River Arbúcies Biophysical Modelling, 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 5.9 River Arbúcies biophysical modelling, final report

Lead contractor: URead Other contractors involved: UB Due date of deliverable: Month 44 Actual submission date: Month 45

Work package: 5 Contributors: Martin Erlandsson, Andrew J Wade, Joan Lluís Riera Rey, Mariàngels Puig, Richard A Skeffington, Sarah J Halliday Estimated person months: 12

TABLE OF CONTENTS ABSTRACT...... 4 1. BACKGROUND ...... 5

1.1. SITE DESCRIPTION ...... 5 1.2. AVAILABLE FLOW AND CHEMISTRY DATA ...... 6 1.2.1. Catalan Water Agency (ACA) data...... 6 1.2.2. Font de Regas – nested catchment sampling of the Arbúcies headwaters ...... 7 1.2.3. Samples nearby the sewage treatment works effluent ...... 7 2. MODEL SETUP ...... 8

2.1. DEFINING SUBCATCHMENTS ...... 8 2.2. METEOROLOGICAL DATA ...... 9 2.3. LAND COVER CLASSES ...... 11 2.4. CROPS, FERTILISERS AND IRRIGATION ...... 12 2.5. STW EFFLUENT DATA ...... 14 2.6. DEPOSITION ...... 15 3. MODEL CALIBRATIONS ...... 16

3.1. HYDROLOGY (PERSIST) ...... 16 3.1.1. Initial data exploration ...... 16 3.1.2. Model calibration...... 17 3.2. NITROGEN (INCA-N WITH PERSIST) ...... 19 3.2.1. Initial data exploration ...... 19 3.2.2. Model calibration...... 20 3.3. PHOSPHORUS (INCA-P) ...... 23 3.3.1. Initial data exploration ...... 23 3.3.2. Model calibration...... 24 3.4. CONCLUSIONS AND DISCUSSION ...... 26 3.4.1. Hydrology...... 26 3.4.2. Nutrients ...... 27 4. MODEL TESTING ...... 28

4.1. METHODS ...... 28 4.2. HYDROLOGY ...... 29 4.3. NITROGEN ...... 29 4.4. PHOSPHORUS ...... 30 5. SENSITIVITY ANALYSIS ...... 32 6. CLIMATE AND LAND USE SCENARIOS ...... 33

6.1. METEOROLOGICAL DATA ...... 33 6.2. DIFFERENCES BETWEEN BASELINE AND CONTROL PERIODS ...... 35 7. LAND USE SCENARIOS ...... 36

7.1. SCENARIO DECRIPTIONS ...... 36 7.2. LAND COVER ...... 37 7.3. FERTILISERS AND MANURE ...... 37 7.4. DEPOSITION ...... 38 7.5. STW EFFLUENT ...... 39 7.6. ABSTRACTION ...... 40 8. RESULTS FROM CLIMATE SCENARIOS ...... 40

8.1. HYDROLOGY ...... 40 8.2. NITROGEN ...... 43 8.3. PHOSPHORUS ...... 44 9. RESULTS FROM LAND USE SCENARIOS ...... 46

9.1. HYDROLOGY ...... 46 9.2. NITROGEN ...... 46 9.3. PHOSPHORUS ...... 48 10. ASSESSMENT OF MACROINVERTEBRATE STATUS ...... 49

10.1. SAMPLING PROCEDURE AND SITES ...... 49 10.2. MACROINVERTEBRATE INDICES ...... 50 10.3. SITE DESCRIPTIONS AND ECOLOGICAL CLASSIFICATION ...... 52 10.4. MACROINVERTEBRATE RELATIONSHIPS WITH PHYSIOCHEMICAL VARIABLES ...... 54 10.5. ANTICIPATED CLIMATE AND LAND USE EFFECTS ON MACROINVERTEBRATES ...... 56 11. CONCLUSIONS ...... 58

11.1. HYDROLOGICAL RESPONSE TO CLIMATE AND LAND USE CHANGE PROJECTIONS ...... 58 11.2. STREAMWATER NITROGEN RESPONSE TO CLIMATE AND LAND USE CHANGE PROJECTIONS ...... 58 11.3. STREAMWATER PHOSPHORUS RESPONSE TO CLIMATE AND LAND USE CHANGE PROJECTIONS ...... 59 11.4. FUTURE ECOLOGICAL STATUS AND RISKS ...... 59 11.5. UNCERTAINIES AND LIMITATIONS ...... 60 12. REFERENCES ...... 61

Abstract

The Arbúcies river is the smallest of the eight main demonstration catchments in the REFRESH project, with a catchment size of 112 km2, and a total length of 28 km. It is a subcatchment of the larger river La Tordera, located near to the coast of Catalunya in N.E. Spain, 55 km N.E. of Barcelona in the Montseny mountain range. As the catchment is largely forested, with only small areas of agriculture (6 %), the Arbúcies is not substantially affected by diffuse pollution from arable land. Instead, the main source of pollutants (with respect to eutrophication) was belived to be the sewage treatment works (STW) of the town of Arbúcies, which serves approximately 7000 inhabitants. This study aimed to calibrate the hydrological and hydrochemical models of PERSiST, INCA-N with PERSiST and INCA-P, to simulate future hydrology and soil- and stream-water nitrogen and phosphorus concentrations under a number of different climate and land use scenarios. The models were calibrated for the period of 2001-2011, and tested for the periods 1996-2000 and 2011-2012. 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. Although spot samples of STW effluent discharge and chemistry were available (typically 1-2 samples/month), a shortage of STW data was still a major source of uncertainty when running the model calibrations, as the nutrient load from the STW was highly variable on both short-term and inter-annual scales. Thus, it was not possible to calibrate the model to capture the temporal variability of the observed in-stream data with great precision. For the climate scenarios, two of the models (KNMI-RACMO2-ECHAM5 and SMHIRCA-BCM) predicted a slightly wetter future with higher average runoff (11-14 %) due to increased winter precipitation, whereas the third climate model (HadRM3-HadCM3Q), predicted a much drier future for all seasons, with a 38 % decrease in average runoff. None of the climate scenarios had any great impact on nutrients (NO3 and soluble reactive phosphorus [SRP] concentrations) , with relative changes within [-8 %; +7 %]. However, for the drier climate scenario, the streamwater total phosphorus (TP) concentrations increased substantially (+36 %) due to decreased dilution of the STW effluent. Furthermore, for the drier climate scenario, the total nutrient load transported by the river decreased substantially (by 39 % for nitrate and 26 % for SRP) due to decreased diffuse leaching. The four land use scenarios described a combination of future possible storylines, ranging from market- to environmental-oriented, and global to more local/national regulations. The land use scenarios had an influence on fertiliser applicationation rates, population, STW effluent, abstraction, and, to some extent, atmospheric nitrogen deposition. In the model-based applications, stream water quality was found to be more sensitive to land use changes under the dry climate scenario (HadRM3-HadCM3Q), due to the impact from the STW. Total phosphorus increased more than two-fold for the marked- oriented land use scenarios when combined with the dry climate scenario. This did however not change the ecological status of River Arbúcies, which was classified as “good“ by the river outlet for contemporary conditions, as well as for all simulated future scenarios (<2.3 mg-N/L of nitrate, <220 µg-P/L of SRP). The modelling results also suggested that the STW effluent was very influential on in- stream nitrate concentrations before an improved nitrogen removal procedure was implemented in 2006. After this was installed however, sources from semi-natural areas dominated, namely deposition and soil mineralisation. Hence, possibilities for improving the water quality with respect to nitrogen by local management are very limited. For phosphorus however, the STW effluent was the dominant source for all simulated scenarios. The biological status with respect to macroinvertebrates was also classified as “good“. However, the macroinvertebrates in the larger catchment of La Tordera are often affected by STW effluents, and prolonged periods of lower stream flow during future climate scenarios in combination with an increasing population pressure may put higher demands on STW treatment efficiencies in order to preserve or not to degrade the macroinvertebrate status.

1. Background

1.1. Site description

The Arbúcies is a tributary to the river La Tordera, situated by the coast of Catalunya in N.E. Spain, approximately 55 km N.E. of Barcelona in the Montseny mountain range (Fig. 1 ). The Arbúcies has a catchment area of 112 km2 and a length of approximately 28 km. The highest point at the catchment boundaries is at approximately 1500 m.a.s.l. The headwaters in the western parts of the catchment, Font de Regas, emerge from numerous springs, from which water is bottled and commercially sold as mineral water. The upper river reaches are is steep and mountainous, approximately halfway along the length the river reaches a gently sloping valley bottom, where the small town of Arbúcies (pop. 6,700) is situated. Here, the effluent from a small sewage treatment plant discharges into the stream. The sewer system is a combined one, with surface runoff water from the town being transported in the same pipes as sewage water. During rainstorms, the capacity may be exceeded, which causes untreated sewage water to discharge into the stream.The river flows through a narrow valley plain with some agricultural areas before it discharges into river Tordera by the small town of Hostalric, at 60 m.a.s.l. The western part of the catchment is occupied by the Montseny National Park, a biosphere reserve which has been protected since 1978. The large altitudal difference creates a mosaic landscape, with Mediterranean tree species such as Holm Oak (Quercus ilex), Stone Pine (Pinus pinea) and Cork Oak (Quercus suber) prevailing at lower altitudes and a sub-alpine climate with typically Central European species such as beech (Fagus sylvatica) and heather (Calluna vulgaris) heathlands at higher altitudes. The bedrock in the area is mainly different types of granites. The catchment is mostly pristine, with 80 % being covered by forests (predominantly evergreen broadleaf). Agricultural areas are mainly nearby the river in the valley plains, and the small (5 km2) extended plain at the very bottom of the catchment, near the confluence with La Tordera, comprise more than half of the total agricultural area in the catchment (Fig. 2).

Figure 1. Location of the Arbúcies catchment in N.E. Spain.

Figure 2. Left: Land use in the Arbúcies catchment. Middle: Map of the dominant bedrock types in the Arbúcies catchment. Right: Elevation map of the Arbúcies catchment.

The Arbúcies river is also one of the experimental sites of the REFRESH project, whereby it has been subject to drought and flooding experiments. The experimental site is situated a few km upstream of the confluence with Riu Tordera. A short strech of the river was artificially dried in the summer of 2011, and was subject to a natural flooding event in the spring of 2011. In conjuction with the experiments, the stream and riparian zone were monitored for physio-chemical varibles, biota and ecosystem processes (van Oosten-Siedlecka et al., 2011; Martin et al., 2013). There is also monitoring data on macroinvertebrates available from the Arbúcies, and from the larger river of La Tordera.

1.2. Available flow and chemical data

1.2.1. Catalan Water Agency data (ACA)

Flow: There is one gauging station approximately 3 km upstream of the confluence with La Tordera. Flow has been measured daily from Oct 1994 to Mar 2011, although there are two gaps in the data (Oct 1999 – Jun 2000, Jan 2005 – Dec 2006). The stream bed in this stretch of the river is sandy, and infiltration in the stream bed can occur during dry summers, causing the stream channel to turn dry completely.

Chemistry: There are three ACA sites with chemistry observations in the Arbúcies catchment (Table 1). Each water sample has been analysed for nitrate, ammonium and phosphate concentrations. Between 1995 and 2006, samples were taken at a site at Hostalric, near the confluence with La Tordera. The sampling frequency was irregular, with an approximately monthly frequency during the period 1995-2002 and seasonally from 2003 to 2006. In 2007, the sample site was moved to near to the gauging station, and an additional, new sampling site located approximately 3 km upstream of the town of Arbúcies was started. These sites were sampled seasonally from 2007 to 2011. Each water sample was analysed for nitrate, ammonium and phosphate, and for the period of 2000-2006, total phosphorus was also measured.

Table 1: Water chemistry sampling sites in Arbúcies Site Period n Solutes analysed Frequency

Hostalric - Riera 1995-2006 77 NO3, NH4, PO4, TP Semi-monthly sampling. d'Arbúcies Summer samples and low flows underrepresented

Capçalera de la riera 2007-2011 16 NO3, NH4, PO4 Seasonal sampling d'Arbúcies

Tram baix de la riera 2007-2011 16 NO3, NH4, PO4 Seasonal sampling d'Arbúcies

1.2.2. Font de Regas – nested catchment sampling of the Arbúcies headwaters

The second source of data consists of three years of daily observations from a nested catchment sampling study in Font de Regas. This data set contains observations from three sampling sites in the headwaters of the Arbúcies river along the main stream channel. The sites follow a gradient of increasing riparian zone width, the first site (catchment size 1.8 km2) lacks a riparian buffer zone, the second site approximately 1.5 km downstream of the first site (catchment size 8.5 km2) has a narrow riparian zone, and the third site 1 km downstream of the second site (catchment size 12.9 km2) has a wider riparian zone.

The sampling started in September 2010 and proceeded for three years, until August 2013. Both discharge and concentrations of chemical constituents (nitrate and ammonium, but not phosphorus) were measured daily using an auto-sampler. Presently the flow data from September 2010 to August 2011 from the second sampling site only are available for analysis.

1.2.3. Samples nearby the sewage treatment works effluent

A third source of data consists of one year of bi-monthly to monthly samples (December 2011 to October 2012) taken nearby, and downstream of the STW effluent in the town of Arbúcies. The effluent discharges to River Xica, one of the larger tributaries of the Arbúcies river, just before the confluence with the main channel.The sampling sites were chosen so that it would be possible to assess the load of nutrients from the STW to the Arbúcies, and investigatehow rapidly nutrients were transformed and removed downstream of the effluent (Fig. 3, Table 2). Samples were analysed for nitrate, ammonium and phosphate.

Figure 3. Map displaying the locations of the five sampling points nearby the STW effluent in the town of Arbúcies. Sampling site 3 is the STW effluent itself.

Table 2: Description of the sampling sites of the “STW data set”. Site Description S1 The Arbúcies main channel, just upstream of the confluence with River Xica. S2 River Xica, just upstream of the STW effluent S3 STW effluent S4 River Xica, just downstream of the STW effluent S5 The Arbúcies main channel, just downstream of the confluence with River Xica. S6 2.9 km downstream of the confluence S7 5.9 km downstream of the confluence S8 9.4 km downstream of the confluence

2. Model setup

2.1. Defining subcatchments

According to the standard procedure for semi-distributed models (Whitehead et al., 1998), the Arbúcies catchment was divided into eight reaches (or subcatchments), according to where observations of chemistry or flow were available (Fig. 4, Table 3).

