration period minus rainy days when transpiration is absent or negligibly low. Critical comments have been made on this method of fast weighing, both on the methodology of measurements and on the method of calculation of transpiration of individual trees in the forest area. A check of this method, however, made by some authors, gave quite admissible results. In general, these results are in agreement with the results of the water balance method (Table 2.16).

Table 2.16 Annual transpiration (mm) from forested areas obtained by different methods

Weight of Transpiration (mm) Forest age Number of green needle Tree species Thermo- Water (years) stems mass weighing balance (kg/ha) method method Pine 10 5000 11500 260 - Pine 33 3010 15965 361 371 Pine 65 892 12050 272 245 Pine 150 460 9000 203 181 Birch 25 2875 6500 335 350 Birch 60 524 5288 323 - Birch 77 412 5564 286 -

The second variant of the gravimetric method comprises the assessment of transpiration from the change in the weight of a tree growing in a vegetative vessel. Later, instead of small vessels, evaporimeters and lysimeters with single trees were used. Studies with big evaporimeters and lysimeters have been made in Germany, the Netherlands and in the USA. Based on the results of transpiration measurements by the gravimetric method, transpiration coefficients have been established, i.e. water loss in grams for the formation of 1 g of dry matter. Some data are given below in Table 2.17 presented by Polster [1954] and Linsley et al. [1962] for conditions in the USA.

Table 2.17 Transpiration coefficients for different tree species

Tree species Data of Linsley et al. Data of Polster Spruce 145 231 Birch 375 317 Ash 244 - Alder 227 - Oak 220 344 Larch 220 257 Scotch pine 213 300 Beech 209 169 Swiss mountain pine 208 -

202 Evaporation from forest

As evident from Table 2.17, transpiration coefficients obtained by these scientists differ greatly for the same tree species. In literature even greater differences in these coefficient values can be found. These differences probably depend on the methodology of coefficient determination. In Valday the gravimetric method for determining evaporation and transpiration with the use of the hydrostatic weighing of lysimeters was used to estimate daily variations in the transpiration of individual trees (spruce, pine, birch) up to 20-25 years old as well as evapotranspiration using hydraulic evaporimeters 3.0 m² in area, 1.5 m high. During the experiments for evapotranspiration, the day surface of the soil monolith, the grass cover and the tree crown were open. During the experiments for transpiration the surface of the soil monolith and the grass cover were covered by a water-impermeable cloth. Thus, evaporation from the soil and from the grass cover was excluded; a change in the mass of the soil monolith was the result of transpiration only. Experiments were carried out over a period of six to 12 hours usually once every five to ten days. During the experiment three recorders registered continuous transpiration and its intensity was estimated in mm/hour. The accuracy of these transpiration measurements was ± 0.1 mm. The use of hydraulic soil evaporimeters made it possible for the first time to obtain continuous daily variations of the transpiration of trees and evapotranspiration of the grass cover. Moreover, it was established that the general type of daily variations in transpiration as well as in evapotranspiration is in agreement with the variations in air temperature and the air humidity deficit, and in particular with the daily variations in the total radiation and radiation balance. The transpiration rate varies from zero in the morning to maximum values at about 1 pm in the afternoon. Then it gradually falls and reaches practically zero in the evening. Smooth daily variations in transpiration and evaporation are observed only on days of fair weather or continuous cloudiness. Analyses of data on transpiration and air temperatures show that there is a definite concord between these components during the first half of the day only when simultaneous variations in air temperature and transpiration are observed; during the second half of the day this concord breaks down. The transpiration rate during this period tends to decrease gradually while the air temperature remains high which is explained by the immense warming of the lower air layer due to a turbulent exchange. A more satisfactory concord between the mean hourly transpiration rate and mean daily air temperature is observed. This fact was used for the development of the so-called thermo-weighing method of transpiration measurement. Observations show that the highest hourly and daily transpiration occurs during the period of intense vegetation. Later, the solar radiation becomes less intense, the sunshine period of the day becomes shorter, and hourly, daily and monthly transpiration values tend to decrease. This is clearly seen in Table 2.18. Monthly transpiration was estimated with the use of coefficients characterising a ratio between daily transpiration and evapotranspiration. Evapotranspiration was estimated for particular months, if possible, for days with similar meteorological situations. These coefficients for particular months during 1956-1957 for individual tree species are given in Table 2.19. It should be noted that these coefficients are applicable to a particular tree only. To correct this coefficient, the results of simultaneous observations of evapotranspiration were used in some years with the use of evaporimeters with a single tree or without a tree, but with similar

203 Forest hydrology – results of research in Germany and Russia plants, if possible. Coefficients have been used to obtain transpiration values during the period May – September for some years (Table 2.20).

Table 2.18 Mean daily transpiration (mm) during a month estimated from evaporimeters 3 m² in surface area with different tree species

Tree species Year V VI VII VIII IX Spruce 1954 0.81 1.23 1.06 0.74 0.37 1955 0.55 1.17 1.22 1.22 0.53 1956 0.71 1.33 1.06 0.74 0.47 Pine 1954 0.84 1.40 1.22 0.84 0.40 1955 0.65 1.30 1.36 1.36 0.60 1956 0.81 1.53 1.19 0.81 0.53 Birch 1954 0.77 1.20 1.03 0.74 0.33 1955 0.52 1.13 1.14 1.14 0.50 1956 0.68 1.30 1.03 0.71 0.43

Table 2.19 Ratio between transpiration and evapotranspiration for spruce and birch

Tree species V VI VII VIII IX Spruce 0.60 0.72 0.75 0.67 0.59 Birch 0.65 0.76 0.82 0.58 0.35

Table 2.20 Transpiration of a single birch and spruce and the weight of fresh leaves/needles, according to observations made in forest hydraulic evaporimeters

Potential Stem Weight of Transpiration evapotran- E Year Age diameter leaves/needles 2 (mm) (E ) spiration (cm) (kg) 2 E0 (mm) (E0 ) Birch 1956 19 2.6 1.080 192 384 0.50 1957 20 2.9 1.040 209 358 0.58 1958 21 3.8 1.610 228 372 0.61 1959 22 4.1 1.890 256 460 0.56 1960 23 4.3 2.560 256 428 0.60 1961 24 4.6 2.725 276 397 0.70 Spruce 1955 20 2.3 2.496 195 418 0.47 1956 21 4.7 2.800 210 384 0.55 1957 22 5.2 1958 23 5.6 1959 24 5.8 1960 25 6.2 1961 26 6.7 3.270 250 397 0.63

204 Evaporation from forest

Table 2.20 also contains data on the amount of the green mass of leaves or needles of single trees. Data on the green mass were obtained in the following way: the weight of leaves of a birch tree (500 leaves) taken off a growing tree was measured; needles of a spruce were weighed from a model tree and by a direct cut off the spruce in the year when the experiment included that tree. Table 2.20 also contains data on water loss for transpiration in the period May – September per unit weight of fresh leaves/needles. Transpiration losses were obtained for particular years with different hydrometeorological conditions, from observations of different trees greatly variable in age. The gravimetric method makes it possible to estimate transpiration of small single trees only. Transpiration values obtained by this method, taking phytomass characteristics into account, provide, however, the validity of another method of determination, which is discussed below.

Determination of transpiration of trees on the basis of characteristics of tree productivity The close relationship between the transpiration of trees and the characteristics of tree productivity has been discussed in literature. In this report, the annual increment of tree stand, the amount of leaves or needles, the increase in height and stem diameter of a tree are used as the characteristics of tree productivity. The experimental data obtained made it possible to establish relationships between transpiration and the following characteristics of productivity: the annual increase in height and stem diameter of trees. As for data on the annual increment of the tree stand, it is rather difficult to obtain such data. Therefore, in our case, we used the latter three characteristics as a tree stand productivity index, which reflect the whole increment quite well. Moreover, the analysis was made on the basis of data on single trees (growing in hydraulic evaporimeters) and on spruce stand (on the experimental forest plot). First, let us consider the results of transpiration determination and productivity indices obtained from single trees for particular years. Figure 2.7 shows the relationship between water loss for transpiration for the period May – September and the amount of green mass of birch leaves and spruce needles. Figure 2.8 shows the ratio of transpiration and the diameter of tree stems at an elevation of 130 cm. It should be noted that relative transpiration values reduced to the same meteorological conditions were used to plot these graphs. As is evident from the graphs, the relative water loss for transpiration is closely connected with the characteristics of single tree phytomass. As expected, a similar dependency of water loss for transpiration on productivity characteristics was found for a forest, too. Table 2.21 shows the relationship between transpiration and the radius increment of tree stems from May to September.

Table 2.21 Transpiration (mm) and radius increment of spruce stem (mm)

Year 1959 1960 1961 1962 Transpiration 154 198 166 161 Increment 1.72 1.90 1.75 1.72

205 Forest hydrology – results of research in Germany and Russia

Figure 2.7 Evaporation ratio of intercepted precipitation to potential evapotranspiration (E2/E0) (May – September) versus the amount of green mass of a single tree (kg) for (a) a birch and (b) a spruce

Data on spruce stem increment were obtained by measuring the stem diameters of 10 trees every year on water balance plot No. 3 and in the Elovy catchment, except 1962 when only five trees were measured. A significant theoretical study in this field was made by Hilmy [1957]. Moreover, an equation was offered to calculate transpiration on the basis of the total tree stand mass and annual tree stand increment: dV E = mV + n (2.47) T dt where V = total tree stand storage measured by the amount of its part above land related to the unit area occupied by the stand (m³/ha) dV / dt = a rate of this value change (m³/ha per year) m and n = coefficients. Coefficient m indicates normal discharge of moisture required for life of unit volume of the biomass; coefficient n is the quantity of moisture lost on average in the stand for unit biomass formation.

206 Evaporation from forest

Figure 2.8 Relationship between transpiration (T) in the period May – September and annual spruce increment (I) (Taezhny catchment)

If coefficients m and n are known, this equation makes it possible to determine transpiration from the data on tree stand storage and on its current increment. Using data on the storage and current increment obtained from the table of tree growth and on transpiration, Hilmy [1957] obtained the following values of these coefficients in the foxberry pine forest: m = 0.25, n = 43 . It should be noted that the use of this equation for the calculation of transpiration is often difficult because it is hard to get the basic data. On the basis of the analysis of experimental data on transpiration of needles and leaves, Hilmy discovered that the so-called coefficient of the transpiration rate (KT ) for a particular type of tree species within a wide age range is a constant value (Tables 2.22, 2.23 and 2.24).

Table 2.22 Coefficients of transpiration activity ( KT ) for pine and birch plantings

Weight of Tree stand age Transpiration K Tree species leaves/needles T (years) (mm) 3 (kg/ha) (m /kg per year) Pine 10 11500 260 0.227 Pine 33 15965 361 0.226 Pine 65 12050 272 0.231 Pine 150 9000 203 0.226 Mean 0.228 Birch 35 6500 335 0.515 Birch 60 6288 323 0.514 Birch 77 5564 286 0.514 Mean 0.514

207 Forest hydrology – results of research in Germany and Russia

Table 2.23 Coefficients of transpiration activity ( KT ) for foxberry pine forest

Tree stand age Weight of needles Transpiration KT (years) (kg/ha) (mm) (m3/kg per year) 10 11000 250 0.227 14 14200 261 0.184 33 17200 345 0.201 65 13800 238 0.172 120 10600 208 0.196 150 8800 182 0.208 Mean 0.198

Table 2.24 Coefficients of transpiration activity ( KT ) for spruce stand

Mass of needles Transpiration K Plot Characteristics T (kg/ha) (mm) (m3/kg per year) Water balance 10 spruce trees 29470 210 0.071 plot No. 3 density 0.7 age 78 years Water balance 10 spruce trees 15820 140 0.088 plot No. 4 density 0.9 age 25 years Water balance Age 39 years 27750 235 0.08 plot No. 4

According to the data of the VB of the SHI for the experimental plot with pines of 10 years old over an area of 1 ha, KT equalled 0.207 m³/kg per year. Moreover, the mass of needles equalled 11,000 kg/ha for 1,615 trees in 1 ha. Transpiration calculated from lysimeter data (3.4 m² in area, 1.7 m high) equalled 250 mm. Until now, the coefficient of transpiration activity has never been estimated for conifers because of inadequate data. Data on the transpiration and mass of needles obtained on water balance plots have made it possible to estimate this coefficient for spruce stands of different ages (Table 2.24).

KT coefficients in Table 2.24 were calculated for water balance plots Nos. 3 and 4 from observation data for 1961 from 13 model trees and for 1958 from 10 model trees. These data show that spruce trees, if compared with birch and pine trees, have the lowest coefficient of transpiration activity equalling about 0.08.

The total annual transpiration ET of the planting can be derived on the basis of the coefficient of transpiration activity KT and weight of needles ( g ) from the following equation:

ET = KT g (2.48)

208 Evaporation from forest

The use of this method provides a quantitative assessment of transpiration and, consequently, also evapotranspiration from forested areas in mountains.

Transpiration measurements by water balance method This method was presented in Russia by G.N. Vysotsky and then it was improved by several scientists. The following equation is applied to obtain transpiration by the water balance method:

ET = P1 − Y1 + Y2 − E1 ± ΔU (2.49) where

P1 = precipitation under the forest canopy

Y1 = surface runoff

Y2 = stemflow

E1 = evaporation under the forest canopy ΔU = the change in soil and subsoil moisture content. The use of this method is connected with multipurpose observations of water balance components in a forest catchment. But the use of this method is often restricted by the difficulty of making a correct assessment of water storage change in soils and subsoils.

Transpiration measurements from evapotranspiration This method is indirect and based on data on evaporation under the forest canopy and data on precipitation intercepted by the canopy. In this case, transpiration is calculated from the difference between evapotranspiration of the forest and its components, i.e. evaporation under the canopy and evaporation of precipitation intercepted by the crowns. Transpiration is estimated by the following equation:

ET = E − E1 − E3 (2.50)

Evapotranspiration of the forest has been accepted from the results of its estimation by the heat balance method. Evaporation under the canopy and evaporation of the intercepted precipitation were calculated by the methodologies described above. These components and transpiration itself were estimated for particular months on forested water balance plot No. 3 and in the Elovy water balance catchment. The difference in monthly transpiration in these areas was not great; it varied within 5%. During the wet and warm seasons evaporation and transpiration on water balance plot No. 3 were usually a little higher than in the Elovy catchment; the opposite was observed during dry years. On the basis of monthly transpiration on these plots, transpiration of the tree stand of the whole forest area in the Taezhny catchment was estimated as the mean weighted value. Table 2.25 gives monthly and seasonal transpiration for 1960-1974. It also contains data on radiation balance ()R and radiation aridity index (J ) for May – September, as well as soil moisture content at the end of the period of intense vegetation (June and July) WVI , WVII .

209 Forest hydrology – results of research in Germany and Russia

Table 2.25 Monthly transpiration (mm) of spruce during the period May – September

J R R W Year V VI VII VIII IX V-IX W V-IX V-VII V-IX V-VI 1960 50 53 46 16 8 184 1.65 964 1261 216 1961 29 59 39 19 10 156 1.15 860 1121 236 381 1962 23 45 49 17 5 139 0.95 718 1000 303 277 1963 52 42 37 23 18 170 1.79 980 1322 259 221 1964 48 61 50 19 6 168 1.83 948 1226 226 184 1965 28 57 39 34 11 169 1.66 910 1230 237 244 1966 37 51 39 35 9 171 1.57 863 1142 206 206 1967 31 52 45 29 9 166 1.59 976 1272 236 204 1968 16 58 40 41 8 177 1.50 834 1200 263 261 1969 25 56 33 36 8 158 1.60 922 1270 236 233 1970 49 30 35 26 11 152 2.25 955 1285 189 155 1971 24 51 33 40 8 156 1.68 930 1226 248 246 1972 29 54 34 25 8 149 2.32 1006 1378 205 166 1973 31 50 44 29 (8) 163 1.41 968 1308 213 181 1974 24 39 40 36 12 168 1.40 818 1128 278 287 Mean 34 51 40 28 9 162

The above data reveal significant monthly and seasonal variations in transpiration during some years. This depends on specific hydrometeorological conditions during the vegetative period. The radiation balance of forest plants and moisture content in soils and subsoils are the most important specific features. Similar transpiration values were also obtained for subsequent periods. They show that maximum transpiration occurs in June when total radiation intensity and radiation balance are maximum. The mean long-term transpiration of spruce in June equals 51 mm; the radiation balance equals 328 mJ/m²; the total values for May – September are 152 mm and 1243 mJ/m² respectively. The above data also show the effect of soil and subsoil moisture content on transpiration, in particular during the period of intense vegetation. For example, in June – July, when soil moisture content is below its critical value (Wcr ) (in 1970 and 1972), the forest plants have a water storage resulting in a lower transpiration rate. In the case of water surplus, when soil moisture content exceeds the level of the least water-bearing capacity (1962), a relatively low transpiration was also observed. A complex effect of the radiation balance and soil moisture content on transpiration is clearly seen in Figures 2.9 and 2.10. These graphs were plotted from transpiration data during May – September for a long observation period. The graph in Figure 2.9 is based on the data of the period 1955-1974, i.e. it covers forest plantings of age class III. The second graph (Figure 2.10) is plotted from the data for 1975-1998 for forest plantings of age class IV. These graphs show a significant deviation of transpiration data in some years. It is shown below that these deviations are regular. As evident from these graphs and from Table 2.25, the highest transpiration was observed during the years when the moistening parameter varied between 1.3 and 2.0. Within these

210 Evaporation from forest values a ratio between the radiation balance and precipitation was optimal. Soil moisture content during the warm season was above the critical level (W ≥ Wcr ).

Figure 2.9 Transpiration (E3) (mm) versus the moistening parameter J (age class III of the tree stand)

Figure 2.10 Transpiration (E3) (mm) versus the moistening parameter J (age class IV of the tree stand)

This confirms a certain optimum in the ratio between heat energy and precipitation at which the plant cover productivity is maximum. In the years when the moistening parameter is higher ( J ≥ 2.0 , i.e. typical of dry seasons), transpiration tends to decrease greatly. This is explained by the soil moisture deficit during the period of intense vegetation. During water deficit the stomata of leaves and needles gradually close and the inner resistance to diffusion may be so high that it determines the rate

211 Forest hydrology – results of research in Germany and Russia of water vapour motion. Thus, lower soil moisture content causes a decrease in transpiration and evapotranspiration. This has been proved by theoretical studies. Studies were made during the dry years of 1959, 1970, 1972, 1979, 1994 and 1999 in the Novgorod region. In some periods during these years water storage in the top soil layer of 1 m depth was often below critical, whereas in the 0-50 cm layer of soil moisture content approached wilting point. Meanwhile, the values of radiation balance were high. As a whole, these dry years were unfavourable for normal plant evolution, for spruce trees in particular because of their shallow root system. The broken rhythm of tree stand evolution was caused by the droughts. The years following the droughts (1971, 1973, 1980, 1993) were not optimal either. As a result, transpiration was not high. Particular notice should be taken of the years with low moistening parameter values (J<1.3) and high soil moisture content. Lower transpiration accompanied by a water surplus is primarily the result of unfavourable conditions for the development of roots because of an oxygen deficit in the poorly aerated zone. Against the background of general transpiration changes in time, a continuous change in tree stand age occurs, i.e. tree aging. This process is accompanied by a gradual transpiration decrease. This is quite evident from the data on transpiration for coupled seasons with similar values of J , R and W , e.g. for 1960 and 1965, for 1960 and 1967, and for 1965 and 1969. This is clearly shown by a comparison of graphs of transpiration change of spruce forests of age classes III and IV. Mean transpiration equalled 166 mm and 151 mm respectively. As shown above, a similar transpiration decrease from 394 mm to 380 mm corresponds to an evapotranspiration decrease for these periods. Finally, the transpiration data analysis made it possible to discover the effect of one more biophysical factor (fruit-bearing factor) on transpiration. In general, fruit-bearing of conifers is usually observed once every three years and is accompanied by a change in the energy balance of the stand. Experimental data analysis shows that against the background of regular change in transpiration due to the moistening parameter during particular years, transpiration tends to decrease greatly. These were the years of abundant fruit-bearing (1978, 1980, 1984) which agreed with the results of observations made in the Valday forest tree nursery and at the Tver forest breeding station. Thus, monthly and seasonal transpiration values obtained by indirect methods have been considered here, i.e. in the case of available experimental data on evaporation under the forest canopy and evaporation of precipitation intercepted by the tree crowns in the catchment. Another variant, the estimation of the transpiration of trees which is based on the use of data on evapotranspiration under the canopy and on intercepted precipitation, is more difficult. In this case it is possible to calculate transpiration from the difference between evapotranspiration of the forest and its components.

Table 2.26 shows the results of calculations of transpiration E3 with the equation for the period May – September 1960-1974. It also contains data obtained by evaporation measurements under the forest canopy and evaporation of precipitation intercepted by tree crowns. Comparative results of transpiration calculation for the vegetation period obtained by the two variants show them to agree well. The difference in transpiration does not exceed 9%. Thus, the method considered makes it possible to estimate water loss for transpiration of leaves or needles of the trees without taking the transpiration of plants and low bushes under the forest canopy into account.

