Rhine basin study: Land use projections based on biophysical and socio-economic analyses
Volume 1. Biophysical classification as a general framework
R.P. Rotter
Report 85.1
DLO Winand Staring Centre/Rijkswaterstaat Institute for Inland Water Management and Waste Water Treatment Wageningen/Lelystad (The Netherlands), 1994
-•• :J ABSTRACT
Rotter,R.P. , 1994.Rhine basin study: Land useprojections based on biophysical and socio-econ omic analyses. Volume 1.Biophysical classification as a general framework. Wageningen (The Netherlands), DLO Winand Staring Centre. Report 85.1; 106 pp.; 8 Figs; 15 Tables; 100 Refs; 7 Annexes; 9 Maps.
Possible future land use for the Rhine basin, as aresul t of climatic change and autonomous socio economic developments, was studied. A biophysical classification system was developed. Bioclimatic types and soil groups were overlaid, resulting in biophysical land types. Regression analysis of weather records yielded relationships between geographical position and temperature characteristics needed for creating current and future temperature data surfaces. Each grid cell was characterized by key soil and climatic attributes and assigned to a specific biophysical land type. Output includes temperature maps, asoi l grouping map, aprecipitatio n map, land suitability maps, and a map with the subregions distinguished.
Keywords: agroclimate,bioclimati c type,biophysica l landtype ,climati c change,climati c variable, crop cultivation, geographical information system, land use potential, regression analysis, soil
ISSN 0927-4537
©1994 DLO Winand Staring Centre for Integrated Land, Soil and Water Research (SC-DLO), P.O. Box 125, NL-6700 AC Wageningen (The Netherlands). Phone; 31 837074200; fax: 31 837024812. Rijkswaterstaat RIZA, P.O. Box 17, 8200 AA Lelystad (The Netherlands) Phone: 31 320070411; fax: 31 320049218.
The DLO Winand Staring Centre is continuing the research of: Institute for Land and Water Management Research (ICW), Institute for Pesticide Research, Environment Division (IOB), Dorschkamp Research Institute for Forestry and Landscape Planning, Division of Landscape Planning (LB), and Soil Survey Institute (STIBOKA).
No part of this publication may be reproduced or published in any form or by any means, or stored in a data base or retrieval system, without the written permission of the DLO Winand Staring Centre and RIZA.
Project 7343 [rep85-l.hm/12-94] Contents
page
Preface 9
Summary 11
1 Introduction 13 1.1 Background and objectives 13 1.2 The problems and priorities of the biophysical impact study 14 1.3 Outline of the report and reader's guide to the biophysical parts 16 2 Geographic description of the Rhine basin 17 2.1 Geographic situation 17 2.2 The main regions and subregions 17
3 Literature review on prevailing land use patterns and biophysical condi tions 21 3.1 Overview on broad land use patterns in the Rhine basin 21 3.2 Description of present-day biophysical conditions in the various regions 22 3.2.1 Agroclimate 22 3.2.2 Soils 24 3.2.3 Altitudinal zonation and vegetation belts 27 3.3 Biophysical classification - conclusions from the literature review 29
4 The new biophysical classification system 31 4.1 General approach 31 4.1.1 Bioclimatic classification 31 4.1.2 Soil and terrain suitability classification 32 4.1.3 GIS techniques 32 4.2 Data 32 4.2.1 Point data 32 4.2.2 Spatial data 35 4.3 Methods 35 4.3.1 Bioclimatic analysis 35 4.3.1.1 Choice of climatic variables 35 4.3.1.2 Regression analysis of climatic variables from selected weather stations 40 4.3.2 Soil and terrain suitability assessment 41 4.3.3 Definition of bioclimatic types 42 4.3.4 Creation of climatic data surfaces and bioclimatic maps using GIS techniques 42 4.3.5 Creation of biophysical land types 43 4.4 Results 43 4.4.1 Results of regression analysis on climatic variables 43 4.4.2 The bioclimatic classification system 45 4.4.3 The biophysical classification system 47 4.4.4 Summary of approach chosen and the major results 50 4.5 Discussion and perspectives 50 4.5.1 Reasons to focus on GIS 50 4.5.2 Interpretation of climatic regression analysis 51 4.5.3 Advantages provided by the classification system 53 4.5.4 GIS input\output: quality aspects 54
References 55
Tables 2.1 Long term mean summer (SP) and winter (WP) precipitation and ratio SP/WPan d annual temperature amplitude (between coldest and warmest month) for selected weather stations inth eRhin e basin. Summer includes themonth sJune ,Jul y andAugust ;winte rinclude s December, January and February. 19 3.1 Composition and characteristics of the major soil groups and subgroups in the Rhine basin (mapping units of CHR/KHR soil map) 25 3.2 Definitions of texture and slope class used in codes of soil subgroups (mapping units of CHR/KHR soil map) 26 3.3 Generalized altitudinal zonation, vegetation and crops (after Ellenberg, 1982; Ehrendorfer, 1983; Thran & Broekhuizen, 1965) 27 4.1 Results ofregressio n analyses climatic variables/indices onaltitud e X( m a.s.1.) (n=40) 43 4.2 Results of regression analyses using other independent variables (units of X are °C, geogr. project ° (i.e. °long.E, °lat.N; ' in decimals) and m a.s.1.) 44 4.3 Regression ofmea n annual temperature onaltitud e and onth erati o (4000- altitude) to latitude (based on 53 data sets) 44 4.4 Class definitions at level 1( = mean annual temperature) 46 4.5 Class definitions at level 2 (= mean annual temperature amplitude) 46 4.6 Class definitions at level 3 (= mean temperature of coldest month, January) 46 4.7 Class definitions at level 4 (= mean annual total precipitation) 46 4.8 Possiblebioclimati c maintype s (18)an dgenera l occurrenceunde r current conditions per main geographic region of the Rhine basin (some spots, e.g. L*m in region 1 and H m in region 3, are not considered) 47 4.9 Soil suitability grouping (based on soil mapping units by CHR/KHR 1978) 48 4.10a Thete nbioclimati c maintype s withth ehighes tare acoverag ei nth eRhin e basin 49 4.10b Predominantbiophysica l landtype s inth eRhin eBasi nunde rbaselin e and future climates (scenario BAU-best) 49
Figures 3.1 Altitudinal limits and georgraphic distribution of forest types (source: Boer & Koster, 1992, p.143: Schematic cross section through the present natural zonation of vegetation in Central Europe (after Ellenberg, 1978)) 28 3.2 Seasonal fluctuations of altitudinal temperature gradient (source:Ozenda , 1987, p.27) 28 4.1a 'Environmental' data space: 40 weather stations characterized by mean annual temperature and precipitation 33 4.1b 'Environmental' data space: 53 weather stations (13 Swiss stations plus 40 stations illustrated in Fig. 4.1 a) characterized by mean annual temperature and precipitation 34 4.2a De Kooy: Current and projected future (scenario BAU-best) annual course of mean air temperatures 37 4.2b Osnabrück: Current and projected future (scenario BAU-best) annual course of mean air temperatures 37 4.2c Trier-Petrisberg: Current and projected future (scenario BAU-best) annual course of mean air temperatures 38 4.2d Nürnberg: Current and projected future (scenario BAU-best) annual course of mean air temperatures 38
Annex 4.1 Weather stations for Rhine basin study (excerpt of SC-DLO data base) - final selection indicated 65 4.2 Additional Swiss weather stations 67 4.3a For weather stations used in climatic analyses: Long-term mean monthly values 69 4.3b Temperature diagrams (monthly mean minimum and maximum) for weather stations used in the climatic analyses 81 4.3c Summarized temperature characteristics for selected weather stations in the Rhine basin, representing the three main biophysical regions 89 4.3d Input for climatic regression analysis 91 4.4 Summary of important GIS working steps required for producing the various Rhine Basin overlays 93
Maps 1 Mean annual precipitation 97 2 Soil suitability grouping 98 3 Bioclimatic types (1.level = mean annual temperature), baseline 1961-89 99 4 Bioclimatic types (1.level = mean annual temperature), decade 2040-49 (BAU-best) 100 5 Bioclimatic types (3.level = mean temperature of coldest month), baseline 1961-89 101 6 Bioclimatic types (3.level = mean temperature of coldest month), decades 2040-49 (BAU-best) 102 7 Classification of land suitability, baseline 1961-89 103 8 Classification of land suitability, decades 2040-49 (BAU-best) 104 9 Bioclimatic types unsuitable for forest and/or crops (baseline) and 'statistical' regions 105 Preface
This report has been prepared by the DLO Winand Staring Centre in Wageningen under contract for Rijkswaterstaat RIZA (Institute for Inland Water Management and Waste Water Treatment) in Lelystad. It is a contribution to a large research project of the International Commission of the Hydrology of the Rhine basin (CHR/KHR), initiated in 1989,o n the assessment of the consequences of changes in climate and land use for the discharge regime of the Rhine. Several institutes from the Rhine riparian countries are collaborating in this project. Coordination is in the hands of the CHR. On the Dutch side, work in the project is being undertaken by the Institute for Inland Water Management and Waste Water Treatment (RIZA) and Utrecht University (RUU). RIZA is also responsible for the development of land use 'scenarios', conceived as projections of future land use.Thi s research has been subcontracted toth eDL OWinan d Staring Centre (SC-DLO).
In a preliminary biophysical study, 'Effects of climate change on crop production in the Rhine Basin' (Wolf &Va n Diepen, 1991),als o conducted by SC-DLO, simu lations were done for afe w climate-soil combinations only, by far not representing the diversity of biophysical conditions in the Rhine basin. In the present study both biophysical and socio-economic factors are considered for the development of land use projections. This volume,Volum e 1,present s abiophysica l classification needed for identifying geo-referenced agro-ecological zones serving as a basis for region-wide land use projections under current and future conditions. Volume 2 describes the impact analysis of the possible climate changes on crop suitability and crop productivity, and in effect on land use patterns and water use. Volume 3 deals with impact analysis for forestry. Volume 4 describes the possible impact on land use in the Rhine basin of both biophysical and socio-economic developments, presented in a number of projections for decade 2040-2049.
The idea of largely using information from literature - as formulated in the project proposal - turned out to be inadequate for the objectives of the biophysical part, i.e to come up with spatial (rather than point-specific) results and to make an impact analysisfo r agricultural production onth ebasi s oflan d use 'potentials' under current andfutur e conditions,rathe rtha nt oprovid eexplanation s for actual land useo rland - cover types. As a consequence of the sparse information from the literature, ameaningfu l com bination of applying asuitabl eproces s model andusin greadil y available spatialdat a of the Rhine basin had tob e searched for and established. This took more time than expected, whereby progress was largely delayed byth emoderat equalit y ofth eavail able spatial data. Asa consequence , the study included large modelling and GIS components, inaddi tion to scanning literature.
Anothercomplicatin g factor wasth edynami cnatur eo fth eavailabl eplausibl e climate scenarios. Thetren d in scientific consensus is towards increasingly smaller changes in mean climatic conditions, but greater variability in weather conditions, and increased risks of sudden changes. Wishing to keep in line with the state of the art required extensive literature research.
Kees van Diepen Project leader
Acknowledgements
This report has benefitted from inputs and comments from many colleagues. First of all,th e SC-DLO staff members Jan-Dirk Bulens and Theo van der Heijden, who made the Geographic Information System on the Rhine basin operational, and provided the geographic in- and outputs for this study. The author acknowledges the discussions and comments and materials received from Kees van Diepen, Frank Veeneklaas and Kees Hendriks of SC-DLO, Joost Wolf (WAU), Free de Koning (AB-DLO), Dr. Grabs, Dr. Schädler and Dipl.-Met. Krähe (CHR/KHR, Climate Project), Prof. Bengtsson (MPI, Hamburg), Prof. Flohn (Universität Bonn), Dr. Willems (Universität Stuttgart) and from my colleagues at the Department Land Evaluation Methods, Tamme van der Wal and Sergei Andronikov.
The digitized administrative (NUTS) maps used in this study were supplied by the CORINE project of DGXI of the Commission of the European Communities. The development of the bioclimatic classification andcarryin g outcro p simulations benefitted from the ongoing tests of the Crop Growth Monitoring System (CGMS), which was being built for the MARS project of the Joint Research Centre at Ispra, Italy.
Many thanks toBar t Parmet andMatthij s Raak (RIZA) forthei r support and counsel.
Reimund Rotter
Wageningen, January 1994
10 Summary
As the mainresult , the biophysical classification of theRhin e Basin using GIS tech niques has been established. Each of the more than 20000 grid-cells (9km 2) within the Rhine drainage area is characterized by specific values and the combination of long-term mean annual temperature, annual temperature amplitude, temperature of the coldest month and annual precipitation - the latter not specific, but available as 200 mm intervals between 400 and 1200 mm. Thethre etemperatur e characteristics areavailabl efo r both thebaselin e and possible future climateaccordin g toth euniforml y applied changescenari o 'business-as-usual, best estimate'(BAU-best), obtained from climate modelling for themi d of next cen tury and used as input for crop modelling (Volume 2).
Inadditio n toth eagro-relevan t climaticdata ,eac h grid-cell ischaracterize d byaggre gated soilan d terrain information thatha sbee n screenedfo r relevancet ocro pproduc tion potentials. Moreover, both the geo-referenced soil and climatic information is also of relevance to forestry (Volume 3).
Thecreate d spatialdatabas e allowst o stratify quantitative analysis of crop production potentials and associated water useusin g dynamic crop simulation models (Volume 2). The representativeness of point-specific simulation results can be assessed and information gapsca nb eidentified . Moreover, asth ethre etemperatur e characteristics distinguished allow to reconstruct the shape of themea n annual temperature course, applying of suitable weather generators (Kramer, in prep.; Semenov, in prep.) for filling the gaps is facilitated.
The map output for the Rhine basin shown in this volume represents only part of the established data base. Nine maps are presented (map annex), consisting of a digitized base map (mean annual precipitation) (Map 1), a soil grouping map (2) derived from a digitized soil map, four new temperature maps (mean annual temperature and mean temperature of the coldest month, for current (Maps 3 and 5) and future conditions (Maps 4 and 6),respectively ) based on adigitize d altitude map and regression analysis, two new maps showing land suitability for crop cul tivation under current (Map 7) and future conditions (Map 8), a synthesis of soil and bioclimatic suitability for agriculture, and, finally, map 9, showing the ad ministrative/statistical regions distinguished for further economic analysis (Veeneklaas, et al., this report, Volume 4).
The land with very high and high suitability for crop cultivation under current con ditions (Map 7) is situated in the less elevated, warmer areas of the Rhine Basin, such as parallel to the Upper and Lower Rhine, Neckar and Main, Lorraine, the Muenster chalk basin and in largepart s ofth eNetherlands .Unde r current conditions, these are the areas with the least restrictions in the main factors limiting crop cul tivation, soil/terrain and temperature characteristics.
11 Assuming conditions according to scenario BAU-best for the mid of next century, the total of the area with very high and high suitability for crop cultivation would expand, the area of marginally suitable land would shrink, butth e area of physically unsuitable land would hardly change (Maps 7 and 8). As the expansion of highly suitable areas is found where there would also be a high demand for urban land (Volume 4), the potential biophysical benefits, expected according to simulation results for scenario BAU-best (Volume 2), are likely to be limited by the projected socio-economic developments.
12 1 Introduction
1.1 Background and objectives
Expected climatechang e according toIPC C consensus (Hougthon etal. , 1990; 1992; Barrow, 1993) would most likely have considerable impacts on agriculture, natural vegetation, and the discharge of the river Rhine, with consequences for supply of drinking water, shipping, etc. Possible negative consequences could be minimized by an effective policy, founded on insight in the effects of climate change on the hydrological processes. Climatechang e mayhav edirec tan dindirec t effects onrive rdischarge .Fo rinstance , changes in precipitation amount or intensity in the Alps would directly affect the risk of flooding. Indirect effects may result from its influence on land use in terms of crop choice and cropping calendar, and hence on crop water use, soil coverage, leaching to deeper soil layers and surface runoff. Thus, through its impact on land use, climate change may cause additional changes in the discharge pattern of the river Rhine. Against this background, in 1989, the International Commission of the Hydrology of the Rhine basin (CHR/KHR) initiated a project to assess the consequences of changes in climate and land usefo r thedischarg e regime of theRhine . Several insti tutesfro m theRhin eriparia n countries arecollaboratin g inth eproject . Coordination is in the hands of the CHR. On the Dutch side, work in the project is being under taken by the Institute for Inland Water Management and Waste Water Treatment (RIZA) and Utrecht University (RUU) (Parmet, 1993). Thecentra l aimo fth eCH Rclimat e changeprojec t ist odevelo p awate r management model for the whole basin with the capability of analyzing the impact of average and extreme discharges by using 'scenarios'. The use of scenarios is a way to cope with uncertainties.A suncertaint y isinheren t topredictin g thefuture , various aspects of the future call for a scenariowise approach, e.g. the nature of the climate changes tob e expected, alternative policies tocomba t the greenhouse effect, political,demo graphic and economic developments. Sometimes it is more appropriate to speak of projections rather than scenarios, as scenario implies adevelopmen t pathway, while a projection refers to one point in the future.
One of the tasks of RIZA within the CHR project is to develop land use scenarios. Thisresearc h hasbee n subcontracted toth eDL OWinan d StaringCentr e (SC-DLO). The present study aims at contributing to a better understanding of climate change effects onlan d usei nth eRhin e basin,resultin g inth edevelopmen t oflan dus e scen arios on the basis of both biophysical and socio-economic factors (Parmet, 1993). In a preliminary biophysical study, 'Effects of climate change on crop production in the Rhine Basin' (Wolf &Va n Diepen, 1991),als o conducted by SC-DLO, simu lations were done for a few climate-soil combinations only, by far not representing the diversity of biophysical conditions in the Rhine basin.
13 The specific aim of the biophysical part of thepresen t main study 'Land use projec tions for the Rhine Basin on the basis of biophysical and socio-economic analysis' is to come up with geo-referenced information on land use potentials under current andfutur e conditions.Th eprojec t proposal specified thata suni t areasfo r the analysis should serve all land types which are represented by unique combinations of crop//soil//climate//altitude.Thes e canb edetermine d only if asensibl e classification for each of these factors exists. Thedevelopmen t of aconsisten t biophysical classification isth e subject ofth epresen t volume.Th e second volume will deal with the impact of climate change on land use potentials. The possible spatial shifts of land use potentials will then be compared with 'autonomous' or socio-economic developments (in the absence of climate change) in view of their relative importance for future land use patterns in the Rhine basin. Considerable changes in land use could also occur inth e absence of climate change. Analysis of expected 'autonomous' developments and its main determinants for the Rhine basin will be presented in the socio-economic part of this combined study (volume 4), concluding with a synthesis of both parts. As there are several interactions between land use and climate change (interrelated side-effects, e.g. on soil thermal and moisture regimes), speed and direction of the former partly co-determines the latter and vice versa. Thefeedbac k mechanism, changei nlan dus e -acceleration/dela y inregiona l climate change, is, however, beyond the scope of the present study.
1.2 The problems and priorities of the biophysical impact study
In order to assess the relative importance of biohysical and socio-economic factors for land usechange s inth eRhin e Basin ataroun d the mid of next century, assuming a possible 'warm scenario', spatial model building is required. The most obvious bottle-neck forth ebiophysica lpar ti sth elac ko fa classificatio n systemtha tcontain s both elements, climate and soils. Ideally, such a classification should refer to the most recent base-line climate period (1961-90) and be flexible for modification for future conditions. It is a major objective of this study to establish such a system taylored to the Rhine basin, by applying GIS in combination with a process model.
Studies that improve understanding of the possible impacts of climate change on the growth and production of crops, grass and timber, and the associated agro- hydrological processes at the regional level are prerequisites for formulating ap propriate measures for mitigating or preventing possible negative effects.
For instance, possible negative effects of climate change on land use (e.g. for some annual crops: lower yield potentials due to a shortened growing season) may be alleviated through preventive actions (e.g.cro p cultivar selection).A tth e sametim e the satisfaction of water requirements of that new cultivar needs to be taken into account. As it is not feasible to examine such interactions for anumbe r of crops and biophysical configurations in experiments, application of simulation techniques is required.
14 Therefore, another objective of this study is the assessment of attainable yields and corresponding water use of important crops for the major biophysical types in the Rhine basin under present and possible future conditions.
Thebiophysica l analysisfocuse s onth erelationship s oflan dus etyp ean d temperature conditions and land use type and water use. In the latter relationship, direct effects of increased C02 concentrations on crop water use are taken into account.
Precipitation isa ver yimportan t meteorological elementt ob econsidere d inth edevel opment oflan dus e scenarios thatthemselve s servea sinpu t for ahydrologica l model for the Rhine basin - the main objective of RIZA. It should be realized that predic tions of precipitation changes in amount and, particularly, distribution under C02 - induced warming are most uncertain. While there is some agreement about rainfall increase in the northern parts, and a decrease in the southern parts of Europe (i.e. south ofth eRhin ebasin) ,ther ei sdisagreemen t aboutchange s inrainfal l seasonality (Parry, 1990; Barrow, 1993; Schoenwiese, 1993) - not to speak of changes in interannual variability.