Table 3. Division of subcatchments for the hydrochemical modelling. Sub- Area Name Observations catchment (km2) 1 Font de Regas 1 1.8 Font de Regas 2 Font de Regas 2 6.7 Font de Regas 3 Font de Regas 3 4.4 Font de Regas 4 Capçalera 27.3 ACA (Chemistry) 5 Arbúcies 15.7 STW effluent 6 Sant Feliu 50.4 ACA (Discharge) 7 Tram Baix 0.7 ACA (Chemistry) 8 Hostalric 5.0 ACA (Chemistry)

Figure 4. Schematic map of the Arbúcies catchment and eight subcatchments. The location of the ACA sampling sites, the gauging station, the Font de Regas sampling sites, and the Arbúcies STW is shown.

2.2. Meteorological data

To drive the hydrological and chemical models, daily temperature and precipitation time series were required for each INCA subcatchment. Data from seven different meteorological stations was used. One of the stations was located within the catchment, in the town of Arbúcies, for which precipitation data only were available. The other stations were located outside the catchment (north and south, east and west) and on different altitudes, from sea level to 860 m.a.s.l. (Fig. 5). Inverse distance weighting was used together with a correction factor for altitude to determine the precipitation in each subcatchment:

(Eq. 1)

Where: ps,d = daily precipitation of for the subcatchment th pn,d = daily precipitation of for the n meteorological station th pn,y = sum of annual precipitation of for the n meteorological station th dn = the distance between the n meteorological station and the centroid of the subcatchment ES = mean altitude of the subcatchment th En = altitude of the n meteorological station

The equation contains a factor for the altitudal effects on precipitation, which is dependent on the relationship derived from the seven meteorological stations:

2 (r = 0.83) (Eq. 2)

Where: =mean daily precipitation E = altitude

The temperature time series were calculated in a similar way:

(Eq. 3)

th Where Ts,d is the daily temperature of the subcatchment and Tn,d is the daily temperature of the n meteorological station

The coefficients for altitudinal dependence in equations 2 and 3 were both estimated from regressions on mean precipitation and temperature from the seven available stations. Since the recorded data from the meteorological stations cover different periods, and since there are data gaps, both longer and shorter, the meteorological time series were composed from several different combinations of stations. For precipitation, six different stations were used: Arbúcies, Viladrau, Santa Coloma, Santa Maria, Anglés and Malgrat (Fig. 5). The list of combinations of stations and the order of priority is given in Table 4. Temperature was not recorded at the station of Arbúcies, instead the station of Tagamanent was used for temperature estimations. This station was not used in precipitation calculations since it deviated substantially from the altitude – precipitation relationship, however, it did not deviate with respect to temperature.

Table 4: The combinations of meteorological stations used to calculate precipitation and temperature, sorted in priority order Priority Precipitation stations Temperature stations 1 Arbúcies + Viladrau + Sta Coloma + Sta Maria Viladrau + Sta Coloma + Sta Maria 2 Arbúcies + Viladrau + Sta Coloma Viladrau + Sta Coloma + Malgrat 3 Arbúcies + Viladrau + Sta Maria Viladrau + Sta Coloma 4 Arbúcies + Viladrau + Anglés Viladrau + Sta Maria + Malgrat 5 Arbúcies + Viladrau + Malgrat Viladrau + Anglés + Malgrat 6 Arbúcies + Sta Coloma + Sta Maria Viladrau + Malgrat 7 Arbúcies + Sta Coloma + Malgrat Tagamanent + Sta Coloma + Sta Maria 8 Viladrau + Sta Coloma + Sta Maria Tagamanent + Sta Coloma + Malgrat 9 Viladrau + Sta Coloma + Malgrat 10 Viladrau + Sta Coloma 11 Viladrau + Sta Maria + Malgrat 12 Viladrau + Anglés + Malgrat 13 Viladrau + Anglés 14 Viladrau + Malgrat

Figure 5. Schematic map of the Arbúcies catchment and subcatchments (red) within the Riu Tordera catchment (black) and the coastline (purple), showing the locations and altitude of the seven meteorological stations used to calculate precipitation and temperature.

2.3. Land cover classes

In INCA, soil properties such as nitrogen process rates are separately defined for the different land classes. A maximum of six different classes is usually recommended to help limit 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 five classes were defined after map data from 2002 (Generalitat de Catalunya, 2004) (Table 5):

1. “Grassland” – Land in the class “Woodland and Grassland”.

2. “Evergreen” – Evergreen trees, defined by the combined classes “Evergreen broadleaf” and “Conifers”.

3. “Deciduous” – Land classified as “Deciduous broadleaf”.

4. “Arable” – Arable land, defined by the combined classes “Irrigated agriculture”, “Non-irrigated agriculture” and “Non-irrigated fruit trees”.

5. “Urban” – Urban areas and artificial surfaces, defined by the combined classes “Industrial”, “Urban”, “Dispersed urban”, “Roads” and “Bare soil”.

Table 5. Land cover distribution for the 8 subcatchments given in as percentages for each subcatchment. The total area (in km2) is shown in the final row for each of the five land cover types.. Subatchment Grassland Evergreen Deciduous Arable Urban 1 21.2 7.3 70.6 0.9 0 2 8.2 53.9 36.8 1.1 0 3 1.7 62.5 35.5 0.3 0 4 5.3 48.3 41 3.2 2.2 5 3.1 58.7 23.4 5.8 9 6 13.1 59 20.6 5.1 2.2 7 67.8 5 16.8 10.4 0 8 23.1 5.6 9.4 55.2 6.7 Total 11.3 50.0 29.2 5.9 3.6

2.4. Crops, fertilisation and irrigation

The cultivation areas of different crop types were given on municipality level (Table 6). Information was collected from the two municipalities of Arbúcies (86.24 km2) and Sant Feliu (61.9 km2), which together cover 87 % of the catchment (Table 7). Fertiliser application rates per crop type were taken from average levels across Spain given by National Association of Fertiliser Producers (http://www.anffe.com) and expert advice from local farmer associations. For nitrogen, 30 % of the fertilisers were inorganic and 70 % were organic. For phosphorus, 27 % was inorganic and 73 % organic. Numbers of livestock and other domestic animals were also collected on municipality level from Catalan Institute of Statistics (http://www.idescat.cat).

Table 6. Crop types and fertilisation rates for the two main municipalities in the Arbúcies catchment N fertiliser P fertiliser Area Area Sant Application demands (kg demands (kg Arbúcies (ha) Feliu (ha) period N ha-1 year-1) N ha-1 year-1) Winter cereal 30 32 105 23.8 Fall Summer cereal 21 235 235 13.5 Spring Fodder 9 39 58 10.9 Spring Ornamental 21 29 39 7.4 Spring Fruit trees 150 45 53 8.1 Spring Other 45 140 98 12.7 Spring

Table 7. Distribution of the subcatchment areas between the municipalities Arbúcies Sant Feliu Outside Subcatchment (%) (%) (%) 1 46 0 54 2 86 0 14 3 100 0 0 4 97 0 3 5 77 0 23 6 52 33 15 7 0 100 0 8 0 93 7

The areas outside the two municipalities of Arbúcies and Sant Feliu were assigned to the nearest municipality, which was Sant Feliu for subcatchment 8 and Arbúcies for all other areas. To calculate the area of each crop in each subcatchment, the area classified as arable land for each subcatchment was multiplied with the proportion of the specific crop area to the total area of arable land in the municipality:

(Eq. 4)

Where subscript “s” denotes subcatchment and subscript “m” denotes municipality.

The amounts of N and P in waste produced by animals (Table 8) were scaled according to the ratio between the area of the Arbúcies catchment assigned to each municipality and the municipality area:

-1 [kg year ] (Eq. 5)

Where LoadM is the total load of manure in municipality M, AArb,M is the area of the Arbúcies catchment assigned to municipality M, and AM is the area of municipality M.

Table 8. Number of animals per municipality and annual mass of nitrogen and phosphorous produced per head. Cattle Sheep Goats Pigs Chickens Rabbits Horses Arbúcies 640 1332 29 622 609 1147 4 Sant Feliu 65 1683 50 1442 597 456 40 N (kg/Yr) 48 5.25 4.5 6 0.32 0.32 48 P (kg/Yr) 24 2.25 1.875 2.25 0.24 0.24 24

Manure produced by pigs, chickens and rabbits was assumed to be applied on arable land. As this manure was not enough to cover the total demand of organic fertilisers, this was assumed to be included in the total fertiliser load on arable land. However, manure produced by cattle, sheep and goats was assumed not to be collected, but instead applied to grassland. For the two municipalities, area-specific rates of manure application rates on grassland were calculated as:

-1 -1 [kg ha year ] (Eq. 6)

Where lman is the area-specific load of manure on grassland for municipality M, and Agrass,M is the area of grassland in municipality M. The load of manure on the grassland area of each subcatchment was then calculated as:

-1 [kg year ] (Eq. 7)

Table 9. Total loads of nitrogen and phosphorus on arable land (organic and inorganic fertilisers) and grassland (manure from cattle, sheep and goats), given per subcatchment. N on arable land N on grassland P on arable land P on grassland Subcatchment (Tn Yr-1) (Tn Yr-1) (Tn Yr-1) (Tn Yr-1) 1 0.0 3.2 0.0 1.6 2 0.0 4.4 0.0 2.2 3 0.3 0.3 0.1 0.2 4 5.8 15.8 1.0 7.7 5 5.7 7.8 1.0 3.8 6 27.5 21.8 5.1 10.5 7 0.9 0.2 0.2 0.1 8 36.6 0.5 7.3 0.2

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 deciduous and evergreen land classes, assumed equal to 10 kg-N ha-1·year-1. For the phosphorus modelling, phosphorus was added as plant residue with a rate of 2 kg-P ha-1·year-1 as solid phosphorus. Total loads for nitrogen and phosphorus on arable land and grassland are given in Table 9.

Just upstream of the gauging station, water is diverted to an irrigation channel. The channel is actively managed, but there are no measurements of the flow in the irrigation channel, and no information on water demands for crops was available. In the absence of this, the assumption was made that 0.2 m3 s-1 is diverted in July-August, and 0.1 m3 s-1 is diverted in May-June and September, under the condition that the flow in the river must not fall below the arbitrarily chosen number of 0.02 m3 s-1 after diversion.

Assuming that the diverted water is used for the 520 ha of arable land in the St Feliu municipality, this would make 3570 m3 ha-1 of water available for irrigation each summer. Together with the average annual summer precipitation (May-September) of 340 mm, this would fulfil a demand of almost 7000 m3 ha-1 of water during the growing period. While this most certainly exceed the needs for irrigation in the municipality, it is still a possible assumption for the diversion of flow to the irrigation channel, as excess water is routed back to the river downstream of the gauging station.

2.5. STW effluent data

The Arbúcies STW provided some limited data on discharge and nutrient concentrations of the effluent. Discharge was measured approximately bi-monthly to weekly from January 1996 to December 2006, thereafter monthly until August 2011. Nitrogen was measured as nitrate and Kjeldahl-N (i.e. ammonium + organic N), approximately fortnightly to monthly from January 1997 to July 2011. The phosphorus concentration in the effluent was measured as TP from August 2001 to August 2011, monthly for the period 2001-2005, fortnightly for the period 2006-2009 and again monthly for 2010-2011. There were also samples from the STW effluent collected by the University of Barcelona from December 2011 to October 2012 (described in section 1.2.3) and analysed for nitrate, ammonium and phosphate.

All chemical constituents were highly variable, as well as the discharge. Nitrate varied from 0.01 to 16.9 mg-N L-1, Kjeldahl-N varied from 1 to 104.9 mg L-1, TP varied from 0.1 to 24 mg L-1, and the discharge varied from 1.8 to 96 L s-1.

In the absence of more frequent data, mean annual values of discharge and chemistry were used as input time series to the model, as no seasonal variations were observed in the STW effluent flow and chemical measurements. As for nitrogen, Kjeldahl-N dominates over nitrate (average ratio 1:3). Thus, one key assumption is how much of the Kjeldahl-N is “available”, either in the form of ammonium, or readily degradable organic N which can be transformed to ammonium by in-stream mineralisation). The proportion of available Kjeldahl-N was calibrated as a model parameter. For phosphorus, the ratio of phosphate to TP was simply assumed to be equal to the ratio of the average PO4 from the “STW” data set sampled 2011-2012, and the average TP from the data provided by the STW sampled 2001-2011 (SRP:TP ≈ 1:9). The sediment from the STW effluent was assumed to be evenly distributed between the grain size classes defined in INCA-P (clay, silt, fine sand, medium sand and coarse sand). Each milligram of sediment was assumed to carry 10 µg P.

In 2006, a nitrogen removal step was implemented at the STW. The concentrations of nitrogen in the effluent thereby decreased by approximately 15 mg-N/L, and also the phosphorus concentrations decreased slightly. The input time series of the STW effluent are displayed in Fig. 6.

Figure 6. Input time series of the STW effluent used in the nitrogen and phosphorus modelling. Upper: Time series of STW discharge, nitrate and Kjeldahl-N concentrations. Lower: Time series of STW discharge, phosphate and particulate phosphorus. Discharge is plotted on the right axis, concentrations on the left axis.

2.6. Deposition

Nitrogen deposition scenarios were developed in the REFRESH Deliverable 1.10 (Jackson-Blake et al., 2011). The deposition was calculated by EMEP-square. 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:

(Eq. 8)

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

+ (Eq. 9)

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.

3. Model calibrations

3.1. Hydrology (PERSiST)

3.1.1. Initial data exploration

Of the two available discharge time series, the short record from Font de Regas 2 (reach 2) and the longer record from St Feliu (reach 6), the latter shows a much more flashy response. For the seven months of corresponding observations, the specific discharge of reach 6 has a lower average value, a lower minimum, a higher maximum, and a higher Coefficient of Variation (CV), compared to the flow of reach 2 (Table 10).