212 Evaporation from forest

Table 2.26 Transpiration ( E3 ) (mm) of spruce stand according to calculated and observed

evaporation under the forest canopy ( E1 ) and evaporation of intercepted

precipitation ( E2 )

Method for determination of 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 transpiration With calculated

data ( E1 and E2 ) 177 159 143 167 174 186 168 168 183 160 146 166 146 With observed

data ( E1 and E2 ) 175 156 139 170 168 169 171 166 176 158 152 156 149 ΔE , % 1 2 3 -2 3 9 -2 2 4 1 -4 6 -2

Estimation of the transpiration of forest plants using Penman's method Unlike some other methods, Penman's method suggests determination of the transpiration of forest plants including trees and plants. It is based on the use of the heat balance equation and some empirical ratios and it is presented as follows: Δ R + E γ T AT E = (2.51) T Δ +1 γ where

EAT = 0.35()1+ 0.537U (e0 − e)

RT = 0.75Q − QB

Here

RT = radiation balance

EAT = dry-up capacity of the atmosphere, i.e. the value which takes account of wind

velocity U and air humidity deficit d = l0 − l Δ = a constant which depends on temperature and equals a tangent slope to a curve which expresses a dependence of saturation viscosity on air temperature at a point corresponding to the mean air temperature γ = psychrometric constant Δ = a dimensionless value upon which R and E depend in the above equation. At air γ T AT temperatures of 10°C, 20°C and 30°C the ratio Δ /γ would equal 1.3, 2.3 and 3.9 respectively Q = total short-wave radiation

213 Forest hydrology – results of research in Germany and Russia

QB = effective long-wave radiation of the Earth's surface. Transpiration under natural conditions is always accompanied by evaporation of water under the plant cover. It can be eliminated artificially. The problem is to separate evapotranspiration under the forest canopy ( Ec′ ) from transpiration of plants (including low bushes) ( Etr′ ) and evaporation from the soil ( Es′ ). The following simple equation may be used for an approximate assessment of the transpiration of plants:

Etr′ = Ec′ − Es′ (2.52)

This equation was realised during the readings of soil evaporimeters installed under the canopy on plots with plants ( Es′ ) and on plots with forest litter without plants ( Es′ ). These observations made on forest experimental plots provided data on the transpiration of plants for specified vegetation periods. Thus, the total transpiration ( ET′ ) obtained by an indirect method consists of two components, i.e. transpiration of needles or leaves and transpiration of plants. A more accurate calculation of this value is also possible using the heat balance method. But this is even more difficult. Table 2.27 gives the results of monthly and seasonal transpiration of forest plants. It also shows (for comparison) the total transpiration of the needle mass of the tree stand and the transpiration of plants obtained from experimental data (by an indirect method). Basic data for a computation of transpiration were obtained by averaging the mean daily values of meteorological elements and radiation characteristics.

Table 2.27 Transpiration (mm) of forest plants using Penman's method (1) and an indirect method (2)

Year Method V VI VII VIII IX V-IX 1975 1 71 64 83 34 2 254 2 49 71 57 49 21 247 1976 1 49 51 64 30 12 206 2 44 59 60 40 23 226 1977 1 41 81 62 30 2 216 2 35 52 8 39 17 191 1978 1 82 64 69 48 0 243 2 37 57 57 42 22 216 1979 1 89 79 52 47 11 278 2 43 66 66 50 26 251

Calculation of transpiration using Penman's method was based on the results of meteorological and heat-balance observations made on the forest tower in a spruce forest on the experimental plot in Valday. Data on air temperature and humidity, wind velocity and radiation balance were obtained at an elevation of 37 m, with an average height of tree stand of 28-29 m. The mean values of meteorological elements and radiation characteristics for 24 hours and for 10 days were averaged to provide basic data for calculations.

214 Evaporation from forest

These transpiration results obtained with two methods for the vegetation period agree well; monthly values, however, differ greatly. This is explained, firstly, by incomplete basic data for some periods, and, secondly, by the not quite correct assessment of the transpiration of the green mass of trees and in particular of the transpiration of plants and bushes under the canopy.

2.6 Ratios between the components of forest evapotranspiration for individual periods

Data on forest evapotranspiration and its components for specified periods were obtained from the results of observations and calculations. Table 2.28 contains these data as a case study for specified months during the period 1970-1974; Figure 2.11 demonstrates a change in these data during May – September 1970-1974. It also shows seasonal variations in the radiation balance during this period.

Table 2.28 Evapotranspiration of the forest ( E ), evaporation under the forest canopy ( E1 ),

evaporation of precipitation intercepted by tree crowns ( E2 ) and transpiration

( E3 ) (mm)

Evaporation Year V VI VII VIII IX V-IX % component 1970 Е 85 71 98 70 47 371 100 Е1 27 31 36 26 17 137 37 Е2 9 10 25 18 20 82 22 Е3 49 30 37 26 10 152 41 1971 Е 68 109 89 75 37 378 100 Е1 27 27 25 21 8 108 29 Е2 17 32 29 14 22 114 31 Е3 24 50 35 40 7 156 40 1972 Е 71 98 90 70 42 371 100 Е1 27 32 38 36 16 149 40 Е2 18 11 17 9 18 73 20 Е3 26 55 35 26 8 149 40 1973 Е 73 108 96 74 37 388 100 Е1 18 35 37 25 7 122 31 Е2 24 23 15 19 22 103 27 Е3 31 50 44 30 8 163 42 1974 Е 66 99 104 81 41 391 100 Е1 21 29 33 24 14 121 31 Е2 19 31 31 21 15 117 30 Е3 24 39 40 36 12 153 39 Mean Е 73 97 95 74 41 380 100 Е1 24 31 34 26 12 127 33 Е2 17 21 23 16 19 96 26 Е3 32 45 38 32 10 157 41

215 Forest hydrology – results of research in Germany and Russia

Figure 2.11 Variations in monthly values of evaporation components from the forest (mm)

( E = evapotranspiration; E1 = evaporation under the canopy; E2 = evaporation

of intercepted precipitation; E3 = transpiration)

It follows from the data above that: • monthly and seasonal evapotranspiration and its components vary widely in some years and depend on variations in hydrometeorological conditions; • seasonal variations in evapotranspiration and transpiration for a long-term period repeat variations in the radiation balance; • the highest values of evapotranspiration, transpiration and evaporation under the canopy are observed in June – July; • on average for the warm seasons of 1970-1974 water loss was as follows: 41% of evapotranspiration was lost for transpiration, 33% for evaporation under the canopy and 26% for evaporation of precipitation intercepted by tree crowns. The analysis of data on evapotranspiration components for adjacent years shows that the ratio between these components differs greatly depending on the moisture content in the catchment during the warm season. This is evident from Table 2.29 which contains data on evapotranspiration components for relatively wet (W) and relatively dry (D) seasons. The moistening parameter (J) for May – September has been accepted as the moistening parameter. Moreover, • evapotranspiration and transpiration during wet seasons are higher than in dry seasons; • transpiration of trees together with evaporation under the canopy comprise the major portion of evapotranspiration during all the years (about 78-80% during wet seasons, and about 70% during dry seasons); • the seasons with different moistening are characterised by a stable ratio (in %) between

the evapotranspiration components ( E1 , E2 , E3 ), equalling on average about 30%, 39%

216 Evaporation from forest

and 41% respectively during wet seasons, and 36-40%, 22% and 41% respectively during dry seasons.

Table 2.29 Evapotranspiration and its components for May – September during wet and dry seasons

Unit of Year Season J E E1 E2 E3 E1+E3 measurement 1958 W 1.42 412 117 119 176 293 mm 100 28 29 43 71 % 1959 D 2.05 405 145 90 170 315 mm 100 36 22 42 78 % 1971 W 1.68 378 108 114 156 264 mm 100 29 30 41 70 % 1970 D 2.25 371 137 82 152 289 mm 100 37 22 41 78 % 1974 W 1.40 391 110 117 164 274 mm 100 28 30 42 70 % 1972 D 2.32 371 149 90 149 298 mm 100 40 20 40 80 % 1985 W 1.47 394 109 120 165 274 mm 100 28 30 42 70 % 1988 D 1.60 381 121 116 144 265 mm 100 32 30 38 %

More detailed information about ratios between the evapotranspiration components from the forest and about their time variations can be obtained from the analysis of data on these components for a long-term period (1955-1999) given in Table 2.30 for individual age classes of tree stand. To this end, they were arranged in 5-year periods with the tree stand age, radiation balance ( B ) and moistening parameter ( J ). The analysis of the evapotranspiration structure is made taking into account the homogeneous hydrometeorological conditions for particular periods. Besides, the transpiration of trees and plants is considered as a single process.

Table 2.30 Components of evapotranspiration (mm) in May – September (1955-1999)

Tree

Period stand age E0 Ec E1 E2 E3 B J (years) 1955-1959 70-75 446 399 118 110 171 32.6 1.53 1960-1964 75-80 467 401 123 114 164 31.2 1.52 1965-1969 80-85 475 397 121 109 168 32.2 1.62 1970-1974 85-90 485 380 124 99 157 33.2 1.79 1975-1979 90-95 465 381 107 115 158 30.9 1.47 1980-1984 95-100 445 367 113 119 136 29.9 1.25 1985-1989 100-105 448 391 117 117 157 31.5 1.36 1990-1994 105-110 461 382 120 106 156 31.5 1.49 1995-1999 110-115

217 Forest hydrology – results of research in Germany and Russia

As evident from Table 2.30, the ratio between the evapotranspiration components depends not only on the moistening rate of the catchment but also on the effect of increasing tree stand age. Besides, the increasing tree age is accompanied by a decreasing evapotranspiration value and transpiration together with lower evaporation under the canopy. This is evident from similar data on radiation balance and aridity index. For example, when a forest became 15 years older, i.e. between the second and fifth periods, evapotranspiration decreased by 20 mm; a change in transpiration together with evaporation under the canopy decreased by

21 mm (about the same value). A similar decrease of Ec and E1 + E3 occurred between the second and eighth periods and equalled 19 mm and 11 mm respectively.

The maximum decrease in Ec and E1 + E3 was observed when the forest aged by 25 years (between the first and sixth periods) and equalled 32 mm and 42 mm respectively. This means that the rate of evapotranspiration and transpiration decrease during that period was, on average, 1.3 mm and 1.6 mm per year. As to the change in evaporation under the canopy and evaporation of intercepted precipitation for a long-term period, they depend on the moistening parameter. It was possible to expect that these dependences had opposite trends, i.e. an increase of seasonal moistening causes a decrease of evaporation under the canopy and an increase of evaporation of the intercepted precipitation; and vice versa, a decrease in moistening causes a decrease in evaporation of the intercepted precipitation and an increase of evaporation under the canopy. In general, similar ratios between the evapotranspiration components are typical of the plants of age classes III and IV. Mean long-term and extreme values of evaporation components from the forest are characterised by the data in Table 2.31. Besides, the higher age class of the forest caused a significant change in the evapotranspiration components. This change mainly occurred during tree stand aging and resulted in a gradual transpiration decrease. For example, for a spruce forest of age class III (1955-1974) transpiration equalled 166 mm on average whereas for a spruce forest of age class IV (1975-1994) it equalled 151 mm, i.e. the decrease was 15 mm. As evident from Table 2.31, the evapotranspiration decrease during these periods was about the same ( ΔE =14 mm). Thus, it follows that a change in evapotranspiration from the forest mainly occurs due to a change in transpiration. Total water loss from soil for transpiration and evaporation under the canopy of the forest of age class III equalled 73%, and that of age class IV 70%.

Table 2.31 Evapotranspiration components for different age classes of forest

Mean Maximum Minimum Mean Characteristic (mm) (mm) (mm) (%)

Age class III (1955-1974) Е 395 409 371 100 Е1 122 149 101 31 Е2 107 157 73 27 Е3 166 184 143 42 Age class IV (1975-1994) Е 381 404 348 100 Е1 116 156 91 30 Е2 115 140 90 30 Е3 151 176 126 40

218 Evaporation from forest

It should be noted that tree stands with other characteristics and forest type have other ratios in the structure of evapotranspiration from the forest. For example, according to the data obtained on the experimental plot in the Tver' region (mixed spruce forest with a tree density of 0.8), the following average ratio was observed over three years (May – September): evaporation under the forest canopy 17%, evaporation of intercepted precipitation 32% and transpiration 51% of evapotranspiration. In all cases, however, transpiration during the warm season is the most significant contributor to evapotranspiration from the forest. The structure of evapotranspiration from the forest in winter with snow cover is estimated from the following equation:

E = Ec + E3 + ET (2.53) where

Ec = evaporation from snow under the canopy

E3 = evaporation from snow intercepted by tree crowns

ET = winter transpiration of trees. Evapotranspiration from the forest in winter has not yet been studied. Of the publications available, mention should be made of Molchanov [1960], who obtained evapotranspiration from the forest equalling 28 mm for the Moscow region, and Rauner [1972], who investigated evaporation from the forest by the heat balance method for particular periods in March – April 1960. Such inadequate data on evapotranspiration from the forest and on its components in winter are associated with great methodological difficulties. Data obtained by some scientists for different time periods show that evaporation from snow under the canopy is about 2 or 3 times less than that from open areas. For example, evaporation from snow under the canopy in the Taezhny experimental catchment in the winter months of December to March in 1967-1969 equalled about 7 mm. Another component of evapotranspiration from the forest or transpiration of conifers in winter in the taiga is assumed by some authors to be insignificant. For example, in the Perm region it equals about 3 mm. In Valday, transpiration of the conifers in the winter months is about 2 mm. Therefore, the value in the taiga may be neglected. But it should be noted that in other climates, e.g. in the USA, quite other ratios may be observed for transpiration in the warm and cold seasons. Evaporation of intercepted solid precipitation, i.e. the third evapotranspiration component, during the winter months for a long-term period averages 30 mm. This amount is distributed over the individual months (in mm) as follows:

XII I II III XII-III 5.6 5.0 6.0 13.0 29.6

The following ratio occurs between the components of evapotranspiration for a spruce forest in winter (December – March): evaporation from snow under the canopy 18%, transpiration 5% and evaporation of snow intercepted by tree crowns 77%. Thus, it means that in winter evaporation from snow intercepted by tree crowns is the major contributor to evapotranspiration from spruce forest. The problem of evaporation from snow

219 Forest hydrology – results of research in Germany and Russia intercepted by tree crowns requires more studies, as do the other components of evapotranspiration in winter time. As a whole, ratios between the components of evapotranspiration from a spruce forest during warm and cold seasons differ greatly. On average, long-term values of evaporation of the intercepted total precipitation during the warm season equal 26%, transpiration 43% and evaporation under the canopy 31% of evapotranspiration, whereas during the cold season these values are 77%, 5% and 18% respectively. As a whole for a year, the structure of evapotranspiration from a spruce forest of mean density is as follows: evaporation under the canopy 29%, transpiration 38% and evaporation of intercepted precipitation 33%.

2.7 Relationship between annual evapotranspiration rates from forested and open territories

Monthly evaporation from forest and open land has been calculated for several years according to the method adopted with data obtained from the Valday branch and the water balance stations and collected in Annex 2.4-2.9. In Table 2.32 the annual mean evapotranspiration from the small catchments Usadjevsky and Tajezhny is shown as well as the relationships between the evaporation rates for five-year periods from 1955 to 1999. This ratio, specified as β = Ef/Eo, features the influence of forest on evapotranspiration. As suggested by Yu.L. Rauner, it is a hydrometeorological index of the forest vegetation.

Table 2.32 Evapotranspiration (mm) from forest and open land (Valday) and Budyko's radiative index of dryness (J = R/LP)

Evaporation Period ΔЕf-o β JV-VIII Ef Eo 1955-1959 476 432 44 1.10 1.60 1960-1964 481 449 32 1.07 1.96 1965-1969 480 436 44 1.10 1.58 1970-1974 465 430 35 1.08 1.81 1975-1979 457 416 41 1.10 1.47 1980-1984 448 418 30 1.07 1.26 1985-1989 475 425 50 1.12 1.44 1990-1994 481 460 21 1.05 1.95 1995-1999 463 453 10 1.02 2.28

The data collected show that in north-west Russia (Novgorod region) evaporation from fir stands is considerably higher than from open spaces. Taking the annual average for a long period of time (45 years), this difference ranges from 10 to 50 mm and the index of the influence of forest (β) varies from 1.02 to 1.12, that is 1.08 on average. Resulting from a combined analysis of the energy and water balances of forest and open land (with the mean values of many years), it is deduced that the difference is caused by the difference in the incoming parts of the energy and water balances.

220 Evaporation from forest

Temporal changes of these components conform well to the radiative index of dryness, in particular for the intensive growth of vegetation. When this index varies within the range of 1.4 to 1.6, there are the greatest values of the vegetation parameter β (Figure 2.12). Similar variations in this factor have been observed in other regions of Russia.

Figure 2.12 Evapotranspiration factor (β) dependent on index of dryness (JV-VIII)

The data collected show that the β–values increase southwards, from the coniferous forest to the forest-steppe zone. Such a change in the β–values is attributed to the forest species, the physiographic features and, most important for the forest-steppe zone, the forests are of limited areal size. Finally, it must be noted that, by evaluating the ratio between forest and open land, not only hydrometeorology but also forest age and forest stand composition have to be taken into account. One of the features of forest, especially coniferous forest, is that it has a much longer growing season than open land vegetation (e.g. cropland). Besides, there is the influence of a much deeper active soil layer under the forest canopy.

221

Water regime of soils in the unsaturated zone

3 Water regime of soils in the unsaturated zone

3.1 General

Soil moisture content, water storage in soils and subsoils in the unsaturated zone and variation in time are the most significant water balance components. Observations of soil moisture content in the network of hydrological and agrometeorological stations are made mainly for a quantitative assessment of evaporation from the land surface and for runoff formation studies. The first results of soil moisture measurements in Valday were obtained in the Priusadebny and Usadievsky small experimental catchments in 1939-1941. In 1951/52 intensive methodological studies were developed to estimate the accuracy of measurements of soil moisture content, volumetric density and water storage in the Priusadebny and Taezhny small experimental catchments. The assessment of moisture content variations in soils and subsoils in the experimental catchments was based on the well-known methodologies of drilling soil samples. The number of boreholes for taking soil samples varied from 8 to 11 in each catchment. The required number of boreholes was estimated statistically on the basis of data on soil moisture content obtained in detailed areal soil surveys. Nowadays, a neutron probe is used for soil moisture measurements. The results of soil moisture measurements are published annually in Materials of Observations at the Valday Branch of the SHI and in Transactions of the State Hydrological Institute ("Trudy GGI"). Studies of the soil moisture regime stimulated a development of methodologies and analyses of hydrophysical soil properties in forested and field catchments. The following parameters were estimated: soil granulometric composition, density, mean density in the catchments, total water-bearing capacity (TC), minimum water-bearing capacity (MC), wilting point, humidity of the capillary break (critical humidity), maximum hygroscopicity, filtration coefficients and water yield. During wet years soil moisture content in field and forested catchments was close to the water amount corresponding to the MC. It should be noted that the total water-bearing capacity (TC) was estimated additionally by experiments, i.e. moisture content measurements with a neutron meter during spring snow melting and durable rainfalls when soils were completely saturated with water. Long-term data analyses on soil moisture content show that in some years in winter in the zone of water surplus water storage in the unsaturated zone in the 0-100 cm layer usually

223 Forest hydrology – results of research in Germany and Russia tends to rise by more than 80-120 mm. This rise is the result of meltwater infiltration during winter thaws, the motion of water under the effect of capillary forces and its migration to the frozen layer. During relatively warm winters water recharge of all soil layers is rather uniform. At the end of winter soil moisture content in the 0-30 cm layer in the field catchment is within the limits of the total water-bearing capacity (TC) and capillary water-bearing capacity (CC); in the forested catchment it is within the limits of the CC and MC. In deeper horizons, water storage in the soil is close to the CC in the field catchment; in the forested catchment it is close to the MC. According to measurements made in Valday, the long-term average soil moisture storage within the upper 1 m soil layer changes spatially. The variations are from 30 to 67 mm throughout a small catchment. Besides, about 65-80% of these are in the upper 50 cm of the soil profile. In summer, soil moisture content is usually reduced by evaporation and transpiration. During dry summer seasons soil moisture depletion reaches 130-150 mm; in wet years it reaches 30- 80 mm. When the summer is wet, the variations in soil moisture content in the field are insignificant and are close to the MC. In the forest during wet years a small decrease of soil moisture content is also observed (uniformly all along the soil profile). In autumn, soil moisture content is replenished by precipitation. Moreover, after a dry summer soil moisture content in the forest and in the field is almost the same (50-60 mm in the top soil 1 m deep). After a wet summer soil moisture content in the field is higher by 20- 30 mm whereas in the forest it is much less because of deeper moisture infiltration into the soil. The analysis of soil moisture content shows that during the year there are two periods of soil moisture replenishment and two periods of its decrease. A general development of water storage in the soil during the year in the forest and in the field is clearly seen in Tables 3.1 and 3.2.

Table 3.1 Soil hydrological parameters

Catchment Type of land WP (mm) MC (mm) TC (mm) Ccr (mm) Taezhny forest 140 290 390 215 Usadievsky field 140 280 380 210

Here, soil moisture content (in the top soil layer 0-100 cm deep) corresponding to the moisture content at wilting point (WP) was accepted for extremely dry vegetation periods (measurements made by soil sample weighing). Minimum water-bearing capacity (MC), total water-bearing capacity (TC) and critical soil moisture content (Ccr) were estimated from the results of measurements using a neutron meter. It should be noted that the critical soil moisture content is the least studied parameter among the other hydrophysical soil properties. According to Rode [1965], the absolute value of this parameter is close to the humidity of the capillary break.

224 Water regime of soils in the unsaturated zone

Table 3.2 Typical moisture content (mm) in loamy soils (cm) at the end of a season in field and forested catchments (Valday)

Winter Total Capillary Minimum Soil water- water- water- Autumn Spring Summer layer bearing bearing bearing 1 2 3 capacity capacity capacity Field 0-10 35 42 47 55 30 29 13 45 34 27 10-20 29 33 38 45 27 24 13 39 32 26 20-30 27 30 33 37 26 25 14 35 31 25 30-40 26 29 29 32 27 25 14 33 30 25 40-50 25 28 25 29 26 23 16 32 28 25 50-60 25 28 24 28 25 23 18 31 28 25 60-70 25 27 24 28 25 24 18 30 28 25 70-80 24 27 24 28 25 24 20 30 28 25 80-90 24 27 24 28 25 24 20 30 28 25 90-100 25 27 24 28 25 24 20 30 28 25 Forest 0-10 27 38 45 56 33 24 17 58 41 26 10-20 24 29 37 50 28 22 15 50 36 25 20-30 25 28 33 34 26 22 16 35 32 25 30-40 25 28 30 31 25 23 16 33 30 24 40-50 25 29 29 29 25 25 18 32 29 24 50-60 25 28 29 26 25 24 17 32 29 24 60-70 24 28 31 25 25 25 18 32 29 24 70-80 23 28 31 25 26 26 19 31 29 24 80-90 24 28 27 25 26 25 19 31 29 24 90-100 24 28 26 25 26 25 20 31 29 24 Note: 1 – mild winters; 2 – winters with mean conditions for soil freezing; 3 – severe winters without thaws

3.2 Estimation of soil humidity and moisture content in soil

Soil humidity in the catchments was measured at permanent sites, usually in the top soil layer 0-100 cm deep; during some periods these measurements were made in the layers of 0- 150 cm and 0-3 cm deep. At first, measurements were made with the use of a borer; since 1989 a neutron meter has also been used. A number of points for soil moisture content measurements were determined as a result of a detailed areal survey in the catchment with a subsequent analysis of the data obtained; the statistical method was used for the analysis. At the selected mean value of the specified error, the following formula was applied to calculate the number of points: t ⋅σ ΔW = α (3.1) α n −1 where σ = mean square-root deviation

225 Forest hydrology – results of research in Germany and Russia

tα = normalised deviation from the selected mean value

ΔWα = error of the selected mean value n = number of points for soil humidity measurements. Detailed areal surveys of soil moisture content were made in many catchments. The number of points varied from 24 to 45. Proceeding from the results of statistical characteristics computation (at the specified frequency of 0.80 and at the specified accuracy of no less than 10%), the required optimal number of points was accepted. This number varied from 8 to 11 for experimental catchments. Data of soil moisture content measurements were established taking the appropriate research programme into account.