Both,temperatur e andévapotranspiratio nlevel s arerelate d toaltitud eabov ese alevel . However, strongcorrelation s areonl yexpecte d withinregion s thatar e homogeneous interm s ofatmospheri c circulation conditions, i.e.region stha t experience airmasse s with similar dominance and properties (maritime - continental). Present land use patterns seemt ob estrongl y related toth epetrographic-morphologica l differentiation of the Rhine basin.
Asmos t ofth ebroa d geological-morphologicalunit s(CHR/KHR , 1978)ca n bechar acterized by distinct altitude ranges, for aroug h geographic schematization, it may suffice to stickt othos eunits .Moreover , theroughl y W- E oriente d slatemountains , Jura and Alps are distinct barriers for northerly and southerly air masses, or, exert at least a considerable modification on their properties. This is reflected in the distinction of three broad geographic regions as proposed by CHR/KHR (1978, Hydr. monograph, Part A):
- The plain (to the S delimited by the northern boundary of the slate mountains) - The central mountain region (to the Sdelimite d by the 'Hochrhein' or axis Basel - Konstanz) - The Alps and its forelands (the remaining southern parts)
Within theseregions ,climat e and soilcondition s andlan dus ear elargel y determined by the local relief/topography, i.e. luv/lee, slope, distance from the sea, etc. Though there is hardly any reliable information on possible climate change at the regional level (Kenny et al., 1993; Schoenwiese, 1993;Rosenzwei g &Parry , 1993), it can be expected, that the marked, present influence of altitude and the spatial arrangement of mountains and valleys on circulation/climate patterns will also modify future circulation systems in a characteristic way (Frankenberg, 1993), as has been illustrated for broader regions (Flohn, 1993).
15 1.3 Outline of the report and reader's guide to the biophysical parts
The biophysical study is divided into three main parts: Part 1: Biophysical classification as a general framework (this Volume, 1) (author: R.P. Rötter) Part 2: Climate change impact on crop yield potentials and water use (Volume 2) (authors: R.P. Rötter & CA. Van Diepen) Part 3: Climate change impact on forest yield potentials and water use (Volume 3) (author: C.M.A. Hendriks)
The socio-economic study and synthesis of biophysical and socio-economic analysis are contained in Volume 4 (authors: F.R. Veeneklaas, L.M. van den Berg, D. Slothouwer & G.F.P. IJkelenstam).
Overview on volumes 1, 2 and 3
Volume 1 (author: R.P. Rötter): The geographic description (chapter 2) and the overview on current climatic condi tions, soils and land useAand cover types in the Rhine basin (chapter 3) are largely based on literature, while in chapter 4 account is given of the newly established bioclimatic classification system and the methods applied to arrive at biophysical land types.
Volume 2 (authors: R.P. Rötter & C.A. Van Diepen): Chapter 2 deals with the uncertainties of (regional) climate change predictions and gives the scenario selected for the current study. Chapter 3 introduces the approach chosen for assessing land use suitability and crop production possibilities under pres ent and possible future conditions. Chapters 4 and 5 give the results, i.e. crop yield potentials and water use for current and future conditions, respectively, followed by a discussion (chapter 6), conclusions and perspectives (chapter 7).
Volume 3 (author: C.M.A. Hendriks): Forests of the past, present and in the future are dealt with in chapters 2, 3 and 4, respectively. Chapter 5 contains discussion and the conclusions.
16 2 Geographie description of the Rhine basin
2.1 Geographic situation
The Rhine Basin with a total area of ca. 185.000 km2 is distributed over nine countries, whereby more than 95% of the area belong tofou r countries, The Nether lands, France, Germany and Switzerland (CHR/KHR, 1978). The basin roughly extends from latitudes 53°30 ' to 46° 30' N, and reaches its widest E -W extension (longtitudes 5°30 ' to 11° 45') atlatitud e 50°N .Thi s roughly corresponds toa lengt h of 700 km and a greatest width of 500 km.
Three broad regions can be distinguished: The Plain, The Central Mountain Region and The Alps and its Forelands. They can be further subdivided according to geologicalunit s (CHR/KHR, 1978,ma pC4.1 ) and altituderange s (CHR/KHR, 1978, map C2).
2.2 The main regions and subregions
(1) The Plain
subregions: 1.1 The Netherlands'plain/delta region 1.2 The Lower Rhine sedimentary basin and The Muenster chalk basin
(2) The Central Mountain region
subregions: 2.1 The (Rhenanian) Slate mountains 2.2 The Upper Rhinegraben and the Mainzer basin 2.3 The Mesozoic cuesta landscape, except 2.4 2.4 a)Blac kForest ,b )Vosges ,c )Odenwal d andd )Pfaelze r Wald
(3) The Alps and its Forelands
subregions: 3.1 The Swiss Jura 3.2 The Molasse basin/Alpine footslopes 3.3 The Northern and Central Swiss Alps
- The Plain A marked boundary in terms of land use is found at the northern border of the Rhenanian slate mountains; the transition from the Eifel to the loess-covered and highlyfertil e lowerRhenania n basini nth ewest , andth etransitio n from theBergisch e Land and Sauerland to the Muenster chalk basin that isintensivel y used for agricul-
17 ture. North of this line, only afe w pleistocene hills and holocene dunes, limited in size,ar ecovere db yforest . Agriculture isprevailin g inth eNetherlands ;larg e propor tions are used for forage production and as pastures.
- The Central Mountain region Aseparatio n ofth eregio n into: 1)th eRhenania n slatemountain s (northern part)an d 2) the mesozoic cuesta landscape (southwestern and southeastern parts), both with altitudes ranging from roughly 300 to approx. 800 m a.s.L, and 3) the less elevated (50-300 m) upper Rhinegraben (central-south) and the relatively narrow valleys of Mittelrhein, Mosel, Ahr and Lahn, and 4) the higher elevated (800-1500 m) Black Forest and Vosges andpart s of Odenwald, Spessart and Pfälzer Wald, seems advis able in terms of land use.
The slatemountain s inth enort h(Hunsrueck , Taunus,Eifel , Westerwald, Bergisches Land and Sauerland) are largely under mixed use, and, at higher altitudes, mainly under forest.
Thecuest a landscapes west of theVosge s (Lorraine) and northeast ofth eBlac k For est(Baden-Wuerttember g andNort h Bayern)are ,o nth econtrary , mainly underagri cultural (or mixed) use; particularly on the 'Muschelkalk' and on parts of the 'Keuper' formations, agriculture is dominating. Larger areas covered by forest can only be found on the steep slopes of the 'Jura' formation.
Under intensive agricultural use (partly 'Spezialkulturen', i.e. special crops: wine, fruits and tobacco) are also, the 'Pfaelzer Huegelland, the Rhine-Main basin, the Hessian depression and, especially, the Upper Rhine basin, including the 'Vo- rbergzonen' that are covered by loess.
Black Forest and Vosges arelargel y covered byforests . Forestry isparticularl y pre vailing in areas with mesocoic sandstones asth eparen t material, like in parts of the BlackFores t andVosge s andt oth enort h inth e 'Haardt' (Pfaelzer Wald), Odenwald and Spessart.
- The Alps and its Forelands The high alpine region with its axis from approx. W to E shows large proportions of unusable land, large areas of forest and only a few areas of mixed use (agricu lture/forestry). The distinct boundary between land use types forest and agriculture also demarcates the boundary between the Alps and its Forelands, the 'Schweizer Mittelland' (subregion:Molass ebasin/Alpin efootslopes) , which again canb eclearl y distinguished from the Swiss Jura to the North; the latter dominated by mixed use and forest.
This geographic subdivision ofth eRhin e Basin, mainly based on geomorphological and topographic criteria, is partly reflected in climatic differences among regions\subregions.Ther eare ,i ngeneral ,difference s inrainfal l seasonality and annual temperature amplitude (Table 2.1).
18 Table 2.1 Long term mean summer (SP) and winter (WP) precipitation and ratio SP/WP and annual temperature amplitude (between coldest and warmest month) for selected weather stations in the Rhine basin. Summer includes the months June, July and August; winter includes December, January and February. Station name lat. long. altitude SP WP SP/WP amplitude (m a.s.1.) (mm) (mm) mean Jan./Jul. temp. (°C) The Plain Eelde 53°07' 6°34' 4 213 188 1.13 14.7 De Bilt 52°06' 5°10' 2 222 202 1.10 14.6 Twente 52°16' 6°54' 14 215 190 1.13 14.8 Osnabrueck 52°15' 8°03' 97 223 217 1.07 16.2 Muenster 51°58' 7°36' 64 208 187 1.11 15.8 The Central Mountain region Zuid-Limburg 51°05' 5°46' 125 216 183 1.18 15.1 Frankfurt 50°03' 8°36' 111 200 136 1.47 18.2
Metz 49°04' 6°07' 191 196 191 1.03 17.0 Trier 49°45' 6°40' 265 215 185 1.16 17.3 Wuerzburg 49°48' 9°58' 268 185 137 1.35 18.8 Nuernberg 49°30' 11°04' 319 211 137 1.54 19.2
Nancy 48°40' 6°13' 225 200 189 1.06 16.9 Saarbruecken 49°13' 7°07' 322 228 207 1.10 17.2 Karlsruhe 49°01' 8°22' 112 224 175 1.28 16.4 The Alps and its Forelands Luxeuil 47°46' 6°21' 278 258 269 0.96 16.9 Basel 47°33' 7°35' 316 252 155 1.63 18.0 Zuerich 47°28' 8°31' 432 385 219 1.76 18.4 Konstanz 47°40' 9°10' 443 306 166 1.84 18.6 Feldkirch 47°16' 9°36' 439 423 206 2.05 19.2
In Table 2.1, stations are arranged from North to South. For the Central mountain region, there are three transects from W to E, illustrating the influence of distance from the sea (continentality) on annual rainfall distribution and annual temperature amplitude. Summarizing, ratio summer to winter rainfall and temperature amplitude increase from North to South but also from West toEas t (= increased continentality). Exceptions are specific modifications as a result of altitude and luv/lee position with respect to the humid air masses. Long-term mean monthly temperature, global radiation and other climatic elements are given in Annex 4.3. The amplitudes between mean temperatures of coldest and warmest month show a distinct W-E differentiation within the regions as well as, generally, an increase from Nto S.
19 3 Literature review on prevailing land use patterns and biophysical conditions
3.1 Overview on broad land use patterns in the Rhine basin
The broad overview given here results from map evaluation. A far more detailed and statistically based overview onpresen tlan dus ei spresente d involum e 4(chapte r 'present land use in the Rhine basin').
Map C.7 of CHR/KHR (1978) (alsoavailabl e ingrid-base d digital form: 3 km x 3 km resolution) shows acoarsel y generalized picture of land use inth eRhin e basin (map scale 1 : 1 500 000), distinguishing between areas that are: a) mainly used for agriculture b) approx. equally used for forestry and agriculture c) mainly used for forestry d) that are neither used for agriculture norfo r forestry, due to physio-geographical constraints (glaciers, rocks, etc.)
The correlation of land use and petrographic-morphological conditions is apparent in the entire Rhine basin. For instance, in region 'The Alps and its Forelands', the high alpine region with its axis from approx. W to E shows large proportions of unusable land (type d) and large areas of forest (type c). The distinct boundary between land use type c and a also demarcates the boundary between the Alps and itsforelands , the 'Schweizer Mittelland' (subregion: Molassebasin/alpin e footslopes), which again can be clearly distinguished from the Swiss Jura to the north; the latter dominated by forest (type c).
Resultsfro m overlays ofdigitize d landus ean d altitudemap s (CHR/KHR, 1978)wer e tabulated (not shown). Altitude ranges as defined in the original digital data base (28classes )hav ebee n re-arranged; altitudeinterval s of 100m wer euse d (upt o 1800 m); above 1800 m two additional classes were distinguished; Summarizing the overlays, land use type (LUT) agriculture (agr) alone slightly exceeds the area covered by the mixed (mix) and forest (for) LU types. In the range 0 - 200 m, agr by far exceeds the other two LUTs taken together and is still superior to total area of the other two in classes 200-300 and 300 - 400 m; Between40 0an d90 0m ,L U agriculture isalread yrelativel y under-represented; above 900 m, forest and mixed LUT are increasingly dominating.
For orientation, between altitude range 0 and 600 m, almost all, i.e. more than95 % of total area of the land use type agriculture (LUT agr.), about 65%o f LUT forest and 85% ofLU Tmixe d agriculture/forest asmappe d byCHR/KH R (1978)ar e found.
21 3.2 Description of present-day biophysical conditions in the various regions
3.2.1 Agroclimate
Climatic, agroclimatic and agro-ecological maps of Europe,th eRhin e basin orpart s ofthe m(Schnelle , 1955;Deutsche rWetterdienst , 1953,1979;Walthe r&Lieth , 1960- 67; Thran & Broekhuizen, 1965; Freitag, 1962, 1965; WMO/UNESCO, 1970; Seemann &Brandtner , 1976;CHR/KHR , 1978;Keller , 1978, 1979; Ozenda, 1987) show that annual temperature amplitude, the ratio summer to winter precipitation and altitude are the main factors determining a regional differentiation that is meaningful interm s of agricultural crops andnatura l vegetation.Thi si s also reflected in the climatic indices/variables used for defining climatic requirements/limits of individual crops (e.g. Papadakis, 1970, 1975;Volz , 1984; Hough, 1990; Kenny & Harrison, 1993).Fo r grapes, for instance, Kenny & Shao (1992) provide an assess ment of the latitude-temperature index (LTI) for predicting climate suitability for grapes. They used LTI in the form: LTI = MTWM*(75-latitude), where MTVM is the mean temperature of the warmest month and value ranges are specified for four grape cultivar groups (e.g.fo r Riesling, Pinot Noir, etc.,460-575) ;thi sLT Iha s been used in combination with a winter severity constraint (mean temperature of the coldest month less than -3°C) for broad assessment of climatically suitable areas in Europe (Kenny & Harrison, 1993).
It mustb eborn e inmind , however, that for finer climatic suitability assessments the local topoclimatic conditions (meso- to microscale) have to be taken into account. This refers particularly to situations, where frost risk and other temperature limits play an important role, asi n wine and fruit tree cultivation (e.g.Bjelanovicz , 1967; Seemann et al., 1979; Endlicher, 1980; Van Eimern & Häckel, 1984). Based on detailed phenological and temperature observations in Switzerland, the following relationshipsbetwee nannua lmea ntemperatur e andagricultura l zoneswer e obtained (Schreiber, 1973 cit. in: Amrein, 1982):
- At annual mean temperatures less than approx. 1°C, the potential upper forest boundary is found; partly highpasture s with 60-80day s duration occur at annual mean temperatures between 0 and 2°C.
Zoneo fmountai n pastures/meadows atannua lmea ntemperature s between 2an d 5-6 °C;a t around 5-6°C,th e climatic boundary for spring-sown cereals is found; pasture duration up to 150 days.
The zone with annual mean temperatures between 5-6 and 8°C is a mixed zone with cultivation of grass and arable crops, in particular, cereals; at favourable placesintensiv e arablefarmin g ispossible ; annual meantemperature s at 7.5-8°C demarcate theboundar y for commercial fruit tree cultivation andopen-ai rhorticul ture.
22 - Between annual mean temperatures 8 and 9 to 9.5°C is the main arable farming zone with good conditions for fruit tree cultivation and open-air horticulture; at around 9.5°C is the boundary for commercial wine cultivation.
- A mixed wine and fruit tree cultivation zone is found at annual mean temperatures between 9.5 and 11°C; good to very good conditions for intensive arable crop cultivation open-air horticulture (including early vegetables) and thermophile fruit trees.
The most elaborate agroclimatic classification covering the Rhine basin is the one of Thran & Broekhuizen (1965). They have adapted an approach from Schnelle (1955) for classifying the agroclimate of Europe. Crop requirements are taken as yardsticks and the various agroclimatic 'sub-provinces' are represented by codes (combinations of numerals and letters). To demonstrate which climatic elements appear most important, their definitions of agroclimatic types are given here:
Digit 1: annual precipitation; numerals 1-9 from very wet to arid - Digit 2: annual temperature; numerals 1-8 from cold to hot - Digit 3: summer temperature (warmest month); numerals 1-7, warm to very hot - Digit 4: winter temperature (coldest month); numerals 1-7, icy to warm Digit 5: special data on rainfall; numerals 1-9, summer dry to winter-rainless Digit 6: special data on temperature; numerals 1-9, spring-mild to winter-sunny
Overview on agroclimate in the Rhine basin For precipitation (digit 1) only numerals 1-4 occur in the Rhine basin, i.e. very wet, constantly wet, wet and moderately wet. For temperature (digit 2), numerals 4 and 5 occur (mountain areas are not accounted for); numeral 4 represents mean annual temperature class +7 to +9 (°C), numeral 5 stands for temperature class +9 to +12 (°C).
Most relevant in the classification system of Thran & Broekhuizen is that it further contains the elements 'duration of the periods spring to midsummer' and 'midsummer to autumn', resulting in various 'phenological types': The letters/letter combinations LL, L, M, Kan d KK refer to the duration (L for long; K for short, in German 'kurz') of the period from spring to midsummer (first half of the growing season), the same letters/letter combinations in lower-case refer to the period midsummer to autumn. The timing of midsummer, autumn, etc., however, is not clearly defined. Their classification system has several other bottle-necks, making it unsuitable for use in the current study. Apart from that it has too many combinations, the major bottle-neck in the classification of Thran &Broekhuize n isth e separation and setting- aside of some mountain areas, without specifying what criteria were used to classify areas as mountain areas or 'non-mountain areas'. Indeed, mountain areas show some specific characteristics that have little in common with the surrounding lowlands (e.g. higher radiation and higher daily temperature amplitude), but they are also influenced by the atmospheric conditions of the surroun dings.
23 Anotherapproach ,bot hdifferen t inth enumbe ran dcombinatio n ofclimati c elements and (mainly) in the methodology of constructing such aclassificatio n will be intro duced in Section 4.3.
3.2.2 Soils
Invariou smaps ,th e soils ofth eRhin ebasi n havebee n divided into main soil groups that are largely related to geological units, especially, toth e parent material. It must be borne in mind, that due to the small scales (1:1000000 and smaller) with map generalization determined by theprevailin g pétrographie conditions, each delimited soil group is more or less heterogeneous pedologically. Different soil maps covering the Rhine basin, e.g. soil maps of Europe scales 1 : 5 000 000 (FAO-UNESCO, 1981) and 1:1000000 (CEC, 1985) both using the FAO soil legend (FAO-UNESCO, 1974;FAO , 1988), show different definitions of soil groups with respect toth erelatio n of predominant and associated soiltypes .W e follow the definitions used in the soil map of CHR/KHR (1978) as shown in Table 3.1.
Spatial distribution of major soil groups
- The Plain Orthic and gleyic Podzols inNort h andEas tHolland , TheLowe r Rhine and Münster basin account for approximately 30%o f the area.Alluvia l Gleysols, mainly inWes t and North Holland cover another 20%. Approximately 20% are covered by orthic Luvisols occurring in the Lower Rhine basin and in aban d parallel to the Northern boundary of the Slate mountains.Histosols ,mainl y in Southwest and North Holland account for another 15% of the area.
- The Central mountain region The Cambisol and Luvisol groups approximately account for 85%o f the area inth e central mountain region; the Rendzina and Podzol groups largely account for the remaining area. In the Slate Mountains, Cambisols associated with Rankers, Lithosols and Podzols are predominant. Most of the Cambisols are of the dystric type. The areas covered by otherpredominan t soilsar erelativel y small,lik eth eLuviso l areanea rth eLaache r See (W of Koblenz), the Podzol-Cambisol area and Luvisol area in the Westerwald. Luvisols and, to a lesser extent, Rendzinas characterize the French and Southwest German cuesta landscapes. Luvisols are mainly associated with Cambisols, the Rendzinas are associated with Lithosols, Cambisols and Luvisols. Larger areas of Podzols aremainl y found inth emos t eastern part of theRhin e basin (Franken), some smaller areas also in the west (Hunsrück, Pfälzer Wald).
- The Alps and its Forelands OrthicLuvisol s associated with eutricCambisol s coverapproximatel y 25% ofregio n 3 (i.eth e northern parts of theMolass e basin).Mainl y dystric Cambisols cover15 % in aban d south of the 'Luvisol belt'. In the eastern parts of region 3,gleyi c Podzols associated with humic Gleysols account for another 10%.
24 Rendzinas associated withCambisol s andLuvisol s arepredominan t inth eSwis sJur a subregion, covering about 15% of region 3. The remaining parts, the higher Alps, are dominated by Rendzinas associated with Lithosols and rock outcrops and by Lithosols associated with Rankers, Rendzinas and rock outcrops. Gleysols are another important group in terms of area coverage.