Table 10. Average, minimum, maximum and CV of specific discharge of flow measurements at Font de Regas (reach 2) and St Feliu (reach 6) from September 2010 to March 2011. Font de Regas (reach 2) St Feliu (reach 6) Average (mm/day) 0.75 0.63 Minimum (mm/day) 0.43 0.09 Maximum (mm/day) 4.29 11.13 CV (%) 79.38 171.33

Furthermore, the observed flow at reach 6 displays some strange and clearly unnatural patterns during the period (Fig. 7).

Figure 7. Stream hydrograph from the gauging station at reach 6. The inset shows an enlargement of a segment of the hydrograph which displays an unnatural behaviour.

3.1.2. Model calibration

To model the hydrology, the new hydrological model 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 the hydrological input data (Hydrological effective rainfall and soil moisture deficit) needed to drive the INCA water quality models.

PERSiST was set up with the five land cover classes described above (section 2.3), and three different soil boxes to represent overland, through- and groundwater flow. Each box was charachterized by nine different parameters, which were specific for each land class. There were 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 was thus 9*3*5 + 9*5 = 180. In reality, only a few of these parameters were actually adjusted in the calibration process.

The model was calibrated for the period January 2001 – August 2011. This period covers observations from both Sant Feliu (January 2001 – December 2004 and January 2007 – March 2011) and Font de Regas (September 2010 – August 2011). Because of reasons mentioned above, regarding the quality of the data from the Sant Feliu gauging station and the uncertainty in the abstraction rates, the calibration was aimed to optimise the model fit with respect to the one-year flow record from reach 2 (Font de Regas headwaters) initially. The calibration was then adjusted to match, as closely as possible, the flow recession rate and the magnitude of the flow peaks observed in reach 6, but with a less strict interpretation of the Goodness-of-fit values.

The model was first calibrated to the flow at reach 2, by adjusting the rain multiplier, evapotranspiration parameters, soil infiltration capacity, drought runoff fraction, soil evapotranspiration adjustment, soil max capacity, direct runoff, soil and groundwater residence times, and the coefficients of the square matrix for the land cover classes of grassland, deciduous and evergreen. The calibration resulted in a Nash-Sutcliffe value of 0.919 (Fig. 8). Thereafter, the parameters for the land cover classes arable and urban were calibrated to simulate the more rapid flow response of reach 6. Furthermore, soil and groundwater residence times were adjusted for all

land cover times to improve the fit for the flow recession curves for this reach. The resulting Nash- Sutcliffe value for reach 6 became 0.629 (Fig.9, Tables A1 and A2).

0.45

0.4

0.35

0.3

- 0.25

m 0.2 Observed flow Modelled flow 0.15

0.1

0.05

0 08/2010 11/2010 02/2011 06/2011 09/2011 Date

Figure 8. Modelled and observed flow at the Font de Regas headwaters (reach 2).

1 -

s 8

m Observed flow 6 Modelled flow 4

0 1999 2002 2005 2008 2010 Year

Figure 9. Modelled and observed flow at St Feliu (reach 6).

In the subsequent modelling of the streamwater nitrogen and phosphorus concentrations, the hydrological parameters of soil and groundwater retained water depth were adjusted to improve the goodness-of-fit (GOF) of the nutrient concentrations. This did however only affect the GOF for flow marginally.

3.2. Nitrogen (INCA-N with PERSiST)

3.2.1. Initial data exploration

For examining the flow-concentration relationships, the modelled flow from PERSiST was used rather than the observed flow records, as the latter contained a two-year gap of data. For nitrate, the flow- concentration relationship was significantely positive for reaches 4 and 7 (monitoring period 2007- 2011) and significantely negative for reach 8 (monitoring period 2001-2006) (Fig. 10). Only data from the calibration period (2001-2011) were used in these initial analyses.

Figure 10. Concentration-flow relationships for streamwater nitrate at reach 4 (2007-2011), reach 7 (2007-2011) and reach 8 (2001-2006).

For the seasonal variability, mean concentrations were highest during summer months for reach 4 (0.95 mg-N L-1) and during winter months for reach 7 (1.65 mg-N L-1). None of these difference between seasons were however statistically significant (pooled t-test, p < 0.05). For reach 8, there was however a statistically significant difference between the mean concentrations of winter months (2.46 mg-N L-1) and those of spring months (1.89 mg-N L-1).

The profile of nitrate concentrations along the river shows a general increase downstream, with a mean nitrate concentration of 0.83 mg-N L-1 in reach 4, 1.26 mg-N L-1 in reach 7 and 2.06 mg-N L-1 in reach 8 (Fig. 11). Since there are no point sources along the short (approximately 3 km) river stretch between the two sampling points, the different mean concentrations and contrasting nitrate dynamics between reaches 7 and 8 almost certainly reflects the different time period for sample collection rather than any spatial variation. The replacement of the monitoring site at reach 8 with the one at reach 7 coincides with the implementation of the improved nitrogen removal treatment of the STW. Furthermore, for reach 4, the nitrate concentrations are below the detection limit (0.5 mg- N L-1) for almost half of the observations.

2.5

N/L

- 1.5 mg 1

0.5

0 Capçalera Tram Baix Hostalric (2001- (2007-2011) (2007-2011) 2006)

Figure 11. Distributions of nitrate at reach 4 (2007-2011), reach 7 (2007-2011) and reach 8 (2001- 2006). Whiskers mark minimum and maximum, upper and lower box boundaries mark the 25th and 75th percentiles, and the line marks the median.

In terms of ecological classification, all three reaches would be classified as “good“, according to the ACA classification system (Table 11). However, it should be noted that the boundary between “high“ and “good“ status is actually below the detection limit of 0.5 mg-N L-1. Furthermore, for reach 8, which essentialy covers the “pre-treatment period“ for the STW, the average value (2.1 mg-N L-1) is close to the boundary for “moderate“ ecological status. Ammonium concentrations are in general very low; for reaches 4 and 7, only one observation each is above the detection limit of 0.08 mg-N L-1. Even in reach 8 (i.e. pre-treatment period), most observations are below detection limit, and only three observations exceed the limit of “high“ ecological status (0.12 mg-N L-1)

Table 11: The boundaries for classification of surface water quality standards. (http://aca-web.gencat.cat/aca/documents/ca/directiva_marc/capitol4_subcapitol4_1.pdf). Class I Class II Class III Class IV Class V

(high) (good) (moderate) (poor) (bad) Nitrate (mg-N/L) 0.45 2.3 5.7 11.3 >11.3 Ammonium (mg-N/L) 0.12 0.39 0.78 3.9 >3.9 Phosphate (µg/L) 40 220 440 870 >870

3.2.2. Model calibration

For modelling streamwater nitrate and ammonium concentrations, INCA-N with PERSiST was used, which is a model that integrates the hydrological model of PERSiST with the nitrogen model INCA-N (Wade et al., 2002a). This is the first application of this model, so there is no existing documentation. However, INCA-N with PERSiST is described by the equations for nitrogen transformation processes and soil temperature from INCA-N (Wade et al., 2002a; Rankinen et al., 2002), 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 calibrated model 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“.

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, groundwater denitrification, initial groundwater nitrate and initial groundwater ammonium. Furthermore, the hydrological parameters of groundwater residence time were adjusted to improve the fit for baseflow conditions. All the remaining parameters were kept at their initial values, defined by “best guesses“ or experience from previous model applications, listed in Tables A3 and A4 (Wade et al., 2002a; McIntyre et al., 2005).

Allthough the two sampling sites of reach 7 (2007-2011) and reach 8 (2001-2006) were separated by less than 3 km, it was not possible to find a common calibration for them both, as they have different annual mean concentrations and flow-concentration relationships. The reduction in nitrogen concentrations of the STW effluent was insufficient to explain these differences alone. However, another possible explanation is that the “available“ proportion of the Kjeldahl-N measured in the STW effluent decreased with the implementation of a nitrogen removal treatment. For this reason, the proportion of “available“ Kjeldahl-N (i.e. ammonium or organic N rapidly mineralised to ammonium) was treated as a variable model parameter with different values pre- and post 1st January 2007.

Allthough these two additional parameters allowed for simulating the contrasting flow-concentration relationships, it was still not possible to achieve Nash-Sutcliffe values >0 for both reach 7 and reach 8. The choice was made to optimise the model calibration to the observations from reach 7, as they cover the period with improved nitrogen removal treatment of the STW, which is more relevant for the future scenarios. The selected calibration had a Nash Sutcliffe value of 0.202, a normalised bias close to zero (-0.026), and captured some of the temporal variability (r2=0.420). For reach 4, the model over-estimated nitrate concentrations somewhat (normalised bias = 0.687), but did capture the temporal variability (r2=0.635). For reach 8, the model under-estimated nitrate concentrations slightly (normalised bias = -0.208), but largely fails to capture the temporal variations (r2=0.117) (Table 12, Figs. 12 & 13). The calibration did not succeed to simulate ammonium, however, nearly all observations after 2006 were below detection limit. Additional goodness-of-fit measures are reported in Table A7.

Table 12: Goodness-of-fit measures for nitrate and ammonium (calibration period). Nitrate Ammonium Norm RMSD Norm RMSD # Period RMSD N-S r2 RMSD N-S r2 bias (norm) bias (norm) 4 2007-2011 0.585 0.596 1.362 -1.196 0.617 0.133 0.049 1.224 -0.516 0.102 7 2007-2011 -0.093 0.504 0.860 0.253 0.464 -0.947 0.031 1.316 -1.628 0.053 8 2001-2006 0.016 0.936 2.215 -3.905 0.137 0.750 0.062 1.042 -0.648 0.165

Figure 12. Modelled and observed nitrate concentrations at reaches 7 (top), 4 (bottom left) and 8 (bottom right).

Figure 13. 1:1-plots of observed and modelled nitrate concentrations at reaches 7 (top), 4 (bottom left) and 8 (bottom right).

3.3. Phosphorus (INCA-P)

3.3.1. Initial data exploration

For phosphate, the flow-concentration relationship was not significant in any of the reaches, but nearly negatively significant for reaches 7 and 8 (p = 0.09 and 0.06 respectively). TP in reach 8 was insignificantly negatively related to flow (p = 0.22) (Fig. 14).

Figure 14. Concentrations vs. flow-relationships for SRP at reach 4 (2007-2011), reach 7 (2007- 2011) and reach 8 (2001-2006), and for TP at reach 8 (2001-2006).

For the seasonal variability, mean phopshate concentrations were highest in summer months for reach 4 – in fact this was the only season when phosphate concentrations were above detection limit. For reach 7, phosphate concentrations were also highest in summer, followed by autumn, spring and winter, with the difference between summer (mean phosphate = 80 mg L-1) and winter (mean phosphate = 20 mg L-1) being significant. For reach 8, the highest mean phosphate concentrations were observed during the winter months, however, winter samples were only taken during two first years. For TP, seasonal differences were very small.

The profile of phosphate concentration along the river is similar to that of nitrate, although with larger differences between reaches 4 and 7 (Fig. 15). The mean concentrations of phosphate were 25 µg L-1 for reach 4, 54 µg L-1 for reach 7, and 87 µg L-1 for reach 8. The mean TP concentration for reach 8 was 140 µg L-1. Although the STW effluent concentrations of phosphorus were less affected by the new technology implemented in 2006 than those of nitrogen, it is still likely that the difference in mean phosphate levels between reaches 7 and 8 was due to the different sample collection periods rather than differences in geographical characteristics and/or nutrient processing. Furthermore, the measurements for reach 4 are even more uncertain than they were for nitrate, as only three of 14 phosphate observations were above detection limit (14 µg L-1 until May 2009, 24 µg L-1 after May 2009).

250

200

150

P/L

- g µ 100

0 Capçalera Tram Baix Hostalric (2007-2011) (2007-2011) (2001-2006)

Figure 15. Distributions of SRP at reach 4 (2007-2011), reach 7 (2007-2011) and reach 8 (2001- 2006). Whiskers mark minimum and maximum, upper and lower box boundaries mark the 25th and 75th percentiles, and the line marks the median.

With respect to SRP, the ecological status would be classified as “good” (<220 µg L-1) for reaches 7 and 8, and “high” (<40 µg L-1) for reach 4 (Table 11).

3.3.2. Model calibration

For modelling the phosphorus, the ambition was to use the new integrated INCA-P with PERSiST model. However, trial runs revealed that the INCA-P with PERSiST sediment routine did not work properly. As it was necessary to simulate the transformation of PP to SRP by desorption, the regular INCA-P model was instead used (Wade et al.; 2002b, Jarritt et al., 2007; Wade et al., 2007), with the time series of hydrologically effective rainfall (HER) and soil moisture deficit (SMD) generated by PERSiST used to drive the model. However, this meant that the phosphorus model was used with a slightly different simulated hydrology than the nitrogen model. Generation of direct runoff, calculation of soil retention and drainage volumes, soil moisture effects on process rates were calculated differently, whereas baseflow index and groundwater retention time were calculated on sub-catchment basis, rather than on land cover basis as in PERSiST.

The following INCA-P model parameters were calibrated including: soil phosphorus terms (Freundlich isotherm, weathering factor, sorption coefficient and equilibrium phosphorus concentrations), plant uptake, process rates response to temperature, immobilisation, maximum soil moisture deficit, ratio of total to available water, initial labile and inactive soil P, water column phosphorus terms (Freundlich isotherm, sorption coefficient, equilibrium phosphorus concentrations and P exchange), reach ecology parameters for macrophytes and epiphytes, sub-catchment direct runoff parameters, groundwater initial values, groundwater volume constants and groundwater process coefficients. All the remaining parameters were kept at their initial values as given in Tables A5 and A6 (Wade et al., 2001; Wade et al., 2002b; Jarritt et al., 2007).

The model was calibrated against SRP of reaches 4 and 7 measured 2007-2011, and SRP and TP of reach 8 measured 2001-2006. The difference between TP and SRP of reach 8 was assumed to be PP, with the STW as the primary source. Small amounts of PP were however also generated from the land phase by erosion and transport by overland flow. In absence of observations of macrophyte or

epiphyte biomass, ecology parameters were calibrated to give an approximate macrophyte biomass of 200 g C m-3, similar to what has been observed in other rivers (Wade et al., 2001).