3.3 Critical water storage in soils and subsoils

This parameter characterises the general dependence of evaporation and transpiration on moisture content in soils and subsoils at the transition from lower values to high ones and can be expressed as follows:

Etr − Ecan ⎛ W −Wwilt ⎞ = f ⎜ ⎟ (3.2) E0 ⎝WMC −Wwilt ⎠ where

Etr = transpiration of the plant cover

Ecan = evaporation under the tree canopy

E0 = evaporativity W = water storage in soils and subsoils.

According to Rode [1965], Wcr is expressed in percent of absolutely dry soil; for chernozems it equals about 75% of the MC value. For a quantitative assessment of the critical water storage in soils experimental data on transpiration and evaporation were used, as well as data on soil moisture content in the top layer 1 m deep for certain 10-day periods in the forested and field catchments of the Valday branch of the SHI; data from other water balance stations were also used. Data for the periods without precipitation (or little total precipitation) were taken for the analysis. Graphs were plotted to show variations in transpiration ( Etr ) and evaporation from land ( Ecan ) depending on the moistening characteristics, e.g. evaporativity value ( E0 ). These graphs show that at all points in different physiographic zones general changes (Etr + Ecan )/ E0 depending on water storage in soils are quite typical. Besides, a certain critical level of water storage is discovered in the graphs; above and below this level a relative rate of transpiration and evaporation from soil tends towards considerable change.

Table 3.3 contains critical water storages (Wcr ) in several catchments. Other soil hydrological parameters are given in this table for comparison. As evident from the analysis of the observation data, the critical water storage in the catchment closely corresponds to the middle value between the WP and MC. Its absolute value equals about 75% of the MC in the zone of coniferous forests (data from the VB of the SHI), about 78% in the zone of mixed forests (data from the Podmoskovnaya water balance

226 Water regime of soils in the unsaturated zone station) and about 79% in the forest-steppe zone (data from the Nizhnedevitskaya water balance station).

Table 3.3 Critical water storages in several catchments (mm)

Catchment Location Wcr WP MC TC Taezhny Valday (Novgorod region) 215 140 290 390

Usadievsky Valday (Novgorod region) 210 140 280 378

Medvenka river Podmoskovnaya water balance station (Moscow region) 265 - 340 455

Yasenok river Nizhnedevitskaya water balance station (Voronezh region) 282 207 356 510

Figures 3.1 and 3.2 show soil moisture content distribution in the top layer 1 m deep (mean monthly values) in the Usadievsky and Taezhny catchments during a dry summer (1972) and during a very wet year (1962). In 1972 soil moisture contents during the dry warm season (May – August) in the field and forested catchments were almost the same. Soil moisture content was a little higher in the forest (170 mm) during the driest period (August) whereas in the field it equalled 140 mm. In winter moisture storage in the forest was much higher than in the field.

Figure 3.1 Soil moisture content distribution (mm) in 1972 in the Usadievsky (row 1) and Taezhny (row 2) catchments

227 Forest hydrology – results of research in Germany and Russia

Figure 3.2 Soil moisture content distribution (mm) in 1962 in the Usadievsky (row 1) and Taezhny (row 2) catchments

In 1962 (wet year) water storage in the forest was higher all the time; i.e. during wet periods the forest stimulates a greater accumulation of moisture in soil. Considering long-term variations in soil moisture content in forested and field catchments it should be noted that mean annual values are quite similar (Figure 3.3). A slight decrease of

Figure 3.3 Mean monthly soil moisture content (mm) in the soil layer 1 m deep during a year in the Usadievsky (row 1) and Taezhny (row 2) catchments 228 Water regime of soils in the unsaturated zone soil moisture content in the forested and field catchments was observed during 1965-1985. It should also be noted (see Chapter 1) that during the observation period no decrease in precipitation was observed in the forested catchment, whereas in the field catchment annual precipitation was constantly increasing. Figure 3.4 demonstrates the distribution of mean soil moisture content during the period of intensive vegetation (May-August) in the forested and field catchments in 1955-1988. Here, similar values of soil moisture content in the top layer 1 m deep were observed during warm and dry seasons; these values were slightly higher in the forest during the wet seasons.

Figure 3.4 Mean monthly soil moisture content (mm) in the top soil 1 m deep during the period May-August in the Usadievsky (row 1) and Taezhny (row 2) catchments

3.4 Variations in the regime of soil moisture content and water table affected by forest clear-cutting

Forest clear-cutting introduces great changes in the regime of the soil moisture content and the water table. Later, during forest regeneration or forest plantation these changes become not so evident and depend on the quantity of precipitation, evaporation from the soil and specific features of the heat regime. Soil moisture content measurements were made at the same sites (in the forest and in clear-cut areas) over several years. The basic differences in the regime of soil moisture content were observed in the top soil (50 mm deep) during particular seasons. The maximum amplitude of soil moisture content variations was observed in the winter months and was explained by the different soil moistening in autumn, during thaws and according to specific features of moisture accumulation during soil freezing.

229 Forest hydrology – results of research in Germany and Russia

As noted above, moisture storage in the top soil layer 1 m deep is usually greater in the forest than in the field during a hydrological year. This difference is most evident during the period of intensive vegetation in wet years. Greater soil moisture content in the forest (if compared with the field) is explained by a longer snowmelting at the beginning of the vegetation period. The opposite situation may be observed in winter because of different temperature gradients in soils in the forest and in the field. This type of soil moisture content variations in forested and field catchments is typical not only of the study region but of other regions in the forest zone of European Russia, which is clearly seen in the data of the Podmoskovnaya water balance station. The regime of soil moisture content in felled areas has its specific features. These are: 1) increase of soil moisture content after forest clear-cutting as compared to an undisturbed forest stand; 2) dependence of soil moisture content on the size of the clear-cut area; 3) gradual decrease of water surplus from year to year during forest regeneration in the felled area. Water surplus in felled areas is favourable because it prevents a sudden reduction of evapotranspiration after the forest clear-cutting and stimulates an additional accumulation of solid precipitation. The maximum increase of water storage was observed in the felled areas just after cutting in summer during the first year; it reached 70 mm in the top layer 1 m deep in bilberry spruce forests and 30-50 mm in cowberry pine forests. Water storage equilibrium in soils in felled areas and in the forest around only occurred at the end of snowmelting. The difference in soil moisture content in the felled areas and in the forest depended on their shading rate (α 0 ). The maximum water surplus was observed in felled areas with 10-20° shading which corresponded to the conditions of the maximum storage of additional precipitation. When the cut area was grown with plants and later, during forest plantation, the soil moisture content in the felled area became the same as in the surrounding forest. Ten years after the forest cut the difference in soil moisture content was insignificant and within the accuracy of soil moisture content determination. More correct conclusions can be deduced from the data on water table variations in the felled areas, which were measured very accurately. The subsurface water regime in the felled areas has its specific features. First, it is characterised by a shallower water table than that of the surrounding forest. In the southern taiga, one year after a forest cut, the water table in a bilberry spruce forest at the end of the summer season in 1973 rose by 50 cm; in the warm season of 1974 the water table rise rose by 20-30 cm. A special experiment was made in a plot close to an area clear-cut 10 years previously for a more detailed study of this event. In the winter of 1977 the forest stand in the study plot was clear-cut and removed. The clear-cutting changed the water table level. The maximum water table rose by 60 cm compared to that of the forested area and it rose by 30 cm compared to the area where forest-cutting had taken place 10 years earlier. The difference between the water tables in the felled area and under the canopy of the bilberry spruce forest (in cm) was as follows:

230 Water regime of soils in the unsaturated zone

Months

II IV VII X 1974 26 0 21 10 1977 9 0 23 27 1978 0 8 11 30 1982 4 9 7 - 1986 4 0 4 10

During spring snow melting the rise of the water table in the felled area began 15-20 days earlier than in the forest. This was also the period of maximum rise and was connected to the beginning of the water yield from snow. A year after the forest-cutting and thereafter, year after year, the water table constantly fell under the influence of the growing forest plantation. This is in accordance with the change in evapotranspiration and soil moisture content depending on the number of years after forest-cutting. The final equilibrium of the water table in the felled area and in the forest was observed about 15 years after forest regeneration.

231

Impact of forest on runoff

4 Impact of forest on runoff

4.1 Impact of forest on mean long-term runoff characteristics

A method of comparison has been applied to estimate the impact of forest on runoff. Runoff values observed in catchments with different percentages of forest coverage with similar physiographic features, if possible, were compared. For the comparison of runoff characteristics in the catchments, it is necessary to take into account the major factors of runoff formation, i.e. topography, soils, hydrological conditions and plants. If the picture is not complete, the results may be contradictory. It is rather difficult to select catchments that differ only in their forest areas, but retain similarity in all other features. In this chapter some research results obtained in European Russia are given. Many authors assume that all forests stimulate a higher river runoff. If the percentage of forest coverage in the catchment is high, the ratio between total runoff from the forest and from the field mainly depends on climate factors. Moreover, this territory is characterised by an unambiguous dependence of a higher mean long-term annual runoff on the percentage of forest coverage of the catchment. The most significant studies were made by V.V. Rakhmanov [1970]. He used a method of comparison and correlation analysis of annual runoff values of rivers in catchments with different percentages of forest coverage and concluded that the effect of forest on the increase of runoff was linear and mainly explained by a higher precipitation above the forest. The relationship between the annual runoff of 12 rivers and forested areas in the Vjatka river basin (Vologda region) is characterised by the correlation coefficient 0.88. Similar results have been obtained for the catchments in the upper Dnieper basin and middle Volga basin. It is shown in Rakhmanov [1970] that higher runoff during the winter and summer low-flow periods can be observed in forested catchments everywhere. Similar studies were made in the northwestern region of European Russia [Fedorov, 1977]. In accordance with the above principle we selected several groups of catchments located within one physiographic zone with similar topography, soils, and percentage of lake and swamp areas. The topographic homogeneity of the catchment was estimated from its mean elevation and mean river slope. This information was taken from the table Basic Hydrographic Characteristics of River Basins at Hydrometric Stations (see manual Basic Hydrological Characteristics (1966)). The soil homogeneity of catchments (soil type and mechanical composition) was estimated from soil maps. As a result, catchments were selected in the central and northwest regions of European Russia for the analysis of the impact of forest on runoff.

233 Forest hydrology – results of research in Germany and Russia

Table 4.1 contains mean long-term runoff values and runoff coefficients for separate homogeneous groups of catchments with different percentages of forest coverage. The basic data on runoff were taken from the tables Basic Hydrographic Characteristics Volumes 2 and 5, 1966, as well as from hydrological yearbooks. The data on precipitation were taken from meteorological monthly bulletins, from tables and climate manuals. The data in Table 4.1 show that a higher percentage of basin forest coverage causes an increase in the annual runoff, the annual runoff coefficient and the deeper layer of runoff during the low-flow period in each group of catchments. This is typical of large and small river basins. The impact of forest on annual runoff in the catchments under investigation can be observed not only over a long-term period but during wet and dry years. The impact of forest in 1962 (wet year) is more evident than in 1959 (dry year). This difference is probably explained by a more evident impact of forest on precipitation during wet years than in dry years. The impact of forest on summer low flow is more significant in large rivers only when their drainage areas exceed 6000 km². Moreover, a higher percentage of forest coverage in the catchment causes a higher runoff during the low-flow period. In smaller catchments this effect is not so great although a trend towards an increase in runoff is evident. A positive effect of forest on runoff during the summer low-flow period in large catchments is observed not only over the long-term period but also in 1962 (wet year) and in 1959 (dry year); the impact of forest on low flow in catchments of less than 6000 km² is observed during wet years only. The increased low flow in a catchment with a larger area of forest coverage is associated with a better infiltration capacity of forest soils as compared to soils in treeless territories. In general, soils which have a higher infiltration capacity allow most precipitation to recharge subsurface water by infiltration, thus increasing the subsurface component of runoff. This statement is proved by a relatively close relationship between the portion of subsurface runoff in the total runoff and forested areas in the catchments (Table 4.1). Figure 4.1 shows graphs of low-flow coefficient (VI-IX) for particular years versus the parameter of moistening (J); these graphs were plotted for two catchments with different percentages of forest coverage but with other relatively similar physiographic features. The low-flow coefficient (η) is assumed to be the ratio between runoff depth (mm) and atmospheric precipitation (mm) for the period June to September. Moreover, the runoff coefficient was calculated from the total precipitation and runoff for June – September; the corrected precipitation was accepted as the mean for a number of meteorological stations. The total radiation balance was obtained from the data of the Nikolaevskoye heat-balance station, the nearest meteorological station with heat-balance observations. It is evident from Figure 4.1 that an increase of the moistening parameter value leads to a lower runoff coefficient. When the moistening parameter is unchanged, during the summer season, the runoff coefficient is higher in the catchment with a larger forested area than in the catchment with less forest coverage. Thus, it is possible to assume that in the forest zone of European Russia greater forest coverage in the catchments leads to higher mean long-term annual runoff; moreover, runoff during the summer low-flow period is higher, too.

234 Impact of forest on runoff

30 20 26 34 26 15 (%) of total

Subsurface flow as part

0.34 0.31 0.32 0.33 0.30 0.28 0.29 0.26 0.37 0.31 0.30 0.29 0.28 0.26 ratio

runoff Annual

256 234 234 245 220 214 220 195 252 209 212 200 204 188 I-XII

44 35 41 48 38 32 39 35 23 40 35 22 22 24

VI-IX

Runoff depth (mm)

96 126 131 119 125 121 132 139 119 142 105 129 140 109 II-V

751 748 730 740 742 754 760 750 684 682 716 694 734 727 (mm) Precipitation

62 51 40 59 40 26 33 24 63 40 52 30 53 44 (%) Forest Second group of catchments, region I (1957-1966) of catchments, region I (1957-1966) Second group

coverage First group of catchments, region I (long-term period) catchments, region I (long-term of First group First group of catchments, region II (long-term period) catchments, region II (long-term of First group with different forest coverage

5990 8230 6820 2480 2270 1440 2950 1410 1710 area 12200 12200 14700 13400 18000 17700 (km²) Catchment

Site

Kingisepp Kholm Guitivo Tolmachevo Sel'tso Sapolje Orsha Slavgorod Uvarovo Velikie Luki Plussa Porkhov Vizgi Rjabovo

River

Table 4.1 Characteristics of runoff Luga Lovat' Velikaja Luga Lovat' Shelon' Dnieper Sozh Kunja Lovat' Plussa Shelon' Issa Sinja

235 Forest hydrology – results of research in Germany and Russia

Figure 4.1 Graphs of low-flow coefficient (η) (VI-IX) for particular years versus the parameter of moistening (J); these graphs are plotted for two catchments with different forest coverage but with other relatively similar physiographic features. I = River Luga (59%), II = River Lovat' (40%)

The impact of forest on runoff characteristics may be different in other physiographic zones (forest-steppes and steppes). According to studies made by A.I. Mikhovich [1981] the impact of forest in the Ukraine is complicated. On the basis of an analysis of the relationship between water balance components, the author came to the conclusion that the water-conserving effect of the forest in different physiographic regions was ambiguous. For example, in north Ukraine (Polesie) the increase of forest coverage in the catchment causes a higher river runoff. Besides, forest stimulates a subsurface runoff increase. In the forest-steppe zone, where oak trees prevail, an increase in subsurface runoff can be observed where soils are loamy. In the steppe zone complete afforestation leads to reduced subsurface runoff and to a lower total runoff. P.F. Idzon [1986] used the method of regionalisation for Russia when he studied this problem. Runoff measured in a great number of pairs of catchments (forested and poorly forested) was taken as the basis for this method. It was assumed that for a great number of pairs of catchments the impact of different factors not taken into account (except forested areas) is compensated by each other. Variants with the use of 88, 135 and 300 pairs of catchments were used for the analysis during different years. 54% of the catchments had drainage areas exceeding 1000 km²; 46% of the catchments were less than 1000 km². Moreover, the minimum flow modules characterising subsurface water recharge in all the regions (except the central part of the forest-steppe zone) were higher in the rivers flowing in forested catchments than in those flowing in catchments where forest coverage was poor. Then, the author separated 433 pairs of catchments to estimate the water-conserving effect of forest; these catchments were evenly located throughout the plains of European Russia. He investigated the probability of exceedance and equality of annual runoff and its components in rivers flowing in forested catchments compared to the areas where forest coverage was poor, taking into account the moistening of the terrain (See Table 4.2).

236 Impact of forest on runoff

Table 4.2 Probability of exceedance, equality and decrease of runoff in rivers flowing in the forested areas compared to those flowing in areas with poor forest coverage according to the zones of water availability (except karst basins) (according to P.F. Idzon [1986])

Increase of more Increase of more Total number of Zone than 5% than 10% cases Water surplus 81 cases = 67% 106 cases = 88% 121 Wet 92 cases = 64% 122 cases = 85% 143 Semi-arid 46 cases = 48% 73 cases = 77% 95

4.2 Experimental studies on the impact of forest on runoff from small catchments

According to the results of numerous observations of the runoff from small catchments, the measured runoff values from forest catchments are, in most cases, lower than those from treeless catchments and from catchments with a small percentage of forest coverage. For example, according to the data of the VB of the SHI, the annual runoff observed over a long- term period (45 years) from the Taezhny forest catchment was 195 mm whereas from the Usadievsky field catchment it equalled 340 mm. Low values of observed runoff from small forest catchments compared with that from treeless catchments, i.e. 1.5-2 times lower, were determined from the data at the Podmoskovnaya, Nizhnedevitskaya and Pridesnianskaya water balance stations. This means that in small forest catchments it is hardly possible to measure the whole amount of runoff in the catchment. Meanwhile, if the determination of precipitation is correct and a methodology for the computation of evaporation from the forest and field has been selected properly, it is possible to calculate quite reliably the runoff from forest and field catchments for particular years and for a long-term period. This results from the water balance equation and from the ratio between water balance components for forest and field catchments. These ratios are fairly stable for young, mature and old forests. A relatively low change in the water regime is characteristic of these evolution stages of the forest phytocenosis under the influence of the tree stand age. Small experimental catchments in the VB of the SHI and some water balance stations were used to study the impact of forest on runoff. The following catchments were selected in the Valday region: the Taezhny forest catchment, the Usadievsky field catchment and the Polomet' river basin (at Dvorets, drainage area = 432 km², mean slope of the main channel = 0.9‰). The Polomet' river basin is a representative basin in the Valday hills according to its physiographic features; its runoff corresponds to the mean runoff from the whole region. The corrected total precipitation and evaporation calculated for particular years and for 5-year periods between 1955 and 1999 were used for the analysis of quantitative ratios between the water balance components (Table 4.3). Moreover, monthly and total annual precipitation in these small catchments was estimated from the readings of three or four precipitation gauges; in the Polomet' river basin these estimates were obtained from seven precipitation gauges evenly distributed over the study area.

237 Forest hydrology – results of research in Germany and Russia

Table 4.3 Water balance components (mm) of the experimental catchments of the VB of the SHI

Taezhny (forest) Polomet' river Usadievsky (field) Period precipi- evapo- runoff precipi- evapo- runoff ΔY % tation ration comp. obs. tation ration comp. obs. 1955-1959 forest 874 493 353 266 871 493 378 379 0 field 851 457 263 390 - - - - - 1960-1964 forest 780 498 263 170 775 502 273 269 2 field 650 461 189 258 - - - - - 1965-1969 forest 775 493 271 179 780 496 284 297 -4 field 734 467 267 318 - - - - - 1970-1974 forest 745 498 222 133 758 498 247 258 2 field 694 457 237 254 - - - - - 1975-1979 forest 813 498 289 173 821 503 318 323 -2 field 734 444 275 303 - - - - - 1980-1984 forest 829 490 322 198 829 495 334 340 -2 field 794 462 337 404 - - - - - 1985-1989 forest 841 502 324 245 853 502 351 354 -1 field 761 449 290 407 - - - - - 1990-1994 forest 835 496 314 273 890 498 392 413 -5 field 787 474 287 ------1995-1999 forest 811 471 289 226 810 471 331 321 3 field 800 467 333 ------Mean forest 811 493 318 207 821 495 326 328 - field 756 460 297 ------ΔP = 811 – 756 = 55 mm or 8%; ΔE = 493 – 460 = 33 mm or 7%;

ΔYcomp. = 294 – 275 = 19 mm or 6%;

ΔYobs. = 317 – 333 = -16 mm or -5%.

Evaporation from the Polomet' river basin, where forest coverage equals 80%, was calculated according to the evaporation from the Taezhny forest catchment and the Usadievsky field catchment. Thus, mean runoff for 5-year periods and for the long-term period was calculated from the mean precipitation and evapotranspiration (Table 4.3). It follows from Table 4.3 that in the majority of cases the observed runoff from a small forest catchment (Taezhny catchment) was much lower than that from a treeless catchment (Usadievsky catchment). Runoff from the Polomet' river basin for the same periods was a little higher than that from small catchments.

238 Impact of forest on runoff

The above data also show that the computed runoff from the Taezhny forest experimental catchment is consistent with the values of the computed and observed runoff from the Polomet' river basin. It should be noted that during particular periods (in wet years) runoff from the Usadievsky field catchment is a little higher than runoff from the Polomet' river basin. This is explained by the non-coincidence of the surface water divide and the subsurface water divide of the small catchment. The values of the computed runoff modulus for the Polomet' river basin equals 10.4 l/s per km², for the Taezhny catchment 10 l/s per km² and for the Usadievsky catchment 9.4 l/s per km². The annual runoff coefficient of variation for the Polomet' river basin CV is 0.17. Thus, the results of the experimental studies on the impact of forest on runoff in the Valday hills territory (south taiga zone) show that the difference between runoff from the forest and runoff from the field for a long-term period is about 10%. Moreover, the differences between the total precipitation ( ΔP ), evaporation ( ΔE ) and runoff ( ΔY ) in the Polomet' river basin (forest) and the Usadievsky catchment (field) are as follows: ΔP = 786 – 735 = 51 mm or 9%; ΔE = 493 – 460 = 33 mm or 8%;

ΔYcomp. = 294 – 275 = 19 mm or 6%;

ΔYobs. = 317 – 333 = -16 mm or 5%. The impact of forest on the water balance components is quite evident in large catchments (in northwestern Russia), e.g. in the Luga and Shelon' river basins. These two basins mainly differ in the percentage of forest coverage. Table 4.4 shows computed and observed mean annual runoff data and data on other water balance components.