Table 3.1 Composition and characteristics of the major soil groups and subgroups in the Rhine basin (mapping units of CHR/KHR soil map) Code Nomenclature and %are a coverage of parent altitude land use &No. predominant associated soils material range (m) soils and/or inclusions pred./assoc. pred./assoc. L Luvisols (9) Lo-3b orthic Luvisols eutr. Regosols loess 40-700 arable crops (9.1) Lo: 90% Lo-2a orthic Luvisols eutr. Cambisols (10%) pleistocene 10-200 arable crops/ (9.2) Lo:60 % hum. Gleysols (10%) sediments/ grassland orth. Podzols (10%) -do- Lo-3a orthic Luvisols dystr. Podzoluvisols loess 0-800 arable crops (9.3) Lo:70 % Lo-2b orthic Luvisols eutr. Cambisols (25%) glacial 50-1500 arable crops/ (9.4) Lo: 50% and var. others deposits grassland Lc-4b chrom. Luvisol Rendzinas (25%) limestone 100-1000 arable crops (9.5) Lc: 60% orth. Luvisols (10%)
B Cambisols (8) Bd-2c dys. Cambisols Rankers (15%) schists, 100-1500 forest, grass (8.1) Bd: 80% grauwacke, arable crops sandst. Bd-2b dys. Cambisols gleyic Cambisols (15%) -do- 150-1000 -do- (8.2) Bd: 80% Bv-5a vert. Cambisols Rendzinas and clay-marl 40-900 grassland, (8.7) various others forest Be-2c/ eutr. Cambisols gleyic Cambis. (10%) limestone/ 50-1800 forest, grass 4b(8.4) Be: 80% marl arable crops Bd-2a dys. Cambisols camb. Arenosols (15%) loamy sand 20-400 forest, arable (8.3) Bd: 80% crops, horti culture
P Podzols (10) Po-la/b ort+hum. Podzols gley. Podzols pleistocene 10-500 forest, arable (10.3) Po+Ph: 90% sands crops Pg-la gley. Podzols hum. Podzols (20%) -do- 5-400 arable crops, (10.4) Pg:40 % Kultosols (20%) grass, forest
G Gleysols (2) Gf-2/4a alluv. Gleysols marine/fluvia- 0-5 arable crops, (2.4) Gf: 100% tile alluvium grass, horticulture G-3/4a eut/dys. Gleys. gley. Luvisols loess-glacial 0-800 grassland, (2.1) Ge+Gd: 60% dys. Podzoluv. (30%) deposits forest, arable crops
25 Table 3.1 continued
E Rendzinas (6) E-2/4b Rendzinas eutr. Cambisols (20%) limestone- 100-1700 arable crops, (6.1) dolomite forest E: 40% chrom. Luvisols (20%) grassland
J Fluvisols (1) J-2/4a calc, eutr. dys. giey. holocene 0-1000 grassland, arable (1.1) dys. Fluvisols J:70% alluvium crops
O Histosols (11) O-a dystric, eutric. Histosols Gleysols peat 0-1500 grassland, (11.1) Od+Oe: 80% horticulture
Soil texture and slope classes The codes of each soil subgroup (e.g.Lo-3a ) contain information on soil type (Lo = orthic Luvisol) and also on texture (3= loams ) and slope class (a =0-3% ) (see Table 3.2).
Table 3.2 Definitions of texture and slope class used in codes of soil subgroups (mapping units of CHR/KHR soil map) Texture Definition code 1 coarse; sandy soils with > 65% sand and < 18% clay 2 medium; sandy loam soils with > 15% sand and < 35% clay 3 medium fine; loamy soils with , 15% sand and < 35%clay 4 fine; clayey soils wit 35-60% clay 5 very fine; heavy clay soils with > 60% clay Slope classDefinition code a slopes 0-3% b slopes 3-15% c slopes 15-25% d slopes > 25%
Slope classes c and d are certainly not suitable for highly mechanized agriculture, though they are of importance for wine. Similarly, heavy clay soils (texture class 5) are not suitable for arable farming.
26 3.2.3 Altitudinal zonation and vegetation belts
An accurate definition of altitudinal belts that are meaningful in terms of land use is often difficult, however, somebroa dclasse swit happroximat e figures (here:fo r upper Rhine basin and Black Forest) can be distinguished (Table 3.3).
The altitudinal limits for vegetation belts in the Rhine basin mainly differ due to the influence of latitude on temperature (Fig. 3.1) and secondly, due to the effects of air humidity (distance from sea; exposure in terms of moist air masses) and differences in irradiation patterns on the altitudinal temperature gradient. The effects of air humidity and irradiation pattern becomes obvious from Figure3.2 , showing variations in the temperature gradient of differently exposed slopes during the year. Thus, for instance, mean annual temperature must, at least, be corrected for latitude, before a meaningful correlation to altitude can be obtained.
Table 3.3 Generalized altitudinal zonation, vegetation and crops (after Ellenberg, 1982; Ehrendorfer, 1983; Thran & Broekhuizen, 1965) Alti Range Plant communities tudinal (m a.s.1.) Pot. natural vegetation* Substit. land use types belt 'rich' 'poor' arable f. grl./past. forestry collin 0-300 Melico Carpinion- wine, v.meadows Quercus & sub fagetum betuli fruit tr. montane 300-500 winter wheat/ Carpinus sugar beets betulus montane 500-900 Abieti- Luzulo- potatoes, m.meadows Picea Fagetum Fagetum rye abies high- 900-1400 Aceri- Pyrolo-/ montane Fagion Luzolo Fagetum sub- > 1400 Vaccinio- - alpine Piceion * potential natural vegetation after Ellenberg (1982); attributes rich and poor refer to the influence of soil properties on the composition of plant communities
Forfurthe r details onth egeograph y ofplan t communities/vegetations and land-cover inth eRhin e Basin, thereade r isreferre d toBoe r &Koste r (1992),Ehrendorfe r (1983 and later) Ellenberg (1978 and later), Amrein (1982) and Lang (1973) and to LANDSAT satellite images as presented in various recent publications and atlasses.
27 ' t Schneeslufo tfc{$ Tonnen,Fichten,Suchen i;>y'"i Alpint Ro»«nsiuie •H Buchen jf»*i uurenen,Arv«o(«Zirb«nl (V*> Suchen mil Sehen K^3 Fichten ttf'tfbi Gehen 1 CCDU Fichten und Tonnen [v'"- ^! 9«rqKiefefn{Krummholi) i- --; Kielern{• Föhren) Harz ttW* iïhón
Rügen N ^^TT»AA^. fnswf^ 500 1000 Km
77ie ieecA occurs only inthe western and southwestern part of Central Europe, influenced by a maritime climate. The conifers occur predominantly inthe more continental parts, also inthe Alps (Inneralpen). With decreasing latitude and increasing size of the mountains thezone borderlines become situated higher. The Europeanfir (Albies alba) also occurs inthe potential natural vegetation of the Vosges,but the Norway spruce does not. WGR -alpine timberline; SGR- snowline; Schneestufe -zone withpermanent snow;Alpine Rasenstufe -Alpine meadows zone; Lärchen, Arven (=Zirben) - Larch andStone pine; Fichten - Norway spruce; Fichten und Tannen - Norway spruce and European fir; Kiefern (=Föhren) - Scots pine; Tannen, Fichten, Buchen - European fir, Norway spruce, Beech; Buchen -Beech; Buchen mitEichen -Beech andOak; Eichen - Oak; Bergkiefern - Mountain pine.
Fig. 3.1 Altitudinal limits and georgraphic distribution of forest types (source: Boer & Koster, 1992, p.143: Schematic cross section through the present natural zonation of vegetation in Central Europe (after Ellenberg, 1978))
1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 JFMAMJJASOND
Seasonal fluctuations ofthe altitudinal temperature gradient. Decrease of the montly mean per 100 metres elevation (from statistics supplied by FRANZ). The gradient value isconsiderably higher insummer thanin winter andslightly higher on the south-facing slopes (adret) than on the north side (ubac).
Fig. 3.2 Seasonal fluctuations of altitudinal temperature gradient (source: Ozenda, 1987, p.27)
28 3.3 Biophysical classification - conclusions from the literature review
The Rhine basin includes a large number of climate-soil environments (Papadakis, 1970; Flohn & Fantechi, 1984; Mueller, 1987; Jongman, 1990; soils: Ganssen, 1972; Deutsche Bodenkundliche Gesellschaft, 1971an d 1986;Semmel , 1977; FAO-UNESCO, 1981; CEC, 1985; soils and climate: Reinds et al., 1992; WRR, 1992; Hofmeister, 1993).
By very rough generalization, they can be assigned to several major broad biophysical regions. Any assessment of present land use and projections of future land use should refer to such regions as a first frame.
Looking at climatic classifications at the global/continental scale (e.g. the ones of Koeppen, 1936 or Troll & Paffen, 1964), the whole Rhine basin is covered by two types. At the European scale, the very detailed system of Thran & Broekhuizen (1965) is already very complicated (6 levels of definition), but still lacks a clear hierarchy, has rather wide intervals (2°C) for mean annual temperature classes and, moreover, leaves mountainous areas out of consideration.
For examining plant-soil-weather interactions in view of possible impacts of climate change on land use patterns, agricultural and forest production and water use, delineation of areas with similar biophysical production potentials and limitations is necessary. This type of biophysical characterization requires, however, amuc h higher spatial resolution.
For the Rhine basin, there is sofa r no appropriate classification that takes into account both, climatic and soil properties in relation to land suitability for crop and forest cultivation.
The map scales of readily available and consistent spatial information on soils, land use and climate largely determine the degree of the generalization. While a special soil map is available at scale 1:1.500 000 (CHR/KHR, 1978) suitable for the purpose, there are no corresponding bioclimatic maps, butjus t various rainfall maps, an altitude map and a very rough land use map in that scale.
In order to arrive at ameaningfu l biophysical classification system for the entire Rhine drainage area, with sufficient spatial resolution and capability to characterize biophysical conditions under current and possible future climates, data integration in a GIS environment (e.g. integration of existing with newly generated point and spatial data) seems most promising.
29 4 The new biophysical classification system
The purpose of biophysical classification in this study is to characterize the study areai nterm s ofit sphysica l suitability forplan tgrowt han dproductio n andt odelineat e representative landunit s (climate-soil-terrain combinations) as abasi sfo r identifying sitesfro m whicht ogeneraliz e (point-specific) quantitativedat ao ncro pyiel dpotential s and water use to areas. There were several reasons for developing a new biophysical classification system for the Rhine drainage area. First, there was no such system for the study area that combined up-to-date information on the land resources (soils, climate, topography and relief) in form of key variables that determine land suitability for crop and forest cultivation at a suitable scale. Second, area-covering soil and topographic data were available in digital format, yet, requiring supplementation by the most important climatic data (for a common baseline period), i.e. climatic variables co-determining land suitability, potential yields and water use of the most important crops and tree species in the study area. Finally, a transparent and flexible system was needed, transparent in its necessarily subjective definitions of soil, terrain and climatic requirements ofplan t communities and flexible for incorporation of alternative defini tions and climate change scenarios.
4.1 General approach
4.1.1 Bioclimatic classification
Examining literatureo nbioclimati c descriptions ofth eRhin eBasin ,i twa sfoun d that, apparently,ther ei sn oclassificatio n system sufficiently detailed and,a tth e sametime , fully covering the entire area (Section 3.3). Generalizing from various (larger scale) regional assessments seemed not practicable due to the diversity of classification systems, sources, etc. in the various countries. Thus, ane w bioclimatic classification system had to be created. Bearing in mind the required examination of shifts in climatic suitability and potential and water use (i.e. potential land use patterns) due to climate change, one of the main objectives of this study, the system had to be designed for covering current (baseline period-) and possiblefutur e (climate change scenario-specific) bioclimatic types.Fo rtha tpurpose , a wide range of point-specifc weather records (period 1961-89)ha s been statistically analyzed to identify key variables that were, at the same time, subject to change, relevant tobioclimati c suitability and correlated withtopographi c variables (Sections 4.2 and 4.3).
31 4.1.2 Soil and terrain suitability classification
Itwas decide d tous eth ereadil y available information on soils (i.e.digitize d soilma p from: CHR/KHR, 1978)fo r thispurpose .Th e soilmappin g unitsdistinguishe d therein provide information on the approx. percentage of predominant and associated soils, texture and slope class (Chapter 3), i.e. variables with relevance to land suitability for WesternEuropea n mechanized agriculture andforestry . Onbasi s ofthes evariables , soil suitability classes were defined, guided by the requirements of various land utilization types (Subsection 4.4.3).
4.1.3 GIS techniques
For the present study, as for environmental change studies in general, linking of Geographical Information Systems (GIS)an dproces s models (such ascro p simulation models) would be desirable. However, it is the lack of the time dimension in spatial data models underlying commercial GIS that prevents aful l integration of those two techniques (Bregt, 1993).Eve n without achieving such aful l integration, it has been showntha tapplicatio n ofGI Stechnique s incombinatio n with aproces s model isver y useful for integrating point and spatial data for biophysical assessments (Rotter & Dreiser, 1994).
4.2 Data
A common database has been used for biophysical classifcation (this volume) and for crop modeling (Vol.2), warranting consistency in data synthesis -e.g . in relating simulated potential yields and water use to biophysical land types. Data types, sources and purpose for using them are described in the following sub sections. Basically, two data types are distinguished: point-/site-specific data and spatial data.
4.2.1 Point data
The point data used consist of weather records (SC-DLO and CHR/KHR data bases), and, for volume 2, other information not necessarily site-specific, but used as site- specific inputs such as soil hydraulic data given by Reinds et al. (1992) and Wolf (1993a-c) and crop cultivars and corresponding planting/sowing dates (Boons-Prins et al., 1993) for crop simulations.
Meteorological data (weather records, period 1961-89)
First of all, daily data on precipitation, minimum and maximum temperature, mean relative humidity, wind speed, globalradiatio nfro m moretha n 50pre-selecte d weather
32 stations (situated inth eNetherlands , Germany, Switzerland, Francean d Austria;Anne x 4.1, SC-DLO data base) relevant for the Rhine basin were used to calculate monthly means for each individual element and, additionally, potential évapotranspiration according toth eformul a ofPenma n (1948,1956),modifie d byFrer e &Popo v(1979) . The weather data were screened for completeness. Moreover, information on data quality (Schoenwiese et al., 1993) was taken into account to make the final selection of weather stations included in the analysis. Datafo r each element andévapotranspiratio n calculations performed for each individ ualyea r werethe n summarized to give thelong-ter m monthly averages for the period 1961-89.Th epurpos e wast o establish reference values for spatial inter- and extrapo lation of calculated water balance and crop water use. A number of these climatic variables or derived variables (e.g. summer rainfall, precipiation/potential evapotransiration index) were used in regression analysis (Section 4.3). Temperature andprecipitatio n data for another 13Swis s weather stations (period 1957-85);Anne x 4.2) were obtained from the CHR/KHR data base and partly included in regression analysis (Section 4.3).
For crop simulation (Volume 2),dail y data were used of precipitation, minimum and maximumtemperature , meanrelativ e humidity, wind speed, global radiation overth e period 1961-89 from 18 selected stations out of 40 weather stations used for establishing the bioclimatic classification.
r\ E E c o
(0
u CD L a
6 7 8 9 10 Mean annua I Temperature Cdegree Celsius}
Fig. 4.1a 'Environmental' data space: 40 weather stations characterized by mean annual temperature and precipitation
33 £ E e w 2500 c o
(0 2000 - E u E (I) L E a 1500 (0 D c El c ia c 1000 E raBEI (0 0) s
' 1 -5 0 S 10 Meanannua l Temperature (degree Celsius}
Fig. 4.1b 'Environmental' data space: 53 weather stations (13 Swiss stations plus 40 stations illustrated in Fig. 4.1 a) characterized by mean annual temperature and precipitation
Representativeness of the 40 weather stations and the 53 weather stations (incl. the additional 13 Swiss stations) relative to the topographic conditions and in terms of mean annual precipitation and temperature, the latter phrased 'environmental data space', is illustrated in Figures 4.1a and 4.1b.
Soil hydraulic data Soil-type specific data on moisture retention capacity (volumetric water content at pF 0, 2, 2.5 and 4.2) were available from various sources, among others, from Reinds et al. (1992) and Wolf (1993a-c). For the present study, the crop growth simulation model WOFOST, version 6.0 (Hijmans et al., 1994; Supit et al., 1994; see, Volume 2) was used with a simple water balance, assuming freely draining soils, without influence of shallow groundwater or slowly permeable layers, taking into account as soil parameters the total available soil moisture between field capacity and wilting point, and maximum rooting depth. For the simulations two soil types were distinguished, characterized by available water capacities of 70 and 140 mm per meter soil depth, and a standard soil depth of 100 cm. It was further assumed that the soil profile was at field capacity at the beginning of the simulated period (sowing or crop emergence).
34 4.2.2 Spatial data
The spatial data and information used consists of digitized maps (from RIZA and CHR/KHR, 1978) and other maps contained in atlasses (Thran &Broekhuizen , 1965; CHR/KHR, 1978; Keller, 1978).
Digitized maps Digitized maps provided by RIZA and used in this study were:altitude , soil and land usemap sdigitize dfro m CHR/KHR (1978)- however ,no tcomplete ,i.e .full y covering theRhin edrainag e area.Digitizatio n ofth emissin g parts ofth ealtitud e and soil maps was carried out by SC-DLO. Furthermore, the annual precipitation map from CHR/KHR (op. cit.) has been digitized by SC-DLO. Thecomplet e digitized altitudema p servesa sa basi sfo r constructing new temperature maps of the Rhine basin by incorporating regression equations (Section 4.3). The complete digitized soil map serves as a basis for a new soil/terrain suitability map (Section4.3) . Subsequently, various overlays areproduced .Anne x4. 4 gives an account of the GIS working steps carried out using system ARC/INFO. The map resolution of the grid-based maps provided is 3 km * 3 km. Hence, all new maps have this resolution. The overlay of altitude and land use map is used to give an overview of broad land use classes in relation to altitude ranges (Chapter 3). A map of administrative boundaries of the European Community (NUTS-2 level) in digital form wasobtaine dfro m EC-DGXICorin eProject . Thisma pha sbee n modified according toth ere-definitio n ofth e 13administrative/statistica l regions distinguished in the Rhine basin by Veeneklaas et al. (Volume 4). The result is used as an overlay to the new maps presented in this volume.
Atlasses The climatic, hydrological, soil\terrain, geological, vegetation and land use maps contained inCHR/KH R(1978) ,Thra n &Broekhuize n (1965)an d Keller (1978),amon g others,wer euse dt oidentif y thosephysica l factors mostimportan t for the distribution of agricultural land and vegetation.
4.3 Methods
4.3.1 Bioclimatic analysis
4.3.1.1 Choice of climatic variables
As aresul t of the literature review and in view of the results from climate modeling (see Volume 2, Chapter 2), it was decided to put emphasis on the analysis of tem peraturecharacteristic s (shape,maxim aan dminim a ofth eannua ltemperatur ecurve) , precipitation and évapotranspiration.
35 Temperature Temperature analyses focused on a spatial differentiation with respect to temperature- altitude dependance and the annual course of temperature. That the annual amplitude (expressed by the difference of mean July and January temperatures) is relevant to a climate change - crop impact study for the Rhine basin, can be illustrated by pres enting data of two geographic regions, each with two contrasting stations: De Kooy and Osnabrueck (for Region 1 - The Plain) and Trier and Nuernberg (Region 2 - Central Mountain region).
At De Kooy (53°N 5°E) a temperature increase of 1.7°Ci n spring means that the mean temperature level 5°C (i.e. the lower limit or boundary of any vegetation period) would shift from end of March to end of February (approx. 30 days) (Figure 4.2a). A gentle increase of temperature during spring is characteristic for stations representing the maritime climate, in contrast to a steep increase characteristic for more continental climates, such as Nürnberg (Figure 4.2d). For Osnabrück (52°N 8°E), the shift would be less pronounced (15-20 days), from the end to the beginning of March. Trier-Petrisberg (50°N 7°E) shows a shift of approx. 20 days (Figure 4.2.c) from mid of March to the end of February. Nürnberg (49°N 11°E )represent s the transitional climate (not anymore maritime mild and not yet continental cold, see Section 4.4). A constant increase of spring mean temperatures of 1.7°C results in an earlier the start of the vegetation period of approx. 10 days to mid March (Figure 4.2d).
Under expected future climate conditions, thepresentl y more maritime areas will also experience larger shifts in the cropping calendar than the more continental ones, especially as it is assumed that temperature increases are not constant over the year, but higher in winter than in summer.
Precipitation and potential évapotranspiration The moisture available for plant growth is best expressed in terms of the seasonal course of soil water content. Good fits to the distribution of vegetation types have been obtained by using climatic indices such as the ratio long-term mean annual precipitation (P) to potential évapotranspiration (pETo). This ratio (P/pETo) charac terizes the humidity/aridity of a climate. Annual potential évapotranspiration varies little among the various regions of the Rhine basin. From 0 up to 2000 m a.s.1., annual pETo values range between 550 and 850 mm.