Similar to the nitrogen model, no calibration was found that gave Nash-Sutcliffe values >0 for all three reaches. Again, the measured concentrations for reach 8 (earlier period) are higher than those for reach 7 (later period).

Table 13: Goodness-of-fit measures for SRP and TP (calibration period). SRP TP Norm. RMSD Nash- Norm. RMSD Nash- Period RMSD r2 RMSD r2 bias (norm) Sutcliffe bias (norm) Sutcliffe 4 2007-2011 -0.257 0.485 0.992 -0.050 0.132 7 2007-2011 0.352 0.029 1.241 -0.663 0.047 8 2001-2006 -0.216 0.036 1.109 -0.276 0.019 -0.157 0.084 0.925 0.119 0.173

The temporal variability for phosphorus was poorly captured for all reaches (r2<0.18). Normalised bias was however reasonably close to zero (between -0.254 and +0.307). A Nash-Sutcliffe above zero was achieved for TP at reach 8 (Figs. 16 & 17, Table 13). The model fit for the flow from INCA-P was slightly worse than that from PERSiST; the r2-value was 0.584 and the Nash-Sutcliffe was 0.579. Additional goodness-of-fit measures are reported in Table A7.

Figure 16. Modelled and observed TP at reach 8 (top left), and SRP concentrations at reaches 8 (top right), 4 (bottom left) and 7 (bottom right).

Figure 17. 1:1-plots of observed and modelled TP concentrations at reach 8 (top left), and SRP concentrations at reaches 8 (top right), 4 (bottom left) and 7 (bottom right).

3.4. Conclusions and discussion

3.4.1. Hydrology

For the hydrology modelling, it was somewhat surprising to note the contrasting flow response between reach 2 and reach 6. The pattern was unexpected since the steep and mountainous headwater catchment of Font de Regas was expected to respond quicker to flow events than the downstream site, which is on a narrow alluvial plain. The relative distribution of the three dominating land cover classes according to the map, grassland, evergreen and deciduous, was similar for subcatchment upstream of reach 2 and 6 respectively, and different rainfall-runoff response among these land classes could therefore not explain the observed differences. In the model calibration, the flashy response observed at reach 6 was instead simulated by a relatively large proportion of direct runoff generated by the arable and urban land cover classes. While this behaviour is expected from urban land, it could potentially also be the case for arable land, if it was subject to extensive ditching. However, another possibility is that the subcatchment of Font de Regas is unrepresentative of the catchment as a whole with respect to the rainfall-runoff response. Local differences in precipitation, soil depth, slope, land cover or bedrock, which are not captured by the available land cover and geological maps, could also be feasible explanations to the observed differences in stream-flow behaviour between the upper and lower reaches. To better explain this, continuous flow measurements from middle reaches would be required, to investigate where along the river the flashier flow patterns start.

3.4.2. Nutrients

By comparing the output from the different land cover classes with the nitrogen load from the STW effluent, the relative contribution from nitrogen deposition, fertilisers and manure, and STW effluent was assessed. The contribution from deposition was estimated as the output of nitrogen (i.e. nitrate + ammonium) from the evergreen and deciduous land classes. The contribution from fertilisers was estimated as the output of nitrogen from the arable land class. The land cover class of grassland recieves significant nitrogen input from both deposition and manure. The contribution from each of these was estimated as the proportion contributed to the total load multiplied by the total leaching.

The nitrogen loadings from the STW before the implementation of the nitrogen removal step was in size comparable to that of deposition (≈15 Tn N Year-1 each), together making up for 87 % of the total output, whereas fertilsers and manure contribute with the remaining 13 %. After the implementation of the nitrogen removal step, the load decreased to below 3 Tn N Year-1, smaller than the contribution from fertilisers and manure. Post-treatment, the contribution from deposition was twice as large as the two other sources combined (Fig. 18).

18 16 14

12 10 8 Pre-2006

Tn N/Year Tn 6 Post-2006 4 2 0 Deposition + Fertiliser & STW effluent natural sources Manure

Figure 18. Modelled contribution from deposition and natural sources, fertilisers and manure, and the STW to the total amount of inorganic nitrogen leaching to the stream.

A similar analysis was also done for the river transport of SRP. Phosphorus leached from grassland and arable land was attributed to fertilisers and manure. As for the STW effluent, the dominant phosphorus species was PP. The contribution of the STW effluent to SRP was calculated as the SRP load from the effluent, plus the total amount of phosphorus desorbed from the particulate phase along the river stretch below the STW. The calculations were performed separately pre- and post- treatment implemented in 2006.

The contribution from the STW dominated both pre- and post-treatment, whereas fertilisers and manure contributed to approximately the same amount of SPR as the natural sources. Pre-2006, the contribution of the STW was around 60 %, which decreased to around 50 % post-2006 (Fig. 19).

0.9 0.8 0.7 0.6 0.5 0.4 Pre-2006

Tn SRP/Year Tn 0.3 Post-2006 0.2 0.1 0 Natural sources Fertiliser & STW effluent Manure

Figure 19. Modelled contribution from natural sources, fertilisers and manure, and the STW to the total amount of SRP leaching to the stream.

The results from the source-partitioning estimations are similar to those of Caille et al. (2011), who investigated nutrient loads and retention for the La Tordera basin (of which the Arbúcies is a subcatchment) between 1996 and 2001. They found that for the La Tordera basin, 94 % of phosphorus emissions could be attributed to urban and industrial sources. For nitrogen, these accounted for 58 %. Merseburger et al. (2005) studied in-stream processing of nutrients in a river reach downstream of an STW in the La Tordera catchment (similar stream size, STW size and land use to the Arbúcies site), where nitrification was found to be the dominant in-stream process, whereas retention of SRP, nitrate and organic nitrogen was small or negligible. This agrees fairly well with the modelled results, where more than 50 % of the ammonium was nitrified along reach 6. Around 20 % of modelled SRP was retained, whereas modelled nitrate increased slightly.

Clearly, with the STW effluent being influential on in-stream phosphorus concentrations, and for nitrogen pre-2006, the lack of high-resolution data in combination with the very large variability in STW effluent chemistry is a large source of uncertainty. Furthermore, the fact that many observations from reach 4 above the STW were below detection limit makes the estimation of the amount of nutrients leaching from natural sources uncertain. In conclusion, high quality observed in- stream data is lacking to model the upper part of the catchment, and high resolution STW data, including a more detailed speciation of nitrogen and phosphorus, is lacking to model the lower part of the catchment with good accuracy.

4. Model testing

4.1. Methods

For testing the model performance versus data not used in the calibration, the calibrated models were run from Jan 1996 to Aug 2012. The model output was then compared to:

1) ACA monitoring data 1996-2000: NO3 and SRP from reach 8 and stream flow from reach 6. 2) Data from the ”STW data set” Dec 2011 – Aug 2012. Sample site S5 (see Table 2) was then assigned to reach 5, and sample site S8 was assigned to reach 6 (NO3 and SRP).

4.2. Hydrology

For stream-flow, the model actually performed conciderably better for the test period than for the calibration period. The r2 value was 0.755, and the Nash-Sutcliffe was 0.713, and the normalised bias was -0.108).

4.3. Nitrogen

The model fit for nitrogen against the ACA data of 1996-2000 was good with respect to temporal variability (r2 = 0.485), and also correctly captures the increasing trend 1997-1999. However, nitrate was considerably over-estimated (normilised bias 0.787). The model fit could be improved to reach a normalised bias close to zero by adjusting the proportion of available Kjeldahl-N to 0.45 (normalised bias = -0.012). This again illustrates the large uncertainties associeted with the STW effluent. However, it only has a bearing on the pre-treatment nitrogen dynamics, which is less relevant when simulating future scenarios.

The model fit for nitrogen against the STW data set was acceptable although under-estimated for reach 5 (just below the STW), but was considerably under-estimated for reach 6. It was a surprising observation from the STW data set that the average stream nitrate concentrations increased by so much between reach 5 and 6 in the STW data set; from 1.17 mg-N L-1 in reach 5 to 1.49 mg-N L-1 in reach 6 (Figs. 20 & 21, table 14). While it is possible that the arable land adjacent to the stream leach more nitrate than simulated by the model, this cannot account for the whole observed increase in nitrate between reach 5 and 6. Even under the (obviously unrealistic) assumption that no nitrogen is taken up by plants, and all fertilisers are lost with runoff, simulated nitrogen can only increase by 0.2 mg-N L-1 between reach 5 and 6. Instead, it is more likely that upland and lowland forests have different dynamics for nutrient cycling, and that from a modelling perspective they could for that purpose be split into different land cover classes.

Figure 20. Modelled and observed nitrate concentrations for the test periods at reach 8 1996-2000 (top), reach 5 2011-2012 (bottom left) and reach 6 2011-2012 (bottom right).

Figure 21. 1:1-plot of observed and modelled nitrate concentrations for the test periods at reach 8 1996-2000 (top), reach 5 2011-2012 (bottom left) and reach 6 2011-2012 (bottom right).

Table 14: Goodness-of-fit measures for nitrate and ammonium (test periods). Nitrate Norm RMSD # Period RMSD N-S r2 bias (norm) 5 2011-2012 -0.945 0.453 0.910 -0.722 0.225 6 2011-2012 -2.526 0.869 0.937 -6.258 0.140 8 1996-2000 0.787 0.921 1.119 -0.872 0.485

4.4. Phosphorus

The model fit for SRP against the ACA data of 1996-2000 was very good for the first 18 months. For the latter part, August 1997-December 2000, the model fit is worse with considerable under- prediction of SRP concentrations (Fig. 19, Table 12). However, recalling that there was no available data from the STW effluent during the test period, it is hardly surprising that the phosphorus could not be well modelled, considering how influential the STW is on in-stream phosphorus concentrations.

For the “STW data set“, phosphate concentrations increased dramatically for the last months of the test period (April 2012 and onwards), to magnitudes unprecedented according to all other available data sets. This was especially the case for reach 5, where phosphate concentrations exceeded 600

µg-P L-1 in August 2012, while they previously seldom exceeded 100 µg-P L-1 anywhere in the river. SRP also increased for reach 6 around the same time, but not as dramatically (up to 115 µg-P L-1). The source of this massive output of phosphorus is not known. The phosphate concentrationsdoes increase in the STW effluent around the same time, up to concentrations of > 1100 µg-P L-1, however, this is not enough to explain more than a small fraction of the observed in-stream increase. One other candidate for the phosphorus source is the construction of an artificial wetland at the time being. A point source would have to emit up to 15 kg of phosphate per day to explain the observed concentrations.

Regardless of what the true explaination for the observed increase in phosphate concentrations is, it is an event that could not be expected to e correctly captured by the calibrated model. In the tables and figures, the GOF is therefore presented calculated against STW data before April 2012 (Fig. 22 & 23, Table 15).

Figure 22. Modelled and observed SRP concentrations for the test periods at reach 8 1996-2000 (top), reach 5 2011-2012 (bottom left) and reach 6 2011-2012 (bottom right).

Figure 23. 1:1-plot of observed and modelled SRP concentrations for the test periods at reach 8 1996-2000 (top), reach 5 2011-2012 (bottom left) and reach 6 2011-2012 (bottom right).

Table 15: Goodness-of-fit measures for SRP (test periods). SRP Norm RMSD # Period RMSD N-S r2 bias (norm) 5 2011-2012 -0.683 30.772 1.042 -0.552 0.003 6 2011-2012 1.146 9.189 2.250 -5.376 0.115 8 1996-2000 -0.790 48.815 0.986 -0.595 0.109

5. Sensitivity analysis

The model-independent nonlinear parameter estimation tool PEST (Doherty, 2005) was investigated as a method for assessing the parameter sensitivity of the INCA-N PERSiST model simulations. PEST is a local sensitivity analysis method which adopts a one-at-a-time (OAT) approach to sensitivity analysis. The method employs the Gauss-Marquardt-Levenberg optimisation algorithm which is noted for its computational efficiency. However, significant limitations of the PEST method have been noted in the literature (Tang et al., 2007). Principally, that when the model being analysed is highly complex and non-linear, which INCA-N PERSiST is, the optimisation processes can easily get stuck either in an area of insensitivity or at local minimum (Doherty, 2008; Karlsen et al., 2012). In addition, as a local sensitivity method the determination of the parameter sensitivity is highly dependent on the initial parameter values selected. This means that in order for the sensitivity analysis to return valid results the model user already has to have identified initial parameter values

close to the optimal parameter set (Bahremand & De Smedt, 2008). Within the Arbúcies catchment the issues with applying PEST were also confounded by the lack of observed system data. Given these issues it was determined that no additional system or model understanding would be attended by undertaking PEST sensitivity analysis on the Arbúcies.

6. 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). Compared to the “observed” precipitation (as calculated according to section 2.2), the KNMI model was in best agreement. The Hadley model had a too small seasonal amplitude, with an over-estimation of precipitation in spring and summer, and an under-estimation in autumn and winter. The SMHI showed the opposite pattern, with too large seasonal amplitude, resulting in an under-estimation of precipitation in spring and summer, and an over-estimation in autumn (Fig. 24). Furthermore, all three models under-predicted the daily mean precipitation of 2.34 mm. The SMHI model showed the largest bias with a mean daily precipitation of 1.77 mm, followed by the KNMI model (2.10 mm) and the Hadley model (2.19 mm).

Figure 24. Mean value (left) and standard deviation (right) for the observed precipitation at reach 5 (1996-2012), and for the baseline of the three climate models (KNMI, SMHI and Hadley) 1981-2010, plotted my month.

For bias correction of precipitation, the power function method described in Leander et al. (2007) was used. The modelled precipitation is modified by the coefficients a and b, according to:

(Eq. 10)

The parameters a and b are chosen to preserve two different quantiles from the observed timeseries, one quantile near the average daily precipitation (QA), and one quantile near the extreme end (QH). Parameter b is then calculated as:

; (Eq. 11)

and parameter a was calculated as:

(Eq. 12)

Where subscript ‘O‘ denotes ‘observed‘ and subscript ‘M‘ denotes ‘modelled‘.

