River Louros Modeling, Final Report

SEVENTH FRAMEWORK PROGRAMME THEME 6: Environment (including Climate Change)

Adaptive strategies to Mitigate the Impacts of Climate Change on European Freshwater Ecosystems

Collaborative Project (large-scale integrating project) Grant Agreement 244121 Duration: February 1st, 2010–January 31st, 2014

Deliverable 5.4 River Louros modeling, final report

Lead contractor: URead Other contractors involved: UPatras Due date of deliverable: Month 48 Actual submission date: Month 48

Work package: 5 Contributors: Martin Erlandsson, Andrew Wade, Yaron Herschkowitz, Christina Papadaki, Paraskevi Manolaki, Eva Papastergiadou Estimated person months: 12

TABLE OF CONTENTS

ABSTRACT...... 4 1. BACKGROUND ...... 5

1.1. SITE DESCRIPTION ...... 5 1.2. AVAILABLE FLOW AND CHEMISTRY DATA ...... 7 1.2.1. Flow data ...... 7 1.2.2. Chemistry data ...... 7 1.3. INITIAL DATA EXPLORATION ...... 8 1.3.1. Mean nutrient levels ...... 9 1.3.2. Seasonal variability for nitrate ...... 10 1.3.3. Seasonal variability for phosphate ...... 11 1.3.4. Flow-concentration relationship for nitrate ...... 11 1.3.5. Flow-concentration relationship for phosphate ...... 11 1.3.6. Spatial variability of nutrients along the river profile ...... 12 2. MODEL SETUP ...... 14

2.1. DEFINING CATCHMENT AND SUBCATCHMENTS BOUNDARIES ...... 14 2.2. METEOROLOGICAL DATA ...... 15 2.3. LAND COVER CLASSES ...... 16 2.4. CROPS, FERTILISERS AND IRRIGATION DEMANDS ...... 17 2.5. NITROGEN DEPOSITION ...... 21 3. MODEL CALIBRATIONS ...... 21

3.1. HYDROLOGY (PERSIST) ...... 21 3.2. NITROGEN MODELLING (INCA-N WITH PERSIST) ...... 23 3.3. PHOSPHOROUS MODELLING (INCA-P) ...... ERROR! BOOKMARK NOT DEFINED. 4. MODEL TESTING ...... 28 5. SENSITIVITY ANALYSIS ...... 28 6. DEFINING CLIMATE AND LAND USE SCENARIOS ...... 29

6.1. METEOROLOGICAL DATA ...... 29 6.2. LAND USE SCENARIOS ...... 30 6.3. MODEL SCENARIOS ...... 31 7. RESULTS FROM CLIMATE AND LAND USE SCENARIOS ...... 31

7.1. HYDROLOGY ...... 31 7.2. NITROGEN ...... 32 7.3. PHOSPHORUS ...... 33 7.4. CONCLUSIONS FROM THE CLIMATE AND LAND USE SCENARIOS ...... 34 7.5. CLASSIFICATION OF ECOLOGICAL STATUS ...... 34 8. MITIGATION MEASURES...... 35

8.1. DECRIPTIONS OF MITIGATION MEASURES ...... 35 8.2. EFFECTS ON NITROGEN ...... 36 8.3. EFFECTS ON PHOSPHORUS ...... 37 9. ASSESSMENT OF BIOLOGICAL STATUS ...... 37

9.1. AVAILABLE DATA AND CALCULATION OF MACROPHYTE INDEX ...... 37 9.2. CLASSIFICATION OF BIOLOGICAL STATUS ...... 39 9.3. RELATIONSHIP WITH PHYSIOCHEMICAL VARIABLES ...... 40 9.4. ANTICIPATED CLIMATE EFFECTS ON ECOLOGY ...... 43

10. CONCLUSIONS ...... 43

10.1. AVAILABLE AND REQUIRED DATA ...... 43 10.2. HYDROLOGY ...... 44 10.3. NITROGEN ...... 44 10.4 PHOSPHORUS ...... 45 10.5. IMPACTS FROM CLIMATE ...... 45 10.6. IMPACTS FROM LAND COVER SCENARIOS AND MITIGATION MEASURES ...... 45 10.7. OTHER PRESSURES ON ECOLOGY ...... 46 10. REFERENCES ...... 45

Abstract

The Louros river is a medium-sized river (catchment size ≈900 km2) situated near the west coast of mainland Greece in a mountainous setting. The catchment is largely rural and relatively sparsely populated, dominated by mountainous shrubland, but with considerable areas of arable land in the lowland plain. The area is wet by Mediterranean standards, with an average annual precipitation up to 1300 mm in the higher parts. The Louros river has been highlighted as vulnerable for eutrophication in previous reports and publications, although records of observed data are very ambiguous. This study aimed to calibrate the hydrological and hydrochemical models of PERSiST, INCA-N with PERSiST and INCA-P with PERSiST, to simulate future hydrology, nitrogen and phosphorus under a number of different climate and land use scenarios. The models were calibrated for the period of 2001-2012. Thereafter, stream-flow, nitrogen and phosphorus concentrations were simulated for the baseline period of 1981-2010, and for the scenario period of 2031-2060, using three alternative climate models and four different land use scenarios. Shortage of data proved to be the most serious obstacle for carrying out the model-based assessment, as data for calibration was collected from six different sources which upon inspection turned out to be in very poor agreement. In the end, the models were calibrated against observations from two different sites with only 11 observations each. While this is far from sufficient for calibration of complex models as the ones used, it was still possible to draw some conclusions from the climate and land use scenario simulations. The modelled changes in discharge were significant for all three climate scenarios, with a reduction in average flow of 20-30 %. In contrast, neither climate, nor land use change had any substantial effect on nutrient concentrations (nitrate and phosphorus). As for the land use change scenarios, the inferred impacts on fertiliser loads were limited, and as the observed time series suggest very limited leaching of nutrients from arable land, the effects from land use scenarios on stream nutrient concentrations were negligible. As for climate, the forecasted reduction in runoff is accompanied by a reduction of the leaching of nutrients from the soils by a similar magnitude. Thus, the modelled net effect from climate change on stream nutrient concentrations became small. However, the total loads of nutrients transported by the river to the recipient of the Amvrakikos gulf were substantially reduced. The reason for the simulated reduction of nutrient leaching from the soils was that longer water residence time in the soil and less runoff meant that more of the nutrients were available for plant uptake. It should be acknowledged that this is a highly uncertain result, as negative effects on plant growth from drought cannot be ruled out. Given this possibility, nutrient concentrations could instead increase substantially in the future. Finally, different mitigation measures with the purpose of improving the water quality of River Louros were considered. These measures all involved reduction of the fertiliser loads in one way or another. The total reduction of fertilisers was up to 75 % of the baseline load for nitrogen and 67 % of the baseline load for phosphorus. The simulated effect of the mitigation measures was negligible for nitrate concentrations, whereas phosphorus concentrations were reduced by 27-32 %. However, the water quality fulfilled the criteria of “good ecological status” for both nitrate, ammonium, total phosphorus and SRP for all simulated scenarios, including the baseline.

1. Background

1.1. Site description

The Louros river is a typical Mediterranean middle-sized river situated in the Epirus region in the western part of the Greek mainland (Fig. 1). The river emerges from a spring lake (Terovo, 300 m.a.s.l.) near the mountain of Tomaros, with has an elevation of almost 2000 m.a.s.l. It is then fed by numerous springs on its course to the sea, the largest one being close to the village of Agios Georgios approximately halfways to its outlet. Downstream of Agios Georgios is a small hydroelectric dam. The river then flows through an agricultural plain where water is taken for irrigation during summer before it discharges in the Amvrakikos Gulf which is a Ramsar (Ramsar Convention Secretariat, 2004) and Natura 2000 (Office for Official Publications of the European Communities, 2000) site, important for nesting and wintering for several rare bird species.

Figure 1. Location of the Louros catchment in western Greece.

The catchment area at the outlet to the Amvrakikos Gulf is approximately 900 km2, and the approximate total length of the main river from source to outlet is 73 km. The mean annual discharge is around 24 m3/s. The catchment is largely rural and relatively sparsely populated, the largest settlements are the towns of Fillipiada (pop 8,400) and Louros (pop 5,200). A large part of the catchment is mountainous with steep slopes and sparse vegetation, but a substantial part of the lowland is arable land (Fig. 2). The Louros provides water for irrigation of about 120 km2 of cultivated land. In the region, more than 100 large and small agriculture industries and fish farms are operating (Ovezikoglou et al., 2004).Water is also taken for drinking water supply and industrial uses (Kotti et al., 2005). It receives treated domestic effluent and effluent from light industrial activities including meat processing, abattoirs, pig farms and a small quantity of olive mill waste water, mostly during the autumn and early winter months. For water management purposes, the catchment has been divided in three subcatchments: “The Uplands” (A), “Arta plain” (B) and “Preveza plain” (C) (Fig. 2) (Skuras et al., 2012).

Figure 2. Land cover of the Louros catchment, and the three subcatchments A, B and C defined for water management purposes.

The upper, mountainous parts of the catchment are largely covered by a permeable layer of fractured limestone, with smaller parts covered by impermeable formations of flysh, whereas the lower parts are covered by alluvial deposits (Leontiadis and Smyrniotis, 1986). The shape of the river network and the valley floor morphology have been significantely altered in the lowlands by the building of barriers to prevent flooding. Alterations of the delta and the coastline are however negligible as the predominantly calcareous bedrock of the catchment results in a small loading of particulate matter and sediments, and the hydromorphological effects from the hydroelectric dam are limited (Kapsimalis et al., 2005; Poulos et al., 2008). As the catchment is situated in a mountaneous area near the west coast of Greece, it recieves relatively large volumes of convective precipitation, and rainfall is high for Mediterranean conditions; the average annual precipitation ranges from 800 mm in the lowlands up to 1300 mm in the mountaineous areas.

The river has been highlighted as vulnerable for eutrophication, and two published studies have classified the water quality as “fair” or “poor to fair“ (Ovezikoglou et al., 2003; Kotti et al., 2005). However, the nutrient concentrations are in general relatively low in the published studies (average nitrate < 1 mg-N/L, average phosphate < 15 µg-P/L), with the exception of phosphate in the study by

Kotti et al. (2005), which reported concentrations of PO4-P of up to 3000 µg /L. Such high phosphate concentrations are however not supported by any other available data, and a possible explanation is that they were given in the wrong unit.

As for being a subject to hydrogeochemical modelling, there are several difficulties with the Louros catchment, associated with:

a) a shortage of data for discharge and nutrients to calibrate the model against, and b) a very complex hydrology with poorly defined catchment boundaries at both the top- and bottom ends of the system. The advantages lies in good knowledge about agricultural practises in the area and hence the nutrient loads, and also in a sound analysis of future scenarios for land use and water utilisation.

1.2. Available flow and chemical data

1.2.1. Flow data

The only site with an available record of observed flow is the dam outlet (approximately at the boundary between subcatchments A and B in Fig. 2), provided by the hydroelectrical power plant. From the dam, the water outflow is regulated by the operation of a water gate, from which the water is led through a system of pipes to the plant turbines, approximately 1 km downstream of the dam. When the plant is running on full capacity, three turbines are in operation, but depending on electricity demands, fewer turbines may be in operation. The water level may occasionally exceed the dam capacity and flow over the levee; this typically happens once or a few times each year. The recorded discharge is the sum of the flow through the turbines and the dam overflow.

The discharge is thus partly regulated by the capacity at which the power plant is operating. However, the dam is shallow, with an approximate retention time of one day, and the storage capacity of the dam is very limited. Still, this discharge record can only give limited insights in the rainfall-runoff relationship of the area, especially for floods when the dam capacity is exceeded. Furthermore, there are no measurements of discharge from the agricultural plain (subcatchments B and C), which is where most of the water for irrigation is taken.

From previous studies, it is known that the runoff coefficient in the area is approximately 0.65 (65 % of the precipitation generates runoff, 35 % evapotranspirates) (Nikolaou, 2001). The base flow index is approximately 0.85, meaning that 85 % of the hydrologically effective rainfall recharges the groundwater, whereas the remaining 15 % goes to soil runoff or direct runoff (Katsanou et al., 2011).

1.2.2. Chemistry data

For water chemistry observations, i.e. nitrogen and phosphorus, several different data sources were available, from agencies, scientific publications and projects of the University of Patras, Department of Biology (UPAT-BIO) (Table 1). Two different data sets were available from the Ministry of Environment (YPEKA), the first sampled for nitrate and phosphate in 1998-1999 at two sites on the main river, plus one sampling site in the Agios Georgios springs, the second sampled for nitrate, phosphate, ammonium and total phosphorus in 2006-2009 at two sites on the main river. One data set was available from the Ministry of Rural Development (MRD), sampled for nitrate in 2002-2004 at three sites on the main river, plus the Vossa tributary. Two data sets from student projects were available. The UPAT-BIO_Manolaki data set was sampled for nitrate, phosphate, ammonium and total phosphorus in 2005-2007 at eight sites on the main river, plus four sites in the Tomaros springs and two sites in the dam. Some of the sites on the main river are located close to each other, so effectively only six of these sites were used. The second student data set is the UPAT-BIO_Papadaki data sets, sampled for nitrate, phosphate, ammonium and total phosphorus in 2011-2012. The sampling sites were changed twice during the period of the sampling, so the UPAT-BIO_Papadaki data can be divided in three subsets, P1 (Aug – Oct 2011) with samples from eight sites at the main river (all below the dam) and two tributaries, P2 (Nov 2011 – May 2012) with samples from nine sites at the main river (three above the dam) plus one site in the Tomaros springs, and P3 (Jun –Aug 2012)

with six sites at the main river, one site in the Tomaros springs, two sites in the Agios Georgios springs and one tributary. Finally, the Ovezikoglou data set (Ovezikoglou et al., 2003) was sampled for nitrate, phosphate, ammonium and total phosphorus in 2002-2003 at three sites on the main river, plus the Vossa tributary. However, from this data set, only annual means from each site, and seasonal means taken over all sites, were available.