36 Longter m monthlymea ntemperature s (baseline) and modifiedace .scenari o BAU-best
.Tmea n (baseline) .Tmea n (BAU-best) . lim.temp .veg .perio d
Fig. 4.2a De Kooy: Current and projected future (scenario BAU-best) annual course of mean air temperatures
Longter mmonthl ymea ntemperature s (baseline)an d modifiedace .scenari o BAU-best
J J A Month .Tmea n (baseline) .Tmea n (BAU-best) . lim.temp .veg . period
Fig. 4.2b Osnabrück: Current and projected future (scenario BAU-best) annual course of mean air temperatures
37 Longterm monthl y meantemperature s (baseline) andmodifie dac e scenario BAU-best
s 2
's* ^\.
1 1 1 1 1 1 1 1 1 1 1 1 J J Month .Tmea n (baseline) .Tmea n (BAU-best) . lim.temp .veg .perio d
Fig. 4.2c Trier-Petrisberg: Current and projected future (scenario BAU-best) annual course of mean air temperatures
Longter mmonthl ymea ntemperature s (baseline)an dmodffle dace . scenario BAU-best
.Tmea n (baseline) .Tmea n (BAU-best) . lim.temp .veg .perio d
Fig. 4.2d Nürnberg: Current and projected future (scenario BAU-best) annual course of mean air temperatures
38 This is because the pETo-determining variables (global radiation, temperature, air humidity and wind speed) show different and, partly, unsystematic and controversial relationships to altitude in the various geographic (sub-)regions. Annual precipitation isfa r more variable, ranging between 400 and 2000 mm and even above in the high Alps. Soil moisture responds to the seasonal course of precipitation and potential évapotranspiration, mediated by the water-storage capacity of the soil.Fo r the summer months, the P/pETo index is less favourable than the annual value for some areas. For cereal cultivation, for instance, irrigation is not practiced as this would not be economic. Under this aspect, the P/pETo ratio during summer would be important with respect to classifying the crop suitability of the various biophysical environments.
However, there are several reasons for not directly using a humidity or aridity index (P/pETo) for bioclimatic subdivison. First, there is no map showing évapotranspiration estimates based on Penman calculations for the Rhine basin. Second, low index values are likely to be found in areas that are all thermally suitable for arable crops and, at the same time, characterized by relatively low annual precipitation. In most parts of the Rhine basin, the annual P/pETo index is in the range of 0.7 to 1.4, though in the Alps and in the highest parts of the Central mountain region, the ratio exceeds 3. Differences of P/pETo among regions during summer (months June, July and August (JJA)) show variations that are relevant in terms of crop water requirement satisfaction. Differences in the 'summer P/pETo index' range from 0.5 (Upper Rhine basin) to 1.5 and more at high elevations. Total potential évapotranspiration in summer (JJA) accounts for approx. 50% of annual pETo (and even above 50% in the more continental parts). In most areas, summer pETo is between 300 and 400 mm and summer precipitation ranges from 180 to 400 mm. The ratios summer to winter precipitation generally show an increase (0.9 to 2 and more) from N to S and from W to E with some exceptions due to altitude and relief.
In effect, areas with low annual precipiation are also the ones with the highest prob ability of water deficits during summer, though there is the tendency that at similar annual total precipitation, the more continental regions experience a higher risk of water deficits - mainly due to the higher pETo levels.
Comparison of calculated évapotranspiration (Penman - Thornthwaite)
Investigating the compatibility of Penman and Thornthwaite estimates of potential évapotranspiration was of relevance, as it was intended to utilize area-covering Thornthwaite estimates, as presented in a study on the hydrology of the Rhine basin (Kwadijk & Van Deursen, 1993), for bioclimatic classification, while using the Penman formula (incorporated in crop simulation model WOFOST) for point- and crop-specific estimates under current and future conditions (Van Diepen et al., 1988, 1989; Hijmans et al., 1994). In the study of Kwadijk and Van Deursen, potential évapotranspiration data for some unevenly distributed stations in the Rhine basin have been calculated using the formula
39 ofThomthwait e(1948 ) andwer efurthe r used toderiv erelationship s between altitude andpotentia l évapotranspiration.Compariso n of newly generated Penman values with potential évapotranspiration data according to Thornthwaite and based on records of 1931-60 (as reported by Mueller, 1987) showed, that: a) annual potential évapotranspiration according to Penman is distinctly higher than that calculated according to Thornthwaite b) this is not due to the small differences in the long-term mean values of the two calculation periods, but mainly the result of systematic underestimation of win ter/spring ETo when using the THORNTHWAITE formula.
Jaetzold (1962)describe d theshortcoming s ofThornthwaite' sformul a asfollows : The summer months appear too dry and the winter months too wet. In winter, Thornthwaite's PETo values are too low, since ,in disagreement with lysimeter data, Thornthwaite fixes pETo at 0 mm for monthly mean temperatures below 0°C. In Thornthwaite's formula, temperature is the overruling determinant of potential évapotranspiration;hence ,Kwadij k &Va nDeurse nfoun d theirdat ahighl y correlated with altitude,particularly , asth ebul ko fthei r stationsrepresent s Swiss altituderanges . Moreover, the mathematic expression used in the Thornthwaite formula makes it too sensitivet otemperatur e changes.McKenn y &Rosenber g (1993)eve n concluded that it is not suitable for use in climate change - crop impact studies. For these reasons, we refrained from using the database of Kwadijk &Va n Deursen.
4.3.1.2 Regression analysis of climatic variables from selected weather sta tions
Invie w ofth epossibl e generation ofdat a surfaces ontemperature ,évapotranspiration , etc. using GIS technology, relationships between altitude (available as spatial data, raster-based at 3k m *3 k m gridresolution ) and anumbe r of climatic variables were analysedb ymean s of simplelinea rregressio n analysis.Th eclimati c variables selected were regarded as possibly relevant to bioclimatic suitability for different crops and vegetations. Long term mean (period 1961 - 89) climatic data were available (see Section 4.2). After data-screening for completeness andhomogeneit y (thelatte rbase d onSchönwies e et al., 1993), 40 stations were used in further analysis. The following climatic variables/derived indices were regressed on the independent variable 'altitude a.s.1.' (ALTI):
- long term mean air temperature in summer (June, July and August) —» mtempsu - long term mean air temperature in winter (December, January and February) -> mtempwi - long term mean potential évapotranspiration in growing season (Apr.-Sep.) —» etgspenm - long term mean potential évapotranspiration in summer (J, J, A) —> etsupenm - long term mean annual total 'growing degree days' above 5°C base temp. —> tsumyear - long term mean total growing degree days above 5°C base temp. (gr. seas.) —> tsumgrs - long term mean precipitation total in summer (J,J,A) —> mprecsu - long term mean precipitation total in winter (D,J,F) —» mprecwi - long term annual mean air temperature —> mtemyear - long term mean annual total precipitation —> mprecyea - long term mean annual total potential évapotranspiration —> metpeyea
40 - ratio summer to winter precipitation —» mprecsu/mprecwi - ratio summer precipitation to summer potential évapotranspiration —> mprecsu/etsupenm - ratio growing degree days in gr. seas, to annual total —» tsumgrs/tsumyear - amplitude : long term monthly mean temperatures (July -January ) —> Imtjul - mtjanl - product of long term annual mean air temperature and latitude —> mtempyea * lati and, with annual mean temperature as an independent variable: - long term mean annual total potential évapotranspiration and, with the ratio longitude to latitude as an independent variable: - amplitude : long term monthly mean temperatures (July - January) and, with the ratio (4000 - altitude) to latitude as an independent variable: - long term annual mean temperature
4.3.2 Soil and terrain suitability assessment
Soils ofth eRhin eBasi ndiffe r greatly (Table 3.1),als owit hrespec t tothei r suitability for agricultural use. It is often difficult to give an accurate suitability definition of each compound soil mapping unit. For this study, we decided to consider only the leading soils in each mapping unit. The first step was to determine all soil properties, that are used for the soil mapping units in the CHR/KHR (1978) soil map, scale 1:1.500.000. The codes of each soil subgroup (e.g.Lo-3a ) contain theinformatio n about genetic soiltyp e (e.g. 'Lo' -orthic Luvisol) and also on texture (e.g. '3' - loams) and slope class (e.g. 'a' - 0-3%) (see Tables 3.1 and 3.2). It was decided to cluster the soil mapping units, occurring in the Rhine Basin, into suitability groups. Based on crop requirements and main soil properties, obtained from the information on soil types from CHR/KHR soil map (1978), from the description of these soils on the Soil Map of the European Communities, scale 1:1.000.000 (1985) and from literature review, all soil types of the Rhine Basin havebee n assigned to four groups. Results of this grouping are given in Section 4.4.3 (see also, Map 2).
Prior to this grouping, criteria were defined. First, the influence of main limiting factors for mechanized agriculture for each soil suitability group was defined:
All soiltype s of groupU hav e high limitations for agricultural use,a sthe y are situated on steep slopes and are characterised by shallow depth of fine-grained earth, by high stoniness of soil profile, etc. They are practically unsuitable for all types of mechanized agriculture.
Other soil groups do not have so many obviously use-limiting properties and their placement in a suitability group has to rely on subjective pondération of various soil properties that limit their use possibilities to some degree. Soil suitability group B includes a wide range of different soils with various kinds of moderate physical and soil-chemical limitations. Soil types, included in soil group CI have some limitations, mainly connected with unfavourable drainage and rooting conditions. The soils of group C2 are more homogeneous and lack any prominent limitations.
41 While soil groups C2 and CI (the latter after improved drainage conditions) are suit able for cultivation ofroo tcrops ,cereals , grassland andforest , soilgrou p Bi s mainly suitable for grassland and forest and, with some restrictions, for cereals.
To check therathe r subjective separation of four soil suitability groups,th e computer system 'ALES' (Rossiter, 1993)wa sapplie d ata late r stagefo r evaluation of soil and physiognomic land units in the Rhine Basin. All soil-terrain combinations occurring in the CHR/KHR soil map have been evaluated for land use suitability. Four Land Utilization Types (LUT) were defined and nine land use and crop productivity re quirements havebee n chosen for eachLU Tan dfo r each soil mapping unit (for details, see Andronikov et al., in prep.). Results of this formalized procedure confirmed the above-mentioned grouping as presented in Table 4.9 (Section 4.4.3).
4.3.3 Definition of bioclimatic types
Bioclimate type in this report is defined by the combination of specific value ranges (long term averages) of a number of climatic attributes that are relevant for the geo graphical distribution ofcro pplan t andtre e species -suc h astemperatur e andprecipi tation. The class boundaries between bioclimatic types are selected from threshold values given in the literature for each of the climatic attributes. Bioclimatic main types and subtypes aredistinguished . Main types are defined by the combination of the most important attributes within the entire study area, while subtypes are defined by attributes that cause differentations within major regions.
4.3.4 Creation of climatic data surfaces and bioclimatic maps using GIS techniques
Foreac hclimati c attributeuse dfo r thedefinitio n ofbioclimati ctype s a geo-referenced data surface isneeded .Primar y sourceso f geographicinformatio n aredigitize d altitude maps and annual precipitation maps,havin g aresolutio n of 3kmb y 3 km.Th erelatio n between climatic and topographic variables is described by means of regression equations obtained from point data. The altitude map is used as a basis to generate derived maps in the form of continous climatic data surfaces. Such derived maps are created in the Geographic Information System (ARC/Info) by incorporating the regression equations, requiring only latitude, longitude and altitude as inputs. By applying bioclimatic threshold values for each climatic attribute to its corresponding continous data surface the spatial distribution of the classes of the single variables can be shown. Overlaying these derived maps, with or without precipitation map, allows to distinguish the defined bioclimatic types or subtypes and to quantify their distribution. The creation of new data surfaces and subsequent bioclimatic classification of the Rhinebasi n arecompletel y automated procedures in aGI S environment. The geogra-
42 phical distribution of resultant bioclimatic types over the Rhine basin under current (base-line 1961-89)a swel la sunde rpossibl efutur e climate(business-as-usua l scenario for decade 2040-49) is obtained. The preparatory and specific GIS work required to produce overlays of data surfaces and polygons is documented in Annex 4.4.
4.3.5 Creation of biophysical land types
Subsequently, by overlaying bioclimatic maps and, in addition, asoi l grouping map, biophysical classification is automated as well.
Specific combinations of soil groups (i.e. the former soil mapping units aggregated on the basis of soil physical and terrain characteristics) with the various bioclimatic types,bot hunde rcurren t andpossibl efutur e climatic conditions,resul ti nmap s show ing current andpossibl e future biophysical land types (for specific GIS work required, see Annex 4.4).
4.4 Results
4.4.1 Results of regression analysis on climatic variables
Table 4.1 gives the corresponding regression equations, correlation coefficients and coefficients of determination, table4. 2 shows results using some other variables than altitude as independent variables. Table 4.3 gives results after taking into account additional temperature data from another 13 Swiss weather stations (listed in Annex 4.2).
Table 4.1 Results of regression analyses climatic variables/indices on altitude X (m a.s.l.) (n=40) regression equation y = a + bX r R2 mtempsu (°C) = 17.5697 - 0.00297944 X -0.554 0.307 mtempwi (°C) = 1.8956 - 0.00268073 X -0.572 0.327 etgspenm (mm) = 630.90 - 0.0404177 X -0.245 0.06 etsupenm (mm) = 374.43 - 0.0216225 X -0.217 0.047 tsumyear (d°C) = 2169.07 - 0.662384 X -0.628 0.394 tsumgrs (d°C) = 1836.75 - 0.484666 X -0.565 0.32 mprecsu (mm) = 194 + 0.182158 X 0.744 0.553 mprecwi (mm) = 144.9 + 0.154366 X 0.651 0.431 mtempyea (°C) = 9.92875 - 0.00355023 X -0.765 0.585 mprecyea (mm) = 655.833 + 0.60365 X 0.745 0.555 metpeyea (mm) = 756.315 - 0.0264534 X -0.115 0.013 mprecsu/mprecwi = 1.30935 + 0.00016731 X 0.14 0.02 mprecsu/etsupenm = 0.506482 + 0.00591253 X 0.798 0.606 tsumgrs/tsumyear = 0.84488 + 0.0000510194 X 0.585 0.34 Imtju l - mtjan I (°C) = 16.5874 + 0.0016853 X 0.272 0.074 mtempyea * lati (°C "lat.N) = 504.88 - 0.2113472 X -0.898 0.81
43 Table 4.2 Results of regression analyses using other independent variables (units of X are °C, geogr. project ° (i.e. °long.E, °lat.N; ' in decimals) and m a.s.l.) regression equation y = a + bX independent variable(s) metpeyea (mm) = 1470 - 14.44 X -0.46 0.213 annual mean temperature I mtjul - mtjan l(°C) = 11.395 + 35.178 X 0.80 0.638 ratio longitude to latitude mtempyea (°C) = -11.2408 + 0.270106 X 0.94 0.889 ratio (4000 - altitude) to latitude mtjan (°C) = 1.63259 - 0.053365 X -0.84 0.703 product longitude times (altitude/100)
Table 4.3 Regression of mean annual temperature on altitude and on the ratio (4000 - altitude) to latitude (based on 53 data sets) regression equation Y = a + bX r R2 independent variables mtempyea = 10.3559 - 0.004694 X -0.96 0.926 altitude mtempyea = -10.2364 + 0.25687 X 0.99 0.985 ratio (4000 - altitude) to latitude
From theseresult s it is obvious thatbioclimati c types can beeasil y defined (R2> 0.6) on basis of attributes such as annual mean temperature, temperature amplitude and mean temperature of coldest month.
Therefore, therelevan t regression equations,expressin g each ofth ethre e temperature characteristics asa functio n of altitudeand/o rlatitud e andlongitude ,wer e incorporated inGI SARC/INF Oan dapplie d toeac ho fth eapprox .2000 0gri d cells (each of9 km 2).
For current climate, the three regression equations were: (1) Y (annual mean temperature) = -10.2364 + 0.256869 X, where X is the ratio (4000 -altitude in m) to latitude) to generate the data surface ANNUAL MEAN TEMPERATURE (2) Y(mea n annual temperature amplitude) =11.5654 + 34.3805 X, where Xi s the ratio geographic longitude to latitude to generate the data surface MEAN ANNUAL TEMPERATURE AMPLITUDE (3) Y (mean temperature of the coldest month) = 1.63259 - 0.0533654 X, where X is the product longitude times (altitude/100) to generate the data surface MEAN TEMPERATURE OF COLDEST MONTH
Takingint o accountth enee d ofpresentin g temperature conditions alsofo r the selected climate change scenario (Business-as-Usual, best estimate for decade 2040-49, i.e. annual mean + 1.75, mean winter months + 2, mean summer months + 1.5 and, consequently, amplitude - 0.5 °C; Volume 2, chapter 2), the three corresponding 'future' temperature characteristics were also regressed and expressed as functions of altitude and/or latitude and longitude.
Regression equations for future climatic data surfaces: (4) Y(annua lmea ntemperature ) =-8.486 4 +0.25686 9 X, whereX i sth erati o (4000
44 - altitude in m) to latitude) to generate the data surface FUTURE ANNUAL MEAN TEMPERATURE (5) Y (mean annual temperature amplitude) =11.0654 + 34.3805 X, where X is the ratio geographic longitude to latitude to generate the data surface FUTURE MEAN ANNUAL TEMPERATURE AMPLITUDE (6) Y (mean temperature of the coldest month) = 3.63259 - 0.0533654 X, where X is the product longitude times (altitude/100) to generate the data surface FUTURE MEAN TEMPERATURE OF COLDEST MONTH
4.4.2 The bioclimatic classification system
The results of the previous sections in combination with known (agro-/bio-) climatic classification systems (among others Thran & Broekhuizen, 1965; Troll & Paffen, 1964) led to the set-up in the following proposal, distinguishing four criteria for classifying bioclimate in the Rhine basin, based on 30-years averages: 1. Annual mean temperature 2. Mean annual temperature amplitude 3. Mean temperature of the coldest month 4. Mean annual precipitation
Bioclimatic main types are defined by attributes 1 and 2 (e.g. maritime - continental Midland temperature zone, Mme; for value ranges, see Tables 4.4 and 4.5). Subtypes are defined by attributes 3 and 4 (e.g. frosty and humid, d3; see Tables 4.6 and 4.7). For instance, the code for the (full) bioclimatic type is: M\mc\d\3 (i.e. for attributes 1: 8 to 8.9°C, 2: 16 to 17.9°C, for 3: -3 to -0.1°C and 4: 800 to 999 mm).
The notation 'bioclimatic' is chosen since most of the class boundaries are related to observed limits for vegetation types and\or crop groups.
The combination of the first two levels yielded (for present conditions) a classification system with six classes of mean annual temperature and (largely) three classes of temperature amplitude. Information at levels 3 and 4 are used to further subdivide the resultant 18 bioclimatic main types.
Classes have been distinguished in such a way that they can also represent future bioclimatic types reasonably well (as expected according to GCM results for the mid of next century). The narrowest class boundaries are found for conditions that are presently most relevant for arable farming. In this way, changes will show up clearly for this sensitive land use type. Moreover, the combination of levels 1, 2 and 3 allows to reproduce the annual temperature curve and, hence, can deliver inputs for weather generators to fill gaps in the weather station network. This is of importance for the application of crop simulators at (sub-) regional scales (Kramer, in prep).
45 Table 4.4 Class definitions at level 1 (= mean annual temperature) Code Name Temperature Approx. altitude range class (°C) (m a.s.1.) valid at 49°N A Alpine zone < 1.0 > 1800 H Highland zone 1.0 - 4.9 1000 - 1800 U Upland zone 5.0 - 7.9 450 - 1000 M Midland zone 8.0 - 8.9 300 - 450 L- Lowland zone 9.0 - 9.9 <300 (mod. cool) L* Lowland zone 10.0 - 10.9 <300 (mod. warm) L+ Lowland zone >11.0 currently not in the (warm) Rhine basin
Table 4.5 Class definitions at level 2 (= mean annual temperature amplitude) Code Name Temperature amplitude class (°C) m maritime < 16.0 mc maritime-continental 16.0 - 17.9 cm continental-maritime 18.0 - 19.9 [c continental > 20.0] class 'c' is restricted to a few intramontane valleys/specific topo-climatic situations
Table 4.6 Class definitions at level 3 (= mean temperature of coldest month, January) Code Name Mean temperature coldest month (°C) [a mild >3] b cool 1.5 - 2.9 c cold 0.0 - 1.4 d frosty -3 - -0.1 e frosty-icy <-3 class 'a' does currently not occur in the Rhine basin
Table 4.7 Class definitions at level 4 (= mean annual total precipitation) Code Name Annual precipitation class (mm) 5 transitional 400 - 599 4 semi-humid 600 - 799 3 humid 800 - 999 2 fully humid 1000 - 1199 1 perhumid > 1200
46 Annual total precipitation (level 4) in combination with information of level 1an d 2, gives also good information on humidity/aridity of the climates. By far not all of the combinations of level 1 and 2, i.e. the possible number of bioclimatic main types occurunde rcurren t climatic conditions in theRhin ebasi n and definitely not all combinations of levels 1,2 , 3an d 4; the coldest month class 'mild' does not occur at all in the Rhine Basin. However, this class as well as combinations presently absent may occur under expected conditions around 2040-49. Likewise, current combinations may disappear completely. Hence,i t ispossibl e toillustrat e shifts inlan d use suitability and, incombinatio n with type-specific estimates on production level and water use, bioclimatic land use potentials. Forth epurpos eo froughl y schematizing thecurren tbioclimati ccondition s inth eRhin e Basin, combinations of level 1an d 2 (i.e the bioclimatic main types) are shown in Table 4.8.