Table 4.4 Water balance components (mm) for the Luga and Shelon' river basins (1970- 1979)

Drainage Forest Precipi- Evapo- Runoff River Difference Station area coverage tation ration (mm) basin (km²) (%) (mm) (mm) comp. obs. mm % Luga Tolmachevo 5990 59 694 500 194 186 8 4 Shelon' Zapolie 6820 26 669 490 179 162 17 11

The mean precipitation in the catchments was used to compute runoff. Evaporation from the catchments for the warm season was estimated using the heat balance method from data at the Belozerka and Nikolaevskaya stations; evaporation for the cold season was estimated from the evaporativity value. It is evident from Table 4.4 that the observed and computed mean annual runoff from the Luga river basin, where forest coverage is greater, is higher than the runoff in the Shelon' river basin.

239 Forest hydrology – results of research in Germany and Russia

Similar results of annual runoff computation are given for the experimental catchments of the Bolkhovskaya water balance station (the zone of broad-leaved forests) in the Orel region (Table 4.5). These are adjacent catchments, i.e. the Vytebet' and Nugr' river basins. Moreover, the mean precipitation values were obtained on the basis of readings of 29 precipitation gauges, including six precipitation gauges installed in forest glades. Evapotranspiration was computed for the warm seasons by the heat balance method and for the cold seasons using the evaporativity value.

Table 4.5 Mean annual values of the water balance components of the catchments with different percentages of forest coverage from 1970 to 1979 (data from the Bolkhovskaya water balance station)

Drainage Forest Runoff (mm) River Precipitation Evaporation area coverage basin (mm) (mm) (km²) (%) computed observed Vytebet' 920 53 704 320 184 179 Nugr' 1010 7 640 475 165 156 9% 10% 12% 15%

The above data show that there is a great difference in the water balance components of these river basins with different percentages of forest coverage (53 and 7%), i.e. 9% in precipitation, 10% in evaporation, 15% in observed runoff and 12% in computed runoff. Thus, the impact of forest on runoff is quite evident in the zone of broad-leaved forests, too. It also follows from the analysis of experimental data (Table 4.5) that this impact is greater than in the zone of coniferous forests.

4.3 Soil moisture content and runoff from forest catchments

Soil moisture content in the catchment is an important factor affecting river runoff formation. This problem has already been discussed in numerous publications. Some aspects of this problem, however, have been studied quite inadequately. This section gives some results of the experimental studies on the dynamics of the soil moisture content and the water table in forest and field catchments and its influence during spring snowmelt and rainfall floods formation. During the studies particular emphasis was placed on the establishment of relationships between individual phases of flood flow and hydrological soil parameters such as total water- bearing capacity of soils (TC), the least water-bearing capacity (LC), wilting point (WP) and soil moisture content at which a capillary break occurs (CB). These parameters characterise, firstly, the capacity of soils and subsoils to retain a definite quantity of water in the soil pores and, secondly, the rate of soil moisture migration; as a whole these parameters determine the rate of soil saturation in catchments. The intensity of runoff formation, evaporation and subsurface water regime are to a great extent characterised by these properties. These parameters are usually determined during the experiments. In this case the TC parameter was

240 Impact of forest on runoff estimated from the results of soil moisture measurements taken with a neutron probe at the time of maximum soil moisture content in the catchment (during the spring snowmelt floods). As evident from Rode [1965], the CB parameter corresponds in fact to the so-called critical soil moisture content. According to A.A. Rode, the critical soil moisture content (in % of absolutely dry soil mass), e.g. for chernozems, is about 0.75 of the LC value. According to the results of these studies, the numeric value of the critical soil moisture content is also 0.75 of the LC value (for the areas of Valday, Kamennaya Steppe and the Dubovskaya hydrological laboratory). To analyse the relationship between flood runoff phases and hydrological soil parameters, the observed daily runoff and mean soil moisture content in the top soil layer 1 m deep in the catchments at the beginning of a certain flood phase were used in this section. This relationship, observed during spring snowmelt floods formation in some experimental catchments, is discussed in greater detail with regard to the Taezhny small forest catchment. The hydrological soil parameters in this catchment are as follows: TC – 390 mm, LC – 215 mm and WP – 140 mm. Table 4.6 gives the flood characteristics of spring snowmelt floods during the period 1989- 1995: maximum water equivalent of snowpack before the flood ( h ), daily runoff hydrograph characteristic at the beginning of individual flood phases (Y0 , Y1 , Ymax ) and water tables ( H0 ,

H1 , H max respectively), as well as water content in soils and subsoils (W1 , Wmax ) measured, if possible, at the beginning of the phase of intensive runoff increase (Y1) and maximum runoff phase (Ymax ). It should be noted that Y0 and Y1 indicate in fact the subsurface runoff component. A review of long-term data on runoff characteristics and on the saturation of soils and subsoils with water in the catchment during the spring snowmelt floods shows that

• runoff formation in the stream and the beginning of the flood rise in the river (Y0 ) occur if soil moisture content is much lower than the least water-bearing capacity (LC),

• the beginning of an intensive increase of daily runoff (Y1 ) from year to year occurs when the mean soil moisture content is about LC and varies between 270 and 295 mm; a certain difference in soil moisture content during some years is explained by non-simultaneous measurements of runoff characteristics and soil moisture content,

• the maximum daily runoff (Ymax ) is observed in years when a high soil moisture content corresponds to the value of the total water-bearing capacity which equals soil moisture content of 380-390 mm at the depth of the water table of about 10-20 cm; an exception may take place during the years (e.g. 1993) when soils and subsoils are little moistened before winter begins (e.g. in the autumn of 1992). When comparing the characteristics of the rate of soil saturation with water in the catchment on the dates of the beginning of the Y1 and Ymax phases, it follows that a similar ratio between soil moisture content during these phases of flood runoff is observed for each spring flood (except extreme years), namely: W LC 1 ≈ ≈ 0.75 (4.1) Wmax TC

241 Forest hydrology – results of research in Germany and Russia

This ratio reflects one of the most important properties of hydrological soil parameters.

Table 4.6 Characteristics of flood runoff and water content in soils and subsoils in the catchment during spring snowmelt floods

Unit of Runoff measure- 1989 1990 1991 1992 1993 1994 1995 characteristics ment Taezhny experimental catchment ( A = 0.45 km²) h of snowpack mm 136 107 94 113 146 157 135 Date of 31/I 31/I 21/III 28/I 1/III 31/III 28/II measurement

Y0 mm 0.10 0.50 0.20 0.27 0.23 0.03 0.50 Date of 1/II 6/II 21/III 11/III 1/III 20/III 1/III measurement

Y1 mm 0.80 0.84 0.84 0.80 0.60 0.54 0.81 Date of 1/IV 8/II 2/IV 25/III 17/III 7/IV 26/III measurement

Ymax mm 7.62 13.6 7.62 6.96 8.66 9.63 9.58 Date of 13/IV 27/II 11/IV 6/IV 9/IV 15/IV 19/IV measurement H of water 0 cm 47 43 95 75 88 190 55 table (wt) H of water 1 cm 47 45 34 37 37 82 38 table (wt)

H max cm 10 7 21 12 13 9 25

W1 mm 277 292 295 270 238* 289 273

Wmax mm (390) 389 (390) 390 352* 381 374

W1 /Wmax 0.71 0.75 0.76 0.73 0.68* 0.76 0.73 Polomet' river (at Dvorets, A = 432 km²)

Y1 mm 1.41 1.29 1.49 1.22 0.89 1.49 - Polomet' river (at Yazhelbitsy, A = 631 km²)

Y1 mm 1.63 2.38 1.74 1.58 1.22 2.15 -

Note: *) As a result of the extremely dry summer/autumn period in 1992, soil and subsoils early in the spring of 1993 were little saturated with water.

242 Impact of forest on runoff

Table 4.6 also shows daily runoff within a larger catchment (the Polomet' river basin where forest occupies about 80% of the drainage area). The Polomet' river basin is a prototype for the Taezhny small experimental catchment. A comparative analysis of daily runoff (Y1 ) for small and large catchments shows that they both depend on the length of the channel network. This dependence can probably be expressed indirectly through the drainage area ( A ) as an exponential function:

b Y1 = aA (4.2) where parameters a and b characterise specific physiographic features of the catchments.

A study of the characteristics of the flood runoff (Y1 ) hydrograph during a spring snowmelt flood shows that daily runoff differs greatly in the case of one and the same soil moisture content in the catchment corresponding to the LC value during the periods of flood rise and flood recession, i.e. these values are higher during the flood recession than during the flood rise which is proved by the data in Table 4.7. It is explained by the impact of well-moistened deeper soils and subsoils on runoff increase (at the end of the flood recession).

Table 4.7 Daily runoff layers (Y1) in the Taezhny catchment during flood rise ( R ) and flood recession ( F )

Year 1990 1992 1993 1994 R F R F R F R F Date 8/II 16/IV 25/III 5/V 17/III 12/V 11/IV 28/IV

Wcr (mm) 292 287 270 271 282 284 337 341 H (cm) 36 64 37 63 37 87 17 22

Y1 (mm) 0.84 1.61 0.80 1.01 0.50 0.58 1.58 4.89

Thus, the results of observation of the conditions of spring snowmelt runoff formation made it possible to establish specific features of relationship between individual phases of the runoff hydrograph and hydrological soil parameters. As expected, a similar relationship between these parameters is characteristic of rainfall floods, too. In this case not only LC, TC and rainfall characteristics but also one other parameter (i.e. parameter of the critical soil moisture content Wcr ) were taken into account during the analysis. Table 4.8 contains a case study of some rainfall floods observed in the Usadievsky and Taezhny catchments and in the Polomet' river basin; runoff characteristics and parameters of soil moisture content in the catchments during individual phases of the runoff hydrograph are also given in Table 4.8. In general, rainfall flood formation can occur during different conditions of soil moisture content in the catchment. For example, in some cases it can happen after a dry period when small streams become dry and low flow is observed in small rivers. In some cases it may be observed if soil moisture content is above or below the LC value.

243 Forest hydrology – results of research in Germany and Russia

Table 4.8 Runoff characteristics during rainfall floods and parameters of soil moisture content

Polomet' Usadievsky catchment Taezhny catchment Characteristics river of floods 4-12/VIII 24-25/VIII 16-18/VI 4-12/VIII 5-10/VIII 3-12/VIII 1989 1990 1995 1989 1993 1989 Rainfall layer (mm) 155 64 63 161 57 149

Y0 (mm) 0.00 0.02 0.00 0.03 0.02 0.17

Y1 (mm) 0.28 0.28 0.27 0.20 0.44 0.47

Ymax (mm) 12.2 3.03 0.65 11.9 3.38 4.37

Wcr (mm) 206 213 215 215 213 215

W1 (mm) 285 277 277 285 260* 285

Wmax (mm) 380 352 - 390 - -

H wt (cm) 150 127 - 169 152 -

H1 wt (cm) 107 111 - 107 135 -

H max (cm) 10 17 - 10 92 -

Wcr /W1 0.72 0.77 0.77 0.75 - 0.75

W1 /Wmax 0.75 0.78 - 0.73 - -

A joint analysis of daily runoff values and mean soil moisture content in the above case studies shows that • when a rainfall begins and the soil moisture content attains the W value, runoff is formed

in a small stream channel, and a flood wave is formed in a small river (Y0 ), • if it rains for a long time (e.g. 4-12 August 1989), soil moisture content attains the LC

value; then a phase of intensive runoff increase (Y1 ) is observed, • if the rain continues, soils and subsoils in the catchment are completely saturated with water, attaining the TC value which is accompanied by the beginning of the maximum

runoff phase (Ymax ). It also follows from the analysis of the conditions of the rainfall flood formation that daily runoff values (Y0 , Y1) depend to some extent on the drainage area. This is clearly seen from the data in Table 4.9 and Table 4.10 where runoff values are given if soil moisture content is close to Wcr and LC values. Runoff values in the catchments for particular days and mean runoff from a series of N years are shown in these tables.

244 Impact of forest on runoff

Table 4.9 Daily runoff values (Y0 , mm) for soil moisture content close to Wcr values

Taezhny Polomet' river Polomet' river Year Date W (mm) H wt (cm) catchment ( A = 432 km²) ( A = 631 km²) cr 1959 31/VII 0.00 0.11 0.22 211 166 ...... 1982 27/VIII 0.02 0.20 0.27 219 174 N Mean 0.01 0.12 0.18 215 167

Table 4.10 Daily runoff (Y1, mm) at soil moisture content close to the LC values in the summer period

Taezhny Polomet' river Polomet' river Year Date W (mm) H wt (cm) catchment ( A = 432 km²) ( A = 631 km²) cr 1959 28/V 0.19 0.36 0.43 278 118 ...... 1985 30/VIII 0.26 0.51 0.45 286 71 N Mean 0.30 0.53 0.57 283 79

Note: Information on the mean soil moisture content (Wcr ) and on the water table ( H wt) is based on the data from the Taezhny catchment (observation well No. 12).

Ratios between soil moisture content during different phases of rainfall flood as well as for the period of spring snowmelt flood are constant and equal 0.75. It is evident from Table 4.11 that the hydrological soil parameters characterise the conditions of flood flow formation and the parameter of the critical soil moisture content can be applied to determine maximum flood runoff (W0 ) corresponding to the runoff observed at the beginning of the flood wave formation, i.e. at the maximum value of soil moisture deficit:

Wcr = W0 (4.3)

Soil moisture content corresponding to the maximum value of runoff (Wmax ) is estimated from the following equation:

Wmax = W0 + P or Wmax = Wcr + P (4.4) where P is precipitation causing flood runoff. This equation is simplified because it does not take into account precipitation loss for infiltration and for evaporation during the flood rise.

245 Forest hydrology – results of research in Germany and Russia

Table 4.11 Daily runoff (Y1, mm) at soil moisture content close to the LC value in winter time

Taezhny Lonnitsa Polomet' river catchment river W H wt Year Date Dvorets Yazhelbitsy cr ( A = 0.45 ( A = 48 ( A = 432 ( A = 631 (mm) (cm) km²) km²) km²) km²) 1961 2/III 0.08 0.21 0.40 0.47 299 73 1962 28/II 0.07 0.21 0.21 0.47 274 121 ...... 1975 3/III 0.14 0.27 0.31 0.46 277 77 1976 31/III 0.13 0.20 0.21 0.39 273 220 1981 25/II 0.11 0.17 0.26 0.37 280 110 N Mean 0.07 0.17 0.24 0.24 280 166

Maximum runoff in the catchment is computed on the basis of water discharge dependence on subsurface runoff:

Qmax = f ()W ; qmax = f (Wmax ) (4.5)

Subsurface runoff includes only that portion of the total soil moisture content which is lost for runoff formation. Moreover, in the case of a shallow water table, maximum discharges of rainfall floods, in forest catchments in particular, are mainly formed by subsurface runoff. The following equation may be written for the discharge at the beginning of the flood rise:

2 −3 q0 = ρW0 ⋅10 (4.6)

An appropriate method was used to determine the ρ parameter for a particular river. Dependence of water discharges upon soil moisture content during rainfall floods can be expressed by the following equation: Q = qA⋅10−3 (4.7) where Q is in m³. This equation is used here to compute maximum discharges of rainfall flood in small catchments. Moreover, the correlations have been preliminarily obtained for each catchment. As a case study, data on the determination of maximum discharges for some rainfall floods in the Polomet' river (at Dvorets, A = 432 km²) are given below:

−3 2 −6 2 −3 W0 = 0.73 Q0 ⋅10 ; Qm = (ρ − 3.7lg A)ω A⋅10 ; Qm = 1.85Wmax ⋅10 (4.8)

Table 4.12 contains the results of checking maximum discharges computed from soil moisture content.

246 Impact of forest on runoff

Table 4.12 Results of checking maximum discharge computations in the Polomet' river at Dvorets ( A = 432 km²)

Period of Q0 P W0 Wmax S Qmax .comp Qmax .obs flood rise (m³/s) (mm) (mm) (mm) (%) 2-8/V 1967 11.2 34 78 112 23.3 22.4 4 11-15/VI 1967 1.54 21 39 60 6.6 5.5 21 21-24/X 1967 3.0 28 40 68 8.5 8.7 -1.4 2-4/V 1968 7.9 17.2 66 83 12.7 13.0 -2.7 6-9/V 1968 11.9 49 80 129 30.7 32.2 -4.6 12-15/VII 1968 0.34 35 13 48 4.3 4.1 4.6 17-19/VII 1968 1.94 20 31 51 5.0 6.6 -24 15-18/VI 1969 6.8 20 60 80 11.8 12.7 -8 10-12/VI 1969 5.5 31 54 85 13.4 12.7 5.5 13-15/VII 1969 0.9 60 22 82 12.5 16.3 -23 1-4/X 1969 1.3 32 27 59 6.4 7.9 -19 16-17/VI 1973 0.9 37 22 59 6.4 6.6 -2 2-7/VII 1974 1.7 46 30 76 10.7 12.1 -15 16-22/VI 1976 9.1 80 69 149 43.5 47.8 -9 30/V-4/VI 1976 3.8 32 46 78 11.3 11.0 2.7 18-23/VII 1976 1.9 29 32 61 6.9 5.2 33 5-12/VIII 1979 0.9 49 22 71 8.8 8.0 10 10-12/VIII 1980 0.7 85 20 105 20.4 19.2 6.3 4-16/VIII 1989 0.8 128 21 149 41.0 46.5 -12

Q − Q where S = max .comp. max .obs 100% (4.9) Qmax .obs Table 4.13 contains basic data for computation and plotting graphs of the relationship 3 3 between ρχ and ( qm ⋅10 − q0 ⋅10 ) and computation of the maximum rainfall flood runoff; Figure 4.4 shows a graph of this relationship for the Polomet' river. Table 4.13 contains the results of checking maximum discharge computations in the Polomet' river for individual floods.

2 −3 q0 = ρ −W0 ⋅10 (4.10)

Wm = W0 + X (4.11) where: X is precipitation. From equations (4.10) and (4.11) it follows that 1 W = q ⋅103 + X ; m ρ 0

2 ⎛ 1 ⎞ ⎜ 3 ⎟ −3 qm = ρ⎜ q0 ⋅10 + X ⎟ ⋅10 ; ⎝ ρ ⎠

247 Forest hydrology – results of research in Germany and Russia

q ⋅103 q ⋅103 m = 0 + X ; ρ ρ

3 3 qm ⋅10 − q0 ⋅10 = ρX ;

Q = qA⋅10−3 (4.12)

Table 4.13 Basic data for plotting graphs of the relationship between ρχ and

3 3 ( qm ⋅10 − q0 ⋅10 ) for the river Polomet' at Dvorets

Period of Year Q q W χ ω q Q ρχ flood rise 0 0 0 m m m 1973 16-23/VI 0.92 2.13 30.3 53 83.3 15.25 6.60 77 1974 21-24/VI 1.00 2.31 31.6 40 71.6 12.95 5.60 66 1974 2-7/VII 1.70 3.94 41.1 42 83.1 28.00 12.1 109 1976 30/V-4/VI 3.76 8.72 64.4 32 96.4 25.40 11.0 66 1976 16-22/VI 8.90 20.6 69.2 80 149.2 111.0 47.8 188 1976 18-23/VII 1.93 4.48 43.9 42 85.9 12.05 5.20 46 1978 4-10/X 2.76 6.40 52.6 50 102.6 32.10 13.9 99 1979 5-12/VIII 0.91 2.10 30.2 59 89.2 18.50 8.00 90 1980 9-15/VIII 0.70 1.62 26.4 82 108.4 44.40 19.2 170 1980 21-27/VIII 3.50 8.10 59.2 49 108.2 25.40 11.0 69 1980 8-12/X 4.72 3.99 41.5 25 61.5 8.45 65.0 72 1981 6-12/IX 1.17 2.70 34.2 25 59.2 9.65 4.18 46 1982 20-25/VIII 1.73 4.01 41.5 28 61.5 8.89 3.84 31 1984 2-10/IX 0.86 1.99 29.3 27 56.3 9.53 4.11 53 1984 27-29/IX 6.24 14.4 79.0 28 107.0 25.95 11.2 39 1986 8-13/VII 0.80 1.86 28.2 61 89.2 30.10 13.0 130 1986 31/VIII-3/IX 0.51 1.18 22.6 54 76.6 12.50 5.40 78

This method has been applied to compute the maximum discharges of rainfall floods in small catchments. Moreover, the correlations were preliminarily obtained for each catchment. A case study of the Polomet' river at Dvorets ( A = 432 km²) explains how to compute maximum floods for a number of rainfall floods. Similar computations for plotting appropriate graphs were made for the Usadievsky and Taezhny catchments. The computed maximum water discharges were checked accordingly (Table 4.12). A comparison of the computed and observed water discharges in the Polomet' River in the Usadievsky and Taezhny catchments gave satisfactory results. More accurate results of maximum discharge computations with the use of the observed data on soil moisture content can be obtained when the soil moisture content contributing to runoff formation is more accurately determined. This is a problem to be investigated in future.

248 Impact of forest on runoff

As a result of the studies on runoff formation during spring snowmelt floods and rainfall floods, specific features of the relationship between daily runoff and hydrological soil parameters and the water table were demonstrated. It has been established that • individual phases of flood runoff are closely related to hydrological soil parameters; • the beginning of a definite phase of the runoff hydrograph is characteristic of each parameter of soil moisture content: (a) if mean soil moisture content in the catchment corresponds to the critical one, a gradual increase of water discharge occurs in the channels of streams or rivers; (b) if soil moisture content equals the least water-bearing capacity, the beginning of an intensive daily runoff increase is observed; (c) if soil moisture content corresponds to the total water-bearing capacity, the maximum water discharge in the river is observed; • one of the most striking properties of hydrological soil parameters has been discovered, namely: each soil difference is characterised by a constant ratio between the critical soil

moisture content (Wcr ), the least water-bearing capacity (LC) and total water-bearing

capacity (TC): Wcr /LC ≈ LC/TC ≈ 0.75; • new data have been obtained on the regime of soil moisture, the water table and evapotranspiration from the catchments for extremely dry and wet years; • a new approach has been developed to assess the conditions of runoff formation based on the use of the relationship between hydrological soil constants and individual phases of the flood runoff hydrograph; • on the basis of the analysis of data on the distribution of soil moisture content and the water table, specific features of soil saturation rate in different sub-areas in the catchment have been indicated; moreover, concave slopes are the most active zones for runoff formation.

