Modelling of Surface Water Quality by Catchment Model SWAT

Catchment represents a logical administrative unit of governance as a biological, physical, economic and social system, which is affected by natural (rain, sun) and human influences (industry, agriculture, population). The effective implementation of the river basin management plans are necessary and should include clear and strong objectives and instructions for maintaining the quality of surface water, even if needs of the society are changed in the future (Wagner et al., 2002).

Studies on Water Management Issues 108 with surface runoff, depending on the geology and soil type.N leaching occur during wet periods of the year, after crops are harvested, fertilizers and mineralized crop biomass residues are exposed to leaching (Glavan & Pintar, 2010), and when N is not actively absorbed by plants and precipitation exceeds evapotranspiration (Rusjan, 2008).
Phosphorus (P) is known as the limiting factor in eutrophication of freshwater ecosystems (Khan & Ansari, 2005).P is a macronutrient required for the life of all living cells that plants absorb directly in the form of ortho-phosphorus (PO 4 3-) (Khan & Ansari, 2005).Excessive use of P fertilizers may lead to P soil saturation, causing P transport with runoff bound to soil particles or through drainage (Bowatte et al., 2006).Most P in inland waters is contributed by point sources (wastewater treatment plants).Due to advances in wastewater, P stripping has put more emphasis on P from agriculture (Buda et al., 2009).
Computer models in modern integrated catchment management are indispensable for studying the levels of pollutants from diffused sources, as they are capable of merging different spatial and environmental data (Dymond et al., 2003;Kummu et al., 2006).Catchment models can be divided into empirical-statistical (GLEAMS, MONERIS, N-LES), physical (WEPP, SA) and conceptual (distributed or partially distributed -SWAT, NL-CAT, TRK, EveNFlow, NOPOLU, REALTA) (Hejzlar et al., 2009;Kronvang et al., 2009a).Models connected with the Geographic Information System (GIS) has gained new values, as they are more accessible and understandable to different target groups.
Agricultural Research Service (ARS) of the U.S. Department of Agriculture is very active in developing models for agricultural hydrology, erosion and water quality.The Soil and Water Assessment Tool (SWAT) model was developed to assist the water managers in examining the impacts of agricultural activities in catchments (Arnold et al., 1998).The SWAT model is widely used for modelling the hydrology in terms of quantity of water (discharge, soil water, snow and water management), quality of water (land use, production technologies, good agricultural practices, agri-environmental measures) and the effects of climate changes (Gassman et al., 2007;Krysanova & Arnold, 2008).This model enables the modelling of long-term (more than 25 years) effects of agri-environmental measures (Bracmort et al., 2006).SWAT model has undergone several refinement and upgrades resulting in different model versions (SWAT2000, SWAT2005 and SWAT2009).The overall desire to adapt the model for the local conditions has resulted in many adaptations like G-SWAT, SWIM, E-SWAT, K-SWAT (Gassman et al., 2007).
The European Commission has, for the purposes of ensuring adequate tools, for the end user, that could meet the current European needs for harmonization and transparency in the quantitative assessment of diffused sources of nutrient losses, financially supported EUROHARP project (Kronvang et al., 2009b).This project compared nine different catchment models for simulation of the non-point sources of pollution from agriculture on numerous catchments in Europe.The results of the project ranked SWAT, along with NL-CAT and TRK models, in the top three of the best (Schoumans et al., 2009).EUROHARP study showed that the modellers are not yet able to propose only on the best and the most appropriate model for all river basins in Europe, because the quality of the models is based on the input data quality along with quality of the modellers (Kronvang et al., 2009a).
The aim of this chapter is to examine modelling of surface water quality by the catchment model Soil And Water Assessment Tool (SWAT).The capabilities of the model were tested 109 through agri-environmental measures and their impacts on quantity and quality of the surface waters.