The bias corrections were done separately for each month and for each subcatchment (Fig. 25). Upon trial, Q85 was chosen as QA for the KNMI and Hadley models, whereas Q87.5 was chosen for the SMHI models, as these quantiles gave a bias corrected average precipitation that was closest to the observed precipitation of the calibration period. For QH, Q99 was chosen for all three climate models. To prevent unrealistically high amounts of daily precipitation, an upper threshold was introduced. This was calculated as the ratio between the maximum modelled precipitation from the scenario period (2031-2060) and the maximum modelled precipitation from the baseline period (1981-2010), multiplied by the maximum observed precipitation (1996-2011):

(Eq. 13)

Figure 25. Mean value (left) and standard deviation (right) for the observed precipitation at reach 5 (1996-2012), and for the baseline of the three climate models after bias correction (KNMI, SMHI and Hadley) 1981-2010, plotted my month.

The KNMI climate model was also in best agreement with observed data for temperature, predicting a mean annual temperature of 13.4 °C, compared to the observed 12.8 °C. The Hadley model over- estimated temperatures significantly, with a mean annual temperature of 15.1 °C, and especially in summer when the difference in average temperatures was around around 5°C. The average annual temperature form the SMHI model agreed well with the observed redicted approximately correct average annual temperatures (12.2 °C), however, the variability in summer was under-estimated (Fig. 26).

Figure 26. Mean value (left) and standard deviation (right) for the observed temperature at reach 5 (1996-2012), and for the baseline of the three climate models (KNMI, SMHI and Hadley) 1981-2010, plotted my month.

The temperature was bias corrected by calulating the Z-score for the modelled temperature series, multiplying this by the standard deviation for the observed time series, and adding the mean value for the observed time series:

Where subscript ‘O‘ denotes ‘observed‘ and subscript ‘M‘ denotes ‘modelled‘.

This method gave mean and standard deviation of the modelled temperature series identical to the observed. Just as for precipitation, the bias corrections for temperature were done separately for each month and for each subcatchment.

Although the bias correction improved the agreement between observed and the modelled meteorological data, the data for the baseline period (1981-2010) still differed slightly between the three different climate models. For this reason, the baseline period was modelled separately for all three climate models, and all changes reported for the scenario period for a given climate model are relative to the baseline period driven by the same model.

6.2. Differences between baseline and control periods

Whereas all three climate models predicted a temperature increase over all seasons to varying extents, the scenarios for precipitation differed somewhat. The SMHI model predicts a slight increase in precipitation (+7 %), the KNMI model predicts nearly no change (+2 %), whereas the Hadley model predicts a decrease (-16 %). The Hadley model predicts both the largest decreases in precipitation and the largest increases in temperature for the summer months.The KNMI model predicts warmer and drier summers and wetter winters, whereas the SMHI model lacks any clear seasonal patterns in the forecasted changes (Fig. 27).

Figure 27. Left: Relative changes in average monthly precipitation between the baseline (1981- 2010) and scenario (2031-2060) periods for the three climate models. Right: Average monthly changes in temperature between the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

7. Land use scenarios

7.1. Scenario descriptions

Four different storylines were defined based on Caille et al. (2007):

1. The World Market scenario: Globalisation and market forces dominate, meaning that land use receives no government subsidies and there is little environmental protection. Intensive agriculture is favoured on the best quality land. For the Arbúcies catchment, agriculture is reduced due to fierce competition with regional and global markets, following the current trend. Abandoned land is not actively managed, and is mostly converted to grassland or shrubland. 10% of arable land is converted to forest, the remaining agricultural land is managed intensively.

2. The National Enterprise scenario: Protectionism at national and trade-bloc (EU) level with food security given the highest priority and land subsidised to provide this. Low environmental protection and regulation. For the Arbúcies catchment, agriculture in marginal lands will be abandoned in favor of other uses, including forest products, but in competition with grasslands and pastures. 10% of arable land is converted to grassland; the remaining arable land is managed intensively.

3. The Global Sustainability scenario: Global co-operation and binding treaties mean that a high degree of environmental protection is enforced. Land use planning is strongly regulated resulting in zoning associated with best utilisation of natural resources.For the Arbúcies catchment, agriculture will be maintained on high quality lands; abandoned marginal lands are actively managed for forest products. 30% of arable land is converted to forest.

4. The Local Stewardship scenario: Decision-making is dominated by local issues, particularly sustainable solutions to local food and energy supply.There will therefore be little large-scale co- ordination, and land use and resources are strongly based on the needs of the local community. For the Arbúcies catchment, agricultural lands are environmentally-managed but are all kept for food security. Forest cover does not increase.

From a modelling perspective, the land cover scenarios will affect land cover, fertilisers and manure, deposition, STW effluent and abstraction for irrigation.

7.2. Land cover

Storylines for each of the scenarios were translated into net percent changes in land cover classes, with changes differing between subcatchments (Table 16) according to Caille et al. (2007). The main drivers for land use change are agricultural land abandonment and urban growth. Abandoned agricultural land is taken up by urban growth if adjacent to towns or low density second home developments, to changes into grass/shrubland if managed for low productivity pastures, or, if not managed, the abandoned agricultural land is eventually converted into forest through ecological succession. Headwater catchments, which are within the limits of the Montseny Biosphere Reserve, tend to remain mostly forested, with slight differences in the amounts of grassland and shrubland compared to evergreen and deciduous forest. Changes with more impact on nutrient export occur at the lower subcatchments, with reductions in agricultural lands mostly in the World Market and Global Sustainability scenarios, and most agricultural land preserved for food security under the Local Stewardship scenario. Urban growth is larger under World Market, National Enterprise and Global Sustainability scenarios, but low under Local Stewardship scenario. Most importantly, scenarios differ more in how land is used under each land use class than by changes in areal cover, as explained below. For example, agricultural land is more fertilised under the Global Sustainability scenario, and urban growth is more compact under the World Market and National Enterprise scenarios than under the Global Sustainability and Local Stewardship scenarios, which means higher impact per urban land unit area in the former that in the latter.

Table 16: Relative land cover of the Arbúcies catchment (%) for the four land use scenarios. Scenario Grassland Evergreen Deciduous Arable Urban Baseline 11.3 50.0 29.2 5.9 3.6 World Market 11.1 50.0 28.9 4.7 5.4 National Enterprise 12.8 47.7 28.0 5.4 6.1 Global Sustainability 8.7 51.4 30.3 4.1 5.5 Local Stewardship 10.4 50.0 29.2 5.9 4.5

7.3. Fertilisers and manure

For each scenario, a uniform relative change in the area specific fertiliser rates was applied for all crops. The changes were: World Market: No change National Enterprise: +50% Global Sustainability: -50% Local Stewardship: -30%

Livestock were assumed to change at the same relative rate as arable land. Taken into account the decreases in arable land area (table 13), all scenarios except for the National Enterprise brought a decrease in fertiliser loads. The Global Sustainability scenario brought the largest decrease, of around 40 %. For the National Enterprise scenario, nitrogen loads increased by 7 %, whereas phosphorus loads were nearly unchanged (Fig. 28).

140

120

100

80 Nitrogen

Tn/Year 60 Phosphorus

0 Baseline WM NE GS LS

Figure 28. Total loads of nitrogen and phosphorus from fertilisers and manure for the four different land use scenarios (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

7.4. Deposition

Nitrogen deposition scenarios were developed in the REFRESH Deliverable 1.10 (Jackson-Blake et al., 2011). According to these scenarios, wet deposition is the same for all land cover, whereas the dry deposition is higher for forest land cover types. The only bearing the four different storylines have on nitrogen deposition is the forest cover, which will have an impact on dry deposition. The wet and dry deposition per subcatchment were calculated from eqs. 8 and 9.

180 160 140

120

100

80 Tn N/Year Tn 60 40 20 0 Baseline Baseline WM NE GS LS (81-10) (31-60)

Figure 29. Total loads of nitrogen from deposition for the baseline period (1981-2010), the scenario period (2031-2060), and for the four different land use scenarios 2031-2060 (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

The difference in deposition between the baseline (1981-2010) and the scenario period (2031-2060) was substantial, with the annual total nitrogen deposition decreasing from around 170 to 130 Tn N year-1. However the difference in deposition between the four storylines was very small, varying from 128.1 (Local Stewardship) to 132.2 (Global Sustainability) Tn N year-1 (Fig. 29). Realistically, the storylines would also have a bearing on nitrogen emission rates, with higher rates for the more market-oriented storylines of World Market and National Enterprise, and lower rates for the more environmental-oriented storylines of Global Sustainability and Local Stewardship. However, this was not accounted for.

7.5. STW effluent

To derive scenario time series for the STW effluent, the starting point was to take the annual means of STW discharge, nitrate, Kjeldahl-N, SRP and PP from the last five years of the calibration period (2007-2011, i.e. the post-treatment period). These values were then randomly assigned to the years of the baseline and scenario periods. The corresponding years of the baseline/scenario periods (i.e. 1981/2031, 1982/2032 and so forth) were assigned the same values.

The specific STW discharge per person inhabitant was then calculated, and multiplied by the forecasted populations for the different storylines. The population equivalent for the Arbúcies STW was 7011 in 2011, and the annual growth rate for the period 2002-2011 was 1.022. The land use scenarios assumed a range in population growth, from assuming the current growth rate will persist (National Enterprise), to a very moderate population growth (Local Stewardship).

World Market: Weak limitations on urban growth; population grows at nearly the mean growth rate (applied for a period of thirty years). Growth rate applied, 1.018; resulting population for the scenario period = 12,000 person equivalents.

National Enterprise: Weak limitations on urban growth; population grows at the mean growth rate. Growth rate applied, 1.022; resulting population for the scenario period = 13,500 person equivalents.

Global Sustainability: Strong limitations on urban growth; most population growth in seasonal inhabitants. Growth rate applied: 1.010; resulting population for the scenario period = 9,450 person equivalents.

Local Stewardship: Very strong limitations on growth. Rate applied: 1.005; resulting population for the scenario period = 8,140 person equivalents.

The nutrient loads from the STW were also modified, according to:

World Market: +20 % in concentration (low environmental regulations; increased overflows) National Enterprise: No change Global Sustainability: -30 % in concentration (assuming soft-engineering post-treatment is applied, such as constructed wetland, in-stream restoration to enhance nutrient retention) Local Stewardship: -50 % in concentration (assuming a part of the sewage produced in Arbúcies is taken to the STW in Hostalric)

The resulting total fertiliser loads are approximately twice of the baseline for the World Market and National Enterprise scenarios, the same for the Global Sustainability scenario, and 35% lower for the Local Stewardship scenario. For nitrogen however, none of the future scenarios have nitrogen loads nearly as high as the pre-2006 loads, which were around 12 Tn N/year (Fig. 30).

4 Nitrogen

Tn/Year 3 Phosphorus

0 Baseline WM NE GS LS

Figure 30. Total loads of nitrogen and phosphorus from the STW effluent for the scenario period (2031-2060) for the four land use scenarios (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

For all scenarios, including the baseline period, the calibrated post-treatment coefficient of 65 % “available” Kjeldahl-N was applied.

7.6. Abstraction

Abstraction rates were affected by both climate and land use scenarios. Relative to the baseline period (where 0.1 m3 s-1 is abstracted in May, June and September, and 0.2 m3 s-1 in July-August), the abstraction rates were increased by the same relative rates as summer precipitation (May- September) decreased. Thereafter, the abstraction rates were also changed by the same relative numbers as the fertiliser loads. This gave a unique abstraction time series for each combination of climate scenarios and land use scenarios (table 17). As before, there is a restriction to the abstraction so that the stream-flow must never fall beneath 0.02 m3 s-1.

Table 17: Relative change in abstraction (%) for all combinations of climate and land use scenarios. KNMI SMHI Hadley Base +23.2 +2.0 +28.8 World Market -0.8 -17.9 +3.7 National Enterprise +31.8 +9.1 +37.8 Global Sustainability -26.1 -38.8 -22.7 Local Stewardship -0.2 -17.4 +4.3

8. Results from climate scenarios

8.1. Hydrology

Somewhat surprisingly, in spite of the forecasted increase in temperatures and the relatively small increase in precipitation, the climate simulations resulted in an increase of average flow for two of

the climate models, by 14.4 % for the KNMI model and by 10.9 % for the SMHI model (for reach 8). However, for the Hadley model, average discharge was substantially reduced, by 37.9 % (Fig. 31). It is somewhat surprising that the KNMI model gave a larger increase stream-flow than the SMHI model, as the SMHI model forecasted lower temperatures and a larger increase in precipitation (Fig. 27). The reason is that the annual distribution of rainfall differs between the models. Whereas the SMHI model predicts a more or less even change in precipitation over the year, the KNMI model displays a clearer shift in precipitation regime, with drier summers and wetter winters. As the evapotranspiration is lower in winter, more winter precipitation will generate more runoff, whereas drier summers will not have a large impact on runoff as only small amounts of runoff are generated during summer months.

0.7

0.6

0.5

/s) 0.4 3 Baseline 0.3 Scenario

0.2

0.1 Mean discharge(m Mean

0 KNMI SMHI Hadley

Figure 31. Average flow in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

Instead, in the simulations, drier summers manifested themselves in drier soils, and an increased water deficit to meet irrigation demands. In the model calibrations, it was assumed that abstraction was restricted so that a discharge of at least 0.02 m3 s-1 must be maintained. Thus, minimum flow at reach 6 where abstraction is taken equalled 0.02 m3 s-1 for all simulated scenarios. However, the number of days when the required amounts of water for irrigation could not be abstracted varied between the different scenarios, and this number increased for the scenario period for all climate models. The number increased more than threefold for the Hadley model (21 to 67 days), and marginally for the KNMI model (23 to 25 days). For the SMHI model, the number of days with water shortage was considerably lower for the baseline period compared to the other models, demonstrating that the bias correction was not successful in correcting for the drier periods. However, the number of days increased two-fold for the SMHI model, from 10 to 21 (Fig. 32).

40 Baseline 30 Scenario

20 Average No. Average of days/year 10

0 KNMI SMHI Hadley

Figure 32. Average number of days with water deficit for irrigation flow in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

As for high flows, the average annual maximum flow increased for all climate models between the baseline and scenario periods, even for the Hadley model. The SMHI model resulted in the largest increase, by more than 100 % (mean annual maximum for the baseline period = 5.3 m3 s-1, mean annual maximum for the scenario period = 10.8 m3 s-1), the KNMI model gave an increase of 40.8 % (6.8 to 9.5 m3 s-1), and the Hadley model gave the smallest increase of 19.0 % (5.2 to 6.2 m3 s-1) (Fig. 33).