4.4 Runoff changes in the experimental catchments during an observation period

Constant changes in water discharges at the outlets of the experimental catchments in the VB of the SHI were observed as from 1947. Some measurements were already made between 1938 and 1941. Discharge measurements throughout the year during this period were made only in the Usadievsky catchment. Figures 4.2, 4.3 and 4.4 give data on the variations in water discharges at hydrological gauge-lines in the catchments from 1948 to 2001 (mean annual values). It should be noted that runoff variations depend on some climate factors, on precipitation in particular. The long-term cyclicity in runoff variations shown in the graphs as a period of about 40 years is characteristic of the total (annual) precipitation (Fig. 4.5). If the cyclic variations were continuous, a certain decrease in water resources available, i.e. lower precipitation and lower runoff, could be expected.

249 Forest hydrology – results of research in Germany and Russia

Figure 4.2 Annual runoff depth variations (mm) in the Usadievsky catchment

Figure 4.3 Annual runoff depth variations (mm) in the Taezhny catchment

250 Impact of forest on runoff

Figure 4.4 Annual runoff depth variations (mm) in the Polomet' river basin at Dvorets

Figure 4.5 Changes in precipitation (mm) observed at the Valday meteorological station

251

Changes in water regimes of catchments affected by forestry practices

5 Changes in water regimes of catchments affected by forestry practices

5.1 General

Forestry practices, clear-cutting in particular, cause fundamental changes in the forest biogeosystem. Forest-felling and mechanised forest exploitation result in the complete elimination of plant cover and greatly disturb the top soil. Therefore, the conditions for the accumulation of precipitation and for the absorption of the total solar radiation as well as hydro-physical soil properties are subject to change. The water table rises and the total moistening of the area becomes higher. Finally, the structure of evapotranspiration and runoff becomes quite different. Where conditions are favourable, this process is reversible; if a natural reforestation or forest plantation takes place, the hydrometeorological regime and the structure of water and heat balance move towards the background level, i.e. the level typical of mature forest. As a result, water-protecting and water-regulating forest capacities are gradually restored. Unfavourable conditions, such as incorrect technology of forest management or the wrong size of felling areas, accompanied by water surplus in the areas subject to cutting can stimulate water- logging of the terrain. Individual aspects of the impact of forestry on the water regime have been studied for many years (e.g. Molchanov [1960]). Recent experiments, however, have shown the necessity for a multi-purpose approach to the solution of the problem relating to all links in the forest biogeosystem. Studies should be based on the analysis of a transformation of water and heat balance components in the whole cycle of the forest evolution, i.e. the first period after cutting, the stage of forest regeneration, and the stages of mature and old forest. This report contains the research results of the impact of forest-cutting on the water regimes of catchments; a numerical assessment of changes in evaporation and runoff from catchments has also been made for the whole cycle of forest evolution.

5.2 Impact of clear-cutting on the conditions of precipitation accumulation

The impact of clear-cutting can be summarised be as follows.

253 Forest hydrology – results of research in Germany and Russia

Firstly, the removal of trees, bushes and plants eliminates the conditions for the retention of precipitation by the land phytomass; thus, a greater quantity of precipitation accumulates in the forest litter, at least before the stable period of forest regeneration. Secondly, a condition is created for the additional accumulation of precipitation on the cut area produced by the "glade effect" (see Chapter 2). This particularly concerns snow storage in small clear-cut areas. The change in precipitation in clear-cut areas that are different in size shows that more solid precipitation (snow) occurs in the clear-cut areas with degrees of shading of 10° to 50° than in the surrounding forest area (see Table 5.1). The mean annual value of change in solid precipitation for clear-cut areas with 10° to 50° shading is about 20% on average. In small cut areas with more than 55° shading precipitation decreases by 10-20%. According to Forsman and Waldenström [1975] snow storage increase in medium-sized cut areas in central Sweden equals 15%.

Table 5.1 Changes in the quantity of solid precipitation depending on the degree of shading in the cut area

Degrees of shading 10° 20° 30° 40° 45°-50° Change in solid precipitation (%) 12 21 23 20 15

5.3 Evaporation from the forest after clear-cutting and during forest regeneration

Experiments on the change of evaporation after forest felling were made over a period of several years in the Novgorod and Tver' regions. The following research objects were observed: 1. areas in spruce and pine forests after clear-cutting (between 0.04 km² and 5 km²); 2. felled areas (from 1 to 20 years and longer after cutting) with forest cultures; 3. control plots in a mature 95 to 100-year-old spruce forest and in a 75 to 85-year-old pine forest. Experimental sites were selected in the Lake Edrovskoe drainage area and in the Berezaika river basin. Special plots were equipped for evaporation, water balance and runoff measurements. Evapotranspiration from the clear-cut surfaces and from the cut areas with forest cultures was estimated by using evaporimeters and by the heat balance method; evapotranspiration from a mature forest was estimated by the water and heat balance method. In general, when forest cultures are planted in the felled area under favourable conditions, forest regeneration begins and the evaporation regime is gradually restored. The evaporation from the surfaces of cut areas depending on the area size is clearly seen in Table 5.2. Table 5.2 contains the results of evaporation measurements in the felled areas of different sizes one or two years after the forest cut; evaporation values were obtained by using

254 Changes in water regimes of catchments affected by forestry practices evaporimeters for pine and spruce forests. It is evident from Table 5.2 that evaporation increases with the increase in size of the felled area. For example, in a pine forest evaporation from the surface of the disturbed forest litter in small cut areas less than 100 m in diameter and with more than 20° shading one year after the felling appeared to be less than 50% of the evapotranspiration from the forest in a control plot. Evaporation from larger felled areas (160 m in diameter and γ = 9.30) equalled 52-61%.

Table 5.2 Evaporation (mm) from soil in felled areas of different sizes

Number Number Evaporation Mean of the of years Observation shading Surface Year felled after period from felled area (degrees) area felling forest mm % Pine forest (Berezaika catchment) 5 9 1 Forest litter 1974 7/VI-7/X 289 150.0 52 11 3 1 Forest litter 1974 7/VI-7/X 289 176.0 61 5 9 1 Forest litter 1974 19/VIII-16/IX 47 26.0 56 11 3 1 Forest litter 1974 19/VIII-16/IX 47 31.0 66 12 43 1 Forest litter 1974 19/VIII-16/IX 47 21.0 45 3 37 6 Plants 1974 12/V-28/VI 130 87.0 67 7 12 6 Plants 1974 12/V-28/VI 130 116.0 87 5 9 3 Forest litter 1976 VI-IX 302 244.0 81 11 3 3 Forest litter 1976 VI-IX 302 283.0 94 16 20 1 Forest litter 1977 2/VI-7/VII 83 52.0 62 18 30 1 Forest litter 1977 2/VI-7/VII 83 34.0 41 16 20 1 Forest litter 1978 29/VI-9/VIII 85 48.0 48 17 70 1 Forest litter 1878 29/VI-9/VIII 85 21.0 25 18 30 1 Forest litter 1878 29/VI-9/VIII 85 38.0 45 17a 45 2 Green moss, cowberry 1979 1/VI-1/VIII 192 70.0 36 18 30 2 Green moss, cowberry 1979 1/IX-1/X 192 104.0 53 16a 5-7 2 Green moss, cowberry 1980 27/VI-25/IX 81.8 17b 60 2 Green moss, cowberry 1980 27/VI-25/IX 40.8 18 30 3 Green moss, cowberry 1980 27/VI-25/IX 69.2 Spruce forest (Lake Edrovskoe drainage area) 1 70 1 Forest litter 1977 20/VI-30/VI 35.0 26 4 20 1 Forest litter 1977 19/VII-4/VIII 135 71.0 53 7 35 1 Forest litter 1977 8/VII-29/VIII 42.0 31 1 70 2 Forest litter 1978 18/V-8/VIII 252 79.0 31 4 20 2 Forest litter 1978 18/V-8/VIII 252 161.0 63 7 35 2 Forest litter 1978 18/V-8/VIII 138.0 55 3 5 7 Plants 1979 13/VI-2/VII 149.0. 69 4a 10 7 Plants 1979 12/VII-7/IX 217 131.0 60 7 35 3 Plants 1979 12/VII-7/IX 102.0 47

The change in evaporation from the surfaces of felled areas, depending on their size, is clearly seen in Figure 5.1.

255 Forest hydrology – results of research in Germany and Russia

Figure 5.1 Change in evaporation (mm) from the surfaces of felled areas as dependent on the shading rate (angle, α°)

In this respect, the results of the experiments on determining evapotranspiration from soil in evaporimeters with single trees should be noted (Table 5.3). The data in Table 5.3 were obtained from observations in three weighted evaporimeters, each 1.0 m² in area and 0.8 m high. Each evaporimeter contained a soil monolith and a growing 7-year-old pine; the monoliths were taken from the same site. Then, the evaporimeters were placed in the centres of three felled areas of different sizes (15 m, 160 m and 2000 m in diameter). During the observation period from 28 June to 25 September the following values were obtained: 44%, 63% and 86% respectively of the evapotranspiration from a pine forest in the control plot. These data demonstrate the dependence of evaporation on the size of the felled area.

Table 5.3 Evapotranspiration (mm) from soil with individual pines in felled areas differing in size (Berezaika catchment, 1980)

Number of felled area and its diameter Mature forest Observation period No. 17 No. 5 No. 11 (80 years old) 15 m 160 m 2000 m 29/V-27/VI 65 28/VI-29/VII 32 57 83 30/VII-25/IX 61 76 100 28/VI-25/IX 93 (44%) 133 (63%) 183 (86%) 212 (100%)

If relative evaporativity is used as a parameter which takes into account the impact of meteorological factors, it follows that both relative evaporativity and evaporation greatly increase with the increase of size of the felled area, in particular with a degree of shading of less than 30° (Figure 5.1). This graph has been plotted from the results of relative evaporativity measurements in small weather shelters. Moreover, changes in evaporation and evaporativity are consistent with the change in radiation balance depending on the size of the felled area.

256 Changes in water regimes of catchments affected by forestry practices

The impact on evaporation of the period after forest felling This impact on evaporation has been investigated carefully at the Valday Branch of the State Hydrological Institute (VB of the SHI) [Fedorov, 1977] and at the Institute of Forest of the Academy of Sciences of Russia [Molchanov, 1960]. It is evident from these studies that the longer the period after the forest cut, under favourable conditions, the greater the stimulation of forest regeneration and, at the same time, the recovery of the evaporation regime in the forest. It is most clearly seen in the evaporation data obtained in the same felling areas over several years. According to our observations in a spruce forest in the Lake Edrovskoe drainage area, evaporation from the soil in June-September 1973 in a felled area with about 5° shading one year after the cut equalled 50% of the evapotranspiration from a mature forest; two years after the cut evaporation from the felled area with spruce plants attained 56%; four years after the cut it equalled 73%. In 1980 mean-weighted evaporation from the surface of the felled area, grown with plants, spruce trees and individual birch trees, equalled 300 mm, or 79% of evaporation from the forest. A similar intensity of forest regeneration and evaporation increase from the surface of the felling area with pines was observed in the Berezaika catchment. For example, in the second year after felling, in 1974, evaporation from the felling area in May – September was 183 mm, 56% of the evaporation from the forest. In 1977 it equalled 67%, in 1978 79% and in 1979 75%. Tables 5.4, 5.5 and 5.6 contain the results of evaporation measurements from felled areas during forest regeneration and from mature forests of spruce and pine. The latter results were obtained using evaporimeters and by the water and heat balance method.

Table 5.4 Evapotranspiration (mm) from a spruce forest and from felled areas of different ages, based on the data of heat balance measurements in the Lake Edrovskoe drainage area

Evaporation (mm) for Surface 10-12/III 1-11/VII 5-15/VI 10-21/VI 5-15/VI 1976 1977 1978 1979 1980 96-year-old spruce forest 20 33 33 41 49 Forest cut area (young birch trees and spruce trees) 21 28 36 37 41 Forest cut in 1973 (overgrown with motley grass, raspberry bushes and young birch trees) 16 26 21 30 42

The data presented show that after forest planting, in felled areas of optimal size, the evaporation regime recovers quickly. For instance, evaporation from 18 to 20-year-old young birch and spruce trees is about the same as from a 100-year-old forest. Maximum evaporation is observed from a 39 to 46-year-old spruce forest. It is 10-14% higher than that from a 100- year-old spruce forest. Evapotranspiration values for the long-term cycle of spruce forest evolution are given in Figure 5.2.

257 Forest hydrology – results of research in Germany and Russia

Table 5.5 Evapotranspiration (mm) from a pine forest and from felled areas of different ages, according to observation data in the Berezaika catchment from 11 May to 25 September 1979

Characteristics of the site Evaporation (mm) Remark

75-year-old cowberry pine forest 306 45-year-old cowberry pine forest 302 Young 20-year-old pine forest 297 Old felling area Felling area No. 5 (α = 90), 4 to 7-year- old trees 236 Forest was cut in 1973. Felling area No. 11 (α = 10-20), 6 to 7- year-old pines 243 Forest was cut in 1973, after fire.

Table 5.6 Evapotranspiration (mm) from felled areas and from a spruce forest of different ages, according to data of the VB of the SHI for May – September

Area 1973 1974 1975 1976 1977 1978 1979 1980 Felling area 300x300 m 190 246 267 269 297 266 280 300 Water balance plot No. 4 (39 to 46-year-old spruce trees) 443 495 473 443 454 422 410 410 Elovy experimental catchment (95 to 100-year-old spruce trees) 390 435 416 421 404 422 406 380

Figure 5.2 Relative evapotranspiration values (VI-IX) for a long-term cycle of spruce forest evolution (1) and the green biomass (Vt/ha) (2) according to forest age (years)

258 Changes in water regimes of catchments affected by forestry practices

Thus, after forest planting or during a natural forest regeneration in the felled area the process of evapotranspiration recovery begins. The intensity of this process differs depending on the stage of forest planting evolution. Besides, this process is greatly affected by the age of the forest. The results obtained are illustrated in Figure 5.3 by the ratio between evaporation from the felled area, plots with stands of mixed ages ( E ) and evaporation from 95 to 100-year-old forest ( E f ). It is evident from Figure 5.2 that annual evapotranspiration from the surface of the felled area two years after the cut is equal to 60% of evaporation from a mature forest. During the fifth year after felling evaporation attains 70%, during the tenth year 80%; 20-25 years after felling evaporation approaches that from a mature forest. A tendency towards an intensive evaporation increase is observed up to a climax of spruce stand evolution (40-45 years old) when evapotranspiration is 15-20% higher than that from a mature forest. Afterwards, evapotranspiration gradually decreases. It is interesting to consider combined graphs of evapotranspiration changes from the forest and the quantity of needles from the spruce plantation of different ages. The first graph is based on evaporation data from felled areas and water balance plots Nos. 3 and 4; the graph of the green needle mass has been plotted from the results of computations in model trees near the same plots.

Figure 5.3 Daily net radiation balance across a felling area, diameter d(m), divided by the radiation value measured at the centre of the felling area (R/Rc)

The quantity of needles was estimated using model trees growing near water balance plot No. 3 (13 trees) and plot No. 4 (5 trees). According to a geo-botanical description prepared at the Institute of Forest in a forested catchment, the mean stand age in the water balance plot No. 3 in 1961 was 78; this age corresponded to the mean stand age in the whole catchment. The needle mass in water balance plot No. 4 was estimated from trees aged 25, 39 and 44 years.

259 Forest hydrology – results of research in Germany and Russia

It is evident from the above graphs that the change in evapotranspiration from the forest during the stand evolution follows strictly the changes in the phytomass of needles in spruce plantations.

5.4 Distribution of some water and heat balance components in the felling area

The distribution of water and heat balance components in the felling area was studied on the basis of series observations at individual points (5 to 7 points) disseminated over the diameter (or over mutually perpendicular diameters) of the felling area. Measurements of radiation balance, albedo, evaporation, relative evaporativity and soil moisture content in the top layer 1 m deep, beneath the forest canopy, were taken. Besides, series observations of the transformations of wind velocity profile, air temperature and humidity were made in one of the felling areas. The results of these observations were published by Fedorov [1977]. Analyses of data of the total solar radiation and radiation balance show that for comparison it is better to use the sums of radiation balance during daylight hours because measurements during random hours are subject to random variations, whereas total values are more stable. A case study giving the distribution of the radiation balance in the felling area in a south-north direction is shown in Figure 5.3. To plot this graph, the data of three series of observations in the daytime from 10.00 a.m. to 3.00 p.m. were used. It is evident that the shape of the curve also depends on the elevation of the sun and on the total solar radiation distribution. The data show that the radiation balance in the centre of the felling area approximately corresponds to the mean radiation balance over the felling area. The distribution of evaporation over the felling area is quite similar.

5.5 Changes in the soil moisture content and the groundwater table after clear-cutting

A sudden change in evaporation from the forest after clear-cutting is accompanied by a sudden change in the regime of soil moisture content and the level of the water table. Later, during forest regeneration, and forest plantation in particular, this process becomes more stable. These changes depend on the quantity of precipitation and evaporation from the top soil, as well as on specific features of heat regime. Experiments were made in spruce and pine forests, on loams and sandy loams respectively. Moreover, these experiments were made in the same felling areas over several years. The results show that soil moisture content and the water table in the felling areas are much higher than in a mature forest. The maximum difference in the water tables in felling areas one and two years after a cut may attain 80 cm. During spring snowmelt floods or during winters with thaws the soil moisture content and water table in felling areas and in the forest become similar (Figure 5.4). In subsequent years, when plants appear and trees grow, evaporation from felling areas gradually increases; differences in soil moisture content and the water table slowly disappear. The maximum difference in soil moisture content is observed in felling areas shaded by α =

260 Changes in water regimes of catchments affected by forestry practices

10-15°. At higher values of α this difference diminishes because of the effect of the surrounding forest stand; if the rate of shading is lower, this value is less due to greater water loss for evaporation from the felled surface (Figure 5.4).

Figure 5.4 Changes in the moisture content and groundwater table (∆W%) in felling areas depending on the shading rate (angle, α°) (Berezaika river catchment)

Forest management causes change in the water table regime. For example, in the Lake Edrovskoe drainage area, the water table in a felling area one year after cutting was 60 cm higher at the end of the warm season compared with that in the forest. During the spring snowmelt flood the water table difference was only 14 cm. In summer 1974 the maximum difference was about 40 cm (Table 5.7). Later, until 1979, this difference varied from 0 to 64 cm from March to November. Besides, the water table difference during the wet months equalled 0-25 cm; in the dry months it was slightly bigger.

Table 5.7 Differences in water tables (cm) in the forest and in felling areas (1974-1979) Lake Edrovskoe drainage area

Months III IV V VI VII VIII IX X XI Year 1974 14 18 38 41 36 1975 8 14 8 5 (20) 1976 31 58 0 11 2 20 25 22 3 1977 27 16 12 27 41 51 53 45 27 1978 64 25 18 41 28 47 38 (40) 6 1979 7 44 25 20 39 39 28 43 65

During the spring snowmelt flood the water table starts to rise earlier than in the forest; in some years this difference in time is 20 days and this is explained by specific features of the conditions of water yield from snow in the felling area and under the forest canopy.

5.6 Runoff change caused by forest cutting

Big changes in the regime of evaporation, soil moisture content and the water table resulting from forest-cutting lead to significant runoff changes during forest evolution. This is clearly

261 Forest hydrology – results of research in Germany and Russia seen in the graph of runoff coefficient change depending on the age of forest plantation (Figure 5.5). To plot this graph the values of this parameter were used; these values were obtained from some experimental areas in the VB of the SHI: 1. runoff plots arranged in felling areas; 2. water balance plots with spruce mixed-aged plantations; 3. the Polomet' forested experimental basin.

Figure 5.5 Changing of runoff coefficient (η) for 1) a felling area and small catchments (Valday data) and 2) river catchments (S.A.Bratsev data) vs forest age (years)

Table 5.8 contains data on water balance components measured in forested areas which differ in stand age. Figure 5.5 also contains data on the runoff coefficient for catchments in the middle taiga. The latter data have been taken from Bratsev [1966]. for catchments with mixed-aged stands (80-115 years) and with similar annual precipitation. As a whole, the above data characterise the regular features of runoff coefficient change in a forested catchment after felling and during subsequent forest regeneration. Besides, two periods should be noted; the first period, before the climax stage of tree stand evolution, is characterised by a gradual runoff decrease, whereas the second period, up to the completion stage of the forest evolution cycle, is characterised by a gradual runoff increase. The highest runoff coefficients are observed in the areas just after felling where evapotranspiration decreases greatly; the lowest values are observed in forest plantations of about 45 years old where evaporation attains its maximum. The results obtained in mixed-aged and mixed-sized fellings together with the use of network observation data in river basins make it possible to discover specific common features and to predict changes in the water regimes of river basins during the whole cycle of forest plantation evolution. These changes depend on many factors during a year, such as soil, the hydrological factor, tree species, topography, forest-cutting technologies and forest regeneration, size of felling areas, drainage areas and the total area of felling. The last two factors can be combined to express the annual cut area in percentage of the drainage area. The use of different combinations of the above factors and the use of the obtained empirical dependences of major water balance components, evaporation and runoff, on the size of felling areas and periods after felling provided a numerical experiment to predict changes in the water regime

262 Changes in water regimes of catchments affected by forestry practices within the catchment caused by forest-cutting and to develop recommendations on the most rational forest management.