Descriptions of the study areas
The river Reka catchment spreads over 30 km 2 and is located in the northwestern part of the country (Goriška Brda) (Fig. 1).Altitude ranges between 75 m and 789 m a.s.l.Very steep ridges of numerous hills, which are directed towards the southwest, characterizes the area.The catchment landscape is very agricultural with higher percentages of forest (56 %) and vineyards (23 %).The river Dragonja catchment area spreads over 100 km 2 and is located in the far southwestern part of the country (Istria) (Fig. 1).This is a coastal catchment (Adriatic Sea), with an altitude ranging between 0 and 487 m a.s.l.The ridges of the hills are designed as a plateau with flat tops and steep slopes.The landscape is largely overgrown with forest (63 %) and grassland (18 %).Steep slopes allow cultivation only on the terraces.Flysch bedrock of the case areas was formed in Eocene as a product of the sea sediments and undersea landslides.Flysch consists of repeated sedimentary layers of sandstones, marl, slate and limestone, which can quickly crumble under the influence of precipitation and temperature changes.Brown eutric soils are shallow and due to silt-loam-clay texture difficult for tillage, with appropriate agro-technical measures (deep ploughing, organic fertilisers) they obtain properties for vine or olive production.In case of inappropriate agricultural activities and land management, we can witness very strong erosion processes.
The favourable climate and terrain influences at the higher average temperature, better lighting, soil temperatures, minimal risk of frost, wind prevents diseases development.Viticulture is economically most important agricultural sector in both areas, with important share of olive and vegetable productions in the Dragonja area, and fruit production in the Goriška Brda.Terracing is typical for both areas and depends on natural conditions, steepness of the slopes (erosion), geological structure (sliding) and climatic conditions (rainfall).In Goriška Brda (Reka) is 78% of vineyards terraced while in the Slovenian Istria (Dragonja) about 18%.Vine and olive growing are the sole agricultural sectors, which can withstand the cost of the terraces installation.Terraces in the Dragonja area are characterized by overgrowing, which results in a disordered ownership structure.
The annual average concentration of sediment in the river Reka catchment for one year of research period (July 2008 -June 2009) was 32.6 mg l -1 , nitrate (NO 3 -) 2.7 mg l -1 and TP concentration of 0.109 mg l -1 .In the river Dragonja catchment average annual concentration of sediment in the research period (August 1989-December 2008), was 29.1 mg l -1 (107 samples), NO 3 -2.7 mg l -1 (87 samples) and TP concentration of 0.043 mg l -1 (92 samples).In January 2007, the highest sediment concentration measured so far, was 1362 mg l -1 .The water quality with exception of sediments does not cause any serious problems in these two study areas.Data shows that sediment concentrations are well in excess of Environment Agency guide level (25 mg l -1 ).

Database development for the model build
Before the modelling a field tour to the research areas and review of available data was carried out (Table 1).Since the available data was insufficient for modelling, we perform additional monitoring of surface water quality at the Reka tributary Kožbanjšček hydrological station Neblo, excavation of soil profiles, laboratory measurements and using established model standards (texture, albedo, organic carbon etc)