Table 1. Summary of the six available data sets of nitrogen and phosphorus species. No. Time samples Constituents Name Source Frequency No. sites period from each analysed site Ministry of 1998- Semi- 2 (main river) YPEKA 1 (Y1) 15 NO -, PO 3- Environment 1999 monthly + 1 (springs) 3 4 Ministry of 2002- Semi- 3 (main river) MRD rural 13-17 NO - 2004 monthly + 1 (tributary) 3 development UPAT- Student 8 (main river) NO -, NH + 2005- 3 4 BIO_Manola project (Uni of Seasonal 9 + 4 (springs) PO 3-, Total- 2007 4 ki (M) Patras) + 2 (dam) P Ministry of 2006- NO -, NH + YPEKA 2 (Y2) Seasonal 11 2 3 4 Environment 2009 P2O5, Total-P UPAT- 4 (full period) Student NO -, NH + BIO_Papada 2011- + 8 (parts of 3 4 project (Uni of Monthly 3-13 PO 3-, Total- ki (P1, P2, 2012 the period) 4 Patras) P P3) + 1 (springs) - + NO3 , NH4 PO 3-, Total- Ovezikoglou Scientific 2002- 3 (main river) 4 Seasonal 4 P (only (O) publication 2003 + 1 (tributary) mean values available)

1.3. Initial data exploration

Unfortunately, initial analyses of the data revealed that the time series from the different data sources were very different, in terms of mean concentration, seasonal variation, and flow- concentration relationship. For the comparison between different data sources, one sampling point located in the agricultural lowlands were selected from each data set. The site furthest upstream was Filippada from the UPAT-BIO_Papadaki data set, and the site furthest downstream was Lympohotitis from the YPEKA2 data set. The distance between Filippiada and Lympohotitis is approximately 17 km. The Ovezikoglou data set consist of mean values from Kalogirou, St. Spiridou and Petra. The tributary of Vossa, draining an area of more intensive agricultural activities, enters the Louros at a point between Kalogirou and St. Spiridou (Fig. 3). Still, the spatial variability in nutrient concentrations along the river is relatively small, according to all available data sets.

Figure 3. Sites used for inter-comparison between the different data sets.

1.3.1. Mean nutrient levels

For nitrate, the difference in mean concentrations from the six different data sources is substantial. The lowest mean nitrate concentrations were recorded for the YPEKA1 data set from Kalogirou, 1998-1999 (0.49 mg N/L). The highest mean nitrate concentrations were recorded for the MRD data set from Kalogirou, 2002-2004 (2.83 mg N/L). Mean concentrations from the other data sets ranges from 0.63 to 1.68 mg N/L (Fig. 4).

For phosphate, the YPEKA1 data set showed extremely high mean concentrations (1.22 mg P/L). As no other data from the Louros supports phosphate concentrations this high, this data set was excluded from further analysis. The variability between the four remaining data sets were still considerable, although less extreme than for nitrate (Fig. 5). The lowest mean phosphate concentrations were recorded for the Ovezikoglou data set (mean from Kalogirou, St. Spiridou and Petra) 2002-2003 (14 µg P/L). The highest mean phosphate concentrations were recorded for the YPEKA2 data set from Lympohotitis, 2006-2009 (43 µg P/L).

4.5

3.5 YPEKA1

3 MRD 2.5

Manolaki

N(mg/L) -

3 2

Papadaki NO 1.5 YPEKA2 1 Ovezioglu 0.5

0 1996 2001 2006 2011

Figure 4. Time series of NO3 from the six available data sets.

0.1 0.09

0.08

0.07 0.06 Manolaki 0.05 Papadaki 0.04 YPEKA2 Ovezioglu

Phosphate (mgP/L) Phosphate 0.03 0.02 0.01 0 2000 2005 2010

Figure 5. Time series of phosphate from the four available data sets.

1.3.2. Seasonal variability for nitrate

The data was grouped in four seasons; winter (Dec-Feb), spring (Mar-May), summer (Jun-Aug) and autumn (Sep-Nov). Lumping all data sets together (excluding the Ovezikoglou data set), the order of the seasons in mean nitrate concentrations was: Summer (mean = 1.08 mg N/L) < Spring (mean = 1.36 mg N/L) < Autumn (mean = 1.73 mg N/L) < Winter (mean = 2.17 mg N/L). This analysis was strongly influenced by the MRD data, and in particular two observations of very high nitrate concentrations (~10 mg N/L). Excluding the MRD data, the seasonal order becomes: Autumn (mean = 0.79 mg N/L) < Summer (mean = 1.04 mg N/L) < Spring (mean = 1.20 mg N/L) < Winter (mean = 1.25 mg N/L). Analysing the data sets separately for seasonal variation is difficult due to the low number of samples. However, there are clearly differences between them in terms of seasonality. The MRD data set showed significantly higher nitrate concentrations in autumn compared to spring (pooled t- test, p < 0.05). The UPAT-BIO_Papadaki data set had significantly higher nitrate concentrations in

spring relative to all other seasons, as well as significantly higher concentrations in winter relative to autumn. The YPEKA2 data set showed significantly higher concentrations in winter relative to summer and spring. The YPEKA1 and UPAT-BIO_Manolaki data sets showed no significant seasonal differences.

1.3.3. Seasonal variability for phosphate

For the seasonal variability of phosphate, the UPAT-BIO_Manolaki, UPAT-BIO_Papadaki and YPEKA2 data sets were analysed together. The seasonal differences were small, the order of the seasons ranked after mean phosphate concentration was: Spring (mean = 0.29 mg P/L) < Summer (mean = 0.30 mg P/L) < Autumn (mean = 0.31 mg P/L) < Winter (mean = 0.40 mg P/L). No statistically significant seasonal differences could be detected for any of the individual data sets.

1.3.4. Flow-concentration relationship for nitrate

Of the individual data sets, the only one displaying a significant flow-concentration relationship for nitrate is the UPAT-BIO_Papadaki data set, for which the relationship is positive (p = 0.003). Lumping the data together, the resulting flow-concentration relationship is, outliers from the MRD data set aside, more-or-less chemostatic, with constant nitrate concentrations and variability along an increasing flow gradient (Fig. 6).

4.5 4 3.5

3 YPEKA1 2.5

MRD N(mg/L)

- 2 3 Manolaki

NO 1.5 Papadaki 1 YPEKA2 0.5 0 5 10 15 20 25 30 Flow (m3/s)

Figure 6. Flow-concentration relationships for nitrate.

1.3.5. Flow-concentration relationship for phosphate

There are no significant flow-concentration relationships for any of the analysed data sets (UPAT- BIO_Manolaki, UPAT-BIO_Papadaki, YPEKA2). The relationship is insignificantly negative for UPAT- BIO_Manolaki (p = 0.22) and UPAT-BIO_Papadaki (0.20), and insignificantly positive for YPEKA2 (p = 0.51). When the data is lumped together, no clear relationship can be seen. Perhaps, a vague pattern can be seen of decreasing concentrations with increasing flow for low to moderate discharge, and increasing concentrations with increasing flow in the higher end of the scale (Fig. 7). This could indicate a mixture of point- and diffuse sources, but again, the relationship is very weak.

0.1 0.09

0.08

0.07 0.06 0.05 Manolaki 0.04 Papadaki 0.03

Phosphate (mgP/L) Phosphate YPEKA2 0.02 0.01 0 5 10 15 20 Flow (m3/s)

Figure 7. Flow-concentration relationships for phosphate.

1.3.6. Spatial variability of nutrients along the river profile

For investigating the spatial variability of nutrient concentrations along the river reach, mean concentrations along the river profiles from UPAT-BIO_Manolaki, UPAT-BIO_Papadaki and YPEKA2 were compared. The two former data sets have several observations from both the upland and lowland parts of the catchment, whereas the latter has one observation point only above the dam, and one in the lowland plain. The concentration profiles were then compared with the cumulative relative area of arable land. The proportion of arable land increases monotoneously along the reach, from 8 % in the first reach to 22 % at the river mouth. A large step increase occurs when the Vossa tributary enters, where the cumulative arable area increases from 11 to 20 %.

For nitrate, the spatial variability along the river reach is completely overridden by the variability between the different data sources (Fig 8). The largest coefficient of variance (CV) for any individual data set is 11.1 % (UPAT-BIO_Manolaki), whereas the CV for the mean values of the different data sets is 53.9 %. The UPAT-BIO_Manolaki data set suggests that the highest concentrations are found in the uplands, after which a considerable amount of nitrate is retained in the dam, where 20 % of the nitrate is lost. The nitrate concentrations then again increase slightly in the lowlands.The YPEKA2 data set agrees with this picture, whereas for the UPAT-BIO_Papadaki data set, the highest concentrations are found in reach 7, just below the dam. Regardless of the differences, none of the data set suggests any substantial nitrate leaching from the arable areas.

Figure 8. Mean nitrate concentrations along the river profile from three different data sets, and the cumulative proportion of agricultural areas (blue area). Reach 0 corresponds to the spring lake of Terovo.

For phosphate, the spatial variability dominates over the variability between different data sources (Fig. 9). The CV for the individual data sets is between 28.6 % (UPAT-BIO_Papadaki) and 66.7 % YPEKA2, whereas the CV for the mean values of the different data sets is 17.7 %. Both the UPAT- BIO_Manolaki and the YPEKA2 show a substantial increase in phosphate when going from upland to lowland (110-180 %). The UPAT-BIO_Papadaki data set also show a general increase in phosphate along the river reach, allthough the highest concentrations are observed just before the confluence with the Vossa tributary.

Figure 9. Mean phosphate concentrations along the river profile from three different data sets, and the cumulative proportion of agricultural areas (blue area). Reach 0 corresponds to the spring lake of Terovo.

2. Model setup

2.1. Defining catchment and subcatchment boundaries

According to the standard procedure for semi-distributed models (Whitehead et al., 1998), the Louros catchment was divided into a number of smaller reaches (or subcatchments), according to where observations of chemistry or flow are available (Fig. 10, Table 2). The Louros was divided in 16 catchments. The groundwater recharge area of the Louros is considerably larger than the topographic catchment. The boundaries of the groundwater catchment were estimated by Dr Evagelos Nikolaou at the Institute of Geology and Mineral Exploration, Preveza. Of the 16 subcatchments, four of them (nos. 1, 2, 4 and 11 in Fig. 10) were assumed to have a groundwater recharge area larger than the topographic catchment. The models were set up in a branched mode with subcatchment 11, representing the tributary of Vossa, being modelled separately.

Table 2. Division of subcatchments for the hydrochemical modelling. Y1 = YPEKA1, Y2 = YPEKA2, M = UPAT-BIO_Manolaki, O = Ovezikoglou, P# = UPAT-BIO_Papadaki Area Subcatchment Chemistry No. Name (km2) (socioeconomics) observations 1 Vouliasta 205 A M 2 Mousiotitsa 48.4 A M, P2 3 Potamia 21.5 A P2 4 Kerasona 89.6 A M 5 Agios Georgios 31.1 A (80 %), B (20 %) Y1, Y2, M, P2, P3 6 Dam Outlet 3.4 B M 7 Pantanassa 6.6 B P1, P2, P3 8 Palia Filippiada 9.9 B P1, P2, P3 9 Filippiada 7.3 B P1, P2, P3 10 Kalamia 5.9 B MRD, Y1, P2, O 11 Vossa 184.1 A (12 %), B (88 %) MRD 12 Petra 15.4 B (54 %), C (46 %) MRD, M, P1, P3, O 13 Lympohotitis 9.8 C Y2 14 Thresprotiko 177.3 C 15 Louros 69.9 C 16 Amvrakikos 78.0 C M

Figure 10. Map of the Louros catchment, subcatchment boundaries and sampling sites. Groundwater recharge areas outside the topographic catchment are denoted Gx. Yellow stars mark YPEKA2 sampling sites, green circles mark UPAT-BIO_Manolaki sampling sites, purple circles mark UPAT-BIO_Papadaki sampling sites.

2.2. Meteorological data

To drive the hydrological and chemical models, daily temperature and precipitation time series are required for each INCA subcatchment. Data from the meteorological stations of Arta, Ioannina and Aktio were used. Arta is the only meteorological station within the catchment, located in subcatchment 11 at an altitude of 12 m.a.s.l. Ioannina is located at an altitude of 483 m.a.s.l. approximately 13 km north of the northern boundary of subcatchment 1, and Aktio is located approximately 13 km south of the outlet to Amvrakikos gulf, at 4 m.a.s.l. The weights given to each meteorological station for each INCA subcatchment were calculated from Theissen polygons.

Precipitation records were adjusted for altitude according to:

(1)

Where PD,S is the daily precipitation for the subcatchment PD,M is the daily precipitation from the meteorological station PY,M is the long-term mean annual precipitation from the meteorological station (ES-EM) is the difference in altitude between the subcatchment and the meteo station

Temperature was adjusted according to:

(2)

with the same subscripts as defined above. The coefficient of 0.77 in equation 1 means that the annual average precipitation increases by 0.77 mm/m.a.s.l (Leontiadis and Smyrniotis, 1986). The annual mean temperature decreases by approximately 0.5 ⁰C / 100 m elevation (Flocas et al., 1983), and the factor was also adjusted by a term proportional to the day deviation from the annual mean temperature, reflecting that the altitudinal effect is larger in winters than in summers. This term was estimated from the temperature difference between the meteorological stations of Arta (12 m.a.s.l.) and Ioannina (483 m.a.s.l.).

2.3. Land cover classes

In INCA, soil properties such as nitrogen process rates are separately defined for a number of different land classes (Table 3). A maximum of six different classes is usually recommended to constrain the number of model parameters. These classes should reflect both soil chemical process rates and hydrological behaviour, and are thus referred to as hydrochemical response units. The following six classes were defined from a digital elevation map, combined with the CORINE land cover map (EEA, 2000):

1. GW-recharge – this class was used to define the groundwater recharge areas outside the topographic catchment (G1, G2, G4 and G11). This class is necessary for technical reasons.