Table 4.8 Possible bioclimatic main types (18) and general occurrence under current con ditions per main geographic region of the Rhine basin (some spots, e.g. L*m in region 1 and H m in region 3, are not considered) m me cm Occurence of bioclimatic main types (y-es x, no 0) Region 1 Region 2 Region 3 A A m A me A cm 0 0 0 0 0 0 0 x x H Hm H me H cm 0 0 0 0 x 0 0 x x U Hm U me U cm 0 0 0 x x X XXX M M m M me M cm X x 0 X X X 0 x x L- L-m L-mc L-cm X x 0 X X X 0 x x L* L*m L*mc L*cm 0 0 0 0 x X 0x0
Considering thecombinatio n ofth efiv e mostimportan t maintype swit hth ethir dlevel , in terms of area coverage, the following combinations are most wide-spread under current conditions: L-mc/c, M mc/c, L-m/b, U mc/d, M cm/d, L-mc/b, L-m/c and M cm/c; Together, theseeigh tcombination s cover about 80%o fth eRhin e drainage area.Com bining all four levels (=climatic data surfaces; 600possibl e combinations) showstha t thefollowin g five combinations occurmos tfrequently , accounting togetherfo r approx. 40% of the Rhine basin: L-m/b4, L-mc/c4, L-mc/c3, M mc/c3 and M mc/c4 Moreover, from the possible combinations of all classes of the four levels as given in tables 4.4 to4.7 ,unde r current conditions only 90combination s occur inth e Rhine basin and only 25 of those 90 cover more than 1% of the total area.
4.4.3 The biophysical classification system
The combination of bioclimatic types and soil\terrain characteristics results in biophysical land types,define d byth e4 climati c attributes andaggregate d soil\terrain characteristics. The soil\terrain characteristics are represented by soil groups Cl, C2, Ban d U.Eac h of these soil groups contains soilmappin g units characterized by similar
47 slope class, soil texture, depth, moisture retention characteristics, soil genesis, etc. (see, Subsection 4.3.2). A summary of the grouping is given below; the composition of each group in terms of soil types is given in table 4.9. Soil groupU : Soil/terrain mapping units characterized by slopes >25 % and soiltype s Lithosols, Regosols and other shallow and/or stony types. U stands for unsuitable Soil group CI: Fluvisols, Gleysols and Histosols predominant Soil group C2: Orthic Luvisols and eutric Cambisols predominant Soil group B: Soils with relatively low moisture retention capacity predominant
Table 4.9 Soil suitability grouping (based on soil mapping units by CHR/KHR, 1978) Mapping Code Soil groups and subgroups** Units Soil group u* 3 R-lb Regosols (90%) 4 I-2d Lithosols (40%), Rendzinas (15%), Rankers (15%) 7 E-2c/d Rendzinas (60%), Lithosols (15%) 9 Bd-2c Cambisols dystric (80%), Rankers (15%), Rocks (3%) 21 Po-lc/d Podzols orthic (50%), Rankers (20%), Rocks (10%) 22 Pg-3c/d Podzols gleyic (50%), Gleysols (25%)
Soil group B 5 Ql-lc Arenosols luvi e (80%) 6 E-2/4,b/d Rendzinas (40%), Luvisols chromic (20%) 10 Bd-2b Cambisols dystric (80%), Cambisols gleyic (15%) 11 Bd-2a Cambisols dystric (80%), Arenosols (15%) 15 Bv-5a Cambisols vertic (90%) 20 Lc-4b Luvisols chromic (60%), Rendzinas (20%) 23 Po-la/b Podzols orthic (45%), Podzols humic (45%)
Soil group CI 1 J-2/4a Fluvisols (75%), Gleysols (20%) 2 G-2/3/4,a/c Gleysols 24 Pg-la Podzols gleyic (40%), Podzols humic (25%) 25 O-a Histosols (80%)
Soil group C2 8 H-3a Phaeozems (90%) 12 Be-2/4,b/c Cambisols eutric (80%) 13 Be-2c Cambisols eutric (60%), Luvisols orthic (30%) 14 Be-2c Cambisols eutric (70%), Rankers (15%) 16 Lo-3b Luvisols orthic (90%) 17 Lo-2a Luvisols orthic (60%), Cambisols eutric (10%) 18 Lo-3a Luvisols orthic (70%), Luvisols gleyic (20%) 19 Lo-2b Luvisols orthic (50%), Cambisols eutric (25%) *) soil suitability groups **) predominant and associated soils (details in: Vol.1, chapter 3) in area coverage
Further it is assumed in this study that this soil suitability grouping will not change under future climatic conditions according scenario BAU-best. The combination ofbioclimati c types (baseline and future conditions) and soil groups
48 by means of GIS (Annex 4.4) resulted in tables giving area coverage (km2) of the various biophysical land types in the entire Rhine basin and per statistical region. Complete results are given in a working document to this report (Van Der Heyden et al., 1994). An example of GIS area statistics (bioclimatic main types, baseline) is given in table 4.10a. Some of the more prominent biophysical land types occuring (under baseline and future climates, according to scenario BAU-best) are presented in table 4.10b.
Table 4.10a The ten bioclimatic main types with the highest area coverage in the Rhine basin Rank Bioclimatic Area coverage main type
(km2) (% of total1») (cumulative %) 1 L-mc 41724 22.55 22.55 2 M mc 35766 19.33 41.88 3 L-m 28827 15.58 57.46 4 M cm 20322 10.98 68.44 5 U mc 17145 9.27 77.70 6 U cm 8388 4.53 82.23 7 L-cm 7056 3.81 86.04 8 M m 4986 2.69 88.73 9 H cm 2700 1.46 90.19 10 L*mc 2358 1.27 91.46 " Total area is 185 000 km2
Table 4.10b Predominant biophysical land types in the Rhine Basin under baseline and future climates (scenario BAU-best)
Biophysical land types
Baseline Future (scenario BAU-best)
CI., U mc/d2 Cl, M mc/d2 C2., L-mc/d2 C2, L*mc/c2 C2.i L-mc/c4 C2, L+m /b4 B, L-m /c4 B, L+m /b3 B, U mc/d3 B, M mc/d3 B, M mc/c3 B, L*mc/b3 B, L-mc/c4 B, L+m /b3 B, M cm/d4 B, L*cm/c4 C2,, L- m /b4 C2, L+m /a4 CI, M m/b4 CI, L*m /b3
49 4.4.4 Summary of approach chosen and the major results
For biophysical classification, point data (temperature characteristics from a number ofweathe r stations) and spatialdat a (digitized altitude andprecipitatio n data surfaces, soil mapping units) were integrated by GIS techniques, resulting in anumbe r of new bioclimatic and, finally, biophysical data images.
In the present study, the GIS Arc-Info (Version 6.1) was applied to generate various temperature data surfaces (for current and future conditions) from a raster-based altitude map of the Rhine basin (for details see Annex 4.4). For each data surface, aspecifi c regression equation relating the value of the climatic variable to location in space (represented by composite variables of altitude and/or latitude and longitude) has been incorporated and applied to each grid cell (3 km * 3km) . Theseregressio n equationswer eselecte dafte r comprehensiveclimati c analysis based on 53 weather stations distributed over the Rhine basin (Section 4.3). Three temperature variables were retained to serve as indices for bioclimatic classification (current and future): Annual mean temperature, mean annual temperature amplitude and mean temperature of January (coldest month). Subsequently, each of these three continuous data surfaces wasaggregate d intoclasses .Ma p outputbot hfo r current and future conditions hasbee n produced for annual mean temperature (Maps 3an d4 ) and mean temperature of the coldest mont (Maps 5an d 6).Fo r mean annual temperature, class boundaries were chosen on the basis of literature on its relation to vegetation and land use types in the Rhine basin (sections 3.1 and 3.2). For defining class boundaries for theothe rtw otemperatur e data surfaces, simulated potential crop yield for Europe (WRR, 1992) and vegetation maps (e.g. Ehrendorfer, 1983;Fanta, 1992) served asguidelines .A fourt h data surface, mean annual precipitation (digitized from CHR/KHR, 1978,Ma p 1),wa s added to refine the bioclimatic classification system. Inth esystem ,bioclimati c maintype s (combination ofdat a surfaces 1 and2 ) and sub types (data surfaces 3 and 4 combined) can be distinguished. Soils grouped according to suitability classes (4 groups, Map 2) were then overlaid with the bioclimatic types to arrive at the various biophysical land types (i.e. soil- climate combinations) and their area coverage in the Rhine basin.
4.5 Discussion and perspectives
4.5.1 Reasons to focus on GIS
The strong focus on GIS techniques in this study has several reasons: By establishing climatic data surfaces using a GIS, variation of threshold values relevant for crops and vegetations isfacilitated . If, for instance,th e climatic suitability for a number of specific crops is to be determined, this can be done easily. This concept and associated applications have been documented earlier (a.o.Enders , 1979; Volz, 1984; Corbett, 1994). Second, the possibilities of spatial data inter-/ and extrapolation allow to make
50 optimum use of available data. For instance, an innovative aspect of the method used for the Rhine basin is the way available weather records are exploited. Data available from a number of weather stations were not a priori considered as a representative sample suited for immediately applying interpolation methods; instead, the relationships between their climatic attribute values and location in space were analysed to identify those variables and relationships providing general rules, applicable for the entire Rhine area. These general rules, in form of regression equations, were then applied to each cell or pixel (=picture element), i.e. to its center coordinates and mean altitude. It should be stressed that the purpose of the classification is not to reproduce climate patterns as close to reality as possible (as practised in conventional mapping by subjective interpolation, e.g. through relating climate variables to vegetation type, relief form, etc.), but to provide a true reflection of the information contained in the point data. The latter had to fulfil the criteria continuous records from 1961 onwards and high observation standards.
4.5.2 Interpretation of climatic regression analysis
First, the selection of stations providing the input for the regression analysis has an influence on the results. The current selection was done after thorough interpretation of available climatic and topographic maps, taking into account data quality. For instance, care was taken to cover the W-E and the N-S extension of the Rhine drainage area, and, to avoid, as far as possible, including overproportionally those weather stations in the sphere of the 'city warming effect'. Special attention was paid to select weather stations representing altitude ranges in proportion to area coverage of those altitude ranges prevailing in the Rhine basin. The climatic variables selected for inclusion in the regression analysis (Table 4.1) was guided by data availability, their meaning for agricultural crops and natural vegetation. Furthermore, care was taken to avoid redundancy.
Regressions on altitude: Mean summer and winter temperatures are both negatively correlated with altitude. For each of the two variables, only about 30% of the variance in the Rhine basin (selected stations, n = 40) is explained by linear regression. One of the reasons is the 'latitude-effect' on mean summer temperature, i.e. places of similar altitude but different latitude will have, for instance, different mean summer temperatures. Likewise, there is a 'distance from the sea' or 'longitude effect' on mean winter temperatures. Total potential évapotranspiration for the growing season and for summer according to PENMAN are slightly negatively correlated with altitude, but the explained variance is close to zero. Neither irradiation, nor wind speed and relative humidity during the growing season and the summer months are closely related to altitude in the Rhine basin, but these elements have a strong influence on calculated (as well as on measured) évapotranspiration levels. Both, degree days per year and per growing season (above daily mean 5°C) are nega tively correlated with altitude. The explained variance amounts to 39 and 32%, respectively. Here, we find again the latitude-effect: places with many days surpassing the base temperature but at relatively low levels (in the North) may come up with the
51 same totals asplace s that surpass thethreshol d less often but at higher levels (higher- elevated and to the South). Summer and winterprecipitatio n and annual precipitation showhig h correlations with altitude. The fact, that summer precipitation shows a stronger correlation to altitude than winterprecipitatio n maypartl yb eexplaine d byth eintercorrelatio n of topographic factors such asaltitude ,relief ,distanc e from the seaan dprecipitatio n type. Particularly the results for precipitation variables are very sensitive to the selection of stations. Moreover, the delimitation of summer (months J, J, A) and winter (months D, J, F) takes no account of thedifferen t timing of precipitation peaks in theRhin e basin.Th e fact thatth erati o summert owinte rprecipitatio n isno tcorrelate d withaltitud e further indicates the complexity of the rainfall pattern in the Rhine Basin. Detailed studies of topographic effects (luv-, lee- exposure, etc.) on annual and sea sonalprecipitatio n totals anddistributio n inth eRhin ebasi n (e.g.Havlik , 1982) suggest thatneithe raltitude ,no rit scombinatio n withlatitud e andlongitud e aregoo d predictors of the spatial pattern (see, for instance, Keller, 1978). The ratio summer precipitation to summer potential évapotranspiration shows a stronger positive correlation with altitude than just summer precipitation. But again, as there is no strong correlation between potential évapotranspiration and topography, it is difficult to reproduce the spatial pattern of this index.
The amplitude between mean January and July temperature is not correlated with altitude. Depending on the topoclimatic situation, a low amplitude can occur at high altitudes with low summer temperatures but relatively high winter temperatures (due to high winter precipitation frequency and cloud cover; e.g. Feldberg) or, at low altitudes due to the proximity/influence of the sea.
Finally, of all the variables annual mean temperature showed the strongest (negative) correlation with altitude. This correlation even increased when taking latitude intoaccoun t intw odifferen t ways (R2 = 0.81) (Table 4.1, last equation) and (R2 = 0.89)(Table 4.2).
Next, distance from the sea orrathe r spatial distance from the influence of moisture- bearing air masses from Wt oN W -fo r the Rhine Basin expressed byth e ratio longi tude to latitude - explained a high proportion of the variance in annual temperature amplitude. The combined influence (product) of land mass (expressed by longitude) and altitude used ason e independent variable explained ahig h proportion of thevarianc e in mean January temperature (Table 4.2).
Inclusion of 13 additional Swiss weather stations improved the correlation between mean annual temperature and altitude, and altitude corrected for latitude.
52 4.5.3 Advantages provided by the classification system
The established bioclimatic classification system for the Rhine basin is not new with respect toit selements :Annua l meantemperature ,mea n annual temperature amplitude, mean temperature of the coldest month and mean annual total precipitation. These elements as well as some of the threshold values used for distinguishing classes, also appear in well-known classification systems (Koeppen, 1936; Troll & Paffen, 1964; Thran & Broekhuizen, 1965).
The current combination ofthes eelement s selected wasfirs t of alldetermine d bythei r strong correlation with position in space (i.e. longitude, latitude and altitude), the availability of data, and, hence, their reproducibility by means of GIS techniques.
The main advantages of the use of GIS techniques (incl. matrix algebra) for the pur pose of the current study versus conventional methods are:reproducibilit y of results, possibility to re-define suitability for specific crops (in terms of soil/terrain and cli mate) and its capability to cover various climate change scenarios.
Reasons for choosing the proposed combination of elements in the bioclimatic clas sification system were:
1. Temperature characteristics are those that can be most easily generated spatially from geo-referenced topographic information such as altitude, longitude and lati tude.
2. If there is any reliable information from GCMs, then it is the information on changes in mean annual temperature and, possibly, on changes in seasonal temperature differences.
3. Forth eRhin ebasin ,i ngeneral ,temperatur e characteristics (seasonality, frost, etc.) are those that determine whether specific crops or tree species can grow.
4. The spatial pattern of precipitation characteristics is complex. Rainfall amount generally decreases from West to East and increases with altitude. However, the decrease as well as the increase depend on the specific relief position (lee, luv, relative altitude in the relief configuration, etc.). As temperature amplitude is to some extent correlated with rainfall distribution (ratio winter to summer precipitation), it was thought that inclusion of mean annual precipitation would suffice for a classification of bioclimatic types at the scale of analysis.
Not yetreflecte d byth ebioclimati c types areirradiatio n levels.Particularl y for arable crops, information on irradiation during spring and summer would be needed for further differentiations in yield-relevant climatic conditions.
53 4.5.4 GIS input\output: quality aspects
Abottle-nec k in this study isth e moderate tolo w quality of the digitized altitude map provided. Digitizing errors were detected inman y places,bu tonl y themos t prominent errors (occuring in Southwest Germany) were corrected. As a warning to the careful map reader: Errors still affect parts of the Vosges, the Northern Black Forest and the Rhön. The resolution of the digitized map (50 m intervals) is a comparatively minor problem, considering that some spots still show errors of +/- 200 m in altitude - corresponding to about 1.7°C in mean annual temperature. On the other hand, except for the Vosges, where still larger parts are affected, errors in altitudes are restricted in areal extent.
Ofcourse ,i ti sdesirabl et o analyseth eassociate d errorpropagatio n inth efina l output.
This is feasible within the GIS environment (Heuvelink, 1993), but was not carried out for the present broad-scale analysis.
Nevertheless, the incorporation of an improved topographic data base would also improve the quality of the output. Theestablishmen t of such adatabas e for the Rhine Basin ata resolutio n of 1 km2i spresentl y inproces s (Grabs and Parmet, pers.comm.) .
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63 Annex ANNEX ad CHAPTER 4
Annex 4.1 Weather stations for Rhine basin study (excerpt of SC-DLO database) - final selection indicated
WMO.NO & NAME Country Latitude Longitude Altitude Included (m a.s.1.) analysis (x = yes)
6235 De Kooy (Netherlands) 52.92 4.78 1 X 6260 De Bilt (Netherlands) 52.10 5.18 2 X 6280 Eelde (Netherlands) 53.13 6.58 4 X 6290 Twenthe (Netherlands) 52.28 6.90 14 X 6370 Eindhoven (Netherlands) 51.45 5.42 20 X 6380 Zuid Limburg (Netherlands) 50.92 5.78 125 X 6476 St-Hubert (Belgium) 50.01 5.24 560 6670 Zürich Airport (Switzerland) 47.28 8.31 432 X 6700 Geneve/Cointrin (Switzerland) 46.15 6.07 416 X 6762 Locarno/Magadino (Switzerland) 46.10 8.52 200 6998 Basel (Switzerland) 47.33 7.35 316 X 7090 Metz/Frescaty (France) 49.08 6.13 191 X 7180 Nancy/Essey (France) 48.68 6.22 225 X 7190 Strasbourg (France) 48.55 7.63 153 X 7280 Dijon (France) 47.27 5.08 222 7292 Luxeuil (France) 47.78 6.35 278 X 7299 Bale/Mulhouse (France) 47.60 7.52 270 X 10129 Bremerhaven (Germany) 53.53 8.58 11 10203 Emden Hafen (Germany) 53.33 7.21 5 X 10224 Bremen (Germany) 53.05 8.80 5 X 10305 Lingen (Germany) 52.52 7.32 21 10315 Münster (Germany) 51.97 7.60 60 X 10317 Osnabrück (Germany) 52.25 8.05 97 X 10338 Hannover (Germany) 52.47 9.70 56 X 10348 Braunschweig (Germany) 52.18 10.27 81 10400 Düsseldorf (Germany) 51.28 6.78 44 10406 Bocholt (Germany) 51.83 6.53 24 X 10410 Essen (Germany) 51.40 6.97 154 10427 Kahler Asten (Germany) 51.18 8.48 839 10438 Kassel (Germany) 51.30 9.45 231 X 10501 Aachen (Germany) 50.78 6.12 202 X 10513 Köln/Bonn (Germany) 50.87 7.13 91 X 10515 Koblenz (Germany) 50.35 8.01 70 10532 Giessen (Germany) 50.58 8.70 186 X 10544 Wasserkuppe (Germany) 50.50 9.95 921 10609 Trier/Petrisberg (Germany) 49.75 6.67 265 X 10637 Frankfurt Airport (Germany) 50.05 8.60 111 X 10655 Würzburg (Germany) 49.80 9.97 268 X 10671 Coburg (Germany) 50.16 10.57 337 10685 Hof (Germany) 50.32 11.88 567 X 10708 Saarbrücken (Germany) 49.22 7.11 322 X 10727 Karlsruhe (Germany) 49.03 8.37 112 X 10729 Mannheim (Germany) 49.52 8.55 96 10738 Stuttgart (Germany) 48.68 9.22 396 X 10763 Nürnberg (Germany) 49.51 11.08 319 X 10776 Regensburg (Germany) 49.03 12.06 366 X
65 continuation Annex 4.1 WMO_NO & NAME Country Latitude Longitude Altitude Included (m a.s.1.) analysis (x = yes)
10803 Freiburg (Germany) 48.00 7.85 269 X 10838 Ulm (Germany) 48.38 9.97 522 X 10852 Augsburg (Germany) 48.25 10.55 461 10866 Muenchen-Riem (Germany) 48.07 11.42 529 10893 Passau (Germany) 48.34 13.28 409 10908 Feldberg (Germany) 47.88 8.10 1486 X 10912 Villingen (Germany) 47.97 8.52 679 X 10929 Konstanz (Germany) 47.68 9.18 443 X 10991 Nordlingen (Germany) 48.85 10.05 300 X 11105 Feldkirch (Austria) 45.27 9.60 439 X
66 Annex 4.2 Additional Swiss weather stations
NO & NAME Latitude Longitude Altitude (m a.s.1.) 1 Davos 46.82 9.83 1590 2 Säntis 47.25 9.33 2500 3 Altdorf 46.80 8.50 451 4 Bern 46.92 7.49 570 5 Neuchatel 7.00 7.09 487 6 Arosa 46.33 7.00 1847 7 Chur 46.86 9.53 586 8 Einsiedeln 7.63 8.55 910 9 Chateux d'oex 46.42 7.33 980 10 Langenbrück 47.50 7.59 740 11 Langnau 46.92 7.78 695 12 Jungfraujoch 46.53 7.95 3572 13 Interlaken 46.70 7.72 574
67 Annex 4.3a For weather stations used in climatic analysis
Long-term mean monthly values of
- Minimum air temperature (°C, TMIN)
- Maximum air temperature (°C, TMAX)
- Daily global radiation (MJ m"2, GBRAD)
- Daily average vapor pressure (mbar, VAPP)
- Windspeed (m s"1, WIND)
- Total precipitation (mm, RAIN)
- Number of rainy days (d, RDAYS)
- Total potential évapotranspiration (mm, PETo)
- Long-term mean accumulated temperature units (d°C) above thresholds: 0°C (TSUMO) 5°C (TSUM5) 8°C (TSUM8)
69 continuation Annex4.3 a
Structure header: WMO no. first year last year station name validity code latitude altitude (ma.s.1. )
Structure data columns 1 to 11: Tmin Tmax Gbrad Vapp Wind Rain Rdays pETo TsumO Tsum5 Tsu
WMO00006235 1961 1988, De Kooy 1000 52.5 1. 0.2 4.8 2.347 6.7 7.1 67. 22. 16.6 97. 12. 0, 0.1 4.8 4.663 6.5 6.8 39. 15. 22.2 82. 9. 0, 1.8 6.9 8.273 7.3 7.0 50. 18. 46.3 139. 24. 2, 4.4 10.2 13.045 8.5 6.4 42. 15. 76.4 218. 75. 22. 8.1 14.3 17.462 10.9 6.1 45. 15. 117.6 346. 191. 101, 11.0 17.2 18.576 13.2 5.8 49. 13. 132.5 424. 274. 184, 13.2 19.0 17.200 15.1 5.9 66. 15. 135.2 499. 344. 251, 13.4 19.5 15.042 15.2 5.8 66. 15. 119.0 510. 355. 262. 11.3 17.5 10.398 13.5 5.9 83. 16. 78.6 432. 282. 192. 8.3 13.8 6.006 11.3 6.1 85. 19. 45.6 344. 189. 101. 4.6 9.2 2.931 8.7 7.3 92. 22. 27.6 208. 74. 22. 1.8 6.1 1.821 7.4 7.1 77. 21. 16.6 133. 29. 3.