Model performance objective functions
The Pearson coefficient of correlation (R 2 ) (unit less) for n time steps (1) describes the portion of total variance in the measured data that can be explained by the model.The range is from 0.0 (poor model) to 1.0 (perfect model).A value of 0 for R 2 means that none of the variance in the measured data is replicated by the model, and value 1 means that all of the variance in the measured data is replicated by the model predictions.The fact that only the spread of data is quantified is a major drawback if R 2 is considered alone.A model which systematically over or under predicts all the time will still result in good values close to 1.0 even if all predictions were wrong.
The Nash-Sutcliffe simulation efficiency index (E NS ) (unit less) for n time steps (2) is widely used to evaluate the performance of hydrological model.It measures how well the simulated results predict the measured data.Values for E NS range from negative infinity (poor model) to 1.0 (perfect model).A value of 0.0 means, that the model predictions are just as accurate as using the measured data average.A value greater than 0.0 means, that the model is a better predictor of the measured data than the measured data average.The E NS index is an improvement over R 2 for model evaluation purposes because it is sensitive to differences in the measured and model-estimated means and variance (Nash & Sutcliffe, 1970).A major disadvantage of Nash-Sutcliffe is the fact that the differences between the measured and simulated values are calculated as squared values and this places emphasis on peak flows.As a result the impact of larger values in a time series is strongly overestimated whereas lower values are neglected.Values should be above zero to indicate minimally acceptable performance.
Root Mean Square Error -RMSE (3) is determined by calculating the standard deviation of the points from their true position, summing up the measurements, and then taking the square root of the sum.RMSE is used to measure the difference between flow (q) values simulated by a model and actual measured flow (q) values.Smaller values indicate a better model performance.The range is between 0 (optimal) and infinity.(3) Percentage bias -PBIAS (%) (4) measures the average tendency of the simulated flows (q) to be larger or smaller than their observed counter parts (Moriasi et al., 2007).The optimal value is 0, and positive values indicate a model bias toward underestimation and vice versa.
Model calibration criteria can be further based on recommended percentages of error for annual water yields suggested from the Montana Department of Environment Quality ( 2005) who generalised information related to model calibration criteria (

Sensitivity analysis
If the model in certain areas has not been used, then it is necessary to carry out sensitivity analysis.Sensitivity analysis limits the number of parameters that need optimization to achieve good correlation between simulated and measured data.The method of analysis in the SWAT model called PARASOL is based on the method of Latin Hypercube One-factorat-a-Time (LH-OAT).LH-OAT combines the advantages of global and local sensitivity analysis (van Griensven et al., 2006).This method performs LH sampling of data at first, followed by OAT sampling.The new scheme allows the LH-OAT to unmistakably link the changes in the output data of each model to the modified parameter (van Griensven et al., 2006).For the sensitivity analysis and calibration a special tool called SWAT-CUP is available which includes all important algorithms (GLUE, PSO, MCMC, PARASOL and SUFI2) of which Sequential Uncertainty Method (SUFI2) was shown to be very effective in identifying sensitive parameters (Abbaspour et al., 2007).
Tool within the model can automatically carry out the sensitivity analysis without the measured data or with the measured data.The tool varies values of each model parameter within a range of (MIN, MAX).Parameters can be multiplied by a value (%), part of the value can be added to the base value, or the parameter value can be replaced by a new value.The final result of the sensitivity analysis are parameters arranged in the ranks, where the parameter with a maximum effect obtains rank 1, and parameter with a minimum effect obtains rank which corresponds to the number of all analyzed parameters.Parameter that has a global rank 1, is categorized as "very important", rank 2 − 6 as "important", rank 7 − 41 (i.e. the number of parameters in the analysis − i.e. flow 7 -26) as "slightly important" and rank 42 (i.e.flow 27) as "not important" because the model is not sensitive to change in parameter (van Griensven et al., 2006).
Sensitivity analysis was performed using the measured data of the river Reka tributary Kožbanjšček (subcatchment 5) and the river Dragonja (subcatchment 14).The analysis was performed for an average daily flow, sediments, TP and NO 3 -.Table 3 represents for each model the first 10 parameters that have the greatest impact on the model when they are changed.The sensitivity analyses demonstrated great importance of the hydrological parameters that are associated with surface and subsurface runoff.
Alpha_Bf factor determines the share between the base and surface flow contribution to the total river flow.Cn2 curve runoff determines the ratio between the water drained by the surface and subsurface runoff in moist conditions.Ch_K2 describes the effective hydraulic conductivity of the alluvial river bottom (water losing and gaining).Surlag represents the surface runoff velocity of the river and Esco describes evaporation from the soil.For the sediment modelling the most important parameters are Spcon and Spexp that affect the movement and separation of the sediment fractions in the channel.Ch_N − Manning coefficient for channel, determines the sediment transport based on the shape of the channel and type of the river bed material.Ch_Cov − Channel cover factor and Ch_Erod − Channel erodibillity factor proved to be important for the Dragonja catchment.Soil erosion is closely related to the surface runoff hydrological processes (Surlag, Cn2).The analysis showed importance of the hydrological parameters that are associated with surface and subsurface runoff (Cn2, Canmx, Sol_Awc), evaporation (Revapmin, Esco, Blai), base flow (Alpha_Bf) and groundwater (Rchrg_Dp, Gwqmn), suggesting numerous routes by which sediment nitrate nitrogen (NO 3 -N) and TP are transported (Table 3).We noticed that the amount of N is also influenced by other parameters that are not included in the sensitivity analysis tool like Rate factor for humus mineralization of organic nutrients active N and P (CMN.bsn),half-life of nitrates and the shallow aquifer (HLIFE_NGW.gw),fraction of algal biomass that is N (Al1.wwq).TP results are significantly affected by the parameters that control surface runoff (Cn2, Canmx, Usle_P).Usle_P factor adjusts the USLE value for a particular land management.This means that the soil loss from the terraced land is different, from non terraced slopes.Parameters which have a significant impact on P, but not included in the sensitivity analysis tool are: fraction of algal biomass that is P (Al2.wwq),P availability index (PSP.bsn),P enrichment ratio for loading with sediment (ERORGP.hru),BC4.swq, benthic sediment source rate for dissolved P in the reach (RS2.swq),organic P settling rate (RS5.swq).