/s) 10 3

6 Baseline Scenario 4

2 Average annual Average max. flow (m

0 KNMI SMHI Hadley

Figure 33. Average annual maximum flow in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

8.2. Nitrogen

The direction of the change in nitrate concentrations differed between the different climate models, as the wetter climate scenarios (KNMI and SMHI) brought a decrease in nitrate, whereas the drier Hadley scenario brought an increase. The differences were however small; for reach 8 the changes were -6.9 % for KNMI (1.08 to 1.01 mg N/L), -3.9 % for SMHI (1.10 to 1.05 mg N/L), and +7.9 % for Hadley (1.10 to 1.19 mg N/L). The total nitrate load transported by the river increased slightly for the wetter scenarios (< +4%), however, the load was significantly decreased for the drier Hadley scenario, by 39 % (Fig. 34).

1.4

1.2

/s) 3 1

0.8 Baseline 0.6 Scenario

0.4

Average annual Average max. flow (m 0.2

0 KNMI SMHI Hadley

Figure 34. Average nitrate concentrations in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

On a seasonal basis, the “wetter” scenarios show the largest decreases in nitrate concentrations for winter months (December-February), whereas the “drier” Hadley scenario show the largest increases in nitrate concentrations for late summer and autumn (Fig. 35).

Figure 35. Average nitrate concentrations per month in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

As for the source-partitioning of the in-stream nitrate, it is more or less unchanged for the two wetter scenarios. For the drier Hadley scenario, however, the contribution from deposition and natural sources (i.e. nitrogen leached from evergreen and deciduous forests, and partly from grassland) is reduced by almost 50 % (Fig. 36). The decrease in deposition is more-or-less balanced by an increase in mineralisation, brought by increases in temperature. In the end however, the amount of nitrogen leached from the soils are furthermost controlled by the amount of runoff generated. For the Hadley scenario, the average number of days per year when the soil water depth is above the retained water depth decreases by more than 50 % for the scenario period relative to the baseline (from 41 to 20 days per year)

Figure 36. Modelled contribution from deposition and natural sources, fertilisers and manure, and the STW to the total amount of nitrate leaching to the stream, for the baseline (1981-2010) and scenario (2031-2060) periods for the three different climate models.

8.3. Phosphorus

The patterns for phosphorus under the scenario runs were similar to those of nitrogen, with some exceptions. The “wetter” scenarios resulted in a slight, but rather insignificant decrease of phosphorus concentrations. For the KNMI model, TP decreased by 8.7 % (from 86.3 to 78.8 µg L-1), and SRP decreased by 7.9 % (from 40.6 to 37.4 µg L-1). For the SMHI model, TP decreased by 0.1 % (from 77.8 to 77.7 µg L-1), and SRP decreased by 6.5 % (from 40.0 to 37.4 µg L-1). For the “drier” Hadley scenario, TP increased significantly, by 36.2 % (from 87.3 to 119 µg L-1), however, SRP instead decreased slightly, by 2.9 % (from 41.0 to 39.8 µg L-1) (Fig. 33). The total load of SRP transported by the river increased by 8 % for both the KNMI and SMHI scenarios, but decreased by 26 % for the Hadley scenario (Fig. 37).

Figure 37. Average concentrations of SRP and TP in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

Seasonally,the TP for the Hadley model increases especially during late summer, for August reaching average values of more than 230 µg L-1. This is due to a higher concentration of PP from the STW effluent. For the wetter scenarios, SRP tended to decrease mostly in spring and early summer. For the Hadley scenario the pattern is somewhat opposite, with increasing concentrations in spring, and decreases instead for late summer and autumn (Fig. 38).

Figure 38. Average concentrations of TP (left) and SRP (right) per month in reach 8 for the baseline (1981-2010) and scenario (2031-2060) periods for the three climate models.

The source-partitioning calculation for stream SRP concentrations indicated small changes between the baseline and scenario periods. The two wetter climate models gave a slight increase in phopsphorus loads from land (both from natural sources from forests, and fertiliser and manure from grass and arable land) by around 13 % (KNMI) and 10 % (SMHI). Similar to the results from the nitrate modelling, the drier Hadley model gave a decrease in leaching from lands, by 29 % from forests, and by 19 % from arable land and grassland. However, the STW effluent is still the dominating SRP source regardless of the climate model used (Fig. 39).

0.7

0.6

0.5

0.4

0.3

Tn P/Year Tn Baseline 0.2 Scenario 0.1

Figure 39. Modelled contribution from natural sources, fertilisers and manure, and the STW to the total amount of SRP leaching to the stream, for the baseline (1981-2010) and scenario (2031-2060) periods for the three different climate models.

9. Results from land use scenarios

9.1. Hydrology

Land use influences stream hydrology to some extent, mostly due to varying demands on irrigation. High flows may also be affected by direct runoff generated by arable and urban land. However, these effects are small, and as they build on a number of uncertain assumptions regarding irrigation and direct runoff from urban and arable land, they are not reported here.

9.2. Nitrogen

The simulations of the land use scenarios displayed a relatively large range of average nitrate concentrations, over-riding the differences between the wetter and drier climate scenarios from the different climate models. Not surprisingly, the market-oriented land use scenarios (World Market, National Enterprise) give increasing nitrate concentrations, whereas the environmental-oriented scenarios give decreasing concentrations. The best-case scenario is the combination of the KNMI climate and Local Stewardship land use scenario, where nitrate concentrations are 57 % (0.86 mg-N L-1) of those of the worst case scenario, i.e. the combination of the Hadley climate and the World Market land use scenario (1.51 mg-N L-1) (Fig. 40). In spite of the relatively large differences between the best case scenario (KNMI + Local Stewardship; 0.86 mg-N L-1) and the worst case scenario (Hadley + World Market; 1.51 mg-N L-1), all combinations of climate and land use scenarios fall within the class of “good“ ecological status (0.45 – 2.3 mg-N L-1) (Table 11).

1.6

1.4

1.2

Land use baseline 1

N/L) WM - 0.8 NE 0.6 GS

Nitrate(mg 0.4 LS

0.2

0 KNMI SMHI Hadley

Figure 40. Average nitrate concentrations in reach 8 for the scenario period (2031-2060) for all combinations of land use and climate scenarios, including baseline land use (i.e. no change) (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

For the source-partitioning of the stream nitrate concentrations, the contribution of deposition and natural sources (i.e. leaching from forests and partly from grassland) is still dominating for all land use scenarios (calculations based on the KNMI climate scenario). The contribution from fertilisers and manure is comparable in size to the contribution from the STW effluent; the proportion from the STW is slightly larger than that from fertilisers and manure for the market-oriented scenarios, and slightly smaller for environmental-oriented scenarios. When added together, the contribution from local anthropogenic sources (i.e. fertilisers, manure and STW effluents) to the total effective load ranges from 26.3 % (Global Sustainability scenario) to 40.5 % (National Enterprise scenario) (Fig. 41).

18 16 14

12 Land use baseline 10 WM 8 NE Tn N/Year Tn 6 4 GS 2 LS 0 Deposition + Fertiliser & STW effluent natural sources Manure

Figure 41. Modelled contribution from deposition and natural sources, fertilisers and manure, and the STW to the total amount of nitrate leaching to the stream, for the scenario period (2031-2060) for the baseline land use (i.e. no change) and the four land use scenarios (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

9.3. Phosphorus

The results from the land use scenario simulations for phosphorus reflected the importance of the STW effluent as a phosphorus source. The market-oriented land use scenarios, World Market and National Enterprise, both brought higher STW loads of phosphorus, resulting in large increases of TP concentrations, especially for the Hadley climate scenario with an increase of 121 % (from 119 to 263 µg L-1), but even for the KNMI and SMHI scenarios of more than 100 % (Fig. 38).

Whereas the baseline scenarios (i.e. KNMI, SMHI and Hadley for the period 1981-2010) all had SRP concentrations just above the limit for “high“ ecological status (40 µg L-1) (Table 11), all three climate change scenarios (i.e. KNMI, SMHI and Hadley for the period 2031-2060) brought SRP concentrations down to below that threshold (Fig. 33). For the land use scenarios, the two market-oriented scenarios (World Market and National Enterprise) resulted in SRP concentrations above 40 µg L-1 (i.e. good ecological status) for all three climate scenarios, whereas the two environmental-oriented land use scenarios (Global Sustainability and Local Stewardship) resulted in SRP concentrations below 40 µg L-1 (i.e. high ecological status) for all three climate scenarios (Fig. 42). There are however no defined thresholds for TP for the classification of ecological status.

Figure 42. Average TP and SRP concentrations in reach 8 for the scenario period (2031-2060) for all combinations of land use and climate scenarios, including baseline land use (i.e. no change) (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

Already for the baseline period, the STW effluent was the dominating source of in-stream SRP. With an increasing future population, and generally decreases in agricultural areas for the land cover scenarios, the STW effluent becomes even more dominating as a phosphorus source (Fig. 43). The exception is the Local Stewardship scenario, where arable areas are kept for food security, and a part of the sewage produced in Arbúcies is taken to the STW in Hostalric. The source-partitioning calculations were based on the KNMI climate scenario.

1 0.9 0.8

0.7 0.6 Natural sources 0.5

0.4 Fertiliser & Manure Tn P/Year Tn 0.3 STW effluent 0.2 0.1 0 Land use WM NE GS LS baseline

Figure 43. Modelled contribution from deposition and natural sources, fertilisers and manure, and the STW to the total amount of nitrate leaching to the stream, for the scenario period (2031-2060) for the baseline land use (i.e. no change) and the four land use scenarios (WM = World Market, NE = National Enterprise, GS = Global Sustainability, LS = Local Stewardship).

10. Assessment of macroinvertebrate status

10.1. Sampling procedure and sites

The ecological status at Arbúcies and its response to land use and climate change scenarios was evaluated using macroinvertebrate ecological quality indices as the response variable. Annually sampled macroinvertebrate data from seven years (1995-2001) was available from 12 sites within the Tordera catchment (of which Arbúcies is a subcatchment). Seven of the sites are located along the main reach of La Tordera, five are located on tributaries, and one site is located in a headwater stream (Fig. 44). Macroinvertebrates were sampled once in autumn. A reach section defined as the wetted perimeter of the stream and a length ten times that width was sampled diagonally and moving upstream, taking care to include all mesohabitats present in the reach. Individual samples were taken with a surber sampler with 0.1 m2 sampling area and 500 µm mesh size. Macroinvertebrates were identified to family level and counted. Full details on sampling design and methodology can be found in Benito de Santos (2008).

Fig 44: Map of the Tordera catchment, including sites sampled for macroinvertebrates (annotations in Table 19) colour coded for ecological status with respect to macroinvertebrates and STWs (annotations in Table 20). The Arbúcies catchment is shaded.

10.2. Macroinvertebrate indices

For each site and year of observations, four different indices of ecological quality were calculated: 1. Taxon richness (number of taxa) 2. BMWPc (Biological Monitoring Working Party for Catalonia, Benito & Puig, 1999) 3. ASPT: Average Score Per Taxon (BMWPc/Taxon richness) 4. EPT families: Number of ephemeropteran, plecopteran, and trichopteran families

The different indices reflect slightly different aspects of the macroinvertebrate response, with the BMWPc, EPT-FAM and obviously Taxon richness mostly reflecting the number of taxa, whereas the ASPT is essentially the BMWPc normalised for number of taxa, and puts more emphasis on pollution sensitive taxa. The correlation matrix indicates that the BMWPc, EPT-FAM and Taxon richness are very similar, whereas the relationship between ASPT and the other three indices are more curvilinear, with some observations displaying relatively high ASPT-score despite low taxon richness (Fig. 45).

Fig 45: Inter-correlations between the four macroinvertebrate indices EPT.FAM Number of ephemeropteran, plecopteran, and trichopteran families), BMWPc (Biological Monitoring Working Party for Catalonia), TAXONS (number of taxa) and ASPT (BMWPc/TAXONS)

Although the ASPT-index may be preferable as an index of water quality (Armitage et al., 1983), the water quality thresholds provided by the CWA are based on the BMWPc-index (Table 18). Therefore, the analyses of relationships between physiochemical variables and the macroinvertebrate status were focused on this index.

Table 18: The boundaries for classification of surface water quality standards. (http://aca-web.gencat.cat/aca/documents/ca/directiva_marc/capitol4_subcapitol4_1.pdf). Class I Class II Class III Class IV Class V

(high) (good) (moderate) (poor) (bad) BMWPc >100 61-99 36-60 15-35 <15

10.3. Site descriptions and ecological classification

The ecological status with respect to macroinvertebrates decreases along the main channel of river Tordera, from top to bottom. In the upper part (site J026), the ecological status is “Very good”, between sites J015 and J083 the status is “Moderate”, between sites J071 and J06s the status is “Poor”, and at the river mouth (site 018) the status is “Very poor”. For the four sampled tributaries the ecological status is varying; for the Arbúcies (J066) it is “Good”, for river Sta Coloma (J087) “Moderate”, for river St Celoni (J124) “Poor” and for river Breda (J115) “Very poor” (Table 19).

Table 19: Locations for macroinvertebrate sampling in the Tordera catchment, BMWPc-score, and average ecological classification. Average Site Number of Catchment Average Location ecological code observations size BMWPc class T0 Headwaters 6 3 147 Very good J026 Main channel (upper part) 7 41 124 Very good Main channel (at J015 confluence with Sant 6 127 49 Moderate Celoni) Main channel (before J083 confluence with River 6 138 43 Moderate Pertegás) Main channel (after J071 confluence with River 7 221 18 Poor Gualba) Main channel (after J073 confluence with River 7 286 32 Poor Breda) Main channel (after J062 confluence with Santa 7 773 27 Poor Coloma) Main channel (river J018 7 882 14 Very poor mouth) J124 River Sant Celoni 4 37 29 Poor J115 River Breda 6 32 4 Very Poor J066 River Arbúcies 6 110 82 Good J087 River Santa Coloma 5 315 54 Moderate

The BMWPc-index displays a considerable variability in the Tordera catchment, both between sites and between years. The “best” site is the headwater site T0, which is classified as having “very good” biological status for all sampled years (1996-2001). The “worst” site is the tributary of Breda (J115), which is classified as having “very poor” biological status for all sampled years (1996-2001). Except for these two sites, all sites display an inter-annual variability in ecological status with respect to BMWPc. Half (6 of 12) of the sites sampled display variations over three different ecological classes over the four to seven years of observations (Fig. 46). Eight of the sites have observations from all years 1996-2001. The average BMWPc-score over these eight sites was lowest for 1996 (43.8), and highest for 1998 (70.6).