WMOI)000626 0 1961 1988, De Bilt 1000 52.1 2. -0.8 5.0 2.519 6.5 3.8 71. 20. 10.3 93. 15. 2. -0.8 5.8 4.838 6.4 3.6 47. 15. 17.7 86. 15. 2. 1.1 8.7 8.097 7.2 3.8 64. 18. 41.9 155. 37. 8. 3.3 12.5 12.837 8.4 3.5 52. 16. 71.4 238. 96. 38. 7.2 16.9 16.318 10.7 3.2 64. 16. 108.1 372. 218. 128. 10.0 19.9 17.661 13.0 2.9 71. 14. 121.7 449. 299. 209. 11.9 21.4 16.490 14.9 2.8 78. 15. 121.6 516. 361. 268. 11.7 21.5 14.710 15.0 2.7 73. 16. 102.7 515. 360. 267. 9.4 18.7 10.304 13.5 2.7 65. 16. 60.7 421. 271. 181. 6.5 14.4 6.310 11.1 2.9 75. 18. 30.3 323. 170. 88. 2.9 8.8 3.113 8.3 3.6 82. 21. 13.8 179. 58. 18. 0.4 5.8 2.054 7.0 3.7 84. 21. 8.6 117. 27. 5.
WMO( »0006280 1961 1988, Eelde 1000 53.1 4. -1.8 4.2 2.177 6.3 5.1 69. 20. 9.8 76. 10. 1. -1.7 4.7 4.381 6.2 4.7 43. 16. 15.8 67. 8. 1. 0.3 7.8 7.696 7.1 5.1 57. 18. 40.1 131. 26. 5. 2.5 11.8 12.511 8.3 4.6 48. 16. 69.7 214. 77. 28. 6.3 16.5 16.026 10.8 4.2 60. 16. 106.6 353. 198. 112. 9.3 19.5 17.372 13.2 4.1 68. 15. 120.9 432. 282. 192. 10.9 20.6 16.055 14.9 4.0 78. 17. 117.5 489. 334. 241. 10.8 21.1 14.319 15.0 3.8 67. 16. 100.7 494. 339. 246. 8.7 18.2 9.858 13.3 3.9 70. 17. 59.4 402. 252. 163. 5.7 13.6 5.786 10.9 4.0 69. 18. 28.8 300. 147. 70. 2.4 8.3 2.858 8.2 5.0 78. 21. 13.2 164. 48. 13. -0.2 5.1 1.717 6.9 5.1 76. 21. 8.1 100. 20. 3.
70 WMO00006290 1961 1988, Twente 1000 52.2 14. -1.3 4.4 2.304 6.4 3.3 70. 20. 8.6 82. 12. 1, -1.2 5.2 4.645 6.3 2.9 44. 15. 14.8 78. 13. 2, 0.9 8.2 7.770 7.2 3.2 61. 18. 37.3 146. 36. 9. 3.2 12.5 12.452 8.5 3.6 53. 15. 70.7 235. 96. 41. 7.1 17.0 16.034 10.9 4.2 64. 16. 112.4 373. 219. 131. 9.8 20.1 16.944 13.4 4.7 74. 15. 128.7 448. 298. 209. 11.5 21.3 16.005 15.1 4.8 77. 16. 128.0 509. 354. 261. 11.3 21.3 14.246 15.0 4.7 64. 16. 109.9 506. 351. 258. 9.2 18.4 10.138 13.5 4.4 58. 15. 66.3 414. 264. 174, 6.3 13.8 6.181 11.0 3.8 59. 14. 31.6 311. 160. 82. 2.7 8.1 2.960 8.2 3.4 73. 19. 11.9 166. 51. 15. 0.1 4.9 1.866 6.9 3.2 76. 19. 6.7 102. 21. 4.
WMO00006370 1961 1988, Eindhoven 1000 51.3 20. -0.6 5.2 2.551 6.6 4.0 66. 21. 12.3 98. 18. 2. -0.5 6.1 4.961 6.5 3.5 48. 16. 19.3 93. 18. 3. 1.4 9.1 8.071 7.3 3.9 65. 19. 44.8 166. 46. 12. 3.7 13.1 12.755 8.5 4.4 48. 17. 80.5 253. 110. 49. 7.7 17.4 16.330 10.8 4.8 65. 17. 123.7 389. 234. 145. 10.5 20.5 17.341 13.3 5.3 73. 15. 140.4 465. 315. 225. 12.2 22.1 16.368 15.1 5.4 77. 15. 141.4 530. 375. 282. 12.0 22.0 14.590 15.1 5.2 62. 15. 122.5 527. 372. 279. 9.8 19.1 10.470 13.6 4.9 56. 15. 75.0 433. 283. 193. 6.9 14.6 6.496 11.2 4.4 64. 17. 38.4 333. 180. 97. 3.1 8.9 3.212 8.4 4.1 72. 20. 16.3 184. 63. 21. 0.7 5.8 2.071 7.1 3.9 72. 21. 10.2 119. 29. 7.
WMO00006380 1961 1988, : Zuid-Limburg 1000 50.5 125. -0.8 4.9 2.660 6.5 5.0 60. 21. 15.4 94. 17. 2. -0.7 5.8 5.061 6.4 4.4 52. 16. 22.4 89. 18. 4. 1.4 8.7 8.115 7.2 4.6 63. 18. 47.3 161. 44. 13. 3.9 12.6 12.601 8.5 4.1 51. 16. 76.6 248. 106. 46. 7.6 17.1 16.255 10.9 3.8 65. 17. 113.4 383. 228. 139. 10.7 20.4 17.181 13.5 3.6 75. 15. 125.8 466. 316. 227. 12.4 21.8 16.565 15.0 3.5 73. 15. 129.4 531. 376. 283. 12.2 21.7 14.571 15.1 3.4 68. 15. 110.1 526. 371. 278. 9.9 18.9 10.742 13.4 3.7 56. 14. 71.0 432. 282. 192. 6.7 14.3 6.759 10.9 4.0 61. 17. 38.7 325. 172. 92. 2.9 8.5 3.411 8.2 4.7 71. 19. 18.8 174. 57. 19. 0.4 5.4 2.187 6.9 4.8 71. 19. 12.2 111. 26. 6.
WMO00006670 1961 1989, : Zürich, Airport 1000 47.3 432. -2.5 2.3 3.429 5.2 1.5 72. 17. 11.7 51. 5. 0. -1.7 4.6 6.138 5.3 1.5 70. 14. 19.9 69. 11. 1. 0.9 8.8 9.888 6.1 1.6 73. 17. 45.9 157. 47. 16. 4.0 13.1 13.800 7.4 1.5 92. 16. 73.9 258. 119. 60. 7.8 17.7 16.769 9.8 1.4 107. 17. 106.6 396. 242. 153. 11.0 21.1 18.547 12.2 1.4 125. 16. 124.0 481. 331. 242. 13.2 23.4 19.089 13.9 1.3 121. 15. 135.0 567. 412. 319. 12.8 22.4 16.356 14.1 1.2 139. 16. 109.7 546. 391. 298. 10.4 19.4 12.480 12.5 1.2 94. 12. 70.8 448. 298. 208. 6.5 13.8 7.625 9.8 1.2 68. 14. 35.9 315. 163. 86. 1.7 7.3 4.298 6.9 1.5 82. 15. 15.8 143. 40. 11. -1.4 3.3 2.902 5.6 1.5 77. 15. 10.2 69. 11. 2.
71 WMO00006700 1961 1989, Geneve 1000 46.2 416. -1.8 3.7 3.876 5.5 2.1 81. 15. 15.3 63. 6. 0. -0.7 5.7 6.432 5.7 2.4 78. 13. 27.2 86. 14. 2. 1.2 9.7 10.610 6.3 2.5 81. 15. 55.8 172. 50. 14, 4.2 14.0 14.994 7.7 2.5 64. 14. 87.6 273. 128. 59, 7.9 18.3 17.623 10.2 2.2 77. 16. 118.0 407. 252. 161. 11.3 22.2 20.202 12.5 2.2 86. 13. 142.4 502. 352. 262. 13.4 25.3 21.150 13.9 2.0 68. 11. 158.8 600. 445. 352. 13.0 24.3 17.948 13.9 1.9 83. 13. 129.4 578. 423. 330. 10.3 20.8 13.667 12.6 1.9 83. 11. 83.1 467. 317. 227. 6.5 14.9 8.218 10.1 1.7 73. 12. 42.3 331. 177. 95. 2.2 8.5 4.642 7.3 2.2 93. 14. 21.5 162. 44. 12. -0.4 4.5 3.229 5.9 2.2 88. 14. 15.3 82. 13. 3.
WMO00006998 1961 1989, Basel 1000 47.3 316. -1.9 3.6 3.728 5.6 1.9 52. 15. 12.2 73. 11. 2, -0.8 5.8 6.139 5.8 1.8 49. 14. 21.9 93. 18. 4, 1.7 10.0 9.648 6.7 1.8 52. 15. 48.6 187. 64. 22, 4.5 14.3 13.780 8.1 1.6 64. 16. 76.5 283. 138. 70. 8.1 18.6 16.470 10.7 1.5 86. 17. 106.6 414. 259. 168. 11.3 22.0 18.598 13.1 1.5 85. 14. 126.5 499. 349. 260. 13.3 24.5 19.189 14.8 1.5 79. 12. 139.0 586. 431. 338. 13.0 23.8 16.478 14.8 1.4 88. 14. 114.3 571. 416. 323. 10.6 20.7 12.508 13.3 1.3 61. 11. 72.9 470. 320. 230. 6.7 15.1 7.915 10.4 1.4 50. 12. 37.4 338. 185. 104. 2.1 8.5 4.578 7.4 1.8 57. 14. 15.8 164. 52. 16. -0.7 4.6 3.251 6.0 1.9 54. 14. 10.7 91. 20. 5.
WMO00007090 1961 1989, Metz 1000 49.0 191. -1.1 3.9 3.091 6.2 3.3 64. 18. 13.3 84. 14. 2, -0.6 5.9 5.809 6.3 3.4 55. 14. 22.8 93. 18. 3, 1.6 9.8 9.422 7.3 3.5 65. 16. 49.7 180. 56. 18, 4.3 13.9 14.047 8.7 3.3 54. 15. 82.5 273. 129. 62, 7.9 18.2 17.363 11.4 3.0 71. 16. 116.3 405. 250. 159, 11.2 21.6 19.232 14.1 2.8 71. 14. 134.9 493. 343. 253, 12.9 23.8 19.373 15.6 2.6 62. 12. 144.8 569. 414. 321, 12.6 23.3 16.367 15.8 2.4 63. 13. 116.5 556. 401. 308, 10.0 20.2 12.109 14.2 2.5 60. 12. 72.5 452. 302. 213, 6.4 14.7 7.114 11.2 2.6 63. 14. 35.7 326. 173. 93. 2.3 8.3 3.828 8.0 3.1 67. 15. 16.6 162. 51. 16. -0.1 4.9 2.648 6.7 3.3 72. 16. 11.2 101. 24. 6.
WMO00007180 1961 1989, Nancy 1000 48.4 225. -1.5 3.7 3.213 6.1 3.5 64. 19. 14.2 80. 14. 2. -1.0 5.8 5.911 6.3 3.6 54. 14. 23.1 90. 17. 3. 1.1 9.7 9.462 7.2 3.6 60. 17. 50.3 172. 54. 17. 3.7 13.8 14.068 8.5 3.5 52. 15. 82.6 264. 120. 56. 7.4 18.0 17.270 11.3 3.2 72. 16. 115.0 394. 239. 148. 10.7 21.4 19.437 13.9 3.0 73. 15. 135.5 481. 331. 241. 12.3 23.7 19.620 15.6 2.8 60. 12. 144.4 558. 403. 310. 12.1 23.1 16.629 15.7 2.6 67. 13. 117.1 545. 390. 297. 9.6 20.1 12.282 14.0 2.7 63. 12. 73.8 446. 296. 206. 6.1 14.6 7.345 11.1 2.9 58. 14. 37.3 321. 168. 90. 1.9 8.2 3.976 7.9 3.4 65. 16. 17.3 157. 49. 16. -0.5 4.7 2.775 6.5 3.6 71. 16. 12.2 97. 23. 5.
72 WMO00007190 1961 1989, Strasbourg 1000 48.3 153. -1.8 3.4 3.155 5.9 2.9 34. 16. 14.6 76. 13. 2. -1.0 5.6 5.874 6.2 3.1 33. 14. 22.6 88. 17. 4. 1.5 10.3 9.556 7.3 3.4 37. 15. 52.5 187. 64. 21. 4.6 14.7 14.134 8.8 3.2 48. 15. 85.4 289. 143. 72. 8.5 18.9 17.287 11.8 2.8 76. 17. 117.7 426. 271. 179. 11.7 22.3 19.270 14.6 2.7 74. 14. 137.3 510. 360. 270. 13.4 24.7 19.543 16.2 2.6 58. 13. 148.4 591. 436. 343. 13.1 24.1 16.703 16.4 2.4 67. 14. 120.7 576. 421. 328. 10.3 20.9 12.438 14.4 2.4 56. 12. 75.9 468. 318. 228. 6.4 14.6 7.165 11.1 2.4 42. 13. 37.3 326. 174. 94. 2.1 8.2 4.009 7.8 2.9 46. 15. 17.9 160. 50. 16. -0.7 4.5 2.741 6.3 3.0 39. 15. 13.1 94. 22. 6.
WMO00007292 1961 1989, Luxeuil 1000 47.5 278. -2.7 4.2 3.581 6.1 2.7 94. 18. 10.9 73. 10. 1. -2.0 6.7 6.295 6.4 2.8 75. 15. 20.6 90. 17. 3. 0.2 10.4 9.794 7.3 3.0 82. 17. 47.3 170. 52. 16. 2.8 14.7 14.290 8.7 2.8 75. 16. 78.4 263. 120. 57. 6.7 18.8 17.181 11.6 2.5 94. 18. 109.3 395. 240. 149. 9.8 22.3 19.699 14.3 2.4 89. 15. 130.6 482. 332. 242. 11.6 24.7 20.245 16.1 2.3 79. 13. 143.7 563. 408. 315. 11.4 24.0 17.159 16.3 2.2 90. 14. 115.6 549. 394. 301. 8.7 21.1 12.896 14.5 2.2 80. 12. 72.1 447. 297. 207. 5.1 15.7 8.087 11.3 2.4 76. 14. 36.0 323. 171. 93. 0.8 8.9 4.495 7.9 2.6 100. 16. 14.5 154. 48. 15. -2.1 4.9 3.145 6.4 2.6 100. 17. 8.6 83. 17. 3.
WMO00007299 1961 1989, ] Mulhouse (FRA) 1000 47.4 270. 2.2 3.7 3.793 5.7 3 55 17 14.2 74 11 2 1.2 5.9 6.336 6.1 2.9 50 15 23.9 91 19 4 1.4 10.2 9.948 7.1 3.1 50 16 53.2 184 62 21 4.3 14.5 14.291 8.7 2.9 58 16 82.2 282 137 69 8.3 18.7 17.224 11.7 2.6 77 17 113.7 418 263 172 11.5 22.2 19.626 14.4 2.5 71 15 136.4 505 355 266 13.5 24.8 20.081 16.3 2.4 63 13 150.1 594 439 346 13.2 24.1 17.141 16.5 2.3 80 14 122.1 577 422 329 10.6 21 12.901 14.6 2.2 55 11 77.7 473 323 233 6.6 15.4 8.117 11.3 2.3 48 13 40 341 188 107 1.8 8.8 4.69 7.8 2.8 57 14 17.5 165 53 17 -1.1 4.8 3.313 6.1 3 55 15 13.5 92 20 5
WMO00010203 1961 1989, ] Emden-Hafen 1000 53.2 12. -1.0 3.3 2.266 6.1 3.5 65. 19. 8.5 78. 9. 0. -1.0 4.0 4.609 5.9 3.4 42. 15. 14.8 71. 8. 1. 1.2 7.3 7.958 6.8 3.6 56. 17. 39.0 139. 31. 6. 3.8 11.4 12.963 8.0 3.1 48. 15. 67.9 229. 87. 32. 7.8 16.3 16.595 10.5 3.0 58. 16. 107.2 375. 220. 130. 11.1 19.3 17.906 13.0 2.7 71. 14. 121.9 456. 306. 216. 13.0 20.5 16.457 14.6 2.7 83. 17. 120.1 519. 364. 271. 13.0 20.8 14.890 14.6 2.7 73. 16. 104.7 524. 369. 276. 10.9 18.0 10.248 13.0 2.9 66. 16. 64.5 433. 283. 193. 7.4 13.5 5.966 10.6 3.1 70. 17. 31.9 324. 170. 88. 3.4 8.0 2.954 8.1 3.6 79. 20. 12.4 175. 53. 14. 0.4 4.6 1.792 6.7 3.6 71. 20. 7.9 104. 20. 2.
73 WMO00010315 1961 1989, Münster 1000 52.1 52. -0.9 3.8 2.521 6.1 2.4 66. 19. 7.5 88. 14. 2. -0.6 4.9 4.967 6.0 2.3 45. 15. 14.5 86. 16. 3. 1.6 8.4 8.274 6.8 2.3 61. 17. 38.0 160. 45. 12. 4.1 12.7 13.051 8.0 2.2 52. 15. 68.6 251. 109. 50. 8.2 17.4 16.819 10.5 1.9 64. 16. 106.6 396. 242. 152. 11.0 20.4 17.883 13.1 1.8 74. 15. 118.3 470. 320. 230. 12.7 21.9 16.860 14.6 1.7 69. 14. 120.1 536. 381. 288. 12.5 21.9 15.076 14.5 1.7 65. 15. 101.5 533. 378. 285. 10.2 18.8 10.599 13.0 1.6 62. 14. 59.5 435. 285. 195. 6.8 14.2 6.546 10.6 1.8 54. 15. 27.8 327. 173. 92. 3.0 8.3 3.211 7.9 2.2 68. 18. 9.9 174. 57. 18. 0.5 5.0 2.026 6.7 2.3 76. 19. 5.8 110. 25. 5.