Calibration and validation
During the model calibration parameters are varied within an acceptable range, until a satisfactory correlation is achieved between measured and simulated data.Usually, the parameters values are changed uniformly on the catchment level.However, certain parameters (Sol_Awc, Cn2, Canmx) are exceptions, because of the spatial heterogeneity.
Firstly manual calibration, parameter by parameter, should be carried out with gradual adjustments of the parameter values until a satisfactory output results (E NS and R 2 > 0.5) (Moriasi et al., 2007, Henriksen et al., 2003).This procedure may be time consuming for inexperienced modellers.In the process of autocalibration only the most sensitive parameters are listed that showed the greatest effect on the model outputs.For each of the parameter a limit range (max, min) has to be assigned.
Validation is performed with parameter values from the calibrated model (Table 4) and with the measured data from another time period.Due to the data scarcity, the model was validated only for the hydrological part (flow).The river Reka water quality data covers only one year of daily observations, which was only enough for the calibration.For the river Dragonja a 14 years long data series of water quality was available, but the data was scarce in the number of observations (for sediment, NO 3 -and TP only 92, 73, 75, 78 measurements).
It should be pointed out that samples taken during monitoring represents only the current condition of the river in a certain part of the day (concentration in mg l -1 ), while the simulated value is a total daily transported load (kg day -1 ) in a river.

Hydrology calibration and validation
Objective functions show that the simulated total flows are within the acceptable range (Table 5, Fig. 2).Correlation coefficient (R2) for a daily flow is influenced by low flows.
Official measurements of a flow showed that on certain days the flow was not present or it was negligible.Model does not neglect extremely low flows, as is evident from the cumulative distribution of the flow (Fig. 2).Errors in flow measurements, in the worst case may be upto 42 % and in best case upto 3 % of the total flow (Harmel et al., 2006).
The E NS values for total flow fall into the category of satisfactory results (Moriasi et al., 2007, Henriksen et al., 2003), R 2 values fall into the category of good results, RMSE into the category of very good results (Henriksen et al., 2003) and PBIAS into the category of very good and good results (Moriasi et al., 2007).The reasons for lower results of the objective functions in the validation lie in the representation of the soil, rainfall and in the river flow data uncertainty.