Figure 46: Time series (1995-2001) of the BMWPc-score for the 12 macroinvertebrate sampling sites in the Tordera catchment.

The ecological status is clearly related to the distance to and size of STWs upstream of the sampling point (Table 20). The tributaries of the River Breda and River St Celoni (classified as “Very poor” and “Poor” ecological status) both only receive discharge from two small STWs each. However, the effluents are located close to sampling points (1 km for River Breda and 2.8 km for River St Celoni), and both of the rivers are relatively small (catchment area < 35 km2). In contrast, the River Sta Coloma serves as recipient for six STWs, two of which are among the largest in the Tordera catchment. However, the distance between the effluents and the sampling point is much longer (> 12.7 km), and the catchment is also relatively large (315 km2). For the main channel of the River Tordera, it can be seen that the ecological status decreases from “Very good” to “Moderate” as the river receives the effluent of the Sta Maria Palautordera STW, and from “Moderate” to “Poor” as it receives the effluent of the St Celoni STW. It can be noted that, somewhat counterintuitively, the flow in the stream channel is actually lower at the river mouth (site J018) than at the upstream sites, due to infiltration to the stream bed, which could potentially explain the even lower ecological status for that site.

Table 20: List of STWs in the Tordera catchment. Treatment abbreviations stand for: BT = Biological treatment (activated sludge), NR = Nitrogen removal (nitrification/denitrification), CT = Chemical treatment only (precipitation of phosphorus). The Breda STW was commissioned in 2004, at the time of macroinvertebrate sampling wastewater was untreated. Person Average Average Map Population N P Name equivalents annual annual Treatment no. (inhabitants) load/PE load/PE (PE) N load P load Sta Maria de 1 4040 15841 7,0 1,5 0,44 0,10 BT+NR Palautordera 2 Riudarenes 863 2000 - - - - BT 3 St Celoni 11809 22800 - - - - CT Vilalba 4 197 2601 - - - - - Canada Park Vidreres 5 Aguaviva 341 72000 - - - - BT Park 6 Tordera 6685 10833 - - - - BT 7 Sils-Vidreres 4929 18083 10,6 0,9 0,58 0,05 BT+NR 8 Arbúcies 3638 9000 9,9 1,8 1,10 0,20 BT+NR Caldes de 9 2395 7100 7,4 - 1,05 - BT+NR Malavella Sta Coloma 10 7787 14666 14,1 6,6 0,96 0,45 BT+NR de Farners 11 Vallgorguina 275 2062 - - - - - Maçanet de 12 2128 5833 - - - - BT la Selva 13 Hostalric 2881 6492 3,0 - 0,46 - BT

14 Breda 3095 5600 - - - - -

15 Gualba 268 1040 - - - - - Riells i 16 1222 3500 - - - - - Viabrea

10.4. Macroinvertebrate relationships with physiochemical variables

Physiochemical parameters considered as explanatory variables included: Stream water - + - 3- concentrations of NO3 , NH4 , NO2 , PO4 and DO, as well as pH, electric conductivity (EC), temperature, and number of dry months in the summer preceding the observations (DryMonth).

When lumping observations from all years and sites, BMWPc shows a negative correlation with all nutrients (NH4, NO3, NO2 and PO4). The strongest correlation was found against logarithmic values of 2 NH4 (r = 0.52). The BMWPc is also negatively correlated with conductivity and temperature, positively with DO concentrations, and negatively with DryMonth (Fig. 47). The fact that BMWPc is

better correlated to NH4, NO2, PO4 and conductivity than to NO3 again indicates that macroinvertebrates are furthermost affected by STW effluents, rather than by diffuse leaching from soils which is the main source of NO3.

Figure 47: BMWPc-score plotted against log(NH4), log(conductivity), log(PO4), NO3, NO2, DO, pH, temperature and DryMonths, all years and sites.

If looking only at the between-site variability, using average values of the BMWPc and the explanatory variables, the strengths of the correlations increase in general (Fig. 48). The highest 2 2 2 correlation was against log(EC) (r = 0.83), followed by log(PO4) (r = 0.78) temperature (r = 0.72) and 2 log(NH4) (r = 0.71). The correlations are strongly influenced by two outlying sites located in the uplands, these are permanent streams with high BMWPc-scores, and low nutrient concentrations, conductivity and temperature, and high DO concentrations. Excluding these, correlations became much lower, with the highest correlation now against DO (r2 = 0.41), followed by log(EC) (r2 = 0.34) 2 and log(NH4) (r = 0.33).

Figure 48: Average BMWPc-score per site plotted against average values per site of log(NH4), log(conductivity), log(PO4), NO3, NO2, DO, pH, temperature and DryMonths

The between-year variability was however difficult to explain. With a few exceptions, there are no significant correlations between the physiochemical variables and the BMWPc-score within each site. It is perhaps not surprising that the momentary concentrations of nutrients and DO are not well correlated with the macroinvertebrate index. However, the maximum temperature, maximum NH4, and minimum DO observed during the preceding summer were tested as explanatory variables without any more significant results. It is likely that the sampling frequency is too low to capture the short duration events of high nutrient/low oxygen concentrations which are critical for the macroinvertebrates. Notably, although permanent streams tend to have higher BMWPc-score when comparing between sites (Fig. 48), there are no apparent correlations between the number of dry months for the preceding the observation and the BMWPc-score within each site. For example, site J062 was permanently wet for the first four sampled years (1995-1998), but ran dry for 2-3 months for the three last sampled years (1999-2001). Yet, this is not reflected in the BMWPc, which instead takes the lowest values for the two first years of sampling (Fig. 46).

10.5. Anticipated climate and land use effects on macroinvertebrates

The future ecological status in the Tordera catchment as reflected by macroinvertebrates is likely to be furthermost influenced by a combination of the nutrient load from STW effluents, and the amount of streamflow available to dilute this load. The respective scenarios for STW nutrient loads

and streamflow are both ambiguous, and within the frames of the alternative climate- and land use scenarios considered in this study, both the STW loads and streamflow may either increase or decrease in the future.

The projected future streamflow varies greatly between the three climate scenarios. Whereas the KNMI and SMHI scenarios both predict a slightly higher stream flow on average, the Hadley scenario paints a darker picture with an average decrease in stream flow of nearly 40 %. However, all three climate scenarios predict drier summers on average, and a lower minimum stream flow. Hence, the critical low-flow situations are likely to become slightly worse for the KNMI and SMHI scenarios, and much worse for the Hadley scenario. The higher temperatures brought by climate change may also have a further negative impact on macroinvertebrates, either directly, or indirectly through lower solubility of O2.

The future scenarios for the nutrient loads from the Arbúcies STW predicts both increases (the two economy-oriented scenarios), decreases (the Local Stewardship scenarios), and no change (the Global Sustainability scenario). However, these scenarios all built on the post-treatment loads of the Arbúcies STW after 2006. Any more precise assessment of the future ecological status of the remaining sites in the Tordera catchment would require individual scenarios for each of the STWs, and ideally also modelling of in-stream nitrogen and phosphorus concentrations to address in-stream processing of STW effluents. However, available information on loads from the STWs is even more scarce than for the Arbúcies STW, and applications of advanced models such as INCA-N and INCA-P is currently not feasible for the whole La Tordera catchment.

Of the STW’s there is only information on annual N loads from six STWs, and on annual P loads from four STWs, including the Arbúcies (Table 20). Furthermore, the STW of Breda, affecting sample site J115, was not commissioned until 2004, so at the time of macroinvertebrate sampling this site was affected by untreated wastewater. For the Arbúcies, which was already reported to have the highest level of treatment during the sample period of 1995-2001 (both biological treatment and nitrogen removal), the improved treatment implemented in 2006 brought a nitrogen reduction of 70 % and a phosphorus reduction of 45 %.

As the land use scenarios predict an increase in population of 25 – 75 %, and the climate change scenarios all to some degree predict prolonged periods of low stream flow, improved standards of the STW waste water treatments will most certainly be required in order not to deteriorate the biological quality further. However, assuming that all STWs in the Tordera catchment will improve their treatments to the same standards as was done in the Arbúcies STW in 2006 will at least more than compensate for the population increase. Furthermore, the land use scenarios makes different assumptions regarding STW treatments, with tertiary nutrient reduction treatments, separate sewer systems, better networking and soft-engineering solutions (lagooning, constructed wetlands) implemented under the environmental-oriented scenarios, and more limited actions under the market-oriented scenarios.

The effects from reduced stream flow are however more difficult to assess without processed-based hydrological modelling of the whole La Tordera catchment, as infiltration to the stream bed, by which many streams occasionally run dry during summer, is not linearly dependent on stream flow.

11. Conclusions

11.1. Hydrological response to climate and land use change projections

 The effects from the climate scenarios on hydrology were different depending on which climate model that was used to drive the hydrological model. For two of three employed climate models (KNMI and SMHI), the mean discharge increased due to higher winter precipitation, by 10-15 %. The third climate model (Hadley) instead predicted a substantial decrease in flow, by almost 40 %.

 Warmer and drier summers predicted by all three climate models will instead be manifested by drier soils and an increasing water shortage for irrigation demands.

 While the simulations of high flows are probably less accurate due to variable precipitation patterns in the area, all three climate models, including the “drier” forecasted an increase in average annual maximum flow; for the SMHI model by more than double.

 One of the important features of the Arbúcies stream is that during warm and dry summers (which, especially for the Hadley climate scenario, will become more frequent) it may run completely dry due to infiltration in the stream bed. While the hydrological model (PERSiST) has been designed to be able to simulate this behaviour by allowing for bi-directional flow and the inclusion of a riparian zone, it was at the time of reporting not possible due to limitations in the model conceptualisation.

11.2. Streamwater nitrogen response to climate and land use change projections

 Before the implementation of the nitrogen removal treatment in the STW in 2006, nitrate concentrations were highly influential on in-stream nitrate concentrations. After 2006 however, leaching from semi-natural land cover types (evergreen and deciduous forests) was the dominating source of in-stream nitrate, accounting for around 70 % of the total load.

 For the climate scenarios, the “wetter” scenarios gave a slight decrease in nitrate concentrations (4-7 %), but a slight increase in the total in-stream transport. The drier scenario gave a slight increase in nitrate concentrations (8 %), but a large decrease in total in- stream transport.

 The land use scenarios had a larger impact on the streamwater nitrate concentrations than changes in the projected climate alone. The “worst-case”-scenario, i.e. the combination of the Hadley climate model and the World Market land use scenario, resulted in nitrate concentrations of 1.51 mg-N L-1. The “best-case”-scenario, i.e. the combination of the KNMI climate model and the Local Stewardship land use scenario, resulted in nitrate concentrations of 0.86 mg-N L-1. The projected increases in nitrate concentrations in the economy-oriented land use scenarios were mainly due to the expected increases in population.

11.3. Streamwater phosphorus response to climate and land use change projections

 For phosphorus, the STW effluent was always the single dominating source. For the calibration period, the STW contributed to 60 % (before 2006) and 52 % (after 2006), respectively. For the land use scenarios, the STW contributed to between 48 % (Local Stewardship) and 63 % (World Market).

 All climate scenarios, both the wetter and drier, resulted in a slight decrease in SRP concentrations (3-8 %). However, the drier Hadley climate scenario resulted in a large increase in TP concentrations (36 %), due to decreased dilution of the STW effluent.

 The simulated effects on phosphorus concentrations from land use scenarios were dominated by the assumptions regarding the STW effluent, and in turn therefore by the trend in population. The SRP concentrations for the best-case “KNMI climate + Local Stewardship land use” scenario (30.0 µg L-1) and the worst-case “Hadley climate + World Market land use” scenario (56.2 µg L-1) differed by almost a factor of two.

11.4. Future ecological status and risks

 According to the ACA criteria (Table 15), the current water quality with respect to nutrients could be classified as “good” but not “high”, as nitrate concentrations fall between 0.45 and 2.26 mg-N L-1. None of the combination of land use and scenarios gave a different classification. Similarly, with respect to phosphorus (SRP), the water quality would be classified as “good” (40 µg-P L-1 < SRP < 220 µg- µg-P L-1) for current conditions, and either “good” or “high” for future scenarios.

 Future effects on average stream flow due to climate change were very different depending on the climate model employed. However, more severe extreme conditions in form of water shortage in summers and higher and more frequent floods, are common for all three climate scenarios.

 Even for the market-oriented land use scenarios, the contribution of local sources (i.e. point sources and diffuse leaching from arable land) to in-stream nitrate concentrations is relatively low. Thus, the potential for improvements of nitrogen concentrations by local management is limited.

 The biological status of the Arbúcies with respect to macroinvertebrates was classified as “good”, based on monitoring data from 1995 to 2001. Considering the improved treatment of wastewater of the Arbúcies STW in 2006, the biological status is likely not at risk in the future under any of the scenarios. However, in the larger catchment of La Tordera, the biological status was worse at many of the sampled sites, due to more influence of STW wastewater. Although there is likely room for improvement of wastewater treatment at most of the STW’s in the Tordera catchment, prolonged periods of decreased streamflow with climate change, as well as increasing population pressure, may make these improvements insufficient for improving or even sustaining the biological status.

11.5. Uncertainties and limitations

 With a 16-year record of observed streamwater chemistry (seasonally and monthly samples) and daily observations of flow from sites nearby the confluence between the Arbúcies and the larger river of La Tordera, there was a fair amount of observed data on hydrology and chemistry to calibrate the models against. However, there were still large uncertainties in the model applications associated with shortage of data.