WMO00010224 1961 1989, Bremen 1000 53.0 5. -1.9 3.1 2.265 6.0 3.4 56. 19. 7.6 73. 10. 1. -1.6 4.1 4.577 5.9 3.3 38. 15. 14.2 70. 10. 1. 0.6 7.8 7.801 6.7 3.4 49. 17. 38.7 140. 34. 8. 3.3 12.5 12.583 8.0 3.1 49. 15. 69.5 236. 96. 40. 7.4 17.5 16.589 10.5 2.9 63. 16. 110.2 387. 232. 143. 10.5 20.7 18.001 13.1 2.7 69. 15. 124.6 467. 317. 227. 12.2 21.8 16.558 14.7 2.7 70. 16. 123.7 528. 373. 280. 11.9 22.0 14.820 14.5 2.6 68. 15. 105.6 526. 371. 278. 9.4 18.6 10.191 12.9 2.6 55. 16. 61.2 421. 271. 181. 6.2 13.7 6.004 10.5 2.9 55. 16. 28.6 309. 156. 78. 2.4 7.9 2.940 7.9 3.4 59. 19. 10.8 160. 47. 13. -0.4 4.4 1.805 6.6 3.4 61. 20. 6.4 96. 20. 4.
WMO00010317 1961 1989, ( Osnabrück 1000 52.2 97. -1.3 3.2 2.428 6.0 2.8 78. 20. 8.1 78. 11. 1. -1.1 4.1 4.756 5.8 2.6 52. 17. 14.4 76. 13. 2. 1.2 7.8 7.989 6.7 2.7 70. 19. 38.2 148. 40. 11. 3.8 12.3 12.544 7.8 2.3 58. 17. 67.5 242. 102. 45. 7.9 17.0 16.343 10.3 2.2 70. 16. 105.2 387. 232. 143. 10.9 20.2 17.544 12.9 2.1 86. 16. 119.1 467. 317. 227. 12.7 21.6 16.456 14.4 2.1 76. 16. 120.4 532. 377. 284. 12.5 21.7 14.627 14.2 2.0 71. 15. 102.2 529. 374. 281. 10.2 18.4 10.202 12.8 2.1 66. 17. 60.7 429. 279. 189. 6.8 13.7 6.326 10.4 2.2 63. 19. 29.1 317. 164. 86. 2.9 7.8 3.089 7.8 2.7 78. 20. 11.6 165. 52. 15. 0.2 4.4 1.928 6.6 2.7 87. 20. 6.8 101. 22. 4.
WMO00010338 1961 1989, ] Hannover 1000 52.3 56. -2.3 2.9 2.391 5.9 3.2 53. 19. 7.8 71. 11. 1. -2.2 3.9 4.588 5.8 3.1 36. 15. 13.6 67. 10. 2. 0.2 7.8 8.035 6.6 3.2 49. 17. 38.2 137. 36. 9. 2.9 12.7 12.640 8.0 2.8 50. 16. 67.8 233. 95. 41. 7.0 17.6 16.782 10.6 2.6 63. 15. 107.8 382. 228. 139. 10.3 20.9 18.326 13.3 2.4 73. 16. 123.6 467. 317. 227. 12.1 22.2 16.990 14.8 2.4 64. 15. 124.1 531. 376. 283. 11.7 22.2 15.078 14.5 2.3 63. 15. 104.9 525. 370. 277. 9.4 18.7 10.429 12.9 2.4 51. 15. 61.2 421. 271. 181. 5.9 13.7 6.247 10.4 2.7 43. 15. 29.1 304. 153. 78. 2.2 7.7 3.031 7.8 3.1 51. 18. 11.4 157. 47. 14. •0.8 4.1 1.873 6.5 3.2 60. 19. 6.5 92. 20. 4.
74 WMO00010406 1961 1989, Bocholt 1000 51.5 24. -0.5 4.1 2.590 6.4 2.9 62. 19. 8.5 94. 16. 2. -0.3 5.3 4.994 6.2 2.7 43. 14. 15.8 92. 18. 3. 1.8 8.8 8.322 7.0 2.8 62. 17. 40.7 168. 48. 13. 4.1 13.0 13.162 8.2 2.5 49. 15. 70.6 256. 113. 51, 8.1 17.7 16.854 10.7 2.3 63. 15. 109.2 399. 245. 154. 11.0 20.8 17.877 13.3 2.2 76. 15. 121.6 477. 327. 237. 12.6 22.3 16.809 14.9 2.1 77. 15. 122.8 542. 387. 294. 12.5 22.2 15.070 14.8 2.0 67. 15. 104.8 538. 383. 290. 10.2 19.1 10.622 13.3 2.0 60. 14. 62.3 440. 290. 200. 7.1 14.5 6.528 10.9 2.3 57. 15. 30.0 334. 181. 98. 3.2 8.5 3.277 8.2 2.7 66. 18. 11.4 179. 59. 19. 0.7 5.2 2.066 6.9 2.8 74. 18. 6.8 114. 27. 6.
WMO00010438 1961 1989, ] Kassel 1000 51.2 231. -2.1 2.3 2.563 5.5 2.0 55. 19. 7.2 62. 7. 1. -1.7 3.9 5.019 5.5 1.8 41. 15. 13.5 66. 9. 2. 0.9 8.0 8.459 6.4 1.9 51. 17. 37.3 147. 40. 12. 4.0 12.9 12.940 7.6 1.7 50. 15. 67.2 253. 112. 53. 8.0 17.7 16.548 10.2 1.5 69. 16. 103.4 399. 244. 155. 11.2 20.8 17.783 12.8 1.5 79. 16. 118.0 481. 331. 241. 12.8 22.4 17.119 14.0 1.4 66. 14. 121.7 546. 391. 298. 12.5 22.3 14.992 13.9 1.4 62. 15. 101.7 540. 385. 292. 9.9 18.9 10.601 12.4 1.4 53. 13. 60.6 433. 283. 193. 6.3 13.5 6.409 9.9 1.6 47. 14. 28.4 307. 155. 78. 2.2 6.9 3.142 7.3 2.0 58. 17. 10.2 143. 38. 10. -0.7 3.4 2.045 6.1 2.0 68. 19. 6.0 80. 14. 2.
WMO00010501 1961 1989, Aachen 1000 50.5 202. 0.0 4.5 2.925 6.2 2.8 63. 20. 11.2 103. 20. 3. 0.0 5.5 5.386 6.1 2.4 55. 16. 17.7 99. 23. 5. 2.3 8.8 8.679 6.9 2.5 68. 18. 43.2 176. 56. 19. 4.7 12.7 12.964 7.9 2.1 64. 17. 70.3 260. 118. 57. 8.4 17.2 16.608 10.4 1.9 77. 17. 105.9 397. 243. 153. 11.3 20.2 17.849 12.9 1.7 82. 16. 118.7 472. 322. 232. 13.1 21.9 17.233 14.3 1.7 81. 15. 123.9 543. 388. 295. 13.1 21.8 15.199 14.3 1.7 76. 14. 104.8 540. 385. 292. 10.9 19.0 11.354 12.8 1.9 58. 14. 67.4 449. 299. 209. 7.7 14.6 7.200 10.5 2.0 64. 15. 34.6 347. 193. 110. 3.6 8.8 3.762 7.9 2.6 72. 18. 14.6 189. 68. 26. 1.1 5.6 2.417 6.7 2.7 72. 19. 9.6 122. 31. 8.
WMOi9001051 3 1961 1989, Köln/Bonn 1000 50.5 91. -1.5 4.4 2.833 6.0 2.7 63. 19. 10.4 90. 16. 3. -1.4 6.0 5.337 5.9 2.5 47. 15. 17.9 89. 17. 3. 1.0 9.6 8.508 6.8 2.5 65. 18. 42.6 169. 51. 15. 3.6 13.8 13.231 8.0 2.3 55. 17. 72.2 261. 118. 55. 7.6 18.4 16.535 10.5 2.1 75. 16. 109.0 402. 247. 157. 10.7 21.4 17.642 13.1 2.0 85. 16. 121.3 481. 331. 241. 12.5 23.1 17.112 14.6 2.0 86. 15. 127.1 551. 396. 303. 12.1 22.9 14.978 14.5 1.8 76. 14. 105.4 542. 387. 294. 9.6 19.8 11.032 12.9 1.9 60. 14. 65.7 441. 291. 201. 6.3 15.0 6.885 10.4 2.2 55. 15. 33.5 329. 176. 96. 2.3 8.9 3.559 7.7 2.5 65. 18. 13.6 173. 57. 19. -0.2 5.6 2.337 6.6 2.5 72. 19. 7.9 110. 26. 7.
75 10532 1961 1989 Giessen (GER) 1000 50.3 186 -2.1 2.4 2.696 5.5 1.9 47 18 8.5 64 8 1 -1.7 4.1 5.181 5.5 1.8 40 14 15.3 66 9 1 1 8.6 8.674 6.4 2 50 16 40.1 155 43 12 3.9 13.4 13.524 7.6 1.8 47 15 71.1 260 117 55 7.9 18.1 16.998 10.2 1.7 66 15 108 403 248 158 11.2 21.4 18.471 12.6 1.7 65 15 125 488 338 248 12.8 23.2 18.041 13.9 1.6 59 13 130.5 558 403 310 12.5 22.9 15.512 13.8 1.5 59 13 106.9 548 393 300 9.7 19.4 11.06 12.3 1.5 48 12 63.4 437 287 197 6.1 13.5 6.564 9.9 1.5 50 13 29.4 304 152 76 1.9 6.9 3.242 7.3 1.9 59 17 11.4 139 37 10 -0.8 3.5 2.227 6.1 1.9 62 17 6.7 81 16 3
10609 1961 1989 Trier-Petrisberg 1000 49.5 265 -1.4 3 2.939 5.9 2.8 60 19 9.9 71 10 1 -1.1 4.9 5.498 5.9 2.7 53 15 18.8 78 13 2 1.3 9 8.937 6.7 2.8 65 17 45 164 47 14 4 13.4 13.528 7.8 2.4 53 15 76.1 261 120 58 7.8 18 17.036 10.3 2.2 69 16 112.2 400 245 154 10.9 21.2 18.524 12.8 2 73 14 126.7 481 331 241 12.5 23.2 18.476 14.1 1.9 71 13 134.5 554 399 306 12.3 22.8 15.688 14.2 1.9 71 14 110.2 543 388 295 9.8 19.6 11.487 12.7 2.1 60 13 69 440 290 200 6.4 14 6.827 10.3 2.2 65 16 33.9 316 164 86 2.1 7.4 3.568 7.6 2.6 75 18 13.3 148 43 12 -0.4 4 2.449 6.4 2.7 72 18 7.9 86 18 4
10637 1961 1989 Frankfurt/Main, Flughafen 1000 50 111 -2.2 3 2.876 5.8 2.4 44 18 8.8 67 9 1 -1.7 5 5.426 5.8 2.3 38 13 16.9 73 10 2 0.8 9.6 8.991 6.7 2.5 52 15 43.8 167 49 14 3.9 14.2 13.789 8.1 2.4 52 15 76.3 271 127 62 7.8 18.9 17.35 10.9 2.2 63 15 114 414 259 168 11.2 22.2 18.862 13.4 2.2 70 14 132 502 352 262 13 24.2 18.562 14.8 2.1 64 13 139.6 576 421 328 12.6 23.8 15.976 14.8 2 66 12 115.3 565 410 317 9.7 20.3 11.744 13.1 2 47 10 69.7 450 300 210 5.8 14.2 6.845 10.3 2 50 13 31.3 309 157 81 1.6 7.6 3.506 7.6 2.3 58 15 11.9 143 40 11 -1 4.1 2.414 6.3 2.4 54 16 7.2 85 18 4
10655 1961 1989 Würzburg ü(GER) 1000 49.5 268 -2.9 1.9 2.924 5.3 2.3 44 18 9.6 55 7 1 -2.2 3.9 5.516 5.4 2.3 37 14 17.1 62 8 1 0.7 8.7 9.084 6.4 2.4 46 15 43.6 154 44 13 3.9 13.6 13.666 7.6 2.2 48 15 75.9 264 122 60 8 18.4 17.153 10.3 2 57 15 112.1 409 254 164 11.2 21.6 18.658 12.9 1.9 72 15 127.5 492 342 252 12.8 23.6 18.506 14.1 1.8 55 13 135 565 410 317 12.6 23.3 15.92 14.1 1.7 58 14 111.3 556 401 308 9.7 19.9 11.676 12.4 1.7 41 12 69.2 445 295 205 5.6 13.9 6.948 9.7 1.8 42 15 32.7 303 151 76 1.5 6.9 3.592 7.1 2.3 49 16 13.3 134 36 10 -1.4 3.2 2.475 5.9 2.3 56 16 8.3 73 14 3
76 10685 1961 1989 Hof (GER) i 1000 50.2 567 -5.5 -0.8 2.867 4.7 2.9 56 19 6.2 19 1 0 -4.9 0.8 5.243 4.8 2.8 43 17 12.6 24 1 0 -2 5.2 8.803 5.7 2.8 50 17 35.2 83 15 3 1.4 10.3 13.196 7.1 2.5 57 16 64.1 177 61 22 5.6 15.4 16.786 9.6 2.3 74 17 99.6 326 173 94 8.8 18.7 18.298 12.0 2.1 76 16 115.3 412 262 174 10.4 20.5 17.838 13.1 2.1 75 15 120.9 479 324 231 10 20.2 15.536 13.0 1.9 80 15 99.6 469 314 221 7.5 17 11.611 11.3 2.1 53 14 61.6 366 217 131 3.7 11.6 7.246 8.8 2.4 54 15 28.8 238 97 41 -0.5 4.5 3.509 6.4 3 56 19 10.2 82 14 2 -3.8 0.7 2.343 5.2 3.1 68 19 5.5 35 4 0
10708 1961 1989 Saarbrücken 1000 49.1 322 -2 2.5 3.065 5.8 2.7 68 20 8.3 60 7 0 -1.4 4.4 5.79 5.8 2.6 57 15 17.7 70 10 1 1.1 8.3 9.295 6.6 2.6 68 17 43.3 153 43 13 3.9 12.7 13.963 7.7 2.3 61 15 75.3 249 111 53 7.8 17.1 17.603 10.2 2.2 83 16 112 385 230 141 10.8 20.3 19.259 12.7 2 82 15 127.2 466 316 226 12.6 22.4 19.408 14 1.9 73 13 136.8 542 387 294 12.4 21.9 16.642 14.1 1.8 73 13 112.5 532 377 284 9.9 18.9 12.299 12.6 2 62 12 70.4 432 282 192 6.2 13.4 7.308 10.2 2.2 70 14 33 303 151 77 1.6 7.1 3.867 7.5 2.5 84 16 11.9 137 37 10 -1 3.6 2.655 6.3 2.7 82 18 6.5 76 14 3
10727 1961 1989 Karlsruhe (GER) 1000 49 112 -1.5 3.7 3.19 5.9 2.1 57 19 9.9 81 14 2 -0.8 5.8 5.719 6 2 52 15 18.6 91 18 4 1.8 10.5 9.432 6.9 2.1 54 16 46.5 195 69 24 4.9 15.1 14.033 8.3 1.9 62 15 77.5 300 153 81 8.9 19.6 17.689 11.1 1.8 82 17 115.3 442 287 195 12.2 22.8 19.522 13.8 1.7 85 15 134.1 526 376 286 14.1 25.2 19.637 15.1 1.7 72 14 145.8 608 453 360 13.8 24.6 16.881 15.3 1.6 67 13 119.5 595 440 347 10.7 21.3 12.515 13.5 1.6 53 11 73.7 479 329 239 6.7 15 7.382 10.6 1.6 58 14 33.9 337 184 102 2.3 8.5 3.939 7.7 1.9 64 16 13.7 166 54 18 -0.4 4.8 2.761 6.3 2.2 66 17 8.6 98 24 6
10738 1961 1989 Stuttgart-Echterdingen (GER) 1000 48.4 396 -3.4 2.6 3.67 5.3 1.8 45 17 9.7 54 6 1 -2.5 4.4 6.021 5.4 1.9 41 14 18.2 65 9 1 0.3 8.7 9.301 6.2 2.1 45 16 44.5 149 43 12 3.5 13.1 12.907 7.6 2 59 16 71.9 249 111 53 7.5 17.5 15.601 10.2 1.8 83 17 102.2 388 233 145 10.8 20.8 17.423 12.7 1.7 96 15 119.6 474 324 234 12.6 23.1 17.802 14.0 1.6 71 13 131 553 398 305 12.2 22.6 15.522 13.9 1.5 81 14 108 540 385 292 9.3 19.7 12.142 12.2 1.4 57 11 69 435 285 195 5.1 14.3 7.797 9.5 1.5 43 12 33.7 301 150 77 0.6 7.7 4.409 6.9 1.8 54 14 11 136 37 11 -2.2 3.6 3.128 5.7 1.9 48 15 8.6 70 13 3
77 10763 1961 1989 Nürnberg(GER ) 1000 49.3 319 -3.9 1.8 3.076 5.2 2 46 18 7.9 45 4 0 -3.2 3.9 5.746 5.3 1.9 38 15 15.3 55 6 1 -0.5 8.5 9.389 6.2 2 47 15 40.6 138 36 10 2.8 13.5 14.096 7.6 2 47 14 72.9 245 107 49 7.4 18.5 17.778 10.1 1.8 66 15 112.4 401 246 157 10.7 21.8 19.498 12.6 1.7 74 14 129.9 487 337 247 12.4 23.7 19.199 13.9 1.6 70 14 136.5 560 405 312 12 23.1 16.55 14.0 1.5 67 14 111.4 545 390 297 8.9 19.7 12.327 12.2 1.5 50 12 67.5 429 279 190 4.8 13.9 7.521 9.4 1.6 45 12 31.5 289 140 70 0.7 6.9 3.809 6.9 1.9 44 16 11.4 125 31 8 -2.3 3.1 2.579 5.7 2 53 17 6.7 65 11 2
10776 1961 1988 Regensburg(GER ) 1000 49 366 -4.7 0.1 3.139 4.9 1.8 45 17 6.5 27 2 0 -3.5 2.6 5.764 5.1 1.8 37 14 13.9 40 3 0 -0.4 7.9 9.584 6.2 1.8 41 14 39.5 131 33 10 3.2 13.6 14.351 7.7 1.7 41 14 72.7 252 113 53 7.5 18.2 17.537 10.5 1.5 64 16 106.9 398 244 154 10.8 21.6 19.35 13.1 1.4 79 14 125.3 486 336 246 12.3 23.6 19.367 14.3 1.3 71 14 132.9 556 401 308 12.1 23 16.711 14.3 1.2 74 14 109 543 388 295 9.2 19.4 12.284 12.5 1.2 49 12 65.8 428 278 189 4.8 12.8 7.282 9.5 1.3 44 12 29.1 273 126 59 0.6 5.6 3.646 6.8 1.7 49 15 10.5 105 21 5 -2.9 1.6 2.599 5.4 1.8 48 16 5.5 44 5 1
10803 1961 1989 Freiburgi . Breisgau(GER ) 1000 48 269 -1.1 4.2 3.493 5.8 2.2 61 18 14.7 94 21 5 -0.2 6.1 6.113 6 2.1 54 15 22.3 103 26 7 2.7 10.3 9.704 6.8 2.3 65 17 52.8 205 79 31 5.6 14.7 14.201 8.1 2.2 82 16 84.1 305 159 87 9.5 19 17.398 10.8 2 108 18 118.9 442 287 196 12.8 22.4 19.853 13.3 2 115 16 140.7 528 378 288 14.8 24.8 20.184 14.9 1.9 98 14 152.5 615 460 367 14.4 24.2 17.376 14.9 1.9 103 14 126.5 598 443 350 11.7 21.1 12.983 13.3 1.8 71 11 81 491 341 251 7.5 15.2 7.913 10.5 1.8 66 14 39.7 352 199 116 2.9 9 4.423 7.5 2.1 71 16 18.5 183 68 27 0 5.3 3.068 6.2 2.2 66 16 12.9 113 33 12
10838 1961 1989 Ulm(GER ) 1000 48.2 522 -4.3 0.7 3.29 5 2 49 19 7 30 2 0 -3.4 2.8 5.82 5.2 1.9 42 16 15.2 45 4 0 -0.6 7.8 9.668 6.1 1.9 45 16 40.7 127 32 9 2.8 12.6 14.098 7.5 1.8 58 15 71.1 232 98 44 6.8 17.2 17.456 10.1 1.7 78 17 105.5 371 217 130 10.1 20.5 19.478 12.7 1.6 99 16 124 458 308 218 11.8 22.7 19.709 14.2 1.5 82 15 133.8 535 380 287 11.4 22.1 16.733 14.1 1.4 85 15 107.7 519 364 271 8.5 19 12.529 12.2 1.4 60 12 67 413 263 174 4.6 12.7 7.347 9.5 1.5 47 14 30.7 269 122 58 0.1 5.8 3.97 6.7 1.8 55 16 10.8 104 21 5 -3 1.8 2.787 5.4 2 49 18 5.7 46 6 1
78 10908 1961 1989 Feldberg(GER ) 1000 47.5 1486 -5.8 -1 4.05 3.9 7.2 174 21 17 17 1 0 -5.8 -1.1 6.463 4 6.5 139 18 21.4 18 1 0 -4.2 0.4 9.268 4.6 6.2 151 21 36.7 32 5 1 -1.4 3.7 13.284 5.5 5.4 142 20 60.5 76 16 4 2.6 8.1 15.949 7.3 4.8 168 22 87.6 170 58 22 5.8 11.4 17.875 9.3 4.4 169 20 103.1 259 122 61 8.3 13.9 18.552 10.6 4.2 163 18 116.5 343 190 112 8.1 13.5 15.944 10.6 4.3 169 18 97.4 335 182 102 6.1 11.3 12.376 9.2 5 124 16 67.7 262 125 63 3.1 8.1 8.639 6.9 5.3 145 16 44.3 183 70 28 -2.2 2.7 4.906 5 6.6 184 18 22.4 66 15 4 -4.6 0.5 3.698 4.2 7.3 193 19 18.9 34 4 1
10912 1961 1989 Villingen(GER ) 1000 47.6 679 -6 1.5 3.714 4.8 1.5 79 18 7.8 28 1 0 -5.2 3.4 6.091 4.9 1.5 69 15 15.8 40 3 0 -2.5 7.1 9.586 5.7 1.5 71 18 37.9 97 17 3 0.7 11.3 13.457 7 1.5 68 16 63.7 181 60 19 4.5 15.8 15.963 9.4 1.5 82 18 92.8 315 163 84 7.7 19.3 17.419 11.7 1.5 91 17 109 405 255 166 9.4 21.7 18.029 13.1 1.3 82 14 118.9 482 327 234 8.9 21.2 15.781 12.9 1.2 90 15 98.3 467 312 219 6.3 18.6 12.457 11.1 1.2 55 12 63.6 373 223 137 2.6 13.5 7.933 8.7 1.3 59 12 31.2 249 106 46 -1.5 6.7 4.538 6.3 1.5 83 15 11.7 99 19 4 -4.6 2.5 3.23 5.2 1.5 86 16 6.1 40 5 1
10929 1973 1989 Konstanz(GER ) 1000 47.4 443 -1.9 2.7 3.385 5.5 1.5 54 18 10.8 56 7 1 -1.4 4.1 5.899 5.5 1.4 49 15 18.3 62 7 1 1.5 9.6 9.918 6.6 1.5 50 17 45.8 175 56 20 3.9 13.4 14.527 7.5 1.5 73 15 74.7 259 117 55 8.2 18.5 17.786 10.4 1.3 84 17 110.8 415 260 170 11.7 21.7 19.583 13 1.3 107 17 129 501 351 261 13.9 24 19.594 15 1.2 111 16 138 587 432 339 13.6 23.3 17.027 15.2 1.1 88 15 113.9 572 417 324 11 20.2 12.666 13.5 1.1 75 13 71.8 468 318 228 6.8 13.4 7.178 10.2 1.2 65 19 34.2 314 161 83 1.9 7 4.142 7.2 1.4 58 16 14 140 34 9 -0.4 4.1 2.88 6.1 1.5 63 17 9.3 82 13 2
10991 1961 1989 Nördlingen(GER ) 1000 48.5 300 -4.6 1.1 3.126 5.1 1.7 40 16 7.7 38 4 0 -3.8 3.1 5.437 5.3 1.5 37 14 14 48 5 1 -0.7 8.1 9.069 6.2 1.6 38 14 38.7 131 32 8 2.5 13 13.285 7.7 1.5 48 14 66 232 97 42 6.5 17.6 16.598 10.4 1.2 71 16 98.2 374 220 132 9.9 20.9 18.182 13.1 1.3 84 15 116.2 463 313 224 11.5 23 18.358 14.3 1.1 74 13 123.8 535 380 287 11.1 22.5 15.455 14.1 1 71 13 99.8 520 365 272 8.2 19.3 11.413 12.1 1 51 11 61.5 413 263 174 4.1 13.3 6.938 9.3 1.2 42 12 29.5 270 123 57 0.2 6.4 3.775 6.8 1.5 46 15 11.4 113 25 6 -3 2.4 2.642 5.5 1.6 44 15 6.7 55 9 2
79 11105 1961 1988 Feldkirch(AUS ) 1000 47.2 439 -4.3 2.1 3.755 4.9 1.5 71 13 11.3 46 5 1 -2.9 4.5 6.621 5.2 1.7 61 11 20.9 64 11 3 0.1 9.2 10.375 6.2 2.1 63 13 49.4 154 47 15 3.7 13.9 14.423 7.7 2.1 81 13 81 264 125 64 7.6 18.3 17.179 10.2 1.9 100 15 112.2 401 247 158 10.9 21.4 18.846 12.7 1.8 133 15 128.9 485 335 246 12.9 23.3 19.068 14.7 1.7 137 14 137.2 560 405 312 12.4 22.5 16.509 15.1 1.5 153 14 111.5 542 387 294 9.6 19.8 13.218 13.2 1.4 92 10 72.9 441 291 202 5.1 14.2 8.333 9.9 1.4 62 9 36.8 300 150 77 0.6 7.8 4.665 6.9 1.5 72 11 16.5 136 39 14 -3.4 2.9 3.185 5.3 1.4 74 12 9.7 57 10 2
80 Annex 4.3b Temperature diagrams (monthly mean minimum and maximum) for weather stations used in the climatic analysis
81 STATION 06235: De Kooy (NL) STATION 06280: Eelde (NL) Long term monthly mean minimum & maximum temperatures Long term monthly mean minimum &maximu m temperatures
I I' M Month -•-Tmin. -*_Tmax.