Sediment, nitrogen and phosphorus calibration
Sediment calibration is essential for the proper P calibration, as P is preferentially transported adsorbed on the sediment particles.Parameters used for the calibration were USLE_P, SPCON, SPEXP, CH_EROD, CH_COV.Simulation results for the river Reka show lower E NS = 0.23 and a good result in predicting the variability of E NSpercentile = 0.83 (Table 6).In the case of Dragonja, model achieved good results for E NS = 0.70 and E NSpercentile = 0.73.PBIAS values fall within the category of very good results as deviation is less than 15% (Moriasi et al., 2007).
Parameters with impact on the N calibration results were FRT_SURFACE, NPERCO, AL1, CMN, HLIFE_NGW.The river Dragonja statistic is lower (E NS = 0.10, E Nspercentile = 0.78) and for the river Reka is in satisfactory range with E NS = 0.40 and E Nspercentile = 0.72 (Table 6).The PBIAS results fall into the very good (Dragonja) and satisfactory (Reka) category (Moriasi et al., 2007).The lower performance of the objective functions is connected to data scarcity in the Dragonja catchment with only 73 measurements in 14 years and in river Reka with only one year of daily data.Therefore, it is difficult to say whether the model is a good predictor of nitrate nitrogen (NO 3 -N ) loads and dynamics.Monthly sampling rate leads to inaccurate estimates of the transported loads of nutrients in rivers (Johnes, 2007); especially NO 3 - (Harmel et al, 2006).

Model performance indicators
An important step before calibrating sediment and water quality parameters is to look at other model performance indicators.Three main parameters are crop growth, evapotranspiration (ET) and Soil Water Content (SWC), as all of them have a great effect on the water balance.Evapotranspiration is a primary mechanism by which water is removed from the catchment.It depends on air temperature and soil water content.The higher the temperature, the higher is potential evapotranspiration (PET) and consequently ET, if there is enough of water in the soil.A simple monthly water balance between monthly precipitation and PET showed that average monthly water balance in the Reka catchment (station Bilje) is negative between May and August (Fig. 3).In the Dragonja catchment (station Portorož) water balance is negative from April to August (growing season) (Fig. 3).Table 6.SWAT water quality parameters, their ranges and the final values chosen for the models calibration periods (Reka 2008(Reka -2009;;Dragonja 1994Dragonja -2008) ) www.intechopen.com

Studies on Water Management Issues 120
Water that enters the soil may move along one of the several different pathways.It may be removed by plant uptake or evaporation; it may percolate past the bottom of the soil profile or may move laterally in the profile.However, plant uptake removes the majority of water that enters the soil profile (Neitsch et al., 2005).The soil water content will be represented correctly if crops are growing at the expected rate and soils have been correctly parameterized.Figure 4 shows the average of HRU for both catchments, with a silt clay soils, with the prevailing surface runoff and slow lateral subsurface flow.Soils exit the field capacity in the spring and return to that state in the autumn (Fig. 4).Soils in the summer are often completely dry with occasional increasing induced by storms.The plant growth component of SWAT is a simplified version of the plant growth model.Phenological plant development is based on daily accumulated heat units, leaf area development, potential biomass is based on a method developed by Monteith, a harvest index is used to calculate yield, and plant growth can be inhibited by temperature, water, N or P stress.(Neitsch et al., 2005).In the crop database a range of parameters can be changed to meet the requirements for optimal plant growth.We used default SWAT database parameters that were additionally modified (Frame, 1992).An example crop growth profile for development of leaf area index (LAI) and plant biomass (BIOM) for vineyard is presented on figure 5.