Table 5.8 Water balance components (mm) during the hydrological year 1. Elovy experimental catchment 2. Water balance plot No. 4 3. Felling area No. 3

Change in Runoff Stand Annual moisture content Runoff Evapo- Year Plot age precipi- in coeffi- ration in soil (years) tation ground- observed computed cient (0-100) water 1958 1 75 774 483 2 4 322 297 0.38 2 25 817 467 0 4 352 346 0.42 1959 1 76 640 469 30 20 239 221 0.35 2 26 670 442 40 20 230 283 0.42 1960 1 77 597 451 -12 16 109 152 0.25 2 27 615 435 -17 16 191 181 0.29 1961 1 78 816 527 7 -9 244 287 0.35 2 28 830 500 7 -9 339 328 0.39 1976 1 93 728 500 -53 14 159 189 0.26 2 43 735 520 -53 14 179 176 0.24 1977 1 94 809 507 -17 -25 226 260 0.32 2 44 795 537 -19 -23 189 216 0.27 3 5 825 411 -34 -20 360 0.44 1978 1 95 779 516 -10 -7 236 246 0.32 2 45 749 516 -11 -10 212 0.29 3 6 763 366 0 4 401 0.53 1979 1 96 624 486 58 27 101 223 0.36 2 46 616 490 50 27 115 203 0.33 3 7 626 333 76 12 376 0.60

5.7 Water regime transformation in the catchment

Forest plantations in the catchment greatly affect the water regime change. During forest evolution this effect is shown in the water-regulating capacity of the forest and it is accompanied by the transformation of a field biogeosystem into a forest one. To study this phenomenon, a long-term experiment in agricultural fields was initiated at the VB of the SHI during the 1950s and 1960s. Observations of water balance components were made in 1951 in two runoff plots 10 x 40 m in area and in 1953 in the Sinyaya Gnilka small experimental field catchment of 1.5 hectares in area. Those experimental areas were located near Valday. Data on water regime changes and from the studies of the water-regulating capacity of the growing forest in the Sinyaya Gnilka catchment are briefly considered.

263 Forest hydrology – results of research in Germany and Russia

From 1953 to 1960, when the catchment surface was an agricultural field (arable land occupied 83% of the catchment and dry meadow occupied 17%), measurements of precipitation, runoff, the water table and soil moisture content were made. In spring 1961 trees were planted on the land (4-year-old spruce and pine trees). They were also planted beyond the catchment (a strip 20 m wide). In 1980, the characteristics of the forest plantation were as follows: tree species – 6S, 4P; mean height – 10.0 m; mean diameter – 14 cm; density – 0.9; age – 26 years. Later, changes in the tree species occurred. For example, in 1985 it was as follows: 7S, 3P at mean height of 13.0 m; in 1995: 7E, 2P, individual birch trees, forest stand – 18.2 m high. The soils were loamy and podzolic. After forest plantings the same water balance components were observed. Additional measurements of evaporation and precipitation under the canopy were made and tree increments measured. Besides, meteorological elements were observed and the quantity of land phytomass of the forest stand was measured. The methodology of studies included a comparison of individual water balance components for the periods before and after forest planting in the study and control catchments. The adjacent Usadievsky field catchment was accepted as a control for the Sinyaya Gnilka catchment. A rate of homogeneity of these two catchments is shown in Fedorov [1977]. Besides, to compare some characteristics of runoff and evaporation, data from the Taezhny catchment, which was covered with wood sorrel spruce forest, and meteorological data from a field plot were used. During the forest plantation development the whole environment there has been subject to a continuous transformation in microclimate, plants and top soil. This development has been quite intensive. For example, during the 20 years following forest plantation, the mean annual increment in the stem height was 50-80 cm and 8-19 mm in diameter (for spruce trees). At the end of the vegetation period in 1984 the needle mass (fresh state) equalled 26 tons/ha. On the whole, the variations in forest plantation increment (from year to year) are conform to the variations of the hydrometeorological features. The maximum increment was observed when the radiation index of aridity equalled about 1.7. Beneath the canopy of the forest plantation a simultaneous transformation of plants species occurred. Oxalis was the first plant species to grow immediately after forest planting. 12 years later, however, the indicators typical of forested areas, goutweed and loosestrife, appeared. The growing forest plantations stimulate a definite microclimate formation very different from the microclimate in field ecosystems. For this purpose, measurements of the air temperature under the tree stand canopy were made from May to September 1979, as well as serial observations of the total and reflected solar radiation and radiation balance. Simultaneously, similar observations were made in the field and in a mature spruce forest. The analyses of simultaneous observations showed that the canopy of the forest plantation at the age of 25 years greatly affected the air temperature regime. On warm days (day temperature of about 20°C) the air temperature under the canopy was 1-2°C lower than in the field. When the air temperature was low (4-6°C), it was 0.5°C higher under the canopy than in the field. Meanwhile, the air temperature under the canopy of the mature forest is usually 2-3°C lower on warm days (compared to that in the field). When air temperature is low (4-6°C), the opposite situation is observed, i.e. under the forest canopy the air temperature is about 1°C higher.

264 Changes in water regimes of catchments affected by forestry practices

In general, the specific features of the heat regime depend not only on the air temperature, but also on the different values of albedo and long-wave radiation (Table 5.9). Here, the albedo difference in the mature spruce forest and in the area with forest plantation is quite insignificant.

Table 5.9 Daily variations in albedo (%) of different surfaces (August 1979)

Observation hours Plot 7 10 13 16 19 Field 22 21 19 22 21 Mature spruce forest 12 11 12 13 14 Sinyaya Gnilka 16 13 13 15 16

Data on the radiation balance in the above three plots showed that the mean deviation between the spruce forest and forest plantation equalled 8%. Above the mature spruce forest the radiation balance value was higher. A development of forest plantation was accompanied by a change in soils, i.e. in hydrophysical and chemical soil properties. Here, the soddy soil horizon was transformed into needle litter. The humus storage was intensive during the biogenic accumulation. For example, according to data of the Soil Laboratory of the Botanical Institute of the Russian Academy of Sciences, the humus content increased from 2.53% to 5.7% twenty years after the forest plantation. Microfauna affected the increase of nitrogen and calcium content. Magnesium and aluminium contents were higher, too. The change in soil chemistry was also evident from a change in pH value towards a higher soil acidity. Forest plantations greatly affected the soil density. This change was most evident in the top soil layer 20 cm deep (see data in Table 5.10). This is explained by the effect of roots and microfauna making soil looser. Twenty-three years after forest planting, pine roots penetrated into soil deeper than 1 m in some places. This intensified the infiltration rate and resulted in a higher filtration coefficient and water yield. Computations show that if we compare data obtained during the first years after planting (before 1970), we see that the mean infiltration coefficient at the end of 1979 was 10% higher and equalled 6.3.10-6 m/s.

Table 5.10 Mean density of soils and subsoils (g/cm³) in the Sinyaya Gnilka catchment

Month and year of measurements Soil layer, cm June 1961 June 1970 July 1973 September 1984 0 – 5 1.45 1.06 1.14 1.09 5 – 10 1.45 1.26 1.42 1.40 10 – 20 1.54 1.44 1.49 1.38 20 – 30 1.62 1.53 1.56 1.59

The forest plantation has greatly affected the subsurface water regime. Groundwater recharge by infiltration in the 10 years after felling doubled; during the next 10-year period it doubled

265 Forest hydrology – results of research in Germany and Russia again and approached values close to those in a mature wood sorrel spruce forest. Meanwhile, the subsurface recharge in the field catchment appeared to be lower than that in the forested catchment. The growing forest plantation is an important factor for precipitation redistribution. For example, in the catchment occupied by a young forest plantation of 13 years old 15% of liquid precipitation were intercepted by plants, the total precipitation being 325 mm; at the age of 27 years 37% of liquid precipitation were intercepted, at the same amount of annual precipitation. The results of solid precipitation measurements show that 15 years after forest planting the snow storage regime became similar to the conditions of snow cover formation in mature forest. The analysis of the conditions of surface runoff formation shows runoff change due to the effect of forest plantation. These changes are mainly explained by variations in soil freeze-up depth and increased infiltration rate of soils and subsoils. The beginning of the intensive surface runoff change on sandy loams occurs sooner than on loams. On the former it begins 5- 10 years after forest planting, on the latter after about 10 years. A change in the water-regulating capacity of the growing plantation can be seen in the transformation of runoff structure (Table 5.11). Here, total runoff for the spring snowmelt flood is based on the observed data; surface runoff was computed by means of hydrograph separation. Moreover, where there is a hydraulic relationship between surface and subsurface waters, subsurface runoff was estimated from the following equation: R = f (HB).

Table 5.11 Characteristics of runoff during spring snowmelt flood for the 1) Sinyaya Gnilka catchment, 2) Usadievsky catchment and 3) Taezhny catchment

Ratio between Total runoff Surface runoff Subsurface runoff subsurface runoff Period (mm) (mm) (mm) (years) and total runoff 1 2 3 1 2 3 1 2 3 1 2 3 1957-1961 187 189 129 162 142 49 26 30 69 13 16 54 1962-1970 175 179 104 158 158 138 33 21 54 19 11 52 1971-1979 87 161 91 60 50 36 38 23 55 44 14 60

The data obtained show that before forest plantation and during the first years after planting the mean total runoff from the experimental catchment (1) and from the control catchment (2) was practically the same; runoff was mainly formed by surface runoff. During that period (1957-1961) subsurface runoff from the Sinyaya Gnilka catchment slightly differed from the portion of groundwater recharge of the Usadievsky catchment and equalled 13% and 16% respectively. Afforestation greatly transformed the structure of runoff. This transformation was extremely intensive after 1970, when the mean ratio between surface runoff and total runoff attained 44% and approached the value typical of the catchment with a mature spruce stand (3). The ratio for the field catchment during that period was almost unchanged. A similar ratio is characteristic of these catchments for annual runoff, too (Table 5.12). For

266 Changes in water regimes of catchments affected by forestry practices example, they appeared to be almost similar before plantation and for the initial period after plantation; runoff coefficients equalled 0.55 and 0.56, respectively, before forest plantation, and 0.41 and 0.43 during the first years after forest planting. Runoff decrease for that period, if compared with the period of plantation, was explained only by a difference in hydrometeorological features of the study periods; the period before plantation was wetter than the following years. This is evident from the mean values of the radiation index of aridity for May – September: 1.37, 1.59, 1.51.

Table 5.12 Annual runoff (mm) from 1) Sinyaya Gnilka with a forest plantation and 2) the Usadievsky field catchment

Period Runoff (mm) Runoff coefficient Mean aridity (years) 1 2 1 2 index 1953-1961 369 374 0.55 0.56 1.37 1962-1970 268 283 0.41 0.43 1.59 1971-1979 166 291 0.23 0.40 1.51

The most significant changes in runoff characteristics were observed in the catchment between 1971 and 1979. The mean annual runoff, when compared with the previous period, decreased by 1.6 and the runoff coefficient decreased by 1.8. Runoff characteristics in the control catchment, however, remained unchanged during this period. After 9-10 years of forest plantation in Sinyaya Gnilka a significant change in runoff characteristics occurred; after 17-20 years these values approached those typical of the catchment with a mature forest. To determine the dynamics of the water-regulating capacity of the growing forest plantation, it is very important to estimate the change in evapotranspiration from the catchment. Evaporation for the years before forest planting was estimated on the basis of data from a soil- evaporation plot 5 km from the catchment. Moreover, a certain difference in the evaporativity of the study areas and the percentage of agricultural land distribution within the catchment before forest planting were taken into account. Evaporation for the warm season (May – September) during the years after forest planting was estimated as a residual term of the water balance equation. Evaporation data from the water surface of a 20 m² tank were also used for a comparative assessment of evaporation from the catchments. Mean evaporation from the catchments and from the tank for particular periods changed insignificantly (Table 5.13). During the period 1971-1979, however, evaporation from the Sinyaya Gnilka catchment increased considerably. For example, this increase equalled 22% when compared with the evaporation from the water surface estimated mainly on the basis of meteorological factors; it increased by 16% when compared with the field catchment and by 11% when compared with the forested catchment. Proceeding from a joint analysis of evaporation and runoff, it follows that evaporation increase from the catchment with growing forest plantation causes total runoff decrease. Thus, forest plantation stimulated the transformation of the field biogeosystem into a forest one. Besides, the state of the plant and soil coverage tends towards a change, thus giving rise to a water-regulating capacity of the growing plantation subject to continuous variations as the plantation grows. This transformation is accompanied, for instance, by a change in the

267 Forest hydrology – results of research in Germany and Russia structure of all water balance components. About 20 years after the forest planting the water regime characteristics approach specific features of the water regime of forest biogeosystems. These comprehensive and, to a great extent, unique studies of water and heat regimes of forested areas make it possible to establish factors, to measure and compute characteristics which determine the water-regulating and water-conserving role of forest. The dependence of many parameters on forest age, tree species, the whole forest structure and certain types of human activity has been estimated.

Table 5.13 Evaporation (mm) during May – September

Catchment 20 m² E E E Period Sinyaya Usadievsky Taezhny tank 1 1 1

(years) Gnilka E2 E3 E20 ( ) ( E1 ) ( E2 ) ( E3 ) E20 1953-1961 401 404 417 369 0.99 0.97 1.08 1961-1970 396 395 514 375 1.06 0.96 1.05 1971-1979 449 385 405 368 1.16 1.11 1.22

5.8 Heat regime changes in catchments affected by forestry practices

Forest plants contribute to a specific heat regime formation. Forests are characterised by specific radiation regime, temperature regime, air humidity and soil moisture content. The heat-regulating role of forest is most evident when ratios between heat balance components are studied. Experiments of heat regime were made on permanent observation sites in the VB of the SHI (Novgorod region) and on semi-permanent observation sites in the Tver' region. Basic heat-balance observations were made on a tower 42.5 m high in a mature spruce forest. Observations in felled areas several years after felling (spruce, pines) were made on a tower 10 m high with special 10-day measurements. These included measurements of the total and reflected solar radiation, radiation balance, air temperature and humidity, wind velocity, temperature of the forest surface (by radiometer) and soil temperature. These experiments were organised to study the effect of forest plantation age on individual heat balance components, on microclimate characteristics and on the heat regime as a whole. In general, heat regime is an energy factor which determines not only microclimate but also, together with precipitation, the condition of water availability in the terrain and the state of the plant coverage, in particular the increment of tree stand. The results of the experiments and subsequent computations made it possible to obtain daily variations in all heat balance components and total values for the warm season in a spruce forest and for two 10-day periods (in June and July) in the plots with pine plantations (Tables 5.14 and 5.15).

268 Changes in water regimes of catchments affected by forestry practices

Table 5.14 Heat balance components for a mature spruce forest (Taezhny catchment) for the warm season in 1984 (daily mean values in cal/cm² per min.)

May June July August September Radiation balance 0.22 0.18 0.18 0.15 0.05 Heat loss for evaporation 0.13 0.12 0.13 0.12 0.04 Turbulent heat flux 0.08 0.06 0.06 0.03 0.002 Heat flux into soil 0.01 -0.01 0.00 -0.002

Table 5.15 Heat balance components in forest plots (daily mean values for June and July, in cal/cm² per min.)

1981 1982 1984 P S P S P S Radiation balance 0.12 0.20 0.12 0.14 0.13 0.13 Heat loss for evaporation 0.11 0.15 0.10 0.08 0.10 0.12 Turbulent heat flux 0.01 0.05 0.02 0.06 0.04 0.01 Heat flux into soil -0.001 0.00 -0.001 0.00 -0.01 0.00

Note: P - in the plot with pines planted in 1974 S - in a mature spruce forest Mean daily values for June and July, in cal/cm² per min. Methods of heat balance and turbulent diffusion were used for computations. When the turbulent diffusion method is applied, it is necessary to have experimental data on wind velocity profiles at different temperature stratifications and to compute aerodynamic parameters of the underlying surface (elevation of the replacement layer and roughness). Apart from the applied importance for the computation of turbulent heat fluxes and water vapour fluxes, determination of these parameters is of particular interest because they characterise aerodynamic properties of the underlying surface. Data from Tables 5.14 and 5.15 were used to plot the dependence of radiation balance, turbulent heat flux, heat loss for evaporation and heat flux into soil on the tree stand age. The data obtained made it possible to plot the appropriate curves at the initial period of forest regeneration, i.e. during the period characterised by the most intensive variations. Observation data and the use of the computed aerodynamic parameters made it possible to estimate heat balance components in felled areas. Data in Figures 5.6 and 5.7 are relative; the absolute values of the radiation balance in the areas just after cutting are about 15% lower than above the mature forest. During forest evolution the radiation balance increases; when the forest is 10 years old, the radiation balance is almost equal to that above the mature forest. The heat balance structure, however, (i.e. the ratio between balance components) is subject to slower change. These variations are most intensive during the ten years after cutting. For example, heat loss for evaporation during the period from 3 to 8 years of the forest stand increases by 50%. Turbulent heat flux is then 40% lower. Heat flux into soil is reduced most intensely (about 2-4 times) when forest canopy appears and develops.

269 Forest hydrology – results of research in Germany and Russia

Figure 5.6 Dependence of latent heat (LE), turbulent heat flux (P), heat flux within soil (B) and the net radiation (R) (7 hour sum) on the shading rate of the felled area (α°)

Figure 5.7 Ratios (dimensionless) of the latent heat (LE), turbulent heat flux (P) and heat flux within soil (B), as divided by the net radiation (R), on forest age (years)

The radiation balance is an energy basis of the heat regime and photosynthesis of the plant cover. Its variations in different types of underlying surfaces explain the specific features of the heat regime in particular territories. Besides, the ratio between radiation balance of different surfaces depends on albedo and long-wave radiation. Permanent observations of albedo were made on two plots (forest and meadow). Table 5.16 shows mean long-term albedo values for a spruce forest and for a meadow. These data show that albedo for the spruce forest (predominant tree species in the middle and southern taiga zone in European

270 Changes in water regimes of catchments affected by forestry practices

Russia) during the vegetative period equals 0.11; during spring snow melting (April) and at the end of the vegetative period (September) it equals 0.15 on average. In winter time albedo equals about 0.30. Albedo for the meadow during the year is twice as high as the albedo value for the forest during the same period. This ratio can attain 4 during the snowmelt period only. Similar ratios between the albedo values are observed during the vegetative period both in forests and felled areas.

Table 5.16 Mean monthly albedo values of the spruce forest

I II III IV V VI VII VIII IX X XI XII

Aforest1 35 24 15 13 11 11 11 11 14 20 26 32

Ameadow2 76 77 66 24 22 23 23 23 23 26 54 80

Afelling 25 21 21 20 22

Aforest/Ameadow 0.46 0.31 0.23 0.54 0.50 0.48 0.48 0.48 0.61 0.77 0.46 0.40

Albedo variations from the values typical of the winter period to those of summer time were observed in a coniferous forest a month earlier on average when compared to those in open areas. Table 5.16 shows albedo values in felled areas one or two years after cutting. A forest regeneration causes gradual changes in albedo, e.g. it equals 0.12-0.14 in the felled area 5-7 years after cutting. A great difference in the albedo values for forested and open areas explains differences in the radiation regime; moreover, the radiation balance of forested areas is always higher than that of the open areas. The total values of the radiation balance and total solar radiation (Figure 5.6.) in a smaller felled area greatly depend on the size of this area. The total solar radiation on the surface of the felled area depends on its size and is calculated with the following formula:

⎡ Qdis 2 ⎤ Q = Qdir ⎢d()d − h ⋅ ctgϕ + d ⎥ (5.1) ⎣ Qdir ⎦ Here:

Qdir = direct solar radiation

Qdis = dispersed solar radiation h = mean stand height around the felled area ϕ = sun elevation d = diameter of the felled area. Experimental data show that the total radiation on a felled area with 45° shading during the warm season is equal to 50% of the total radiation on an open site. These factors also determine the specific features of the radiation balance. On average, the radiation balance of the mature forest during the vegetative period is 1.1 times higher in June/July and 1.3 times higher in August/September when compared with that of the felled areas. Such ratios are also typical of larger felling areas (with less than 10° shading). The total daily values greatly depend on the size of the felled area (Figure 5.6). To plot the dependence of the radiation balance on the size of the felled area, observation series in the daytime (from 10 am to 3 pm) were used. The result was that the most significant changes in the radiation

271 Forest hydrology – results of research in Germany and Russia balance were observed in the felled areas with 45° to 20° shading. For example, if the shading rate exceeded 45° (a=150 m), the radiation balance was doubled. In this case it was 1.6 times greater in a pine forest. It is evident that this distribution also depends on the sun elevation. To estimate the radiation balance values in the case of missing direct observations, two curves of the total daily radiation balance above the forest and the meadow were plotted. A comparison of these curves shows that the following equation can be used:

Rn = aR + B (5.2) where: a = 1.10 , B = 1.0 MJoule/m² per day. These values of a and B can be used to compute the radiation balance of the mature forest above quality class III because the radiation regime, just like the heat regime as a whole, depends on the age and density of the plantation (Figure 5.7). Changes in the albedo values during transient periods stimulate changes in the radiation balance. In autumn, the radiation balance decrease in the forest and in the field occurs almost simultaneously, i.e. in September/October. According to data in the Heat Balance Atlas, a zero isoline of the radiation balance is fixed in the middle of March (in Valday). At that time the radiation balance of coniferous forests is equal to 100 MJoule/m² per month on average. Data on the radiation balance distribution make it possible to estimate change in evaporation depending on the size of the felled area. In felled areas with 12° and 20° shading heat loss for evaporation equals 60% of the radiation balance on average (Figure 5.6). These data show that evaporation increases with an increase in size of the felled area. Dependence of the total radiation on the size of the felled area shows that the radiation balance and, consequently, all heat balance components depend on the size of the felled area. Besides, most incoming heat is lost for evaporation. In fact, evaporation is the component mainly providing interaction between the water and heat regimes. Turbulent heat exchange with the atmosphere and heat exchange with the soil also affect the ratio between the radiation regime and evaporation. Besides, the dependence of certain heat balance components (evaporation and turbulent heat exchange) on the age of the felled area is very evident (Figure 5.7). As a whole, the dependence of evaporation on the number of years after forest felling is in agreement with the data obtained by the water balance method; maximum heat loss for evaporation from a forest plantation is observed when this plantation is 20 to 50 years old. This period is characterised by minimum values of turbulent heat fluxes. Relatively high values of this heat balance component in the felled area are explained by a great heating of the felled area surface, during the first years after felling, in particular when the vegetative cover is poorly developed. The surface heating causes a rise of temperature gradients both in the lower air layer and in the soil, thus increasing the turbulent heat flux and heat flux into the soil (Figure 5.7). During the forest plantation development an ever-increasing part of the radiation balance is contributed to transpiration; a lesser part is contributed to heat exchange. Then, turbulent heat exchange tends towards some increase, probably because of the increase of the turbulent exchange coefficient, because it depends on the air temperature gradient. A tree stand development causes a rise of exchange coefficient due to the increase of the geometric size of the forest stand.