2. Mountains – All other areas above 700 m altitude.

3. Urban – All other areas classified as Urban or bare rocks (defined by CORINE land cover classes 1.1.2, 1.2.1, 1.3.1, 1.3.3, 3.3.1 and 3.3.2).

4. Agriculture – Defined by the total area of crops and grazing area collected by the Department of Economics, scaled after the relative distributions of CORINE agricultural land cover classes (2.1.1, 2.1.2, 2.1.3, 2.2.2, 2.2.3, 2.3.1, 2.4.2, 2.4.3) between the INCA subcatchments (see more in detail under “Crops, fertilisers and irrigation demands”)

5. Shrubs & natural grassland – The remaining area were split between the two classes “Shrublands” and “Forests”, according to their relative distribution in the INCA subcatchment. Shrubs & Grass included land defined by CORINE land cover classes 3.2.1, 3.2.3, 3.2.4 and 3.3.3.

6. Forest & wetlands – Forests & wetlands included land defined by CORINE land cover classes 3.1.1, 3.1.2, 3.1.3, 4.1.1 and 4.2.1.

The CORINE land cover map and the distribution of each cover class in the sub-catchments were constructed by the UPAT-BIO_Department of Biology (Papadaki et. al. 2012).

Table 3. Land cover distribution (%) for the 16 subcatchments. The star denotes that the size of the subcatchment is a calibrated parameter. Catchment Shrubland Agriculture Mountain Forest Urban GW-recharge Area (km2) 1 24.1 8.5 38.7 2.2 0.4 26.1 219* 2 34.2 7.3 44.3 3.0 0.0 11.2 48.4 3 60.4 17.8 21.8 0.0 0.0 0.0 21.5 4 43.5 9.6 36.5 0.9 0.3 9.1 89.6 5 65.3 11.5 20.8 2.4 0.0 0.0 31.1 6 80.3 0.5 0.0 5.7 13.4 0.0 3.4 7 78.1 21.9 0.0 0.0 0.0 0.0 6.6 8 49.7 25.1 0.0 22.0 3.1 0.0 9.9 9 29.1 41.3 0.0 22.3 7.4 0.0 7.3 10 0.0 82.2 0.0 11.7 6.1 0.0 5.9 11 18.0 39.7 10.9 0.6 2.9 27.9 184.1 12 40.5 47.7 0.0 6.9 4.8 0.0 15.4 13 61.8 36.0 0.0 1.5 0.7 0.0 9.8 14 61.7 21.2 12.0 3.4 1.8 0.0 177.3 15 73.3 19.2 2.2 4.9 0.5 0.0 69.9 16 35.9 41.6 0.2 20.0 2.3 0.0 78.0

2.4. Crops, livestock, fertilisation and irrigation demands

Four different crops were identified by the Department of Economics as being the most interesting from a management perspective. These include: maize, cotton, medic and citrus. Together, these crops cover approximately 50 % of the arable area, but they account for almost 90 % of the fertilisers applied. Crop areas were given per subcatchment (A, B and C) (National Statistical Service of Greece, 2001).

To distribute the crops over the smaller units used in INCA (i.e. Fig. 10), the relative distribution of agricultural areas according to the CORINE land cover data was used. First, CORINE classes “Non- irrigated arable land” and “Permanently irrigated land” were grouped in one class referred to as “Intensive agriculture”, the classes “Complex cultivation patterns” and “Land principally occupied by agriculture, with significant areas of natural vegetation” were grouped in one class referred to as “Non intensive agriculture”, and the classes “Fruit trees and berry plantations” and “Olive groves” were grouped in one class referred to as “Plantations”.

Annual cultivations were assumed to be grown on land classified as “Intensive agriculture” and “Non- intensive agriculture”. The total areas of arable land in each INCA-subcatchment (Aagri,INCA) were estimated as the sum of land classified as “Intensive agriculture” and 50 % of the land classified as “Non-intensive agriculture”. Permanent crops were assumed to be grown on land classified as “Plantations” and “Non-intensive agriculture”. The total areas of plantations in each INCA- subcatchment were estimated as the sum of land classified as “Plantations” and 50 % of the land classified as “Non-intensive agriculture”. Grazing was also included in the INCA-class of agricultural land, and was assumed to include land classified as “Non-intensive agriculture”.

The area of each crop of annual cultivations in each INCA-subcatchment (Table 4) was calculated by taking the total area of the crop in the larger subcatchment (Acrop,X), multiplied by the ratio between

the agricultural area in the INCA-subcatchment (Aagri,INCA) and the total area of agricultural land in the larger subcatchment (Aagri,X): Acrop,INCA = Acrop,X* Aagri,INCA/Aagri,X (3)

Table 4. Areas of each crop type in each subcatchment (km2). Other Agri Cotton Maize Medic Pulses Potato Fert UnFert Garden Grazing Citrus Olive Nuts Area

1 0.10 1.09 1.32 0.06 0.07 0.15 1.29 0.22 13.13 0.03 0.80 0.32 18.6 2 0.02 0.21 0.25 0.01 0.01 0.03 0.25 0.04 2.48 0.01 0.15 0.06 3.53 3 0.02 0.22 0.27 0.01 0.01 0.03 0.26 0.04 2.71 0.01 0.16 0.07 3.83 4 0.08 0.84 1.02 0.05 0.06 0.12 1.00 0.17 4.84 0.01 0.31 0.13 8.62 5 0.04 0.47 0.64 0.04 0.05 0.04 0.51 0.06 1.31 0.18 0.20 0.06 3.59 6 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.02 7 0.01 0.16 0.23 0.01 0.02 0.01 0.17 0.02 0.40 0.21 0.17 0.05 1.45 8 0.02 0.27 0.38 0.02 0.03 0.02 0.29 0.03 0.69 0.36 0.28 0.08 2.48 9 0.02 0.33 0.47 0.03 0.04 0.02 0.36 0.03 0.84 0.43 0.34 0.10 3.01 10 0.04 0.54 0.76 0.05 0.06 0.03 0.58 0.06 1.35 0.70 0.56 0.16 4.87 11 0.56 7.58 10.62 0.63 0.81 0.50 8.15 0.81 18.62 12.15 9.77 2.82 73.0 12 0.12 1.18 1.45 0.09 0.12 0.11 1.13 0.12 1.55 0.58 0.76 0.14 7.33 13 0.08 0.57 0.57 0.03 0.05 0.07 0.45 0.06 0.81 0.03 0.77 0.03 3.53 14 0.73 5.46 5.46 0.32 0.48 0.69 4.35 0.57 7.64 0.49 10.9 0.49 37.6 15 0.15 1.13 1.13 0.07 0.10 0.14 0.90 0.12 4.48 0.22 4.76 0.22 13.4 16 0.81 6.06 6.06 0.35 0.53 0.76 4.82 0.63 5.87 0.27 6.00 0.27 32.5 ∑ 2.81 26.11 30.63 1.75 2.44 2.72 24.52 2.98 66.72 15.69 35.9 5.01 217

Table 5. Crop types and their respective water demands, and rates of nitrogen and phosphorous fertilisers. Water demands (m3 N (kg ha-1 yr-1) P (kg ha-1 yr-1) ha-1 yr-1) Annual cultivations Cotton 7000 110 50 Maize 5500 240 240 Medic 3000 80 80 Pulses 0 0 60 Potatoes 2500 150 200 Other Fertilised (melons) 6000 100 200 Unfertilised (cereals, wheat, etc.) 0 0 0 Gardens / vineyards 6500 150 120 Permanent crops Citrus 8000 300 40 Olives 2000 35 0 Other (nuts) 0 0 0

The demands for irrigation and fertilisers were collected from stakeholders (Psaltopoulos et al., 2013) (Table 5). The amount of irrigation applied each year was calculated as the deficit between the

total summer (May-September) precipitation and the total water demand. The total amount of water abstracted for summer irrigation was then distributed between summer months according to: May – 10%, June – 20%, July – 30%, Aug – 30% and Sep – 10%. The timing of fertiliser applications were estimated from information from stakeholders (Psaltopoulos et al., 2013) (Table 6).

Table 6: Timing of fertiliser applications for different crop types, given in % per month. Mar Apr May June July Nov Dec Annual cultivations Cotton 0 0 27.3 0 73.7 0 0 Maize 0 17.2 17.2 32.8 32.8 0 0 Medic 0 50 50 0 0 0 0 Pulses 0 0 27.3 0 73.7 0 0 Potatoes 0 0 27.3 0 73.7 0 0 Other Fertilised 0 0 27.3 0 73.7 0 0 Gardens 0 0 27.3 0 73.7 0 0 Permanent crops Citrus 23.3 23.3 0 23.3 0 0.15 0.15 Olives 23.3 23.3 0 23.3 0 0.15 0.15

Information on numbers of livestock (including cattle, sheep, pigs and poultry) was also available on the economic subcatchment level (A, B and C) (National Statistical Service of Greece, 2001). Livestock was distributed over the sixteen model subcatchments in the same way as the arable areas (eq. 3), where cattle and sheep were assumed to be proportional to the grazing area, and pigs and poultry were assumed to be proportional to the arable land (Table 7). The total nutrient loads from livestock and animals were then calculated from Table 8 (Taiganides, 1978).

Table 7. Number of animals in each subcatchment.

Cows Sheep Poultry Pigs Goats

1 114 5998 54784 746 1812 2 22 1134 10516 143 343 3 23 1237 11224 153 374 4 42 2211 42314 576 668 5 15 1258 16413 387 354 6 0 12 63 2 3 7 8 963 4898 139 260 8 14 1647 8375 237 445 9 17 1998 10161 288 539 10 27 3222 16528 468 870 11 351 40277 235585 6536 10909 12 53 3737 23566 961 1075 13 55 2013 3280 424 661 14 522 18974 31534 4072 6232 15 307 11135 6512 841 3657 16 401 14566 34997 4519 4785 ∑ 1972 110382 510750 20490 32987

Table 8: Amount of nutrient produced per animal. Nutrient Cows Sheep Goat Hens Pigs Nitrogen (kg·year-1·head-1) 48 5.25 4.5 0.32 6 Phosphorous (kg·year-1·head-1) 24 2.25 1.875 0.24 2.25

As the time series analyses revealed a possible impact from point sources, the manure from pigs was based on anecdotal evidence assumed to be discharged directly into the river instead of being applied on agricultural fields. Although it is acknowledged that the pig farms are not solely responsible for the point source discharges of nutrients, this still served as a useful assumption for modelling purposes. The size of the point source effluents were subsequently calibrated in the model applications. Summed together, almost half of the total fertiliser load, and one third of the manure is applied on the subcatchment 11, i.e. the Vossa tributary (table 9).

Table 9: Total load of nutrients from inorganic fertilisers and manure per subcatchment. Fertilisers and manure are both assumed to be applied on arable land, except for manure from pigs, which is assumed to be discharge directly to the river as a point source. Nitrogen (Tn/Yr) Phosphorus (Tn/Yr) Sub-catchment Fertiliser Manure Total Pigs Fertiliser Manure Total Pigs 1 47.6 62.6 110.2 4.5 44.9 32.8 77.6 1.7 2 9.1 11.9 21.0 0.9 8.6 6.2 14.8 0.3 3 9.7 12.9 22.6 0.9 9.2 6.7 15.9 0.3 4 35.3 30.2 65.5 3.5 34.6 17.4 52.0 1.3 5 24.8 14.2 39.0 2.3 19.9 7.8 27.7 0.9 6 0.2 0.1 0.3 0.0 0.1 0.1 0.1 0.0 7 13.2 8.2 21.4 0.8 7.4 4.0 11.4 0.3 8 22.6 14.0 36.6 1.4 12.6 6.9 19.5 0.5 9 27.5 17.0 44.4 1.7 15.3 8.3 23.6 0.6 10 44.5 27.4 71.9 2.8 24.8 13.5 38.3 1.1 11 701.1 352.8 1053.9 39.2 358.0 176.0 534.0 14.7 12 65.9 34.6 100.5 5.8 49.3 17.4 66.7 2.2 13 25.1 17.3 42.4 2.5 22.0 7.9 29.9 1.0 14 258.2 162.8 421.1 24.4 212.5 74.5 286.9 9.2 15 65.5 91.7 157.2 5.0 44.3 40.8 85.2 1.9 16 257.2 128.5 385.6 27.1 234.7 59.8 294.5 10.2 Total 1608 986 2594 123 1098 480 1578 46

A 50-50 split between nitrate and ammounium was assumed for nitrogen addition. For phosphorus, 70 % was assumed to be added as solid P, and 30 % as liquid P. Finally, biological fixation of nitrogen was included as an extra source of nitrogen for the non-arable land use classes. This was assumed to equal 4 kg-N/(ha·year) for shrubland, mountains and the GW-recharge land classes, and 10 kg- N/(ha·year) for forests. For the phosphorus modelling, phosphorus was added as plant residue with a rate of 1 kg-N/(ha·year) for shrubland, mountains and the GW-recharge land classes, and 2 kg- P/(ha·year) for forests.

2.5. Nitrogen deposition

Annual values of dry and wet deposition of nitrate and ammonium were calculated per EMEP grid square (Jackson-Blake et al., 2011). The Louros catchment is covered by three different EMEP squares. Wet deposition was separately calculated for forested and non-forested land cover. To calculate the N-deposition for a subcatchment, the fraction of the subcatchment covered by each

EMEP-square was calculated (fEMEP), and the wet deposition of nitrate and ammounium was calculated as:

; (4)

Where DS,w is the wet deposition of nitrate or ammonium for a subcatchment, and DEMEP,W is the wet deposition of nitrate or ammonium of EMEP-square i. The dry deposition was calculated as

+ (5)

Where DD,w is the dry deposition of nitrate or ammonium for a subcatchment, DEMEP,for-D is the dry deposition of nitrate or ammonium for forested areas, of EMEP-square i, and DEMEP,nonfor-D is the same for non-forested areas.