Agri-environmental scenarios
The aim of this scenario was to investigate possible effects of the agri-environmental measures on the river water quality.To achieve the aim seven different scenarios were applied to the study area EVP, EKO20, EKO100, S35, S50, STV35, ETA.
The field erosion buffer strips scenario (EVP) is a function of how to minimize influences of diffuse pollution resulting from agricultural activities without drastic management changes.They are planted or indigenous bands of vegetation that are situated between source areas and receiving waters to reduce surface runoff velocities and to remove pollutants from surface and subsurface runoff.The effectiveness of strips is closely correlated with their slope and width (Dillaha et al., 1989).An option of 3 m wide strips was modelled on all arable (AGRC, AGRR), vineyard (VINE), orchard (ORCI, ORCE) in olive grove (OLEA) HRUs.
Organic farming scenarios on 20 % of the area (EKO20) and on the 100 % area (EKO100) aim to reduce the use of mineral fertilizers and to reduce the intensity of production.Special organic rotations with green manure and composted farmyard manure were created.The lack of P was compensated with the use of triple-superphosphate that is allowed in organic production.Both organic scenarios were designed to ensure normal production for the market.
Steep meadows, being an agricultural landscape, should be cut regularly, but due to the steep slopes and the associated costs and risks, are abandoned and overgrown.Scenarios having steep meadows with slope inclination above 35 % (S35) and 50 % (S50) should prevent overgrowth.To verify the effects of scenarios on water quantity and nutrients transport, meadows (TRAV) of both case studies located on slopes greater than 35 % and 50 % were changed into the forest (FRSD) (Fig. 6).In the S35 scenario 18 % (Reka) and 3.6 % (Dragonja) of grassland was changed into forest, which is equivalent to 1.43 % (Reka) and 0.67 % (Dragonja) of the total catchments.In the S50 scenario only 2 % (Reka) and 0.3 % (Dragonja) of grassland was changed into forest, which is equivalent to 0.16% (Reka) and 0.06% (Dragonja) of the total catchments.Conservation of vineyards on steep slopes has proved to be difficult because of unprofitable production.Economic reasons were followed by a trend of wine production abandonment.
In the steep vineyards scenario (STV35), all vineyards on the slopes greater than 35 % were changed into forest, to verify the environmental impact of abandonment of vineyards on steep slopes (Fig. 7).In the STV35 scenario, 17 % (Reka) and 1.4 % (Dragonja) of grassland is changed into forest, which is equivalent to 3.93% (River) and 0.06% (Dragonja) of the total catchments.Extensive grassland scenario (ETA) objective was to determine what would be the impact on water quantity and quality, if the whole grassland would be overgrown with forest.
Extensive grassland use with one cutting is widespread in both areas.Whole grassland in the Reka (8 %) and Dragonja (18%) catchments area was turned into a forest (Fig. 8).
Fig. 8. Hydrological response units with the grassland land use (TRAV) and slope classes for the Reka and Dragonja catchment

Results and discussion
The base scenario indicates a high average annual variability in the transport of the sediment, total nitrogen (TN) and total phosphorus (TP) in the river flow (Table 7).The standard deviations for the Reka subcatchment 8 reveal that the sediment, TN and TP 2/3 of transported quantities are expected in the interval 1,844 ± 1,075 t sediment year -1 , 88,728 ± 63,255 kg TN year -1 and 3,489 ± 2,993 kg TP year -1 and for the Dragonja subcatchment 14 in the interval 4,804 ± 1,576 t sediment year t -1 , 163,763 ± 98,949 kg TN year - 1 and 3,489 ± 11,742 kg TP year -1 .7. Average annual flow (m 3 s -1 ) and river load of sediment (t year -1 ), total nitrogen and total phosphorus (kg year -1 ) for the Reka subcatchment 8 and Dragonja subcatchment 14 (1994−2008)

River flow
Changes in average annual flow between base and agri-environmental scenarios are minimal for both catchments for the research period.Maximum changes on an annual basis are less than 0.5 % (Table 8) and on a monthly basis close to 1% (Reka) and 5% (Dragonja) (Fig. 9).Student t-statistics for average annual flows reveal that the results of the agrienvironmental scenarios are not statistically different from the base scenario (Table 9).Table 9. Review of statistically significant results of Student t-statistics for average annual flow and average annual load of sediment, total nitrogen and total phosphorus

Sediment
Impacts of agri-environmental scenarios EVP, EKO20, EKO100, S35, S50, STV35, ETA on an average annual load of sediment transported with the flow are evident for certain scenarios (Table 8).Statistically significant changes in the Reka catchment have been calculated for the EKO100 scenario, while the EVP scenario result is slightly lower to be statistically significantly different (Table 9).The river Dragonja results show that changes in the scenarios EVP, EKO20, EKO100 and ETA are statistically significantly different from the base scenario (Table 9).The biggest differences between scenarios in transported sediment load are in autumn and winter months, when the loads for scenarios EKO100 (Reka) and ETA (Dragonja) get considerably reduced (Fig. 10).