272 Changes in water regimes of catchments affected by forestry practices

Daily variations in the turbulent heat exchange are similar to daily variations in the radiation balance. Maximum values are observed in June when the radiation balance is significant but transpiration does not attain its maximum. On average, the turbulent heat exchange in the felled areas equals 20 to 30% of the radiation balance. It is a little higher when compared with that of the field. When the shading rate of the felled area increases from 45° to 20°, the turbulent heat flux tends to be twice as high; if the size of the felled area is smaller, it tends to decrease. The turbulent heat exchange in a felled area 50 m in diameter is three times lower in the daytime than that in the 300x300 m felling area. Turbulent heat exchange variations are observed together with the forest plantation development, i.e. they depend on the period after cutting. Turbulent heat exchange in felled areas is affected by the air temperature gradient, which is higher just after forest-cutting, and by the aerodynamic roughness of the surface. During the first years after forest-cutting, when plants grow and trees appear, heat loss for evaporation tends to increase considerably while turbulent heat exchange decreases. 15-20 years after cutting it attains the value observed in the mature forest. Heat flux into the soil is maximum just after forest-cutting. A forest plantation development stimulates a gradual decrease of heat flux into the soil and the heat flux is 20-30% less in the mature forest than in the felled area that is 300 m in diameter. The dependence of this characteristic on the size of the felled area is similar to that of the turbulent heat exchange.

273

Annex

Annex 1.1 Long-term precipitation throughfall (Р1, mm) and the interception in mm (Р2) and in percent (Р3)

Para- XI- IV- Annual I II III IV V VI VII VIII IX X XI XII meter III X totals Fir stand (water balance plot No.3), 1951–1969

Р1 (mm) 38 30 29 28 40 57 71 60 47 54 34 43 174 357 531 Р2 (mm) 5 6 13 15 22 22 26 24 23 26 7 8 39 158 197 Р3 (%) 12 17 31 35 35 28 27 29 33 32 17 16 18 31 27 Fir stand (water balance plot No.4), 1957–1969

Р1 (mm) 29 26 22 27 45 51 67 69 48 57 34 43 154 364 518 Р2 (%) 4 5 11 11 17 19 22 20 20 23 7 7 34 132 166 Р3 (%) 12 16 33 29 27 27 24 22 29 29 17 14 18 27 24 Fir stand (Elovy watershed), 1956–1969

Р1 (mm) 41 30 26 28 43 54 67 66 44 55 33 41 171 357 528 Р2 (%) 5 5 12 12 19 16 24 23 20 23 7 7 36 137 173 Р3 (%) 11 14 32 30 31 23 26 26 31 30 18 15 17 28 25 Planted fir and pine stand (water balance plot No.5), 1969–1971

Р1 (mm) - - - 30 27 54 57 25 53 63 - - - - - Р2 (%) - - - 12 14 13 19 16 25 30 - - - - - Р3 (%) - - - 29 34 19 25 39 32 32 - - - - - Birch and aspen (the same at the plot No.16), 1969–1971

Р1 (mm) - - - 35 28 53 61 28 55 63 - - - - - Р2 (%) - - - 7 13 14 15 13 23 30 - - - - - Р3 (%) - - - 17 32 21 20 32 29 32 - - - - -

275 Forest hydrology – results of research in Germany and Russia

Annex 2.1 Long-term mean values of the radiative index of dryness (J in MJ) for May to September in the European part of Russia

Actinometrical Actinometrical Actinometrical No. J No. J No. J site site site 1 Arhangel'sk 63 17 Kaunas 68 33 Nignedevitskaja 78 2 Kotkino 60 18 Toropets 63 34 Kursk 77 3 Eletskaya 55 19 Torzhok 66 35 Kamennaja Step' 95 4 Irael' 64 20 Moskva 70 36 Tseben'ki 128 5 Petrozavodsk 63 21 Nizny Novgorod 81 37 Kishinev 110 6 Kargopol' 53 22 Minsk 64 38 Poltava 108 7 Ust'-Vim' 71 23 Smolensk 68 39 Volgograd 199 8 Voejkovo 63 24 Kushnarenkovo 96 40 Djanibek 204 9 Novaja Ladoga 63 25 Pinsk 77 41 Herson 137 10 Pribaltijskaja 69 26 Vasilevichi 74 42 Askanija Nova 139 11 Tejrikoja 73 27 Paveletsk 78 43 Velikaja Anadol' 119 12 Nikolaevskoje 64 28 Samara 126 44 "Gigant" 120 13 Valdai 61 29 Kovel' 56 45 Derkul' 115 14 Kostroma 69 30 Beregovo 67 46 Ribinsk 84 15 Nolinsk 75 31 Borispol' 80 47 Visokaja Dubrava 75 16 Shilute 60 32 Konotop 98 48 Pridesnjanskaja 80

Annex 2.2 Evapotranspiration totals (mm) for several periods of the year (1955-1999) (Tajezhny forest catchment)

Year I - III IV V - IX X XI – XII I - XII 1955 9 22 390 28 10 454 1956 14 16 389 13 3 430 1957 14 35 399 22 15 478 1958 14 36 412 17 11 485 1959 21 40 405 20 9 488 Mean 14 30 402 20 10 467 1960 20 46 390 13 3 465 1961 21 41 410 25 0 492 1962 15 45 401 17 11 483 1963 4 36 409 13 8 466 1964 11 31 397 21 5 461 Mean 14 40 401 18 5 473 1965 11 45 391 18 5 465 1966 19 25 405 22 10 474 1967 23 36 404 25 9 489 1968 15 44 405 17 3 479 1969 11 35 382 20 14 456 Mean 16 37 397 20 8 473 1970 23 32 371 17 4 440 1971 8 45 378 18 5 450 1972 19 41 371 14 11 449 1973 15 43 388 18 9 467 1974 23 43 391 25 10 484

276 Annex

Year I - III IV V - IX X XI - XII I - XII Mean 18 41 380 18 8 458 1975 19 46 384 25 4 472 1976 15 42 373 17 4 446 1977 11 42 366 20 8 436 1978 11 42 383 20 11 462 1979 11 24 464 20 6 461 Mean 13 39 381 20 7 455 1980 11 41 354 22 5 429 1981 13 37 372 20 5 442 1982 20 43 362 18 15 450 1983 5 39 394 20 8 462 1984 15 42 355 21 4 432 Mean 13 40 367 20 7 443 1985 23 35 394 16 8 468 1986 15 39 385 26 11 470 1987 23 36 402 30 4 483 1988 15 46 381 26 5 463 1989 25 37 391 21 3 470 Mean 20 39 391 24 6 471 1990 18 55 356 16 1 446 1991 13 37 394 21 10 473 1992 18 36 426 11 6 496 1993 25 41 395 25 8 492 1994 10 50 400 25 5 489 Mean 17 44 394 20 6 479 1995 17 39 394 24 6 479 1996 14 42 357 18 6 436 1997 11 40 373 12 6 441 1998 16 39 413 23 3 493 1999 14 50 378 18 6 465 Mean 14 42 383 19 5 463

277 Forest hydrology – results of research in Germany and Russia

Annex 2.3 Evapotranspiration totals (mm) for several periods of the year (1955-1999) (Usadjevsky grassland catchment)

Year I - III IV V - IX X XI – XII I - XII 1955 7 22 340 26 8 403 1956 11 16 348 11 2 388 1957 11 35 374 20 12 452 1958 11 36 356 16 9 429 1959 17 40 403 16 7 485 Mean 11 30 364 18 8 431 1960 16 46 368 11 2 443 1961 17 41 358 23 0 439 1962 12 45 371 15 9 452 1963 3 36 413 11 6 469 1964 9 31 380 19 4 443 Mean 11 40 378 16 4 449 1965 9 45 378 16 4 452 1966 15 25 398 20 8 466 1967 18 36 344 23 7 428 1968 12 44 356 15 2 429 1969 9 35 340 18 11 413 Mean 13 37 363 18 6 438 1970 18 32 362 15 3 430 1971 6 45 327 16 4 398 1972 15 41 360 12 9 437 1973 12 43 360 16 7 438 1974 18 43 356 23 8 448 Mean 14 41 353 16 6 430 1975 15 46 372 23 3 459 1976 12 42 323 15 3 395 1977 9 42 329 18 6 404 1978 9 42 332 18 9 410 1979 9 24 354 18 5 410 Mean 11 39 342 18 5 416 1980 9 41 332 20 4 406 81 10 37 369 18 4 438 82 16 43 353 16 12 440 83 4 39 352 18 6 419 84 12 42 320 19 3 396 Mean 10 40 345 18 6 420 1985 18 35 322 14 6 395 1986 12 39 325 24 9 409 1987 8 36 353 28 3 428 1988 12 46 353 24 4 439 1989 20 37 348 19 2 426 Mean 14 39 340 22 5 419 1990 18 55 346 16 1 436 1991 13 37 372 21 8 451 1992 18 36 396 11 5 466 1093 25 41 369 25 6 464 1994 10 50 425 25 4 476 Mean 17 44 381 20 5 459

278 Annex

1995 17 39 357 24 5 442 1996 14 42 344 18 5 423 1997 11 40 373 12 5 441 1998 16 39 409 23 2 489 1999 14 50 382 18 5 469 Mean 14 42 373 19 4 453

Annex 2.4 Monthly evapotranspiration (mm) from forested and grassland areas (Veliko- Anadol'skaja water balance station)

Year Site V VI VII VIII IX X Total 1956 field 86 130 114 96 59 30 515 forest (93) (122) ( 93) 110 60 26 - 1957 field 58 - 98 91 - - - forest 78 - 130 124 - - - 1958 field 101 95 93 77 66 44 476 forest (96) (90) (106) (79) 62 43 476 1959 field 95 110 132 109 52 21 519 forest - - 131 106 49 20 - 1960 field (89) 118 111 76 68 23 453 forest (80) (95) 115 122 60 23 (495) 1961 field 68 106 85 97 48 26 421 forest (60) (103) 121 96 48 25 (453) 1962 field 82 82 89 85 51 29 411 forest (66) 86 86 73 46 25 382 1963 field 100 85 121 104 79 27 516 forest (85) 111 122 104 74 24 520 1964 field 73 112 82 86 77 30 460 forest (74) (84) (114) 79 71 28 450 1965 field 112 140 121 88 75 30 566 forest (94) (114) - - 70 30 - 1966 field 112 68 129 84 65 36 494 forest (84) (100) 114 102 70 40 510 field 488 Mean forest 473

Note: Year 1. Evapotranspiration values for several months were calculated using the heat-water balance method (HWB-2) 2. The values in parentheses are given as the potential evaporation.

279 Forest hydrology – results of research in Germany and Russia

Annex 2.5 Monthly evapotranspiration (mm) at Streletskaja Step' (grassland area) and at Duboshino (forested area)

Year Site V VI VII VIII IX Totals % 1947 field 53 74 70 71 54 322 100 forest 64 89 97 86 54 390 21 1948 field 109 100 80 75 59 423 forest 112 105 86 81 54 438 4 1949 field 90 94 93 61 56 394 forest 113 95 133 101 51 493 25 1950 field 77 87 69 80 55 368 forest 102 94 74 87 46 403 10 1951 field 95 92 58 60 68 432 forest 88 140 82 52 47 409 -5 1952 field 87 90 68 75 51 395 forest 100 124 105 75 56 460 16 1953 field 86 93 104 75 50 408 forest 99 120 94 85 39 437 7

Notes: 1. Evapotranspiration was calculated by the heat-water balance method (HWB) taking the critical soil moisture into account. 2. Evapotranspiration from forested areas was calculated like that from the field using the specific ratio (forest evapotranspiration to field evapotranspiration), whereby the soil moisture values were accepted as if measured in the forest.

Annex 2.6 Evapotranspiration (mm) from forest as calculated by the HWB-2 method, Podmoskovnaja water balance station, forested plot, αf =100%

I II III IV V VI VII VIII IX X XI XII V-IXYear 1970 1 3 13 52 95 104 99 62 31 17 5 0 391 482 1971 3 2 8 52 105 103 81 67 28 17 6 1 384 473 1972 1 4 12 51 90 106 98 76 22 20 6 8 392 494 1973 2 5 19 50 91 94 86 66 39 21 9 2 376 484 1974 2 8 18 64 81 110 91 53 47 38 6 2 382 520 1975 0 2 14 56 96 92 94 63 43 24 5 2 388 491 1976 2 3 12 60 78 105 97 71 39 13 6 3 390 489 1977 0 2 20 56 93 104 74 59 24 21 7 2 354 462 1978 1 4 20 57 93 104 84 50 27 22 10 0 358 472 1979 2 3 13 57 133 94 65 55 33 22 5 1 380 483 1980 0 5 12 56 69 128 109 74 37 23 6 1 417 520 1981 0 5 11 55 105 133 97 48 31 25 7 2 414 519 1982 1 3 21 53 90 115 87 54 37 17 9 3 383 490 1983 0 3 8 56 100 90 79 56 46 25 5 4 371 472 1984 4 4 11 63 105 102 93 70 38 19 2 1 408 512 1985 1 3 14 54 96 112 102 93 38 19 2 6 441 540

Notes: Evaporation was calculated a) for months V-X – with the HWB-2 method, b) for months XI-IV – as the potential evaporation.

280 Annex

Annex 2.7 Evaporation (mm) from grassland as calculated by the HWB-2 method, Podmoskovnaja water balance station, Log (ravine) Polevoy

I II III IV V VI VII VIII IX X XI XII V-IX Year 1970 1 3 13 52 99 96 67 38 24 17 5 0 324 415 1971 3 2 8 52 105 84 68 49 25 17 6 1 331 420 1972 1 4 12 51 90 98 88 75 28 20 6 8 379 481 1973 2 5 19 50 93 105 90 68 40 21 9 2 396 504 1974 2 8 18 64 81 121 89 62 11 38 6 2 364 502 1975 0 2 14 56 85 88 92 61 79 24 5 2 405 508 1976 2 3 16 60 73 94 91 68 39 13 6 3 365 468 1977 0 2 20 56 84 99 72 56 24 21 7 2 335 443 1978 1 4 20 57 84 95 82 48 27 22 10 0 336 450 1979 2 3 13 57 118 87 63 53 33 22 5 1 354 457 1980 0 5 12 56 76 118 112 55 36 23 6 1 397 500 1981 0 5 11 55 105 106 72 57 32 25 7 2 372 477 1982 1 3 21 53 92 112 99 68 47 17 9 3 418 525 1983 0 3 8 56 95 76 74 51 39 25 5 4 335 436 1984 4 4 11 63 105 101 92 71 42 19 2 1 411 515 1985 1 3 14 54 95 95 94 74 31 19 2 6 389 488 Notes: Evaporation was calculated a) for months V-X – with the HWB-2 method, b) for months XI-IV – as the potential evaporation.

281 Forest hydrology – results of research in Germany and Russia

Annex 2.8 Precipitation (P, mm) and evapotranspiration (E, mm) calculated by the water budget (with observation data at the Streletskaja Step', Kursk Region)

Forested area Virgin land Year Soil layer Period Р Е Period Р Е 0 – 100 435 345 1947 0 – 200 11/IV-2/X 291 530 10/IV-1/X 291 353 0 – 350 682 370 0 – 100 538 482 1948 0 – 200 12/IV-2/X 366 687 10/IV-1/X 364 529 0 – 350 776 555 0 – 100 14/IV- 500 406 1949 0 – 200 295 13/IV-21/IX 296 22/IX 566 423 0 – 350 604 425 0 – 100 473 386 1950 0 – 200 14/IV-4/X 317 498 6/V-7/X 328 437 0 – 350 553 468 0 – 100 523 - 1951 0 – 200 16/IV-15/IX 360 588 7/IV-18/IX 392 454 0 – 350 588 451 0 – 100 429 421 1952 0 – 200 21/IV-4/X 277 574 22/IV-22/IX 260 440 0 – 350 625 440 0 – 100 403 457 1953 0 – 200 13/IV-5/X 190 538 10/IV-3/X 281 485 0 – 350 610 512 Notes: 1. The water balance observations were made at a forest stand and on virgin land. 2. The basic soil water properties are as follows:

Forest stand Virgin land Layer (cm) FC SM WP FC SM 0 – 100 353 300 165 360 315 0 – 200 664 520 290 675 540 0 – 300 1093 820 440 1090 825

FC = field capacity SM = specific (capillary tension) soil moisture WP = wilting point of the soil

282 Annex

Annex 2.9 Ratio of the evapotranspiration (α) from forested and unforested areas in the Medvenka catchment for the growing season (V-IX) by various moisture years (JV-X) from data obtained at the Podmoskovnaja water balance station

May to September All the year round Year JV-IX forest field α forest field α 1970 391 324 1.21 482 415 1.16 3.27 1971 384 331 1.16 473 420 1.13 2.77 1972 392 379 1.03 494 481 1.03 3.18 1973 376 396 0.95 484 504 0.96 2.97 1974 382 364 1.05 520 502 1.04 3.24 1975 388 405 0.96 491 508 0.97 2.61 1976 390 365 1.07 489 468 1.05 1.49 1977 354 335 1.06 462 443 1.04 1.73 1978 358 336 1.07 472 450 1.05 1.51 1979 380 354 1.07 483 457 1.06 3.87 1980 417 397 1.05 520 500 1.04 1.10 1981 414 372 1.11 519 477 1.09 3.25 1982 383 418 0.92 490 525 0.93 1.60 1983 371 335 1.11 472 436 1.08 6.64 1984 408 411 0.99 512 515 0.99 - 1985 441 389 1.13 540 488 1.11 2.20 Average 389 369 1.06 494 474 1.04 -

Note: Evaporation from forested and unforested areas was calculated by the HWB-2 method (from data obtained on grassland and forested water balance plots)

283

References

Andrejanov, V.G. and Babkin, V.I. (1974) Method of calculating evapotranspiration from a river watershed for short-term time intervals of several years. (In Russian). Trans. of SHI, 217, 70-92. Bratsev, S.A. (1979) Hydrological role of forest in the conditions of the Komi Republic. (In Russian). Bull. of the Russian Academy of Science, Geogr. Series, 6, 45-56. Budyko, M.I. (1956) Heat balance of the Earth surfaces. (In Russian). Leningrad, Gidometeoizdat, 256 pp. Budyko, M.I. and Zubenok, L.I. (1961) Evaluating the evapotranspiration from land surface. (In Russian). Bull. of the Russian Academy of Science, Geogr. Series, 6, 3-17. Delfs, J. (1958) Der Einfluss des Waldes und des Kahlschlages auf den Abflussvorgang, den Wasserhaushalt und den Bodenabtrag. Mitteil. Niedersächs. Landesforstwaltung, Hannover, 3. Dubov, A.S., Bykova, L.P. and Marunich, S.V. (1978) Turbulence within vegetation cover. (In Russian). Leningrad, Gidrometeoizdat, 180 pp. Fedorov, S.F. and Marunich, S.V. (1985) Hydrological role of forest. (In Russian). Obninsk, 42 pp. Forsman, A. and Waldenström, A. (1975) Effect of clear-cutting in forest on snow accumulation. Nord. Inst. Hydrol. Decade Rept., 8, 18-19. Golubev, V.S. (1962) Estimating the accuracy of precipitation measurements with Tretjakov's rain gauges. (In Russian). Trans. of the SHI, 95, 4-13. Guide for the Russian hydro-meteorological service for the correction of measured precipitation (1969). (In Russian). Leningrad, Gidrometeoizdat, 64 pp. Guidebook for the Regional Hydro-meteorological Services No. 89. (1974) (In Russian). Leningrad, Gidometeoizdat, 96 pp. Hilmy, G.F. (1957) Theoretical bio-geophysics. (In Russian). Moscow, 220 pp. Idzon, P.F. (1986) Influence of forest on water resources. (In Russian). Moscow, Nauka, 168 pp. Kharitonov, L.P. (1977) Model for the throughfall calculation under mixed stands. (In Russian). Forest science, 1, 77-83. Kittredge, J. (1951) Impact of forest on climate, soil and water regimes. (Quoted from Russian edition: Moscow, 456 pp). Kuzmin, P.P. (1960) Formation of snow cover and the methods for evaluation of the snow water storage. (In Russian). Leningrad, Gidrometeoizdat, 172 pp. Leyton, L. and Rodda, J. (1970) Forest and precipitation. (In Russian). In: Reports of foreign scientists at the International FAO Symposium on the Influence of forest on the environment, Moscow, 3-20. Leonard, R.E. (1967) Mathematical theory of interception. In: Sopper, W.E. and Lull, H.W (Eds.) Proc. of Intl Symp. on Forest hydrology, Oxford, UK, 131-136.