Table 10: Area specific deposition of nitrogen per subcatchment (kg-N/(ha·year))

Dry NO3 Wet NO3 Dry NH4 Wet NH4 ∑N 1 1.30 2.54 0.79 2.01 6.64 2 1.31 2.60 0.78 1.98 6.66 3 1.26 2.65 0.71 1.94 6.57 4 1.28 2.65 0.72 1.94 6.60 5 1.30 2.65 0.74 1.94 6.64 6 1.36 2.65 0.79 1.94 6.75 7 1.26 2.65 0.71 1.94 6.57 8 1.64 2.65 1.03 1.94 7.26 9 1.64 2.65 1.03 1.94 7.27 10 1.46 2.65 0.88 1.94 6.94 11 1.27 2.65 0.72 1.94 6.59 12 1.38 2.65 0.81 1.94 6.79 13 1.29 2.65 0.73 1.94 6.62 14 1.32 2.65 0.76 1.94 6.67 15 1.35 2.64 0.77 1.92 6.68 16 1.80 2.46 0.63 1.42 6.31

3. Model calibrations

3.1. Hydrology (PERSiST)

There are many complications that makes it difficult to set up and calibrate a hydrology model for the Louros river. These include:

1) The catchment boundaries are difficult to define. The upper layer of permeable limestone is underlain by an impermeable layer with a different slope. This means that the groundwater recharge area is larger than the topographic catchment. The size of the additional recharge are contributing to

the Louros river was estimated by Dr Nikolaou to be approximately 140 km2 by reach 6, plus an additional 50 km2 contributing to subcatchment 11 downstream of the dam.

2) The fractured bedrock cause the groundwater to form subterranean streams, emerging as springs that feeds the river in several places. For this reason, the subcatchment boundaries are not always certain, and especially between the springs of Terovo and the springs of Agios Georgios (corresponding to subcatchments 2-5), the actual contributing areas are likely to be smaller than what is assumed in the model.

3) In the lowland plains of subcatchment 11, the topography is very flat, and the area is traversed by drainage ditches reaching towards the gulf. The runoff from this subcatchment may therefore go in two different directions; either to the Louros river, or else to the Amvrakikos gulf. According to Dr. Nikolaou, during summer, only approx. 80 % of the runoff from subcatchment 11 contributes to the streamflow in river Louros. To simulate this in INCA, the model was set up in a branched version, with reach no. 11 (the tributary of Vossa) simulated separately and feeding into reach no. 12. The 20 % of the runoff that goes directly to the gulf during summer months is represented by an abstraction time series.

4) The only discharge record comes from the hydroelectric dam, were the discharge through the turbines and the overflow is summed. The hydroelectrical plant has three turbines, and number of turbines operating depends on both dam water levels and the demands for electricity. Thus, the flow record from the dam only gives limited insight in the rainfall-runoff relationship.

5) The lack of flow observations in the lowland part makes it impossible to evaluate assumptions regarding abstraction for irrigation, and the contribution of the Vossa tributary to the flow in river Louros.

To model the hydrology, the new hydrological PERSiST (Precipitation, Evapotranspiration & Runoff Simulator for Solute Transport) was used (Futter et al., 2013). PERSiST was designed in the same conceptual framework as the suite of INCA-models, and can be used to generate hydrological input data (Hydrological effective rainfall and Soil moisture deficit) needed to drive the chemical INCA models.

PERSiST was set up with the six land cover classes described above (section 2.3), and three different soil boxes: One quick box, one soil box and one groundwater box. Each box is charactherized by nine different parameters, which are specific for each land class. There are nine additional land cover specific parameters, related to properties such as snow melt, evapotranspiration and base flow index. The total number of parameters in the model is thus 9*3*6 + 9*6 = 216. In reality, only a few of these parameters were actually tuned when calibrating the model to the observed flow.

PERSiST was calibrated against the observed flow for the period Jan 2001 – Sep 2012. The priority was to capture the water balance correctly according to information from previous studies (Nikolaou, 2001; Katsanou et al., 2011). The precipitation multipliers were set equal to one (i.e. the precipitation was assumed to be exactly equal to what has been calculated in section 2.2), the base flow index was fixed at 0.85, and the soil boxes properties (retained water depth, max capacity) for the different land classes were pre-defined by “best guesses“. For the GW recharge land class, only the groundwater percolation will contribute to stream-flow in the Louros river, whereas direct runoff and soil flow will run off in a different direction. This was simulated by setting the rain multiplier to 0.85 for this land class, and thereafter route all water to percolation to the groundwater box. The model was then manually calibrated by adjusting the evapotranspiration parameters together with the area of GW1, to: 1) Achieve runoff coefficients similar to those reported in Nikolaou (2001), and 2) Balance between modelled flow and observed flow at reach 6 (i.e. a relative error close to zero). The

calibration was then adjusted by changing drought runoff fraction, residence times and velocity parameters for reach 6 (the dam) to improve the model fit to recorded discharge. The velocity parameters were estimated from velocity measurements from the UPAT-BIO_Manolaki data set.

/s) 3 40 Modelled flow 30 Observed flow

Discharge(m 20

0 1999 2002 2005 2008 2010 2013

Figure 11. Modelled and observed outflow from the hydroelectrical dam at reach 6.

With the calibrated model a Nash-Sutcliffe value of 0.77 was achieved. The highest observed flows are not captured by the model; these are events when there is water flowing over the barrier (Fig. 11). Compared to the values estimated from Nikolaou (2001), the runoff coefficient was slightly higher for the subcatchment defined by the dam outlet (66 % compared to 64 %), and slightly lower for the subcatchment defined by the sampling site at Petra (62 % compared to 63 %) (Table 11). The mean relative error was 0.28 %. In the subsequent modelling of nitrogen and phosphorus, the hydrological parameters of Soil retained water depth, Groundwater retained water depth, Drought runoff fraction and Groundwater residence time were adjusted to improve the goodness-of-fit (GOF) of the nutrient concentrations. This resulted in a slight decrease in the GOF measures for the hydrological model, giving final values of both r2 Nash Sutcliffe of 0.72. The runoff coefficient and water balance were not affected by these adjustments. The parameters of the final PERSiST calibration can be found in Tables A1 and A2 (appendix).

Table 11: Comparisons between calculated and modelled runoff coefficients Runoff coefficient Runoff coefficient (Nikolaou) (Modelled) Zita (Reach 1) 65 % 65 % Ag. Georgios (Reach 6) 64 % 66 % Petra (Reach 12) 63 % 62 %

3.2. Nitrogen (INCA-N with PERSiST)

For modelling nitrate and ammonium, INCA-N with PERSiST was used, which is a model that integrates the hydrological model of PERSiST with the nitrogen model INCA-N. This is the very first application of this model, so there is no existing documentation of this model. However, it can simply be described as the equations for nitrogen transformation processes and soil temperature from INCA-N (Wade et al. 2002a), implemented into the hydrological framework of PERSiST (Futter et al., 2013). The process rates are however modified by soil moisture in a different way that in “classic“

INCA-N. All soil process rates are multiplied by a soil moisture factor defined by the two parameters “Zero rate depth“ and “Max rate depth“. The soil moisture factor has a value of zero at the soil water depth equal to “Zero rate depth“, and increase linearly with soil water depth to a value of one at “Max rate depth“.

Considering the very large differences between the different data sources with respect to mean concentrations, seasonal variability and flow-concentration relationships, it was not possible to calibrate the model to all, or even more than one, of the different data sources. As the calibration period was chosen as 2001-2012, the initial approach was to retain the YPEKA1 data set (sampled 1998-1999) for model testing. The MRD data was not used for calibration since it showed up to ten- fold higher concentrations than the other data sources. Thus, the model was separately calibrated for the remaining data sets of YPEKA2, UPAT-BIO_Manolaki and UPAT-BIO_Papadaki. Of these, the model was not successfully calibrated to the UPAT-BIO_Papadaki data set, as no parameter set was found that could explain the seasonal dynamics with observed nitrate concentrations peaking in early spring.

The model could however be calibrated to the YPEKA2 and the UPAT-BIO_Manolaki data sets with reasonably good results. Only some of the model parameters were calibrated, namely: Soil denitrification,soil nitrification, soil mineralisation, plant NO3 uptake, plant NH4 uptake, zero rate depth, max rate depth, in-stream nitrification, in-stream denitrification, initial groundwater nitrate and initial groundwater ammonium. Furthermore, the size of the point source (effluent concentration of ammonium) was also calibrated, the hydrological parameters of groundwater residence time was adjusted to improve the fit for baseflow conditions, and the drought runoff fraction was adjusted to keep more nitrogen in the soil during the dry summer months. All the remaining parameters were kept at their initial values. The parameters of the final INCA-N with PERSiST calibration can be found in Tables A3 and A4 (appendix).

The calibrations were primarily focused on: 1) Capturing the mean nitrate concentrations along the river profile, and 2) Capturing the seasonal nitrate dynamics of the lowland sites. One complication for the model performance evaluation was that the exact sampling dates were not known. For the UPAT-BIO_Manolaki data set, the sampling data was given within a window of two-five days, and for the YPEKA2 data set, only the months of the samples were given. Therefore, the samples were assumed to represent a mean over the given period, and the GOF measures were calculated against the averaged modelled output values over the same period. For both the YPEKA2 and UPAT- BIO_Manolaki calibrations, the temporal dynamics (measured by r2) was poorly captured above the dam. However, at the sites located in the lowland plains, the temporal dynamics were well captured for both calibrations, with r2 > 0.65 for reach 13 (YPEKA2) (Fig. 12) and reach 16 (UPAT- BIO_Manolaki). Nash-Sutcliffe values were in general less than zero for sites upstream the dam, but positive for sites downstreams. The normalised bias is alternating between positive and negative above the dam for the UPAT-BIO_Manolaki calibration, however, it is substantially larger for the site at the dam (reach 6), indicating that the denitrification in the dam is under-estimated by the model calibration (Table 12). Additional GOF measures can be found in Table A7 (appendix).

Table 12: Goodness-of-fit measures for the two alternative model calibrations of INCA-N with PERSiST against the YPEKA2 and UPAT-BIO_Manolaki data sets YPEKA2 UPAT-BIO_Manolaki Reach Norm. RMSD Nash- Norm. RMSD Nash- RMSD r2 RMSD r2 bias (norm) Sutcliffe bias (norm) Sutcliffe 1 - - - - - 0.038 0.057 1.056 -0.116 0.020 2 ------0.794 0.044 1.036 -0.704 0.011 4 ------0.259 0.078 0.951 0.028 0.110 5 -0.200 0.215 1.091 -0.231 0.01 0.314 0.086 1.134 -0.384 0.204 6 - - - - - 0.735 0.136 1.068 -0.681 0.009 12 ------0.102 0.128 0.936 0.113 0.162 13 -0.078 0.108 0.576 0.662 0.725 - - - - - 16 ------0.377 0.083 0.598 0.500 0.659

1.25

1.15

1.05

N/L) - 0.95 Modelled 0.85

Nitrate(mg Observed 0.75

0.65

0.55 2005 2006 2008 2009

Figure 12. Modelled and observed (YPEKA2) nitrate concentrations at reach 13 (Lympohotitis).

Ammonium concentrations were also generated as output from the model. However, the model was not specifically calibrated against ammonium concentrations, as they were always low and as the temporal dynamics is difficult to capture. For the YPEKA2-calibration, ammonium was substantially over-estimated for reach 5 (normalised bias = +2.49). For reach 13, the average modelled concentrations agree well with the observed (normalised bias = -0.29), but the temporal dynamics are poorly captured (r2 = 0.033).

3.3. Phosphorus (INCA-P with PERSiST)

Similar to the nitrogen modelling, a new and prevously unpublished model was used for the modelling of phosphorus. The model, INCA-P with PERSiST is simply a model which integrates the hydrology from PERSiST with the soil and in-stream phosphorus processes from the well-established INCA-P model (Wade et al., 2002a, Wade et al., 2007). The process rates are, just as in INCA-N with

PERSiST, modified by a soil moisture factor defined by the two parameters “Zero rate depth“ and “Max rate depth“. In the available data sets, measured phosphate often exceeds total phosphorus. While this obviously indicates that the measuring uncertainty is large, it also suggests that the fraction of particulate phosphorus is small compared to phosphates. Therefore, the INCA-P was ran without calibration against particulate phosphorus or sediment concentrations with the assumption that SRP ≈ TDP ≈ TP. A small amount of particulate phosphorus was however generated from agricultural land by flow erosion. As the different data sources were in better agreement than for nitrogen, only one model calibration was used in this case. The model was primarily calibrated against the YPEKA2 data set for the period 2001-2012. The UPAT-BIO_Manolaki and UPAT-BIO_Papadaki data sets were not used for calibration, but were instead used to evaluate the calibration.

Model parameters that were calibrated included: soil phosphorus terms (Freundlich isotherm, weathering factor, sorption coefficient and equilibrium phosphorus concentrations), plant uptake, process rates response to temperature, immobilisation, zero rate depth, max rate depth, initial labile and inactive soil P, reach ecology parameters for macrophytes and epiphytes, and groundwater phosphorus terms. All the remaining parameters were kept at their initial values. Soil phosphorus terms and initial values were calibrated so that the arable soil became regularly depleted in phosphorus, in order to assure that the added solid phosphurus eventually became desorbed. In absence of observations of macrophyte or epiphyte biomass, ecology parameters were calibrated to give an approximate macrophyte biomass of 200 g C/m3, similar to what has been observed in other rivers. The parameters of the final INCA-P with PERSiST calibration can be found in Tables A3 and A4 (appendix).