Total nitrogen
The effect of agri-environmental scenarios on the annual TP transport in the river flow has proved to be negligibly small, due to the small proportion of land on which the scenarios were set up (Table 8).The results of the agri-environmental scenarios for the TN transport in both catchments are not statistically significantly different from the base scenario (Table 9).Large monthly variations in the loads of TP transported were typical for the scenarios with higher levels of organic matter (EKO20, EKO100, ETA) (Fig. 11).The decomposition of the organic matter is difficult to control, monitor and predict.However, on an annual basis, the variation between months are equalized.

Total phosphorus
The effects of agri-environmental scenarios on the TP transport in the stream are low (Table 8) and may be observed in scenarios EKO 100 and EVP (Reka) and ETA (Dragonja) (Fig. 12).Student t-statistics for average annual TP load in both catchments are not statistically significantly different (Table 9).In case of Rivers, maximum difference between the scenarios resulting in cooler and wetter period of the year, and in the Dragonja catchment, in the warmer and more stormy period.

Scenario evaluation
The evaluation of impacts of the agri-environmental scenarios on the sediment and nutrients transport processes on the catchment level was performed in the light of the EU Water Framework Directive (WFD 2000/60/ES) and Republic of Slovenia legislation.Both set guide concentrations with the purpose of limiting impacts of excessive levels on flora and fauna in the rivers.When interpreting the concentrations we need to have in mind the geological and pedological characteristics of the catchment.There is also the question of whether to consider set guide levels for the rivers that do not represent an economic interest (Lohse, 2008), however rivers are not only economic asset.When recommending possible agri-environmental mitigation measures to deliver water quality improvements, careful evaluation and prioritization of each measure has to be performed according to its positive and negative issues on the environment, agriculture, social life and economy (Bockstaller et al., 2009;Everard, 2004;Glavan et al., 2011).
The results of the scenarios demonstrate that in the Reka and Dragonja catchments major problems with the concentrations of NO 3 -and TP are excluded, as both are lower than the limit values (Table 10).Nevertheless, the results reveal the difficult path to achieve the recommended value for sediment in both catchments, especially in the case of the river Reka catchment.With the realization of agri-environmental scenarios for the Dragonja catchment, particularly the EVP and ETA, we could expect reduction of the sediment concentration below the recommended level and consequently water quality improvements.In the Dragonja catchment, the guide concentration of 25 mg l -1 was reached with the scenarios EVP, EKO20, EKO10 and ETA.However, in the Reka catchment, scenarios sediment reductions are not sufficient to reduce the concentration below the guide level.This leads us to thinking, that catchment is dominated by certain land use (vineyard) and soils, which have a negative impact on the river concentrations (Komac & Zorn, 2007;Petek, 2007;Volk et al., 2009).
The EKO100 scenario is considering the low proportion of land involved in organic production in research areas almost impracticably, since it would require too much labour-intensive work, which results in a higher final price of the crop.Organic production is advised in the areas with long-term organic fertilization where soils were sufficiently enriched with organic matter and nutrients to supply plants for a several decades (Mihelič et al., 2009).In the Dragonja catchment, which is subject to a high degree of afforestation, the scenario EVP reflected in the significant concentration reduction below the recommended value.We used 3 meters wide vegetation bands that have reflected a 14 % (Reka) and 31 % (Dragonja) reduction of sediment in the watercourse, but with broader bands, an even greater impact could be achieved.For the effectiveness of the bands, the identification of critical points is important (Garen & Moore, 2005;Wolfe, 2000).A small proportion of the area can have a significant impact on the sediment, N and P loads in the watercourses.Limit and guide concentrations (mg l -1 ) set by EU directives and Slovenian regulations: Sediment (river) 25 mg l -1 ; Nitrate (NO3-) in drinking water 50 mg l -1 and in surface water 14,08 -30,8 (very good state) and 28,6 -41,8 mg l -1 (good state); Total phosphorus (TP) for salmonid waters 0,2 mg l -1 and for cyprinid waters 0,4 mg l -1 .