 The stream-flow at the gauging station was affected by abstraction for irrigation and potentially also infiltration to the stream bed. Furthermore, the hydrograph displayed some unnatural behaviour, indicating that the discharge record was not of the highest quality.

 The largest obstacle for carrying out the chemical modelling for the Arbúcies is the lack of data describing the STW effluent. From the available data, it is evident that the nutrient load from the STW effluent is highly variable on both shorter and longer timescales, and that this variability has a substantial impact on the in-stream water quality. However, only irregular and infrequent measurements on STW effluent discharge and chemistry were available. Furthermore, the distribution between different nitrogen and phosphorus species of the effluent is highly uncertain, severely limiting the possibilities to model in-stream transformations of nutrients.

 It would also have been desirable to have high quality monitoring data on nitrogen and phosphorus from headwaters as, especially for nitrogen, diffuse leaching from forests was the most important source of in-stream concentrations. Such high quality data is now available for nitrogen from the Font de Regas headwaters.

 In spite of these uncertainties, the assessment of ecological status with respect to nutrient concentrations is likely to be robust for the future climate- and land use scenarios. Although the land use scenarios were defined to cover a wide range of possible futures with respect to population pressure, STW efficiency and fertiliser rates, all the simulated combinations of nitrate and SRP gave average annual concentrations safely below the respective limits for good ecological status (2 mg-N L-1and 100 µg-P L-1 respectively). Thus, the ecological status with respect to eutrophication is likely not at risk for the Arbúcies.

 The above-mentioned uncertainties however do have a bearing on the results from the source-partitioning calculations (Figs. 37 & 39). As the concentrations of both SRP and nitrate in the reach above the STW were usually below detection limit, it is likely that the contribution of the (semi)-natural areas to in-stream phosphorus concentrations were over- estimated. If so, the relative contribution from the STW would be more important than the modelled results indicated.

 One major uncertainty for the nitrogen scenarios is that the deposition of nitrogen was calculated without considering differences in emissions between the storylines. As diffuse leaching from forested areas is the major source of nitrogen in the Arbúcies, this is an important and potentially large error source. Under scenarios of higher N-deposition, nitrate concentrations in the Arbúcies stream could possibly exceed the threshold for good ecological status.

 Observations of both stream-flow (much flashier response to rainfall at the downstream site compared to the headwaters) and nitrogen (an increasing gradient of nitrate concentrations down-stream of the STW, see section 4.3) indicate that upland and lowland areas might

behave differently, hydrologically and chemically. To examine this better, more spatially extensive data on water chemistry are necessary, as well as more refined map data.

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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. Grassland Evergreen Deciduous Arable Urban Initial snow depth 0 0 0 0 0 Snow multiplier 1 1 1 1 1 Snow melt 0 0 0 0 0 Degree day melt factor 3 3 3 3 3 Rain multiplier 0.8 0.8 0.9 0.8 0.8 Degree day evapotransp. 0.3 0.35 0.4 0.35 0.25 Growing degree threshold 2 2 3 2 2 Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Initial water depth 0 200 4030 0 120 4030 0 120 4030 0 170 4030 0 120 4030 Relative area index 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Infiltration 1000 50 100 1000 50 100 1000 200 100 1000 15 100 1000 25 100 Retained water depth 0 200 4000 0 150 4000 0 150 4000 0 200 4000 0 150 4000 Drought runoff fraction 0 0.05 0 0 0.05 0 0 0.05 0 0 0.25 0 0 0.2 0 Residence time 2 5 150 2 5 100 1 5 120 1 5 60 2 5 60 Evapotransp. adjustment 1 2 0 1 2 0 1 2 0 1 2 0 1 2 0 Relative evapotrans. Index 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 Max capacity 1000 300 5000 1000 300 5000 1000 300 5000 1000 300 5000 1000 300 5000

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. Grassland Evergreen Deciduous Arable Urban Q S Gw Q S Gw Q S Gw Q S Gw Q S Gw Q 0.02 0.98 0 0 1 0 0 1 0 0.2 0.8 0 0.25 0.75 0 S 1 0.25 0.75 1 0.15 0.85 1 0.15 0.85 1 0.25 0.75 1 0.25 0.75 Gw 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. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. Grassland Evergreen Deciduous Arable Urban Soil denitrification 0.001 0.001 0.001 0.005 0.1 Soil fixation 0 0 0 0 0 Soil nitrification 0.3 0.3 0.3 0.5 2 Soil mineralisation 1.3 1.2 1.2 2.4 0.4 Soil immobilisation 0.001 0.001 0.001 0.001 0.01 DR initial nitrate 1 1 1 1 1 DR initial ammonium 0 0 0 0 0 Soil initial nitrate 2 2 2 2 2 Soil initial ammonium 0 0 0 0 0 Nitrate uptake rate 0.02 0.02 0.02 0.05 0.02 Ammonium uptake rate 0.02 0.02 0.02 0.05 1 Growth season start day 1 1 1 60 1 Growth season length 365 365 365 300 365 Growth curve offset 0.66 0.66 0.66 0.66 0.66 Growth curve amplitude 0.34 0.34 0.34 0.34 0.34 Maximum N uptake 5000 5000 5000 5000 5000 Zero rate depth 30 15 15 40 50 Max rate depth 200 150 150 250 150 Q10 for soil processes 2 2 2 2 2 Base temperature for Q10 30 30 30 30 30 Diff.between max. temp. 4.5 4.5 4.5 4.5 4.5 Soil thermal conductivity 0.7 0.7 0.7 0.7 0.7 Specific heat capacity 6.6 6.6 6.6 6.6 6.6 Snow depth factor -0.025 -0.025 -0.025 -0.025 -0.025

Table A4: Reach and sub-catchment specific parameters in the calibrated INCA-N model. For the proportion of “available” Kjeldahl-N, the left value is pre-2006 and the right is value is post-2006. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. 1 2 3 4 5 6 7 8 Velocity ‘a’ 1 1 1 1 0.82 0.82 0.82 0.82 Velocity ‘b’ 0.5 0.5 0.5 0.5 0.45 0.45 0.45 0.45 In-stream denitrification 0.01 0.01 0.01 0.01 0.1 0.1 0.1 0.1 In-stream nitrification 1 1 1 1 10 10 10 50 “Available” Kjeldahl-N - - - - 1/0.65 - - - Initial groundwater NO3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1 Initial groundwater NH4 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Groundwater denitrification 0.005 0.0055 0.0035 0.004 0.004 0.004 0.0025 0.0025

Table A5: Land phase parameters in the calibrated INCA-P model. Parameters in bold were calibrated, whereas the other parameters were kept at their initial values. Grassland Evergreen Deciduous Arable Urban DR initial flow 0.001 0.001 0.001 0.001 0.001 DR initial TDP 0 0 0 0 0 DR initial PP 0 0 0 0 0 Soil initial inactive P 0.04 0.04 0.04 0.04 0.04 Soil initial labile P 0.002 0.002 0.002 0.002 0.002 Soil initial flow 0.01 0.01 0.01 0.01 0.01 Soil initial TDP 0 0 0 0 0 Initial sediment store 10 10 10 10 10 Max Soil moisture deficit 240 240 240 240 240 Total to available water 2 2 2 2 2 DR residence time 1 1 1 1 1 Soil residence time 3 3 3 3 3 Vegetation index 1 1 1 1 1 Immobilisation 0.001 0.0015 0.0015 0.0008 0.0015 Soil clay (%) 2 2 6.5 2 6.5 Soil silt (%) 2 2 6 2 6 Soil fine sand (%) 13 13 11 13 11 Soil medium sand (%) 63 63 56.5 63 56.5 Soil course sand (%) 20 20 20 20 20 Infiltration 50 50 50 50 50 E for splash detachment 0.005 0.005 0.005 0.005 0.005 E for flow erosion 0.003 0.003 0.003 0.003 0.003 a for splash detachment 3 3 3 3 3 Soil depth 1.5 1.5 1 2 1 Soil bulk density 1200 1200 1200 1450 1400 Freundlich isotherm 5 5 5 5 5 Weathering factor 0.0005 0.0003 0.0003 0.0012 0.0003 Sorption coefficient 2.5 1 1 3.5 0.5 Equilibrium TDP 0.5 0.2 0.2 1 0.2 Q10 for soil processes 1.5 1.5 1.5 1.5 1.5 Base temperature for Q10 30 30 30 30 30 Plant P uptake 0.2 0.08 0.08 0.075 0.08 Growth season start day 1 1 1 60 1 Growth season length 365 365 365 300 365 Growth curve offset 0.66 0.66 0.66 0.66 0.66 Growth curve amplitude 0.34 0.34 0.34 0.34 0.34 Maximum P uptake 500 500 500 500 500 Diff.between max. temp. 4.5 4.5 4.5 4.5 4.5 Soil thermal conductivity 0.7 0.7 0.7 0.7 0.7 Specific heat capacity 6.6 6.6 6.6 6.6 6.6 Snow depth factor -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. 1 2 3 4 5 6 7 8 Velocity ‘a’ 1 1 1 1 0.82 0.82 0.82 0.82 Velocity ‘b’ 0.5 0.5 0.5 0.5 0.45 0.45 0.45 0.45 Temp dependency (macrophytes) 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 Half-saturation P (macrophytes) 0.1 0.1 0.05 0.05 0.025 0.025 0.025 0.025 Growth rate (macrophytes) 0.035 0.035 0.035 0.035 0.035 0.025 0.035 0.035 Death rate (macrophytes) 0.00015 0.00015 0.00015 0.00015 0.00015 0.00015 0.00015 0.00015 Self shading constant 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 Temp dependency (epiphytes) 1.066 1.066 1.066 1.066 1.066 1.066 1.066 1.066 Half-saturation P (epiphytes) 0.1 0.1 0.05 0.05 0.05 0.05 0.05 0.05 Growth rate (epiphytes) 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 0.0015 Death rate (epiphytes) 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Proportion of P (macrophytes) 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 0.0054 Initial bed sed. clay 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Initial bed sed. silt 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 Initial bed sed. fine sand 1 1 1 1 1 1 1 1 Initial bed sed. medium sand 5 5 5 5 5 5 5 5 Initial bed sed. course sand 5 5 5 5 5 5 5 5 Water column sorption coefficient 600 600 600 600 600 600 600 600 WC equilibrium P concentration 600 600 600 600 600 600 600 600 WC Freundlich isotherm 2 2 2 2 2 2 2 2 P exchange 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Stream bed sorption coefficient 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 SB equilibrium P concentration 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 SB Freundlich isotherm 9 9 9 9 9 9 9 9 Porosity 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Shear velocity scaling factor 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 0.0005 Entrainment scaling factor 3·10-6 3·10-6 3·10-6 3·10-6 3·10-6 3·10-6 3·10-6 3·10-6 Background sediment scaling factor 2·10-8 2·10-8 2·10-8 2·10-8 2·10-8 2·10-8 2·10-8 2·10-8 Non-linear coefficient 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 Base flow index 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 Threshold soil zone flow 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Rainfall excess proportion 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 Flow Erosion scale factor 7 7 7 7 7 7 7 7 FE direct runoff threshold 0 0 0 0 0 0 0 0 FE non-linear coefficient 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 Transport Capacity scaling factor 8 8 8 8 8 8 8 8 TC direct runoff threshold 0 0 0 0 0 0 0 0 TC non-linear coefficient 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 Initial groundwater flow 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 Initial GW TDP concentration 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 Initial GW inactive P 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 Maximum GW effective depth 10 10 10 10 10 10 10 10 Proportion filled pore space 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 GW residence time 25 25 25 25 25 25 25 25 Aquifer sorption coefficient 2 2 2 2 2 2 2 2 Aquifer equilibrium TDP concentration 0.003 0.003 0.003 0.003 0.003 0.003 0.003 0.003 Aquifer bulk density 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-mea-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 4) 1.998 0.235 0.585 0.596 0.548 1.362 0.390 0.970 0.785 0.785 0.617 -1.196 0.336 0.996 0.893 1.362 0.585

NO3 (Reach 7) 1.132 -0.054 -0.093 0.504 0.501 0.860 0.422 0.725 0.681 0.681 0.464 0.253 0.172 0.237 0.713 0.860 -0.093

NO3 (Reach 8) 2.381 0.007 0.016 0.936 0.936 2.215 0.786 1.861 0.370 0.370 0.137 -3.905 -2.879 1.000 1.858 2.215 0.016 SRP (Reach 4) 0.022 -0.121 -0.257 0.485 0.469 0.992 0.234 0.495 0.363 0.363 0.132 -0.050 0.756 0.997 0.280 -0.992 -0.257 SRP (Reach 7) 0.982 0.008 0.352 0.029 0.028 1.241 0.022 0.987 0.217 0.217 0.047 -0.663 -0.115 0.393 1.116 -1.241 0.352 SRP (Reach 8) 0.635 -0.007 -0.216 0.036 0.035 1.109 0.029 0.901 0.137 0.137 0.019 -0.276 -0.389 0.728 0.907 -1.109 -0.216 TP (Reach 8) 0.585 -0.014 -0.157 0.084 0.082 0.925 0.051 0.572 0.416 0.416 0.173 0.119 0.323 0.728 0.536 -0.925 -0.157

Table A8: Mean values of nitrate, TP and SRP for the lowest reach (reach 8) for all combinations of land use scenarios, and the WFD class of chemical status. Baseline Scenario Baseline Scenario Baseline Scenario KMNI KNMI KNMI KNMI SMHI SMHI SMHI SMHI Hadley Hadley Hadley Hadley period period period period period period WM NE GS LS WM NE GS LS WM NE GS LS KNMI KMNI SMHI SMHI Hadley Hadley NO (mg- 3 1.08 1.01 1.19 1.17 0.92 0.86 1.10 1.05 1.22 1.20 0.95 0.90 1.10 1.19 1.51 1.46 1.04 0.93 N/L) TP (µg/L) 86.3 78.8 165.2 156.7 69.6 53.4 77.8 77.7 162 155.3 68.6 53.1 87.3 118.9 262.9 242.9 105.7 76 SRP (µg/L) 40.6 37.4 47.6 47.8 30.8 30 40 37.4 48.3 49.2 31 30.4 41 39.8 56.2 56 32.9 31.5 WFD class good good good good good good good good good good good good good good good good good good (chemistry)