Table 13: Goodness-of-fit measures for the model calibration of INCA-P with PERSiST against the YPEKA2 data set. One observation from Jan 2006 was excluded from these calculations. SRP TP Norm. RMSD Nash- Norm. RMSD Nash- Reach RMSD r2 RMSD r2 bias (norm) Sutcliffe bias (norm) Sutcliffe 5 0.515 0.025 1.632 -1.928 0.186 0.703 0.022 1.588 -2.017 0.054 13 -0.241 0.027 1.173 -0.433 0.130 -0.283 0.022 0.667 0.475 0.556

The model was primarily calibrated against TP, as the TP analyses were assumed to be more accurate. The GOF was strongly influenced by the very first observation from Jan 2006, which was under detection limit, allthough the model predicted very high phosphorus concentrations for this month. When including this observations, the GOF-measures for TP were: (Normalised bias = -0.034; Normalised MSD = 1.076, r2 = 0.085). After removing this observation, the goodness-of-fit for the lower reach (no. 13) was satisfactory, with a Nash-Sutcliffe value of 0.475 for TP and an r2 of 0.556 (Fig. 13). However, the model could not capture the temporal dynamics above the dam, with low r2- values for reach 5. The calibration was not entirely successful in capturing the concentration profile, as phosphorus was over-estimated above the dam, and slightly under-estimated below the dam (Table 13). Additional GOF measures can be found in Table A7 (appendix).

0.18

0.16

0.14

0.12

0.1 Modelled 0.08

TP (mg/L) TP Observed 0.06

0.04

0.02

0 2005 2006 2008 2009 2010

Figure 13. Modelled and observed (YPEKA2) total phosphorus concentrations at reach 13 (Lympohotitis).

Table 14: Goodness-of-fit measures for INCA-P with PERSiST when tested against the UPAT- BIO_Manolaki and UPAT-BIO_Papadaki data sets. UPAT-BIO_Manolaki UPAT-BIO_Papadaki SRP TP SRP TP Norm. RMSD Norm. RMSD Norm. RMSD Norm. RMSD Noh RMSD r2 RMSD r2 RMSD r2 RMSD r2 bias norm bias norm bias norm bias norm 1 -0.97 0.02 0.56 0.72 -0.26 0.01 2.40 0.02 ------2 -0.93 0.02 1.53 0.04 -0.44 0.02 2.07 0.07 6.50 0.03 5.04 0.28 -0.72 0.06 0.87 0.31 3 ------0.99 0.03 1.76 0.19 -0.46 0.69 0.99 0.29 4 -0.55 0.02 1.22 0.08 -0.02 0.02 3.96 0.14 ------5 -0.42 0.02 1.15 0.03 -0.35 0.01 1.35 0.54 0.94 0.02 2.16 0.06 -0.48 0.21 0.94 0.53 6 -0.35 0.02 1.61 0.11 -0.47 0.02 1.25 0.09 ------7 ------0.00 0.02 1.60 0.09 -0.85 0.07 0.84 0.46 8 ------0.06 0.03 1.51 0.04 -0.75 0.13 0.99 0.02 9 ------0.12 0.02 1.14 0.08 -0.40 0.51 1.00 0.02 10 ------0.31 0.03 1.20 0.02 -0.60 0.07 0.84 0.56 12 -0.30 0.05 2.18 0.17 1.27 0.04 4.31 0.02 -0.83 0.03 1.04 0.01 -0.81 0.05 0.84 0.70 16 0.35 0.04 3.14 0.41 0.35 0.06 1.77 0.04 ------

For the UPAT-BIO_Manolaki data set, the model fit for SRP, measured as normalised RMSD and r2 is best for the uppermost reaches, and the model fit then decreases downstream. The temporal dynamics are not well captured in most of the reaches with a few exceptions, notably SRP for reaches 1 and 16, and TP for reach 5. Modelled phosphorus concentrations were in general under- estimated above the dam and over-estimated below the dam (Table 14).

For the UPAT-BIO_Papadaki data set, the pattern is the opposite, with the model fit improving downstream. The model fit for SRP is very bad in the upper reaches, as the modelled values are much higher than the observed. The model fit for TP is relatively good along the whole river reach, especially for the two most downstream reaches of the data set, with r2 > 0.5.

The difference between observed TP and SRP was small and often even negative for the YPEKA2 and UPAT-BIO_Manolaki data sets. However, for the UPAT-BIO_Papadaki data set, TP is significantly higher than SRP, especially for the upper reaches. Since the model was calibrated against the YPEKA2 data set, the modelled PP was low. Consequently, SRP tended to be over-estimated and TP was under-estimated when the modelled values were evaluated against the UPAT-BIO_Papadaki data set.

The patterns of the modelled phosphorus concentrations along the river profile agree well with the UPAT-BIO_Manolaki data, although modelled concentrations are higher. Phosphorus concentrations decrease from the spring lake to the dam, and then increase again as the river reaches the agricultural plain. The main difference is that while the observed phosphorus again decrease between reach 12 and reach 16, the modelled concentrations continues to increase. The UPAT- BIO_Papadaki data set displays a monotonic increase in SRP when going downstream, whereas the TP in contrast drops at the agricultural plain after the confluence with the Vossa tributary.

Although the three different data sets were in better agreement for phosphorus than for nitrogen, there were still substantial differences in terms of mean concentrations, temporal dynamics and the distribution between different phosphorus species. As it is unlikely that the model can be fitted to all aspects of the three data sets, it was decided to keep the parameter set that was calibrated to the YPEKA2 data set.

4. Model testing

Initially, the planned approach was to test the model performance against data sampled prior to the calibration period (i.e. the YPEKA1 data set, sampled 1998-1990). However, because of the substantial differences between the different data sources, this type of split-sample test on independent data could not be done. However, the hydrological model calibration was tested against observed flow 1994-2000.

Table 15: Comparisons between calculated and modelled runoff coefficients for the test period (1994-2000). Runoff coefficient Runoff coefficient (Nikolaou) (Modelled) Zita (Reach 1) 65 % 63 % Ag. Georgios (Reach 6) 64 % 66 % Petra (Reach 12) 63 % 62 %

For the test period, the model underestimated the observed flow to a larger degree than for the calibration period. The mean error taken over all observations was -8 %, and the annual minimum flow was underestimated by 19 % on average. The modelled runoff coefficient was lower for the test period than for the calibration period for reach 1 (63 % vs. 65 %), but similar for reaches 6 and 12 (Table 15). This indicates that the differences in model performance between the calibration and test period are related to the uppermost reach. For this reach, the meteorological time series are mostly influenced by the Ioannina meteorological station, whereas time series for the other subcatchments are more influences by the Arta meteorological station. The stations of Ioannina and Arta recorded almost identical mean daily precipitation for the calibration period (>0.2 % difference), whereas for the test period, the Ioannina station recorded 12 % lower precipitation than the Arta station. One possibility is therefore that the under-prediction of flow for the test period is caused by too low measured precipitation for the Ioannina station during the test period.

5. Sensitivity analysis

Sensitivity analyses were not carried out as the uncertainties of the observed nutrient concentrations and assumptions regarding runoff and abstractions are likely to be at least as important as the parameter uncertainty of the model calibrations.

6. Defining climate and land use scenarios

6.1. Meteorological data

Meteorological data from three different climate models were used to define the meterological time series for the scenario period: 1) KNMI-RACMO2-ECHAM5 (abb. KNMI), 2) SMHIRCA-BCM (abb. SMHI) and 3) HadRM3-HadCM3Q (abb. Hadley) (Christensen et al., 2009). The planned approach was to use the power function method for downscaling the climate scenarios (Leander et al., 2007). However, upon inspection it was evident that the time series from the three different climate models differed substantially, both in-between, and also compared to the observed meteorological data. The optimized coefficients in the power function resulted in unrealistic precipitation amounts, especially for summer months. Allthough there are more refined methods for downscaling, involving correction of number of wet days, such an approach would be uncessesary complicated, and not justified in light of the large uncertainties in the model calibrations themselves. Instead a simpler method was chosen, where observed meteorological time series were adjusted by the average difference between the control and scenario periods for each month and for each of the three climate models. For precipitation, the relative change in average monthly precipitation was calculated for each model:

(6)

(7)

Precipitation time series for the scenario period were then derived by replicating the observed precipitation time series, so that 2001-2010 represented the periods of 2031-2040, and 2051-2060, and 1991-2000 represented the period of 2041-2050. The time series were then adjusted according to:

(8)

(9)

The predicted relative changes in precipitation did not differ substantially between the three climate models, with the least change predicted by the KNMI model (-12 %) followed by the SMHI model (-14 %) and the Hadley model (-16 %). The seasonal patterns in precipitation change were seemingly random, except for the month of July for which all three climate models predict a large decrease in precipitation (55-65 %) (Fig. 14). For temperature, the three climate models were more different, with the SMHI model predicting the smallest increase (+1°C on average), the Hadley model predicting the largest increase (+2.2°C), and the KMNI predicting an intermediate decrease(+1.8°C). Seasonal pattens are also more pronounced, with a smaller increase in winter temperatures and a larger increase in summer temperatures (Fig. 15).

40 30 20 10 0 -10 KNMI -20 -30 SMHI -40 -50 Hadley -60

-70 Relative precipitatin Relative change (%) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Month

Figure 14. Relative changes in average monthly precipitation between the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models KNMI, SMHI and Hadley.

3.5 3 2.5 2 KNMI 1.5 SMHI 1 Hadley

Temperature changeTemperature(ºC) 0.5 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Month

Figure 15. Average monthly changes in temperature between the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models KNMI, SMHI and Hadley.

6.2. Land use scenarios

Four different storylines were defined, translating into four different land cover scenarios: A1, A2, B1 and B2. The ‘A‘-scenarios represent a more market-oriented future and ‘B‘-scenarios represent a more environmental-oriented future. Furthermore, ‘1‘-scenarios represent a more globalised world, whereas ‘2‘-scenarios represent a world with stronger national or local regulations (Psaltopoulos et al., 2013). However, all four land cover scenarios assume a higher agricultural production to some degree. In terms of translating the scenarios into model inputs, the land use scenarios will affect the land cover areas, fertilisation rates, abstraction rates and deposition.

Table 16: Changes in land cover relative to the baseline for the four land use scenarios. Arable Permanent Grassland Forest crops A1 +5 % +2.5 % +0.5 % -5 % A2 +10 % +5 % +3 % -10 % B1 +4 % +2.5 % +0.25 % -5 % B2 +8 % +5 % +0.5 % -10 %

In the land cover matrix, arable, permanent crops and grassland were all lumped into the class “Arable”, whereas forest is a class of its own. After changes in the “Arable” and “Forest” classes had been calculated, the land class “Shrubland” was adjusted to sum the relative land class covers to 100 % (Table 16).

The areas of each arable crop (cotton, maize, medic, pulses, potatoes and other fertilised) and permanent crop (citrus and olives) were then increased according to table 16, and the number of livestock was increased at the same proportional rate as grassland area. Given this, new area specific fertiliser rates were calculated for the INCA-class of “Arable” (Table 17). The deposition rates were adjusted by recalculating the dry deposition rates from the forest area cover according to eq. 5.

Table 17: Changes in nutrient loads relative to the baseline for the four land use scenarios. A1 (% of A2 (% of B1 (% of B2 (% of Base (Tn/Yr) baseline) baseline) baseline) baseline) N P N P N P N P N P 2590 1580 102.6 103.4 106.0 107.4 102.2 102.4 104.3 105.7

6.3. Model scenarios

The calibrated models were first run for the control period, 1981-2010, to define a baseline for flow, nitrate and phosphorus concentrations. Observed meteorological data 1991-2010 were used to run the model, with the 2001-2010 data assigned to the period of 1981-1990 for which no observed data was available.

The models were then run for the scenario period of 2031-2060, using all possible combinations of the three climate models and the four land use scenarios, plus a fifth land use scenario representing a baseline (i.e. no change). The total number of scenarios ran for each model was thus 3*5 = 15. The nitrogen model was run with the “YPEKA2”-calibration, as the YPEKA2-data was provided by a government agency as opposed to the UPAT-BIO_Manolaki data set, and also since the GOF was higher for the YPEKA2 calibration.

7. Results from climate and land use scenarios

7.1. Hydrology

The modelled effects from climate change on the hydrology were significant. For reach 13, the mean flow for the control period was 16.6 m3/s. This number decreased by 14.9 % (to 14.1 m3/s) for the KNMI model, by 18.3 % (to 13.6 m3/s) for the SMHI model, and by 27.7 % (to 12.0 m3/s) for the Hadley model. There was an even greater effect on the annual minimum flow. The average annual minimum flow for the control period was 8.1 m3/s for reach 13, this number decreased by 20.6 % (to

6.5 m3/s) for the KNMI model, by 5.5 % (to 6.1 m3/s) for the SMHI model, and by 29.3 % (to 5.8 m3/s) for the Hadley model. Land cover change further accentuated this change slightly due to increased irrigation, especially for the minimum flow. The A2 scenario (with the largest increase in irrigation) brought a further reduction in average annual minimum flow, resulting in a 21.6 % (KNMI), 26.6 % (SMHI) and 30.2 % (Hadley) decrease relative to the baseline period.

Figure 16. Left: Mean discharge (reach 13) for the baseline simulation as well as all combinations of land use and climate scenarios. Right: Change in average discharge (reach 13) relative to the baseline simulation for all combinations of climate and land use scenarios.

7.2. Nitrogen

The impact from climate change was small, and the results from the different climate models were contrasting. Whereas the KNMI model brought a slight decrease in nitrate concentrations, the SMHI and Hadley models resulted in a slight increase. The baseline nitrate concentration for reach 13 was 0.877 mg-N/L, the climate from the KNMI model resulted in a decrease by 2.1 % (to 0.858 mg-N/L), the climate from the SMHI model caused an increase by 1.3 % (to 0.888 mg-N/L), and the climate from the Hadley model caused an increase by 1.8 % (to 0.893 mg-N/L). The modelled effect from the land use scenarios was very small. For the A2 scenario with the most extensive increase in agriculture, the nitrate concentrations increased by 0.5 % relative to the baseline land cover under all three climate models (Fig. 17).