Average annual concentration (mg l -
Table 10.Impacts of the alternative scenarios on the average annual concentration (mg l -1 ) of the sediment, nitrate and total phosphorus Following the trend of afforestation of agricultural land, the ETA scenario could become practicable, under which all grassland (18 %) would be overgrown by forest.However, such a scenario is not viable, since larger farmers round up their vineyards and olive groves and reduce overgrowth.However, this process is considerably slower than natural afforestation, which has affected the water cycle and erosion processes in the last decade (Globevnik, 2001).Sediment reductions in the catchment are expected with progressive land abandoned with afforestation and with parallel establishment of buffer zones on larger agriculturally rounded areas.The negative effect of erosion buffer zones is an exclusion of a certain percentage of agricultural land from agricultural production.At 3 m wide buffer zones on 1 ha of land (10,000 m 2 ) the loss of the land in production would be 12 % (1,200 m 2 ).An important element, which partially contributes to increased sediment loads in the river Dragonja are cliffs and steep eroded slopes without vegetation, which are eroded at the foothills by the river and torrential tributaries.
in the model, which can lead to appropriate modelling of nutrients pathways and to account for the nutrient lag times in the groundwater.7. Physical landscape spatial variability within catchments (topography, soils, land use, land management etc.) have important influence on the model results.This means that pollutant sources and loads are not evenly distributed in space.Rather than impose blanket agri-environmental measures in the model, it is better to target key source areas or HRU combinations that deliver excessive loads.8.The scenarios assume that all farmers in the catchment take up the structural measures or the changes in land use and management uniformly.However, field work shows that this is not the case.A close cooperation with all key stakeholders on local, regional, national and transnational level and financial support, like EU Common Agriculture Policy, which enable areas to develop in a sustainable way, is necessary.
At the end of this chapter we would like to increase awareness that model results and their interpretation by the modeller must lead to constructive discussion, which aims to achieve and maintain good water quality in research catchments, which is the objective of the Water Framework Directive and other legislation related to water.

Acknowledgments
Financial support for this study was provided by the Slovenian Research Agency founded by the Government of the Republic of Slovenia.Contract number: 1000-06-310163.

Fig. 1 .
Fig. 1.The river Reka and Dragonja catchment case areas divided in sub-catchments

Fig
Fig. 3. Comparison of simulated and measured (Environment Agency of Republic of Slovenia -EARS) water balance (mm) for the Reka subcatchments 8 and Dragonja subcatchment 14

Fig. 6 .
Fig. 6.Hydrological response units with the grassland land use (TRAV) and slopes greater than 35 % and 50 % for the Reka and Dragonja catchment

Fig. 7 .
Fig. 7. Hydrological response units with the vineyard land use (VINE) and slopes greater than 35 % for the Reka and Dragonja catchment

Table 2
. Model calibration hydrology criteria by Montana Department of EnvironmentQuality (2005)For the detection of statistical differences between the two base scenarios and alternative scenarios Student t-test statistics should be used (α = 0.025, degrees of freedom (SP = n-1)), for comparing average annual value of two dependent samples at level of significance 0.05 (5).Variable, which has approximately symmetrical frequency distribution with one modus class, is in the interval x ±s expected 2/3 of the variables and in x ± 2s approximately 95% of the variables and in x ± 3s almost all variables.Confidence interval (l 1,2 ) (6) for Student distribution for all sample arithmetic means ( x ) can be calculated (6).

Table 4 .
Hydrological parameters, ranges and final values selected for the calibration of models (SWAT) for the rivers Reka and Dragonja catchments

Table 5 .
Daily time step river flow performance statistics for the rivers Dragonja and Reka for the calibration (2001-2005) and validation periods