STATION 06260: De Bilt (NL) STATION 06380:Zuid-Limbur g (NL) Long term monthly mean minimum &maximu m temperatures Long term monthly mean minimum &maximu m temperatures
M J J Month
STATION 07190: Strasbourg (FRA) STATION 07090: Metz (FRA) Long term monthly mean minimum &maximu m temperatures Long term monthly mean minimum &maximu m temperatures
STATION 07292: Luxeuil (FRA) STATION 07180: Nancy (FRA) Long term monthly mean minimum &maximu m temperatures Long term monthly mean minimum & maximum temperature
83 STATION 10203: Emden, Hafen (GER) STATION 10315: Muenster (GER) Long term monthly mean minimum & maximum temperatures Long term monthly mean minimum & maximum températures
STATION 10317: Osnabrueck (GER) STATION 10224: Bremen (GER) Long term monthly mean minimum & maximum temperatures Long term monthly mean minimum & maximum temperatures
M I I Month
STATION 10400: Duesseldorf (GER) STATION 10338: Hannover (GER) Long term monthly mean minimum & maximum temperatures Long term monthly mean minimum & maximum temperatures
F M A M j y M
STATION 10406: Bocholt (GER) STATION 10438: Kassel (GER) Long term monthly mean minimum &maximu m temperatures Long term monthly mean minimum & maximum temperatures
J l' M
S O N D
84 STATION 10513:Koeln/Bon n(GER ) Long term monthly meun minimum &maximu m temperatures STATION 10532:Giesse n(GER ) Long term monthly mean minimum & maximum temperatures
STATION 10609:Trier-Petrisber g (GER) Long term monthly mean minimum &maximu m temperatures STATION 10501:Aache n(GER ) Long term monthly mean minimum &maximu m temperatures
J F M S O N D 1 F M A
STATION 10708:Saarbruecke n(GER ) Long term monthly mean minimum & maximum temperatures STATION 10637:Frankfurt/Main ,Airpor t (GER) Long term monthly mean minimum &maximu m temperatures
STATION 10655:Wuerzbur g(GER ) STATION 10685:Ho f(GER ) Long term monthly mean minimum &maximu m temperatures Long term monthly mean minimum & maximum temperatures
85 STATION 10763:Nuernber g(GER ) Long term monthly mean minimum & maximum temperatures STATION 10727:Karlsruh e(GER ) Long term monthly mean minimum & maximum temperatures
j A s o STATION 10776:Regensbur g(GER ) Long term monthly mean minimum &maximu m temperatures STATION 10738:Stuttgart-Echterdinge n (GER) Long term monthly mean minimum & maximum temperatures
SON
STATION 10803: Freiburg i. Breisgau (GER) STATION 10908:Feldber g(GER ) Long term monthly mean minimum &maximu m tempérât Long term monthly meun minimum & maximum temperatures
STATION 109929:Konstan z (GER) STATION 10838:Ul m(GER ) Long term monthly mean minimum & maximum temperatures Long tenu monthly mean minimum &maximu m temperatures
86 STATION 10912: Villingen (GER) STATION 06670:Zueric h (SWI) Long term monthly mean minimum & maximum temperatures Long term monthly meun minimum & maximum temperatures
STATION 11105: Feldkirch (AUS) STATION 06998: Basel (SWI) Long tenu monthly mean minimum & maximum temperatures
87 Annex 4.3c Summarized temperature characteristics for selected weather stations in the Rhine basin, representing the three main biophysical regions
Station name Altitude Annual Coldest Warmest mean mean ampli month month ampli (m a.s.1.) max. min. tude mean mean tude temp. temp. (°Q temp. temp. (°C) Region I. Eelde 4 12.6 4.4 8.2 1.1 15.8 14.7 De Bilt 2 13.3 5.2 8.1 2.1 16.7 14.6 Twente 14 12.9 5.0 7.9 1.6 16.4 14.8 Osnabrück 97 12.7 5.6 7.1 1.0 17.2 16.2 Münster 64 13.1 5.8 7.3 1.5 17.3 15.8 Region II. Zuid-Limburg 125 13.3 5.6 7.7 2.1 17.2 15.1 Frankfurt 111 13.9 5.1 8.8 0.4 18.6 18.2 Metz 191 14.0 5.6 8.4 1.4 18.4 17.0 Trier 265 13.4 5.4 8.0 0.8 17.8 17.0 Würzburg 268 13.2 5.0 8.2 -0.5 18.3 18.8 Nürnberg 319 13.2 4.2 9.0 -1.1 18.1 19.2
Nancy 225 13.9 5.2 8.7 1.1 18.0 16.9 Saarbrücken 322 12.7 5.2 7.5 0.3 17.5 17.2 Karlsruhe 112 14.7 6.1 8.6 1.1 19.6 18.5 Region III. Luxeuil 278 14.7 4.2 10.5 1.3 18.2 16.9 Basel 316 14.3 5.7 8.6 0.9 18.9 18.0 Zürich 432 13.1 5.2 7.9 -0.1 18.3 18.4 Konstanz 443 13.5 5.7 7.8 0.4 19.0 18.6 Feldkirch 439 13.3 4.4 8.9 -1.1 18.1 19.2
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FH FH s_e t.> r-» t.- . t-r-xo\NeoNr«FHt>r Preparatory work included: Digitization of the missing Dutch part in the spatial (altitude, soil,etc. ) database sprovide d byRIZA ,fittin g ofma pprojection s (ace.t oRIZA , projection used in Rhine basin atlas (CHR/KHR, 1978) is unknown) of SC-map of administrative boundaries and grid-based Rhine basin maps provided by RIZA; subsequently, overlays of administrative boundaries (NUTS-2 level) and other base maps; correction of RIZA altitude map (containing severe errors - probably digitizing errors); plotting of complete Rhine basin soil map, and individual bio-climatic data surfaces, digitizing of annual precipitation map (asthi s was considered important for the bio-climatic classification, but not yet available in digitized form). Furthermore, a number of intermediate steps were required: 1. Re-coding of NUTS-2 boundaries and digitizing of Swiss administrative boundaries to delimit the 13administrativ e units (or, moreprecisely , 'statistical' units) distinguished in the Rhine basin for comparison of land use statistics and biophysical potentials. 2. Overlay of the hierachically-ordered (bio-)-climatic data surfaces a) Overlay BIO_CLIM and BIO_AMP, i.e. bio-climatic types at the first level (=annua l mean temperature) and second level (=annua ltemperatur e amplitude) The resultant map gives the bio-climatic main types b) Overlay map of main bio-climatic types (i.e. levels 1 and 2 combined) with the subtypes - level 3 (temperature of coldest month) and - level 4 (annual precipitation classes used to define humidity/aridity) c) Overlay all four (bio-) climatic data surfaces the resultant map showing all bio-climatic type combinations occurring in the Rhine basin 3. Preparing tables, showing area coverage (km2) of the main types (step 3a) and all main type/sub-type combinations (step 3c) for the entire Rhine basin and per region (i.e. the 13 statistical units distinguished) 4. Producing and plotting soil suitability map. From the 27 mapping units distinguished by RIZA, no.s 3,4,5,7,15,21,22,26 and 27 were re-coded (unsuitable for mechanized agriculture) The final output (maps/overlays) was achieved by carrying out the following tasks: 5. Using the resultant map (step 5 ) to generate a 'soil grouping map', a generalization of the former, whereby individual mapping units were grouped as follows: units 1 and 2 --> SOIL group CI units 8, 12, 13, 14, 16, 17, 18, 19, 25 -> SOIL group C2 units 6, 9, 10, 11,20 , 23, 24 --> SOIL group B all other mapping units occurring are re-coded as 'unsuitable' (SOIL group U) 93 6. Produce and plot bio-climatic suitability map. It is the overlay of all 4 climatic data surfaces; however, in addition to step 3c, all Alpine (A) types/combinations (un suitable for forest and crops) and Highland (H) combinations (unsuitable for most crops) have to be re-coded, as given in brackets, for that purpose. 7. Prepare tables showing area coverage for resultant maps of steps 5,6 and 7 (as done in step 4 for bio-climatic types 8. Overlay soil and bio-climatic suitability maps —> to identify bio-physical types and prepare tables (similar to steps 3 and 7) 9. Incorporate three new regression equations (i.e.fo r future climate) i.e. replace regression equations for current climate and repeat steps x-y required to arrive at the results (suitability maps and corresponding tables), but now for future climate conditions. The individual (27) mapping units of the CHR/KHR soil map were grouped as follows (an earlier, first grouping being indicated): SOIL group CI: units 1, 2, 24 and 25 SOIL group C2: units 8, 12, 13, 14, 16, 17, 18, 19 (25 deleted from here) SOIL group B: units 5, 6, 10, 11, 15, 20, 23 (9, 24 deleted from here) SOIL group U: units 3, 4, 7, 9, 21, 22 (5, 15 deleted from here) GLACIER, OPEN WATER (units 26 and 27) 10. Short description : Specified classes contained in the two (baseline and future) data surfaces 'BIO-CLIMATIC TYPES (1.level....)' have to be combined with specified elements from the data surface 'SOIL SUITABILITY GROUPING' to come up with two new synthesized surfaces 'CLASSIFICATION OF LAND SUITABILITY FOR CROP CULTIVATION' (one for baseline, the other for future climate). Each of the two new maps will contain 5 classes of land, defined (i.e. by overlaying elements of soil grouping and each, the baseline & future mean annual temp, data sur face): Class 1 : Soil groups C1,C2 linked with temp, zone L+, L* Class 2 : Soil groups C1,C2 linked with temp, zones L- Class 3 : Soil groups C1,C2,B linked with temp, zone M and soil group B linked to L-,L*,L+. Class 4 : Soil groups C1,C2,B linked with temp, zone U Class 5 : Soil group U linked with (all) temp, zones A,H,U,M,L-,L*,L+ and soil group C1,C2,B linked with temp, zones A and H The first title is the same for the two maps, sub-titles are as in the data surfaces BIO- CLIM TYPES (1.level..) In the legend, which applies to both maps, the classes 1 to 5 will have the following designations 1 Very high 2 High 3 Moderate 4 Marginal 5 Physically unsuitable 94 Make plots in A 4 format (black and white) (—>fo r higher suitability, darker hatches or grey sh. e.g. for class 1almos t black). Add figures of longitude and latitude to the overlaying grids. 11. Spatial statistics on the basis of the output of TASK 10, i.e. calculate area cover of each suitability class for each of the statistical regions (km2an d %)an dfo r the entire Rhine basin. 95 MAP 1 RHINE BASIN MEAN ANNUAL PRECIPITATION AND STATISTICAL" REGIONS Legend > = 400 and < 600 mm > = 600 and < 800 " > = 800 and < 1000 " >= 1000 and < 1200 " >= 1200 and < 2000 " > = 2000 mm LAND USE PROJECTIONS FOR THE RHINE BASIN proj.ni: 7343 map composition: Theo van dei Heijden & Jandirk Bulens digitized trom CHR/HKR, 1978 all rights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 97 MAP 2 RHINE BASIN SOIL SUITABILITY GROUPING Legend *) FOR MECHANIZED AGRICULTURE AND "STATISTICAL" REGIONS Soil group C1 Soil group C2 Soil group B Soils unsuitable for agriculture Open water, glaciers, snow *) For explanation of soil grouping see text VI - LAND USE PROJECTIONS FOR THE RHINE BASIN proj.nr: 7343 map composition: Theo van det Heijden & 46 Jandirk Bulens soil grouping by Andronikov, Van Diepen & Roettei, based on CHK/KHR 1976 all lights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 98 MAP 3 RHINE BASIN BIO-CLIMATIC TYPES (1 level = mean annual temperature) BASELINE 1961 - 1989 AND "STATISTICAL" REGIONS Legend Temp. (C) Name oI temperature belt < 1 Alpine zone 1 j > = 1 and < 5 Highland zone ['"'"] > = 5 and < B Upland zone | j > = 8 and < 9 Midland zone [',"'," i > = 9 and < 10 Lowland zone (mod. cool) > = 10 and < 11 Lowland zone (mod. warm) LAND USE PROJECTIONS FOR THE RHINE BASIN pro] nt 7343 map composition: Theo van der Heijden & 46 Jandirk Bulens bio climatic classification by Roettei & Van Diepen, 1994; based on own calculations all rights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 99 MAP 4 RHINE BASIN BIO-CLIMATIC TYPES (1, level = mean annual temperature) DECADE 2040 - 49 (BaU-BEST SCENARIO) AND "STATISTICAL" REGIONS 4 5 Legend Temp. (C) Name of temperature belt Alpine zone Highland zone Upland zone Midland zone Lowland zone (mod. cool) Lowland zone (mod. warm) Lowland zone (warm) LAND USE PROJECTIONS FOR THE RHINE BASIN proj nr 7343 map composition: Theo van der Heijden & 46 *"$*'•$' Jandirk Bulens ** >ï, '^ bio-climatic classification by Roettet & s''z2k';' Van Diepen, 1994; based on own calculations , Ji / ail rights reserved, January 1994 '", ' The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 100 MAP 5 RHINE BASIN BIO-CLIMATIC TYPES (3. level mean temp, of coldest month) BASELINE 1961 - 89 AND -STATISTICAL" REGIONS Legend Mean temp, coldest month (degrees C) Symbol >= 3 Jandirk Bulens bio-climatic classification by Roettet & Van Diepen, 1994; based on own calculations all tights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 101 MAP 6 RHINE BASIN BIO-CLIMATIC TYPES (3. level = mean temp, of coldest month) DECADE 2040 - 49 (BaU-BEST SCENARIO) AND "STATISTICAL" REGIONS Legend Mean temp, coldest month (degrees C) Name symb 1 ! >= 3 mild a r~i 1.5 - 2.9 cool b tna 0.0 - 1.4 cold c ill -3 - -0.1 frosty d •1 < -3 frosty/icy e LAND USE PROJECTIONS FOR THE RHINE BASIN proj.ni: 7343 map composition: Theo van der Heijden & 46 — Jandirk Bulens bio-climatic classification by Roetter & Van Diepen, 1994; based on own calculations all rights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 102 MAP 7 RHINE BASIN CLASSIFICATION OF LAND SUITABILITY FOR CROP CULTIVATION BASELINE 1961 - 1989 AND "STATISTICAL" REGIONS Legend *) ^^| Very high •i High Moderate lllllll Marginal Physically unsuitable *) for explanation of land suitability classes, see text map composition: Theo van der Heijden Jandirk Bulens land suitability classification by Roetter & Van Diepen, 1994; based on their soil grouping and bio-climatic maps all rights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 103 MAP 8 RHINE BASIN CLASSIFICATION OF LAND SUITABILITY FOR CROP CULTIVATION DECADE 2040 - 49 (BaU-BEST SCENARIO) AND -STATISTICAL" REGIONS Legend *) Very high High Moderate Marginal Physically unsuitable *) for explanation of land suitability classes, see text LAND USE PROJECTIONS FOR THE RHINE BASIN proj.nr: 7343 map composition: Theo van der Heijden & 46L Jandirk Bulens land suitability classification by Roettet & Van Diepen, 1994; based on their soil grouping and bio-climatic maps all rights reserved, January 1994 The WINAND STARING CENTRE WAGENINGEN, The NETHERLANDS 104 MAP 9 RHINE BASIN BIO-CLIMATIC TYPES UNSUITABLE FOR FOREST AND/OR CROPS BASELINE 1961 - 1989 Legend AND "STATISTICAL" REGIONS Alpine temperature zone 4 5 unsuitable for forests and crops Highland temperature zone unsuitable for most crops 105