Figure 17. Left: Mean nitrate concentrations (reach 13) for the baseline simulation as well as all combinations of land use and climate scenarios. Right: Change in average nitrate concentrations (reach 13) relative to the baseline simulation for all combinations of climate and land use scenarios.

The small modelled net change in nitrate concentrations for the climate scenarios is because the amount of nitrogen leaching from the soils decrease by approximately the same rates as the runoff under the investigated climate scenarios. While the average discharge decreased by between 15 and 28 %, the amount of nitrogen leaching from the soils decreased by 15 % (KNMI and SMHI models) to 25 % (Hadley). Thus, the net result on nitrogen concentrations was small; the amount of nitrogen transported to the Amvrakikos gulf is however substantially reduced, by 16.3 % for the KNMI climate, 17.0 % for the SMHI climate, and by 26.5 % for the Hadley climate. The main reason for the simulated decline in nitrate leaching is that longer water residence time in the soil and less runoff meant that more of the nutrients were available for plant uptake. Furthermore, due to lower atmospheric deposition, the external loads were around 5 % lower for the future scenarios.

The very small changes in nitrogen concentrations brought by changes in land use is partly because the change in total external loads for the whole catchment is relatively small (<4.3 % relative to the baseline land cover), and partly because nearly all the additional fertiliser load is balanced by higher plant uptake.

It should also be noted that the modelled changes in nitrate concentrations are very small compared to the differences between observed nitrate of the different data sets. While the model predicted changes of nitrate in the range [-4.2 %, +2.5 %], the difference between the YPEKA2 and the UPAT- BIO_Manolaki data sets was 41 %. Additional statistics for the lowest reach (reach 15) can be found in Table A8 (appendix).

7.3. Phosphorus

Similar to the results from the nitrogen modelling, the climate effects on phosphorus concentrations were contrasting dependent on which climate model that was used. However, as opposed to the results from the nitrogen models, the Hadley climate model resulted in the lowest phosphorus concentrations, whereas the SMHI model gave the highest phosphorus concentration. The baseline SRP concentration for reach 13 was 43.2 µg/L, the climate from the Hadley model resulted in a decrease by 4.4 % (to 41.3 µg/L), the climate from the KNMI model caused a decrease by 2.8 % (to 42.0 µg/L), and the climate from the SMHI model caused an increase by 5.8 % (to 45.7 µg/L). The modelled effect from land use scenarios was larger than for nitrogen. Under the A2 scenario, with the most extensive increase in agriculture, phosphorus increased for all climate models. Relative to the baseline (1981-2010), SRP concentrations increased by 1.2 % (to 43.7 µg/L ) for the Hadley model, 2.8 % (to 44.4 µg/L ) for the KNMI model, and by 12.0 % (to 48.8 µg/L) for the SMHI model (Fig. 18).

Figure 18. Left: Mean SRP concentrations (reach 13) for the baseline simulation as well as all combinations of land use and climate scenarios. Right: Change in average SRP concentrations (reach 13) relative to the baseline simulation for all combinations of climate and land use scenarios.

Just as for nitrate, the modelled response of phosphorus concentrations to climate changes is mainly due to a combination of decreased leaching due to higher removal rates from the soil brought by longer soil water residence times, and less dilution due to reductions in flow. The amount of SRP leaching from the soil decreased by 16.9 % (SMHI), 18.7 % (KNMI) and 35.6 (Hadley). The amount of SRP transported to the Amvrakikos gulf was reduced by 31.1 % (SMHI), 34.4 % (KNMI) and 46.6 % (Hadley).

Although the results from the three climate models differ somewhat, the tendency is that the increase of phosphorus concentrations will be more pronounced during summer months, whereas they will remain unchanged or even decrease during the winter months. As an exception to this pattern stands the month of July, for which phosphorus concentrations remained nearly unchanged. This is likely due to the very low precipitation forecasted, which results in less phosphorus leaching from the soil. Additional statistics for the lowest reach (reach 15) can be found in Table A8 (appendix).

7.4. Conclusions from the climate and land use scenarios

The most significant result from the climate scenarios was that of hydrology, with a decrease in mean flow by between 14 % (KNMI Baseline) and 30.9 % (Hadley A2). For the nutrients, the combination of lower leaching due to enhanced removal rates from the soil, and less dilution due to lower flows resulted in small modelled changes in concentrations. The effects from changes in land use were neglibible for nitrogen, but had some effect on phosphorus concentrations.

The modelled climate effects on nutrient concentrations were controlled by two key factors: 1) The amounts of runoff, and 2) The amounts of nutrients leaching from the soil. Since both these decreased in similar proportion, the resulting effect on stream concentrations were small. The transported nutrient loads were however significantly reduced. The nutrient leaching from the soils is controlled by the removal process rates (i.e. plant uptake, as well as denitrification and immobilisation for nitrogen). The climate impact on process rates is dual; higher temperatures speed up process rates, but a larger soil moisture deficit would slow them down. However, in these model calibrations, the temperature effect dominated, and hence, climate change increased soil process rates. It should be acknowledged that the scarcicity of observed data makes it impossible to disentangle the different effects from temperature and soil moisture on soil process rates, and so these results must be interpreted with caution.

The forecasted changes should also be viewed in the light of the uncertainty of the contemporary observations. Especially for nitrogen, the forecasted changes are completely overridden by the uncertainties of the contemporary observations, for which the relative differences between the data sets are much larger.

7.5. Classification of ecological status

Greece has yet to adopt a full set of standards of chemical thresholds for freshwater quality. Currently, the only legally binding standards applying to the modelled constituents are thresholds for - + groundwater concentrations of NO3 (11.3 mg-N/L) and NH4 (0.39 mg-N/L) (Psaltopoulos et al., 2013). In this report, the evaluation of ecological status was made against the standards proposed by Brunel et al. (1997) for French rivers, which also agrees well with the standards adopted by the Catalan Water Agency used to classify the Arbucies study catchment (Erlandsson et al., 2013). According to these standards, the Louros river water quality was classified as “good” (class II) with

respect to nitrate and ammonium, and “high” with respect to SRP and TP for all combination of climate model scenarios and land use classes.

However, Skoulikidis et al. (2006) proposed an alternative classification system for Greek medium- sized rivers, which puts much stricter limits on nitrogen concentrations, which reflects the naturally low nitrogen concentrations in the area. According to these alternative standards, the water quality would be classified as “moderate” with respect to nitrate and ammonium.

Table 18: The boundaries for classification of surface water quality standards. Quality Classes according to Brunel et al. (1997) High (I) Good (II) Moderate (III) Poor (IV) Bad (V) Nitrate (mg-N/L) 0.45 2.3 5.6 11.3 >11.3 Ammonium (mg-N/L) 0.07 0.39 1.55 6.22 >6.22 SRP (µg/L) 65 163 196 653 >653 TP (µg/L) 100 300 600 1000 >1000 Quality Classes according to Skoulikidis et al. (2006) Nitrate (mg-N/L) 0.22 0.60 1.30 1.80 >1.80 Ammonium (mg-N/L) 0.024 0.060 0.20 0.50 >0.50 SRP (µg/L) 70 105 165 340 >340 TP (µg/L) 125 165 220 405 >405

8. Mitigation measures

8.1. Decriptions of mitigation measures

Four different mitigation measures were considered and implemented in the nutrient models. The mitigation measures include set aside of land, reducing fertiliser loads, and growing nitrogen fixating legumes to reduce the needs for fertilisers (Psaltopoulos et al., 2013). However, in a modelling context, they are all simply represented as reduced fertiliser loads to varying degrees. All the four considered mitigation measures considers only four of the crops: Medic, maize, cotton and citrus. These constitute 52 % of the total arable area, but 86 % of the total fertiliser load (livestock manure excluded).

Mitigation measure 1: Set aside 25 % of medic, maize and cotton areas. Reduce fertiliser loads by 25 % for citrus and the remaining areas of medic, maize and cotton.

Mitigation measure 2: Set aside 5 % of medic, maize and cotton areas. 20 % of medic, maize and cotton areas under nitrogen trapping legumes. 20 % of cotton areas without any fertilisers applied, 20 % of maize areas with 50 % reduction in fertiliser loads. Reduce fertiliser loads by 25 % for citrus and the remaining areas of medic, maize and cotton.

Mitigation measure 3: Set aside 30 % of medic, maize and cotton areas. Reduce fertiliser loads by 30 % for citrus and the remaining areas of medic, maize and cotton.

Mitigation measure 4: Mitigation measure 2: Set aside 5 % of medic, maize and cotton areas. 25 % of medic, maize and cotton areas under nitrogen trapping legumes. 25 % of cotton areas without any fertilisers applied, 25 % of maize areas with 50 % reduction in fertiliser loads. Reduce fertiliser loads by 30 % for citrus and the remaining areas of medic, maize and cotton.

New fertiliser rates and abstraction demands were calculated from table 5, together with new crop areas and the specified fertiliser reductions. The resulting reductions in total fertiliser load were between 75-81 % of the baseline loads for nitrogen, and between 67 and 74 % of the baseline load for phosphorus (Table 19).

Table 19: Changes in nutrient loads relative to the baseline for the four mitigation measures. Mit 1 (% of Mit 2 (% of Mit 3 (% of Mit 4 (% of Base (Tn/Yr) baseline) baseline) baseline) baseline) N P N P N P N P N P Baseline 2590 1580 80.8 74.4 80.6 74.4 77.3 70.2 77.0 69.8 Best case 2750 1620 80.5 74.1 79.8 73.2 77.0 69.8 76.1 68.7 Worst case 2650 1700 80.2 73.9 78.8 71.7 76.7 69.5 75.2 67.3

Instead of running all possible combinations of climate models, land use scenarios and mitigation measures, only the “best- and worst-case” combinations of climate models and land use scenarios were considered. For nitrogen, the “best-case” was the combination of the KNMI climate model and the B1 land cover scenario and the “worst-case” was the combination of the Hadley climate model and the A2 land cover scenario. For phosphorus, the “best-case” was the combination of the Hadley climate model and the B1 land cover scenario and the “worst-case” was the combination of the SMHI climate model and the A2 land cover scenario. All four mitigation measures were then simulated for the baseline period (1981-2010), the best- case and the worst-case scenarios.

8.2. Effects on nitrogen

The simulated effects from the mitigation measures on nitrate concentrations were marginal. The most radical mitigation measure, where fertiliser loads for the four most important crops were reduced by almost 50 %, reduced nitrate concentrations by less than 2.5 %. The mitigation measures had a larger effect on winter concentrations (Fig. 19). Additional statistics for the lowest reach (reach 15) can be found in Table A9 (appendix).

Figure 19. Left: Mean nitrate concentrations (reach 13) for the baseline, “best case” and “worst case”, without mitigation measures, and with mitigation measure 1-4. Right: Mean monthly nitrate concentrations (reach 13) for the baseline with and without mitigation measures.

8.3. Effects on phosphorus

The proposed mitigation measures had a more significant effect on phosphorus concentration according to the model simulations. The mitigation measures reduced total phosphorus concentrations by 23-28 % and SRP concentrations by 27-32 %. The differences between the four mitigation measures were however small (< 4% difference in reduction). On a seasonal basis, the mitigation measures mostly reduced the high phosphorus concentrations during late autumn and winter months (Oct – Jan), for which phosphorus concentrations were reduced by 35-45 % (Fig. 20). The reason for this is that the solid fertilisers that constitute 70 % of the total load will slowly desorb over time. With the lower fertiliser loads employed under the mitigation measure simulation, this store (in the model represented as labile P) will run out earlier (Sep-Dec) then for the model simulations without mitigation measures (Nov-Feb). Additional statistics for the lowest reach (reach 15) can be found in Table A9 (appendix).

Figure 20. Left: Mean SRP concentrations (reach 13) for the baseline, “best case” and “worst case”, without mitigation measures, and with mitigation measure 1-4. Right: Mean monthly SRP concentrations (reach 13) for the baseline with and without mitigation measures.

9. Assessment of biological status

9.1. Available data and calculation of macrophyte index

Macrophyte data (taxon name and abundance class) was collected from 17 sites along the Louros river (Manolaki et al., 2011), including samples from the spring lake, the dam and an adjacent pond. As a base for the calculations of a macrophyte index, the IBMR (Indice Biologique Macrophytique en Rivière - Macrophyte Biological Index for Rivers) index was used (Haury et al., 2006). To each species of a given taxa list a specific value is assigned ('cote spécific'). The index was then calculated based on data of percent cover of species and the specific species-values:

IBMR = Σ (Ei * Ki * CSi ) / Σ (Ei * Ki);

Where i is the serial number of taxon, Ei is the stenoecic coefficient of the i-th taxon (i.e. the higher th the coefficient, the lower the tolerance to nutrient enrichment), Ki is the abundance class of the i th taxon, CSi is the specific value ('cote spécific') of the i taxon.

To better capture the specific conditions of Greece, the IBMR was normalised (IBMRGr) based on comparisons with all Greek sites of the Mediterranean Geographical Intercalibration Group for rivers (MEDGIG). First, MEDGIG sites were selected as “stressed” or “unstressed” based on hydromorphological characteristics of the sites. This was done by employing a PCA analysis with the hydromorphological variables as explanatory variables (Fig. 21). Successional PCA models were applied for hydromorphological variables which passed the criterion of redundancy for each spatial level separately. From the hydromorphological variables that were initially tested, only those that were highly correlated with the main axis of the PCA (AX1) (r> 0.700) were used for the estimation of pressure gradient.