285 Forest hydrology – results of research in Germany and Russia

Linsley, R.K., Kohler, M.A. and Paulhus, D.L.H. (1962) Applied hydrology. (Quoted from Russian edition: Leningrad, Gidrometeoizdat, 778 pp). Mendel, O. (1993) Precipitation interception of forest plant. Modelling the hydrological cycle of forested catchments. (In Russian). In: Results of research after international geophysical projects, Moscow, The National Geophysical Committee of Russian Academy of Science, 37-47. Mendel, O., Miklanek, P. and Koniček, A. (1993) Modelling the potential and actual evapotranspiration. (In Russian). In: Results of research after international geophysical projects, Moscow, The National Geophysical Committee of Russian Academy of Science, 86-93. Merriam, R.A. (1960) A note on the interception loss equation. J. Geophys. Res., 65 (11). Mikhovich, A.I. (1981) Water-protective forest plants. (In Russian). Kharkov, Prapor, 64 pp. Molchanov, A.A. (1960) Hydrological role of forest. (In Russian). Moscow, Russian Academy of Science Publishing House, 488 pp. Monteith, J.I. (1965) Evaporation and environment. In: Frogg, G.E. (Ed.) The state and movement of water in living organisms. Symp. of Soc. Experimental Biol., Academic press, 19, 205-234. Morozov, G.F. (1959) A treatise on the forest. (In Russian). Moscow, Goslesbumizdat, 456 pp. Oke, T.R. (1977) Boundary layer climates. London, Methuen & Co Ltd, Halsted Press Book, John Wiley & Sons, New York, 359 pp. Osukh, B. (1993) Precipitation interception on an example of a forested watershed. (In Russian). In: Results of research after international geophysical projects. Moscow, National Geophysical Committee of Russian Academy of Science, 47-55. Penman, H.L. (1968) Vegetation and water. (Quoted from Russian edition, Leningrad, Gidrometeoizdat, 162 pp). Polster, H. (1954) Gesichertes und Ungesichertes über den Wasserhaushalt des Waldes. Forst und Jagd, 7/3 (4). Rauner, Yu.L. (1972) Heat balance of vegetation cover. (In Russian). Leningrad, Gidrometeoizdat, 206 pp. Rakhmanov, V.V. (1981) Forest hydrology. (In Russian). Results of Science and Technique, Forest Science Series, Moscow, VINITI, 1982 pp. Rakhmanov, V.V. (1970) Dependence of river runoff on watersheds' forestage. (In Russian). In: Reports of Soviet scientists at the International FAO Symposium on The influence of forest on the environment, Moscow, 1, 60-78. Rode, A.A. (1965) Fundamentals on soil water, vol. 1. (In Russian). Leningrad, Gidrometeoizdat, 664 pp. Rutter, A.I. (1967) An analysis of evaporation from a stand of Scotch pine. In: Sopper, W.E. and Lull, H.W. (Eds.), Proc. of Intl Symp. on Forest hydrology, Oxford, UK, 404-417. Reference book on the USSR climate, 3rd issue (1965). (In Russian). Leningrad, Gidrometeoizdat, 520 pp. Turc, L. (1955) Le bilan d'eau des sols. (Quoted from Russian edition). Institut national de la recherché agronomique, Versailles. Tuebes, C. and Ouryvaev, V. (Eds.) (1971) Representative and experimental basins. An international guide for research and practice. (In Russian). UNESCO, Leningrad, Gidrometeoizdat, 428 pp.

286 References

Voronkov, N.A. (1973) Water cycle and water consumption of pine stands. (In Russian). Moscow, Timber Industry Publ., 184 pp. Zinke, P.Y. (1967) Forest interception studies in the United States. In: Sopper, W.E. and Lull, H.W. (Eds), Proc. of Intl. Symp. on Forest Hydrology, Oxford, UK, 137-161.

287

List of figures

1.1 Dependence of underestimation of precipitation (∆P%) measured by Tretjakov rain gauge upon wind velocity (U m/s) on an open area and on the glades of different sizes …………………….…………………………... 143 1.2 Difference between snow storage (∆P%) on indicatory plots, on glades and on felled areas depending on the shading rate (angle, α°) in the centres of the glades ……………………………………………....…………………... 145 1.3 Difference between solid precipitation (∆P%) measured by Tretjakov precipitation gauges from 16 November 1973 to 31 January 1974, on indicatory plots and on glades depending on the shading rates (angle, α°) in the centres of the glades ……………………………………………………. 146 1.4 Relationship between the amount of liquid precipitation intercepted by the canopy (ΔP) and total precipitation on the glade (P) ….…………………… 149

1.5 Ratio of annual precipitation "forest to open land" (Kforest) dependent on the radiative index of dryness (JV-IX) …………………………………………... 156 1.6 Total annual precipitation (mm) (data from the Valday meteorological station), 1926-2001 ……………………...…………………………………. 160 1.7 Total annual precipitation (mm) in the Usadievsky field catchment, 1948- 2001 ………………………………………………….……………………... 160 1.8 Total annual precipitation (mm) in Taezhny forest catchment area, 1948- 2001 ………………………………………………………………………… 161 1.9 Differences (mm) between the total annual precipitation in the Taezhny and Usadievsky catchments ………………………………………………... 162 1.10 Differences (in mm) between the total precipitation in Taezhny and Usadie for the warm (1) and cold (2) seasons ...……………………………………. 162 1.11 Change in mean annual air temperature 1926-2000 (°C) (data of the Valday meteorological station) …………….…………………………………….. 163 1.12 Total annual precipitation (mm) 1950-1981 for the field (row 1) and for the forest (row 2) (data from Podmoskovnaya water balance station) …….…... 164 1.13 Differences in annual precipitation (mm) on forest and field at the Podmoskovnaya water balance station ……………...……………………... 164 1.14 Differences (mm) in precipitation between forest and field over the year (data obtained at Podmoskovnaya water balance station) …………………. 165

2.1 E0 = f ()d for the zone of coniferous forests on a monthly basis .………… 181

2.2 Dependence of evapotranspiration (mm) from the forest (E3) on soil moisture content (J) during May-September ………………………………. 191

289 Forest hydrology – results of research in Germany and Russia

2.3 Ratio of evapotranspiration to potential evapotranspiration (E/Eo) versus forest age (years) …………………………………………………………… 191

2.4 Dependence, E1 = f ()JV −IX , of evapotranspiration (mm) under the forest canopy (∑E1) on the moistening parameter (JV-IX) ………..……………….. 194

2.5 The E1/E =f(JV-IX) ratio for the Taezhny small experimental catchment ..….. 194 2.6 Evaporation ratio of intercepted precipitation to potential evapotranspiration (E2/E0) under the forest canopy in the Taezhny small experimental catchment versus the moistening parameter (J) (May-September) …..……. 198 2.7 Evaporation ratio of intercepted precipitation to potential evapotranspiration (E2/E0) (May-September) versus the amount of green mass of a single tree (kg) for (a) a birch and (b) a spruce …………………………..……….. 206 2.8 Relationship between transpiration (T) in the period May-September and annual spruce increment (I) (Taezhny catchment) …………………………. 207

2.9 Transpiration (E3) (mm) versus the moistening parameter J (age class III of the tree stand) ………………………………………………………………. 211

2.10 Transpiration (E3) (mm) versus the moistening parameter J (age class IV of the tree stand) ………………………………………………………………. 211 2.11 Variations in monthly values of evaporation components from the forest

(mm) ( E = evapotranspiration; E1 = evaporation under the canopy; E2 =

evaporation of intercepted precipitation; E3 = transpiration) ……………... 216

2.12 Evapotranspiration factor (β) dependent on index of dryness (JV-VIII) ……... 221

3.1 Soil moisture content distribution (mm) in 1972 in the Usadievsky (row 1) and Taezhny (row 2) catchments …...………………….…………………... 227 3.2 Soil moisture content distribution (mm) in 1962 in the Usadievsky (row 1) and Taezhny (row 2) catchments …………....……………………………... 228 3.3 Mean monthly soil moisture content (mm) in the soil layer 1 m deep during a year in the Usadievsky (row 1) and Taezhny (row 2) catchments ….……. 228 3.4 Mean monthly soil moisture content (mm) in the top soil 1 m deep during the period May-August in the Usadievsky (row 1) and Taezhny (row 2) catchments ………………………………………………….………………. 229

4.1 Graphs of low-flow coefficient (η) (VI-IX) for particular years versus the parameter of moistening (J); these graphs are plotted for two catchments with different forest coverage but with other relatively similar physiographic features. I = River Luga (59%), II = River Lovat' (40%) …………. 236 4.2 Annual runoff depth variations (mm) in the Usadievsky catchment …..…... 250 4.3 Annual runoff depth variations (mm) in the Taezhny catchment …..……… 250 4.4 Annual runoff depth variations (mm) in the Polomet' river basin at Dvorets 251 4.5 Changes in precipitation (mm) observed at the Valday meteorological station ………………………………………………………………………. 251

290 List of figures

5.1 Change in evaporation (mm) from the surfaces of felled areas as dependent on the shading rate (angle, α°) ………………………………...…………… 256 5.2 Relative evapotranspiration values (VI-IX) for a long-term cycle of spruce forest evolution (1) and the green biomass (Vt/ha) (2) according to forest age (years) ………………………………………………………………….. 258 5.3 Daily net radiation balance across a felling area (diameter d(m)) divided by the radiation value measured at the centre of the felling area (R/Rc)………. 259 5.4 Changes in the moisture content and groundwater table (∆W%) in felling areas depending on the shading rate (angle, α°) (Berezaika river catchment) 261 5.5 Changing of runoff coefficient (η) for 1) a felling area and small catchments (Valday data) and 2) river catchments (S.A.Bratsev data) vs forest age (years) …..……………………………………………………………… 262 5.6 Dependence of latent heat (LE), turbulent heat flux (P), heat flux within soil (B) and the net radiation (R) (7 hour sum) on the shading rate of the felled area (α°) ………….………………………………………………….. 270 5.7 Ratios (dimensionless) of the latent heat (LE), turbulent heat flux (P) and heat flux within soil (B), as divided by the net radiation (R), on forest age (years) ……………………………………………………………………… 270

291

List of tables

1.1 Deviations of precipitation measured above the forest, in per cent of precipitation measured in glades (ΔP), depending on wind velocity above the forest ……………………………………………………………………. 142 1.2 Amount of solid precipitation (mm) in deciduous forest and in the glades based on the data of snow course surveys and snow measurements using the Tretjakov precipitation gauge in late winter …………………………… 144 1.3 Solid precipitation (mm) measured in glades in a coniferous forest and on an indicatory plot (in a deciduous forest) between 26 November 1973 and 31 January 1974 (snow survey data) ……………………………………….. 145 1.4 Solid precipitation measured with precipitation gauges in glades in a coniferous forest and on the indicatory plot in a deciduous forest, and the difference between these measurements …………………………………… 147 1.5 Characteristics of the plots where precipitation measurements under the canopy were made ………………………………………………………….. 148 1.6 Interception of liquid precipitation (%) by spruce trees for the warm season (April-October) depending on the depth of rainfall ………………………... 150 1.7 Interception of precipitation by different tree species at Istrinsky base station ………………………………………………………………………. 150 1.8 Frequency of deviations between total precipitation calculated by the Hilmy formula and precipitation measured under the forest canopy during individual rainfalls …………………………………………………………. 152 1.9 Quantity of the measured and corrected precipitation (mm) using standard precipitation gauges in the Usadievsky catchment and on the experimental plot for precipitation measurements (May-October 1989-1995) …………... 154 1.10 Quantity of solid precipitation (mm) in the catchment and on the plots with zero balance of snow transfer ………………………………………………. 154 1.11 Quantity of solid precipitation on the indicatory plot in the forest and in the field catchment ……………………………………………………………... 155

1.12 Coefficient of the impact of forest (Kforest) on precipitation on the basis of data from coupled (forest and field) catchments (for total precipitation from May to October) ……………………………………………………………. 157 1.13 Coefficient of the impact of forest on precipitation based on data from Valday, water balance stations and permanent stations in forest catchments 158 1.14 Changes of the mean annual air temperature (ToC) and precipitation (P, mm) over decades by the data of Valday Branch of the SHI and Water Balance Stations (WBS) …………………………………………………… 163

293 Forest hydrology – results of research in Germany and Russia

2.1 Evaporation (mm) from forest, calculated by 1) the heat balance method, 2) the water balance method and 3) the complex method …………………….. 172 2.2 Evaporation (mm) from a spruce forest calculated by the heat balance method (HB) and by the method based on the use of similarity theory (ST) for 10-day periods ………………………………………………………….. 174 2.3 Evapotranspiration (mm) from a mature spruce forest calculated by different methods (data from the experimental plot of the VB of the SHI) ... 176 2.4 Monthly values of potential evapotranspiration (mm) from a forest computed by 1) the heat balance method and 2) the graphs of relations between mean monthly potential evapotranspiration and conventional deficit of air humidity ……………………………………………………… 180

2.5 Transition coefficient values (K0 ) …………………………………………. 181 2.6 Monthly potential evapotranspiration (mm) from the forest computed by the heat balance method (1) and calculated by the graphs of mean monthly potential evapotranspiration versus a conventional deficit of air humidity (2) …………………………………………………………………...……… 182 2.7 Values of γ (%) depending on the modulus coefficient of evaporation th from the i plot ( Ei / E ) and its percentage of the area occupied by this

plot ( fi ), according to P.P. Kuzmin [1960] ………………………………... 184 2.8 Soil moisture content (mm) in the 0 to 50 cm soil layer in the Usadievsky small experimental catchment ……………………………………………… 186 2.9 Mean-root-square error of evaporation measurements by two evaporimeters ………………………………………………………………. 187 2.10 Monthly evaporation (mm) from different kinds of farmland as mean values based on observations by a group (cluster) of evaporimeters ………. 188

2.11 Evapotranspiration (E) and potential evapotranspiration (E0) from the forest during the warm season and during the year (Taezhny small experimental catchment) …………………………………………………… 190 2.12 Mean evapotranspiration and its components (mm) for age classes III and IV of a spruce forest during May-September ………………………………. 192

2.13 Evaporation (mm) under the forest canopy ( E1 ), potential

evapotranspiration (mm) above the forest ( E0 ) and K1 = E1 / E0 …………. 196

2.14 Coefficient of parts of potential evapotranspiration of the forest (K1) and radiation aridity index (JV-IX) for May-September …………………………. 197

2.15 Results of the computation of evaporation of intercepted precipitation ( E2 ) during the period May-September …………………………………………. 199 2.16 Annual transpiration (mm) from forested areas obtained by different methods …………………………………………………………………….. 202 2.17 Transpiration coefficients for different tree species ……………………….. 202 2.18 Mean daily transpiration (mm) during a month estimated from evaporimeters 3 m² in surface area with different tree species …………….. 204 2.19 Ratio between transpiration and evapotranspiration for spruce and birch …. 204

294 List of tables

2.20 Transpiration of a single birch and spruce and the weight of fresh leaves/ needles, according to observations made in forest hydraulic evaporimeters . 204 2.21 Transpiration (mm) and radius increment of spruce stem (mm) …………... 205

2.22 Coefficients of transpiration activity ( KT ) for pine and birch plantings …... 207

2.23 Coefficients of transpiration activity ( KT ) for foxberry pine forest ……...... 208

2.24 Coefficients of transpiration activity ( KT ) for spruce stand ……………….. 208 2.25 Monthly transpiration (mm) of spruce during the period May-September … 210

2.26 Transpiration ( E3 ) (mm) of spruce stand according to calculated and

observed evaporation under the forest canopy ( E1 ) and evaporation of

intercepted precipitation ( E2 ) ……………………………………………… 213 2.27 Transpiration (mm) of forest plants using Penman's method (1) and an indirect method (2) …………………………………………………………. 214 2.28 Evapotranspiration of the forest ( E ), evaporation under the forest canopy

( E1 ), evaporation of precipitation intercepted by tree crowns ( E2 ) and

transpiration ( E3 ) (mm) ………………………………...………………….. 215 2.29 Evapotranspiration and its components for May-September during wet and dry seasons …………………………………………………………………. 217 2.30 Components of evapotranspiration (mm) in May-September (1955-1999) ... 217 2.31 Evapotranspiration components for different age classes of forest ………... 218 2.32 Evapotranspiration (mm) from forest and open land (Valday) and Budyko's radiative index of dryness (J = R/LP) ……………………………………… 220

3.1 Soil hydrological parameters ………………………………………………. 224 3.2 Typical moisture content (mm) in loamy soils (cm) at the end of a season in field and forested catchments (Valday) ………………………………..... 225 3.3 Critical water storages in several catchments (mm) ……………………….. 227

4.1 Characteristics of runoff with different forest coverage …………………… 235 4.2 Probability of exceedance, equality and decrease of runoff in rivers flowing in the forested areas compared to those flowing in areas with poor forest coverage according to the zones of water availability (except karst basins) (according to P.F. Idzon, [1986]) ..…………………………………………. 237 4.3 Water balance components (mm) of the experimental catchments of the VB of the SHI …………………………………………...……………………… 238 4.4 Water balance components (mm) for the Luga and Shelon' river basins (1970-1979) ………………………………………………………………… 239 4.5 Mean annual values of the water balance components of the catchments with different percentages of forest coverage from 1970 to 1979 (data from the Bolkhovskaya water balance station) …………………………………... 240 4.6 Characteristics of flood runoff and water content in soils and subsoils in the catchment during spring snowmelt floods …………………………………. 242

295 Forest hydrology – results of research in Germany and Russia

4.7 Daily runoff layers (Y1) in the Taezhny catchment during flood rise ( R ) and flood recession ( F ) …..……………………………………………………. 243 4.8 Runoff characteristics during rainfall floods and parameters of soil moisture content ……………………………………………………………. 244

4.9 Daily runoff values (Y0 , mm) for soil moisture content close to Wcr values . 245

4.10 Daily runoff (Y1, mm) at soil moisture content close to the LC values in the summer period ……………………………………………………………... 245

4.11 Daily runoff (Y1, mm) at soil moisture content close to the LC value in winter time …………………………………………………………………. 246 4.12 Results of checking maximum discharge computations in the Polomet' river at Dvorets ( A = 432 km²) …...……………………………………….. 247 4.13 Basic data for plotting graphs of the relationship between ρχ and

3 3 ( qm ⋅10 − q0 ⋅10 ) for the river Polomet' at Dvorets …...……………… 248

5.1 Changes in the quantity of solid precipitation depending on the degree of shading in the cut area ……………………………………………………… 254 5.2 Evaporation (mm) from soil in felled areas of different sizes ……..……..... 255 5.3 Evapotranspiration (mm) from soil with individual pines in felled areas differing in size (Berezaika catchment, 1980) ……………………………... 256 5.4 Evapotranspiration (mm) from a spruce forest and from felled areas of different ages, based on the data of heat balance measurements in the Lake Edrovskoe drainage area .…………………………………………………... 257 5.5 Evapotranspiration (mm) from a pine forest and from felled areas of different ages, according to observation data in the Berezaika catchment from 11 May to 25 September 1979 .………………………………………. 258 5.6 Evapotranspiration (mm) from felled areas and from a spruce forest of different ages, according to data of the VB of the SHI for May-September . 258 5.7 Differences in water tables (cm) in the forest and in felling areas (1974- 1979) Lake Edrovskoe drainage area ………………………………………. 261 5.8 Water balance components (mm) during the hydrological year: 1. Elovy experimental watershed; 2. Water balance plot No. 4; 3. Felling area No. 3 263 5.9 Daily variations in albedo (in %) of different surfaces (August 1979) .……. 265 5.10 Mean density of soils and subsoils (g/cm³)in the Siniaya Gnilka catchment 265 5.11 Characteristics of runoff during spring snowmelt flood for the 1) Siniaya Gnilka catchment, 2) Usadievsky catchment and 3) Taezhny catchment ….. 266 5.12 Annual runoff (mm) from 1) Siniaya Gnilka with a forest plantation and 2) the Usadievsky field catchment ……………………………………………. 267 5.13 Evaporation (mm) during May-September ………………………………… 268 5.14 Heat balance components for a mature spruce forest (Taezhny catchment) for the warm season in 1984 (daily mean values in cal/cm² per 1 min.) ...… 269 5.15 Heat balance components in forest plots (daily mean values for June and July, in cal/cm² per min.) ..…………………………………………………. 269

296 List of tables

5.16 Mean monthly albedo values of the spruce forest ………………………….. 271

Annex

1.1 Long-term precipitation throughfall (Р1, mm) and the interception in mm (Р2) and in percent (Р3) …………………………………………………….. 275 2.1 Long-term mean values of the radiative index of dryness (J in MJ) for May to September in the European part of Russia ………………………………. 276 2.2 Evapotranspiration totals (mm) for several periods of the year (1955-1999) (Tajezhny forest catchment) ………………………………………………... 276 2.3 Evapotranspiration totals (mm) for several periods of the year (1955-1999) (Usadjevsky grassland catchment) ………………………...……………….. 278 2.4 Monthly evapotranspiration (mm) from forested and grassland areas (Veliko-Anadol'skaja water balance station) ………………….…………… 279 2.5 Monthly evapotranspiration (mm) at Streletskaja Step' (grassland area) and at Duboshino (forested area) ……………………………………………….. 280 2.6 Evapotranspiration (mm) from forest as calculated by the HWB-2 method, Podmoskovnaja water balance station, forested plot, αf =100% …………… 280 2.7 Evaporation (mm) from grassland as calculated by the HWB-2 method, Podmoskovnaja water balance station, Log (ravine) Polevoy ……………... 281 2.8 Precipitation (P, mm) and evapotranspiration (E, mm) calculated by the water budget (with observation data at the Streletskaja Step', Kursk Region) ……………………………………………………………………... 282 2.9 Ratio of the evapotranspiration (α) from forested and unforested areas in the Medvenka catchment for the growing season (V-IX) by various moisture years (JV-X) from data obtained at the Podmoskovnaja water balance station ……………………………………………………………… 283

297

Publications in the series IHP/HWRP-Berichte

The German contribution to the International Hydrological Programme (IHP) of UNESCO and to the Hydrology and Water Resources Programme (HWRP) of WMO is coordinated by one national committee. Both programmes in Germany are managed by the IHP/HWRP Secretariat. Scientific papers originating from the work of the German IHP/HWRP National Committee are published in the series IHP/HWRP-Berichte under ISSN 1614-1180.

Heft 1 Problematik der Wasserbewirtschaftung der Insel Föhr F. Steinmann und H. Ketelsen, 74 pp, Koblenz 2004 Heft 2 Studies in Mountain Hydrology Out of print Edited by Andreas Herrmann and Ulrich Schröder, 104 pp, Koblenz 2004 Heft 3 Value of Water – Different Approaches in Transboundary Water Management German IHP/HWRP National Committee, 128 pp, Koblenz 2005 Heft 4 Runoff from Nepalese Headwater Catchments – Measurements and Modelling M. Konz, L.N. Braun, W. Grabs, A. Shrestha and S. Uhlenbrook, 166 pp, Koblenz 2006 Heft 5 Irrigation control: towards a new solution of an old problem G.H. Schmitz, N. Schütze and T. Wöhling, 224 pp, Koblenz 2007 Heft 6 Forest hydrology – results of research in Germany and Russia B. Beudert, B. Klöcking, Benjamin Marcq, Jörg Niederberger, H. Puhlmann, R. Schwarze, K.-H. von Wilpert, S.F. Federov and S.V. Marunich (dec.), 308 pp, Koblenz 2007

299

IHP/HWRP - BERICHTE

HEFT 6 KOBLENZ 2007

Forest hydrology – results of research in Germany and Russia Forest hydrology – results of research in Germany and Russia

AUS DER ARBEIT DES HEFT 6/2007 HEFT DEUTSCHEN IHP/HWRP - NATIONALKOMITEES

ISSN 1614-1180 IHP – INTERNATIONAL HYDROLOGICAL PROGRAMME OF UNESCO HWRP – HYDROLOGY AND WATER RESOURCES PROGRAMME OF WMO IHP/HWRP - BERICHTE