3 Axis2

Site 24 Site 20 Site 29

Site 16 Site 25 Site 8 Agrrevic Site 30 Cha_Morp 1 Site 2331 Site 28 Site 27 Site 19 Site 26 Site 21 Axis 1 Site 5 -2 Site 9 0 2 4 Site 7 Site 6 Loc_Hab_ Site 1 Site 18 Site 11 Hyd_Alt Site 4 Site 14 Site 22 Site 10 Water_Ab Site 3 -1 Site 2 Site 17

Site 15

Site 1213

-3

Figure 21: PCA analysis showing the hydromorphological pressure gradient.

Stressed sites were then defined as sites with a score on the first principal component axis above the 75th percentile, and unstressed sites as sites with a score on the first principal component axis below th the 25 percentile. The IBMRGr was then calculated according to Böhrem et al (2004) as:

IBMRGr = (IBMR-IBMRstr)/ (IBMRunstr-IBMRstr)

Where IBMRunstr corresponds to the upper limit of the IBMR value under reference conditions and th was set equal to the 75 percentile of the unstressed MEDGIG sites (12.7), and IBMRstr corresponds to the lower limit of the IBMRs value under the worst attainable conditions and was set equal to the th 5 percentile of stressed MEDGIG sites (Fig. 22). To determine the boundaries of IBMRGr index in a National Level the median of the unstressed sites was used as a boundary for the high/good ecological class (0.75). According to the Common Implementation Strategy (Pollard and van de Bund,

2005) the other quality classes were proposed to have the same range. In order to achieve this, the range from 0 to 0.75 was divided into four classes with equidistant boundaries:

High: IBMRGr >0.75

Good: 0.75< IBMRGr <0.56

Moderate: 0.56< IBMRGr <0.37

Poor: 0.37< IBMRGr <0.19

Bad: IBMRGr <0.19

Figure 22: Box whisker plots showing the range IBMR index in stressed and unstressed sites (all IC_Reference Sites included plus site GR_003_06/07).

9.2. Classification of biological status

Using the original IBMR-index, values in the Louros ranged from 11.8 to 6.9 (Table 21). Of the 17 sites assessed, two are characterized as having “Good” ecological status, seven as “Moderate”, six as “Poor” and two as “Bad” (Table 20). The normalized IMDBGr-index in general upgrades all sites by one class or more. Of the 17 sites, eight are characterized as having “High” ecological status, three as “Good”, four as “Moderate” and two as “Poor”. Sites assessed as having “High” ecological class (S1 and S4) are characterized by low water temperature (<16 Co) and salinity (EC <600 µS). “Poor” sites (S14 and S17) have higher water temperature (>17.5 Co) and salinity (EC >1200 µS), high water alkalinity (> 48) and relatively high levels of ammonia (>0.06).

Table 20: IBMR Estimation and Normalization for the whole dataset

No of Quality Quality Site Score IBMR IBMRGr Stress Classification Classification code IBMR taxa PC_1 (IBMR) (IBMRGr) S1 11.4 0.808 10 -1,43 Unstressed Good (IBMHigh S2 11.7 0.847 7 -1,43 Unstressed Moderate High S3 10.1 0.604 4 -1,43 Unstressed Moderate Good S4 12.1 0.913 4 -1,43 Unstressed Good High S5 10.9 0.734 4 -1,25 Neither Moderate High S6 11.1 0.761 4 1,01 Neither Moderate High S7 11.0 0.745 7 -1,43 Unstressed Poor High S8 9.8 0.560 9 2,82 Stressed Moderate Good S9 11.3 0.788 12 -1,19 Neither Moderate High S10 11.0 0.751 13 -1,43 Unstressed Moderate High S11 10.1 0.617 7 -1,43 Unstressed Poor Good S12 8.1 0.315 6 2.53 Stressed Poor Moderate S13 8.6 0.384 10 2.53 Stressed Poor Moderate S14 7.9 0.290 13 3,77 Stressed Bad Poor S15 8.4 0.362 21 2.77 Stressed Poor Moderate S16 8.1 0.318 12 -0,58 Neither Poor Moderate S17 7.9 0.289 13 -1,33 Neither Bad Poor

9.3. Relationship with physiochemical variables

For investigating the relationships between the macrophyte index and physiochemical variables, three of these sites were omitted, two of them being sites within the dam reservoir (S12 and S13), and one which is a temporary pond situated outside the main channel (S15). The IBMR values of each site were correlated with the average measured values of nine physiochemical variables: water temperature, water velocity, electric conductivity (EC), ammonia (NH4), alkalinity, nitrate (NO3), total nitrogen (TN), total phosphate (TP) and orthophosphate (SRP). For the relationship with water velocity, samples from the spring lake (S1-S4) were also omitted (Table 21).

Table 21: Water quality variables, IBMR values and respective ecological quality class of 14 sites along the Louros catchment. Velocity units are in m per second, and chemistry are mg per litre.

Site no Reach Velocity Temp Ammonia EC ALK Nitrate TN TP SRP IBMRGr Quality class 1 Spring lake 0.00 13.4 0.047 336.0 85.4 0.296 0.350 0.046 0.007 0.808 High 2 Spring lake 0.00 12.7 0.043 339.0 85.4 0.313 0.372 0.043 0.007 0.847 High 3 Spring lake 0.26 12.7 0.046 340.0 85.4 0.332 0.384 0.042 0.007 0.604 Good 4 Spring lake 0.03 13.4 0.041 350.0 85.4 0.286 0.333 0.044 0.005 0.913 High 5 1 0.47 13.3 0.028 337.0 97.6 0.590 0.627 0.017 0.004 0.734 High 6 1 0.62 15.0 0.085 351.0 97.6 0.572 0.683 0.014 0.007 0.761 High 7 2 1.26 15.5 0.035 258.0 61.0 0.651 0.717 0.032 0.007 0.745 High 8 4 0.93 15.5 0.019 590.0 134 0.652 0.676 0.018 0.011 0.560 Good 9 5 1.00 14.1 0.009 562.0 61.0 0.665 0.689 0.019 0.002 0.788 High 10 5 1.05 15.8 0.020 531.0 122 0.630 0.687 0.021 0.002 0.751 High 11 5 0.54 15.6 0.017 519.0 159 0.630 0.665 0.017 0.002 0.617 Good 14 12 0.19 17.5 0.063 1279.0 48.8 0.583 0.669 0.034 0.009 0.290 Poor 16 16 0.25 20.3 0.087 3900.0 48.8 0.419 0.517 0.023 0.015 0.318 Moderate 17 16 0.27 19.8 0.083 8088.0 48.8 0.381 0.474 0.038 0.041 0.289 Poor

The best predictors for the decrease in IBMR values were salinity (EC), using power curve (y = 5.78x-0.35, R² = 0.778), and water temperature, with a linear regression (y = -0.073x + 1.76; R² = 0.71). When the samples from the spring lake had been removed, velocity was significantly positively correlated with IBMRGr (y = 0.42x + 0.31; R² = 0.59). SRP was also found to be correlated with IBMR, and was able to explain 47 % of the variability in IBMR values. No monotonous relationships with other variables were found, including ammonia and nitrate (Fig. 23).

Figure 23. Changes in the Macrophyte Biological Index for Rivers (Indice Biologique Macrophytique en Rivière – IBMR) in relation to selected physic-chemical variables. Each point represents one of 14 sampling sites within the river Louros catchment.

9.4. Anticipated climate effects on ecology

With the exception of SRP, the response of the IBMR-index to the modelled nutrient concentrations (nitrate, ammonium, TP) was insignificant. The modelled improvements brought by the mitigation measures suggested that SRP concentrations at the lower end of the river could decrease from 40 to around 30 µg/L. However, according to the regression analyses, this will not cause any significant degradation of the biological status with respect to macrophytes.

However, there was a clear and significant relationship with salinity, water velocity and temperature, all of which will be affected by future climate scenarios. As for temperature, under the simple assumption that the average water temperature will follow air temperature, an increase in stream temperature of around 2°C can be expected. The IBMR-response to temperature was linear, with approximately 3°C separating each IBMR-class (Fig. 20). Thus, based on the observed data, future temperature increases are likely to have a negative effect on the classification of ecological status.

As for salinity, it is affected by many different climate-related factors that are not trivial to quantify, or even estimate; sea-salt deposition, weathering rates and salt water intrusion in the lower reaches. However, assuming that these factors all remain unchanged, salinity will also increase due to less dilution of sea salt and weathering products. The forecasted decrease in runoff of 25 % would result in an increase of salinity of around 33 %, which is also likely to cause a decrease in the classification of ecological status, especially in the higher reaches where the salinity is low as the IBMR relationship with salinity is inverse exponential (Fig. 20).

As the relationship between discharge and water velocity was nearly linear, a decrease in discharge of around 25 % would bring a decrease in water velocity of a similar size. This could potentially, according to the regression models, also cause a significant degradation of the macrophyte status, although not to the same degree as the effects from temperature and salinity. It should be acknowledged that there is a strong collinearity between the physiochemical variables, with increasing or decreasing trends in temperature, salinity and water velocity along the river. However, the effects from these variables on macrophyte communities have been highlighted in previous studies on macrophyte communities (Barendregt & Bio, 2003), especially in Mediterranean areas (Khedr & El-Demerdash, 1997).

10. Conclusions

10.1. Available and required data

 The overwhelming obstacle for the application of the REFRESH modelling framework on the river Louros is the lack of long term monitoring data. This applies both to the hydrology and chemistry, but especially to the latter. Even if the total number of chemistry samples is sufficient, the samples originates from six different sample campaigns, analysed with different methods, and the different data sources are clearly not in good agreement.

 In practise, the model results presented in the report builds on a calibration against 11 samples. For parameter-heavy models like INCA-N or INCA-P, a minimum of three years with monthly samples analysed with a consistent method is recommended, to be able to assess important model responses such as for example storm flow wash out of nutrients, temperature response of soil processes, and the relative importance of soil vs. in-stream processes.

 On a more positive note, the spatial coverage of the chemical sampling is satisfactory with samples taken within the same program taken along the whole river reach, from the spring lake to the outlet. Considering that the river profiles of nutrient concentrations are in relatively good agreement across all the analysed data sets, the conclusions drawn regarding leaching of nitrogen and phosphorus from the agricultural areas should be more or less certain.

10.2. Hydrology

 The most significant and ecologically relevant change from the modelled scenarios was the reduction in discharge. The modelled average discharge for reach 13 decreased by between 15 % (KNMI) and 28 % (Hadley), and the average annual minimum discharge decreased between 21 and 29 %. This also translates to slower water velocities and a longer retention time.

 Quantitatively, these figures must be seen as very uncertain, as there are no discharge records from the river below the dam. This means that the model performance could not be evaluated against observed flow data for the lower reaches. Thus, the validity of the important assumptions made regarding the contribution of runoff from the Vossa tributary, and the amounts of abstraction for irrigation were not possible to evaluate. Nevertheless, the reduction of flow is still likely to be substantial under the modelled climate scenarios.

10.3. Nitrogen

 For nitrate, the largest uncertainty lies not in the model applications, but rather in the contemporary observations. With a range in average nitrate concentrations from the six different data between 0.48 and 2.8 mg-N/L, the highest and lowest average concentrations differ by almost a factor of six.

 The ecological classification with respect to nitrate differs both between data sets and between classification systems. Using the classification proposed by Skoulikidis (Table 18), the ecological status for the lower reaches of the Louros (Fig. 3) could be classified as anything between “Good” (the YPEKA1 data set) and “Bad” (the MRD data set). However, with the classification system proposed by Brunel (Table 18), which agrees better with classification systems used for other study catchments in the REFRESH project (i.e. Erlandsson et al, 2013), the classification would instead be “Good” for all data sets considered, except for the MRD data set for which the classification would be “Moderate”.

 All the analysed data sets except one show approximately equal or lower nitrate concentrations in the agricultural lowland compared to upstream observation sites. The exception is the MRD data set, which show a modest increase of around 20 % between reach 10 and reach 12 where the Vossa tributary enters river Louros. Thus, the nitrate leaching from the agricultural field appears to be limited, or alternatively, excess nitrate is efficiently removed by in-stream processes in the lowland part. Other sources of nitrate may instead be of importance, such as point sources (fish and hog farms), deposition or groundwater.

 Considering the apparently limited nitrogen leaching from arable land, the results from the INCA-N application, showing that in-stream nitrogen concentrations are relatively insensitive to climate and land use changes, is not surprising.

10.4. Phosphorus

 In comparison with the nitrate observations, the phosphorus concentrations are in better agreement between the analysed data sets. The average concentrations of SRP of the four data sets range between 14 and 43 µg/L, i.e. the highest and the lowest average concentrations are separated by approximately a factor of three.

 In contrast to nitrate, the phosphorus concentrations were significantly higher in the agricultural plain than in the uplands. Thus, there is evidence for some leaching of phosphorus from the agricultural fields. However, all the four data sets still had average TP concentrations < 100 µg/L and SRP concentrations < 50 µg/L. The ecological status with respect to phosphorus would thus be classified as “High”, both according to the Brunel and Skoulikidis classifications (Table 18).

 The modelling results showed some limited increases in phosphorus for the SMHI climate scenario, and very small net changes for the KNMI and Hadley scenarios. The modelled effects from the proposed mitigation measures were high and brought down the SRP concentrations well below the threshold.

10.5. Impacts from climate

 Common for both the nitrogen and phosphorus modelling applications was that the modelled climate effects on stream concentrations were small. However, the total amount of nutrients transported to the Amvrakikos gulf was substantially reduced. These results are mainly due to two modelled climatic effects on hydrochemical processes; 1) Lower runoff due to decreased precipitation and increased evapotranspiration caused by increased temperatures, and 2) Increased net removal of nutrients from the soil (by plant uptake, denitrification and immobilisation) caused by longer residence times for soil water.

 While the reduction in runoff is a relatively certain effect, the climate effects on the net removal rates are much more uncertain. Aside from the soil water residence time, modelled soil process rates are dependent on soil nutrient concentrations, and modified by both temperature and soil moisture. These might possibly be coupled in a way that is not represented in the model, for example, the respiration response to temperature in a Mediterranean ecosystem has been shown to decrease with increasing drought (Reichstein et al., 2002). Less efficient removal of nutrients from the soil in combination with lower runoff could therefore result in increased nutrient concentrations under climate change.

10.6. Impacts from land cover scenarios and mitigation measures

 Considering that there is no evidence from the observed data that the arable areas are leaching nitrogen, it is not surprising that neither the land use scenarios, nor the mitigation measures had any substantial impact on nitrogen concentrations. As the model was calibrated to simulate uptake of essentially all the applied fertilisers, and as both the land cover scenarios and the mitigation measures only applies to fertiliser loads, the modelled net effect was very small.

 For phosphorus, the model was calibrated to simulate some leaching from arable land, and thus the land cover scenarios and mitigation measures did make a difference. The modelled

effects from the land cover scenarios were still limited, simply because the scenarios do not assume any major increases in phosphorus loads (102-108 % of the baseline load). The modelled effects from the mitigation measures was more substantial as they implied a bigger change in phosphorus loads (67-75 % of the baseline load)

10.7. Other pressures on ecology

 The efforts of modelling the future state of the Louros river were mainly directed towards nutrients. However, although a shortage of monitoring data makes the modelled results highly uncertain, the current status of nutrient concentrations is good, and excess nutrients are not likely to exert any major pressure on the river ecology in the future either.

 Instead, future stressors on river ecology are likely to arise from climate change. Increased salinity and temperatures were highlighted as potential risks in the analysis of ecological data.

11. References

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Appendix

Table A1: Parameters in the PERSiST model. Parameters in bold were calibrated (in either the hydrological or the chemical modelling), whereas the other parameters were kept at their initial values. Q, S and GW denotes quick, soil and groundwater boxes. Shrub Agri Mountain Forest Urban GW Recharge Initial snow depth 0 0 0 0 0 0 Snow multiplier 1 1 1 1 1 0.85 Snow melt 0 0 0 0 0 0 Degree day melt factor 3 3 3 3 3 3 Rain multiplier 1 1 1 1 1 0.85 Degree day evapotransp. 0.115 0.15 0.107 0.16 0.08 0.115 Growing degree threshold 0 0 0 0 0 0 Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Initial water depth 0 300 5500 0 600 5500 0 130 20500 0 500 5500 0 500 5500 0 300 5500 Relative area index 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Infiltration 1000 300 100 1000 300 100 1000 300 100 1000 300 100 1000 300 100 1000 300 100 Retained water depth 0 300 5200 0 630 5200 0 150 20200 0 530 5200 0 530 5200 0 300 5200 Drought runoff fraction 0 0.2 0 0 0.2 0 0 0.2 0 0 0.2 0 0 0.2 0 0 0.2 0 Residence time 1 5 150 1 5 150 1 5 150 1 5 150 1 5 150 1 5 150 Evapotransp. adjustment 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 Relative evapotrans. Index 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 Max capacity 1000 600 10000 1000 900 10000 1000 300 25000 1000 800 10000 1000 800 10000 1000 600 10000

Table A2: Square matrices of the PERSiST model. Numbers in bold were calibrated in the chemical modelling, whereas the other parameters were kept at their initial values. Q, S and GW denotes quick, soil and groundwater boxes. Shrub Agri Mountain Forest Urban GW Recharge Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Q 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 S 1 0.15 0.85 1 0.3 0.7 1 0.15 0.85 1 0.15 0.85 1 0.15 0.85 1 0 1 Gw 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1

Table A3: Land phase parameters in the calibrated INCA-N model. For parameters that differed between the YPEKA2 and UPAT-BIO_Manolaki calibrations, the left value is from the YPEKA2-calibration, the right value is from the UPAT-BIO_Manolaki calibration. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. Shrub Agri Mountain Forest Urban GW recharge Soil denitrification 0.005/0.04 0.01/0.08 0.001/0.02 0.05/0.25 0.05 0.005/0.04 Soil fixation 0 0 0 0 0 0 Soil nitrification 0.4 0.02/0.4 0.2 0.4 0.4 0.4 Soil mineralisation 0.4/0.7 0.4/0.7 0.1/0.4 1/0.7 0.7 0.4/0.7 Soil immobilisation 0.1 0.1 0.1 0.1 0.1 0.1 DR initial nitrate 1 1 1 1 1 1 DR initial ammonium 0 0 0 0 0 0 Soil initial nitrate 1 1 1 1 1 1 Soil initial ammonium 0 0 0 0 0 0 Nitrate uptake rate 0.02 0.14/0.18 0.01 0.2 0.02 0.02 Ammonium uptake rate 0.02 0.14/0.18 0.01 0.2 0.02 0.02 Growth season start day 1 31 1 60 1 1 Growth season length 365 200 365 300 365 365 Growth curve offset 0.66 0.66 0.66 0.66 0.66 0.66 Growth curve amplitude 0.34 0.34 0.34 0.34 0.34 0.34 Maximum N uptake 5000 5000 5000 5000 5000 5000 Zero rate depth 0 15/30 0 10 30 0 Max rate depth 50 200 30 100 100 50 Q10 for soil processes 2 2 2 2 2 2 Base temperature for Q10 30 30 30 30 30 30 Diff.between max. temp. 4.5 4.5 4.5 4.5 4.5 4.5 Soil thermal conductivity 0.7 0.7 0.7 0.7 0.7 0.7 Specific heat capacity 6.6 6.6 6.6 6.6 6.6 6.6 Snow depth factor -0.025 -0.025 -0.025 -0.025 -0.025 -0.025

Table A4: Reach and sub-catchment specific parameters in the calibrated INCA-N model. The left value is the value from the YPEKA2-calibration, the right is the value from the UPAT-BIO_Manolaki calibration. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. 6 11 1 2 3 4 5 7 8 9 10 12 13 14 15 16 (Dam) (Vossa) Velocity ‘a’ 0.4 0.6 0.6 0.7 0.8 0.0015 0.1 0.1 0.1 0.1 0.05 0.025 0.025 0.025 0.025 0.01 Velocity ‘b’ 0.45 0.35 0.35 0.35 0.2 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.67 0.5 0.5 0.5 In-stream denitrification 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.07/0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 In-stream nitrification 2 2 2 2 2 0.5 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 0.5/2 Effluent coefficient 8/1 8/1 8/1 8/1 8/1 1 1 1 1 1 1 1 1 1 1 1 Initial groundwater NO3 0.9/0.6 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.9/0.7 0.8/1 0.8/0.9 0.8/0.9 0.8/0.9 0.8/0.9 0.8/0.9 Initial groundwater NH4 0.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Groundwater denitrification 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table A5: Land phase parameters in the calibrated INCA-P model. The left value is the value from the YPEKA2-calibration, the right is the value from the UPAT-BIO_Manolaki calibration. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. Parameter related to erosion and sediment transport are not shown, as the sediment routine was not used. Shrub Agri Mountain Forest Urban GW recharge DR initial TDP 0 0 0 0 0 0 DR initial PP 0 0 0 0 0 0 Soil initial inactive P 0.02 0.05 0.02 0.02 0.02 0.02 Soil initial labile P 0.002 0.02 0.002 0.002 0.002 0.002 Soil initial TDP 0.01 0.01 0.01 0.01 0.01 0.01 Zero rate depth 0 10 0 15 15 0 Max rate depth 50 200 30 100 100 50 Vegetation index 1 1 1 1 1 1 Immobilisation 0.001 0.01 0.001 0.001 0.001 0.001 Freundlich isotherm 5 5 5 5 5 5 Weathering factor 0.0003 0.1 0.0003 0.0003 0.0003 0.0003 Sorption coefficient 0.5 3.5 0.5 0.5 0.5 0.5 Equilibrium TDP 0.1 3.5 0.1 0.1 0.1 0.1 Q10 for soil processes 2 2 2 2 2 2 Base temperature for Q10 30 30 30 30 30 30 Plant P uptake 0.04 0.12 0.04 0.05 0.05 0.04 Growth season start day 1 1 1 60 1 1 Growth season length 365 365 365 300 365 365 Growth curve offset 0.66 0.66 0.66 0.66 0.66 0.66 Growth curve amplitude 0.34 0.34 0.34 0.34 0.34 0.34 Maximum P uptake 5000 5000 5000 5000 5000 5000 Diff.between max. temp. 4.5 4.5 4.5 4.5 4.5 4.5 Soil thermal conductivity 0.7 0.7 0.7 0.7 0.7 0.7 Specific heat capacity 6.6 6.6 6.6 6.6 6.6 6.6 Snow depth factor -0.025 -0.025 -0.025 -0.025 -0.025 -0.025

Table A6: Reach and sub-catchment specific parameters in the calibrated INCA-P model. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. Parameter related to erosion and sediment transport are not shown, as the sediment routine was not used. 6 11 1 2 3 4 5 7 8 9 10 12 13 14 15 16 (Dam) (Vossa) Velocity ‘a’ 0.4 0.6 0.6 0.7 0.8 0.0015 0.1 0.1 0.1 0.1 0.05 0.025 0.025 0.025 0.025 0.01 Velocity ‘b’ 0.45 0.35 0.35 0.35 0.2 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.67 0.5 0.5 0.5 Temp dependency (macrophytes) 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 Half-saturation P (macrophytes) 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Growth rate (macrophytes) 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 Death rate (macrophytes) 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 0.00005 Self shading constant 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 74 Proportion of P (macrophytes) 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 Temp dependency (epiphytes) 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 Half-saturation P (epiphytes) 1 1 1 1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Growth rate (epiphytes) 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 Death rate (epiphytes) 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 Proportion of P (macrophytes) 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 Initial groundwater TDP 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Initial groundwater inactive P 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Groundwater sorption coefficient 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 Groundwater eq. conc. 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 Bulk density 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700 2700

Table A7: Additional goodness-of-fit measures for NO3 of the YPEKA2-calibration, and for TP and SRP. σ* = Normailsed STD; B = Potential bias; B*= Normalised bias; RMSD = Root-mean-square-deviation; RMSD’ = Unbiased RMSD; RMSD*’ = Normalised unbiased RMSD; ABSD’ = Unbiased absolute difference; ABSD*’ = Normalised unbiased ABSD; R = Linear correlation coefficient; P_R = Pearson’s correlation coefficient; r2 = Coefficient of determination; N-S = Nash-Sutcliffe; log(N-S) = Logarithmic Nash-Sutcliffe; S3 = Taylor skill score; CF = OSPAR cost function; x = x-target; y = y-target. σ* B B* RMSD RMSD' RMSD*' ABSD' ABSD*' R P_R r2 N-S Log (N-S) S3 CF x y

NO3 (Reach 5) 0.349 -0.039 -0.200 0.215 0.211 1.091 0.157 0.811 -0.099 -0.099 0.010 -0.231 -0.268 0.957 0.778 -1.091 -0.200 NO3 (Reach 13) 0.613 -0.015 -0.078 0.108 0.107 0.576 0.091 0.490 0.851 0.851 0.725 0.662 0.647 0.597 0.480 -0.576 -0.078 TP (Reach 5) 1.022 0.009 0.703 0.022 0.020 1.588 0.016 1.263 -0.233 -0.233 0.054 -2.017 -0.719 0.618 1.399 1.588 0.703 SRP (Reach 5) 0.928 0.007 0.515 0.025 0.023 1.632 0.021 1.452 -0.432 -0.432 0.186 -1.928 -0.658 0.724 1.513 -1.632 0.515 TP (Reach 13) 0.771 -0.009 -0.283 0.022 0.021 0.667 0.019 0.613 0.746 0.746 0.556 0.475 0.447 0.348 0.624 -0.667 -0.283 SRP (Reach 13) 1.070 -0.005 -0.241 0.027 0.026 1.173 0.022 0.981 0.360 0.360 0.130 -0.433 -0.080 0.338 1.029 1.173 -0.241

Table A8: Mean values of nitrate, TP and SRP for the lowest reach (reach 16) for all combinations of land use scenarios, and the WFD class of chemical status. KMNI KMNI KNMI KNMI KNMI SMHI SMHI SMHI SMHI SMHI Hadley Hadley Hadley Hadley Hadley Baseline Base A1 A2 B1 B2 Base A1 A2 B1 B2 Base A1 A2 B1 B2

NO3 (mg-N/L) 0.771 0.744 0.745 0.748 0.745 0.746 0.770 0.771 0.774 0.771 0.772 0.761 0.763 0.766 0.763 0.764 TP (µg/L) 61.7 62.4 64 65.9 63.7 64.9 66.3 68 70 67.6 69 62.6 64.1 65.9 63.8 65 SRP (µg/L) 54.7 54.1 55.7 57.5 55.3 56.6 57.8 59.5 61.5 59.1 60.5 52.9 54.4 56.2 54.1 55.3 WFD class for good good good good good good good good good good good good good good good good chemistry

Table A9: Mean values of nitrate, TP and SRP for the lowest reach (reach 16) for all mitigation measures, and the WFD class of chemical status. Best Best Best Best Worst Worst Worst Worst Baseline Baseline Baseline Baseline Best Worst Baseline case case case case case case case case Mit1 Mit2 Mit3 Mit4 case case Mit1 Mit2 Mit3 Mit4 Mit1 Mit2 Mit3 Mit4 NO (mg- 3 0.771 0.759 0.759 0.757 0.756 0.745 0.730 0.730 0.728 0.727 0.766 0.751 0.750 0.748 0.747 N/L) TP (µg/L) 61.7 45.4 45.3 43.4 43.2 63.8 48.7 48.2 46.8 46.2 70 51.3 49.8 48.9 47.6 SRP (µg/L) 54.7 38.5 38.4 36.5 36.2 54.1 39.2 38.6 37.3 36.7 61.5 42.9 41.4 40.5 39.1 WFD class for good good good good good good good good good good good good good good good chemistry