Hydrological and water quality modeling in a medium-sized basin using the Soil and Water Assessment Tool (SWAT)

Desalination 250 (2010) 274–286

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Desalination j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / d e s a l

Hydrological and water quality modeling in a medium-sized basin using the Soil and Water Assessment Tool (SWAT)☆ Vassilios Pisinaras, Christos Petalas, Georgios D. Gikas, Alexandra Gemitzi, Vassilios A. Tsihrintzis ⁎ Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece

a r t i c l e

i n f o

Article history: Received 24 November 2007 Accepted 1 August 2008 Available online 21 October 2009 Keywords: SWAT model Calibration Verification Hydrology Nutrients Management scenarios

a b s t r a c t The newest version of Soil and Water Assessment Tool (SWAT2005), coupled with a GIS interface (AVSWATX), was applied to Kosynthos River watershed located in Northeastern Greece. The 440 km2 drainage basin was discretized into 32 sub-basins using an automated delineation routine. The multiple hydrologic response unit (HRU) approach was used and the basin was discretized into 135 HRUs. The model was calibrated and verified using continuous meteorological data from three stations, and runoff and nutrient concentrations measured at four monitoring sites located within the main tributaries of the watershed, for the time period from November 2003 to November 2006. Calibration and verification results showed good agreement between simulated and measured data. Model performance was evaluated using several statistical parameters, such as the Nash–Sutcliffe coefficient and the normalized objective function. The validated model was also used to test the effect of several land use change and crop management scenarios in runoff and nutrient loadings. The study showed that SWAT model, if properly validated, can be used effectively in testing management scenarios in Mediterranean watersheds. The SWAT model application, supported by GIS technology, proved to be a very flexible and reliable tool for water decisionmaking, especially under the need for harmonization with the Water Framework Directive. © 2009 Elsevier B.V. All rights reserved.

1. Introduction In recent years many efforts have been made worldwide on the abatement of point source pollution; as a result, the major cause of water quality deterioration of the water bodies is mostly associated today with non-point source pollution, due to the intensification of agricultural activities and the development of large urban centres [1]. The EU Water Framework Directive (WFD) is relatively new legislation that establishes an integrated approach on management and protection of Europe’s aquatic environment. The principal objective of the WFD is to achieve good chemical and ecological status for receiving waters by 2015, and mandates Member States to develop river basin management schemes. This planning mechanism is intended to ensure integrated management of the river environment, providing a decision-making framework for setting environmental objectives. However, the management of water quality from non-point sources would require very expensive monitoring efforts. Mathematical modeling is a necessary step in the implementation of the WFD. The application of different types of models is required at

☆ Presented at the 1st Conference on Environmental Management, Engineering, Planning and Economics (CEMEPE), Skiathos, Greece, 24–28 June, 2007. ⁎ Corresponding author. Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, Greece. Tel.: +30 25410 79393; fax: +30 25410 78113. E-mail addresses: [email protected], [email protected] (V.A. Tsihrintzis). 0011-9164/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.desal.2009.09.044

different stages of the legislative process [2,3], starting with relatively simple ones during the characterization phase of the WFD, and more complex ones during the river basin management planning stage. To end up with a successful river basin management plan, in addition to describing current conditions, a variety of environmental conditions should be evaluated with the use of mathematical models, in an effort to forecast short and long-term impacts on the aquatic system. In the case of diffuse agricultural pollution, various land management options have to be tested with the model. Because of the complexity of the hydrologic processes, hydrologic– based, distributed parameter models and GIS constitute a powerful combination for water quantity and quality assessment [4,5]. There are several reasons that enforce the combination of the aforementioned models with GIS for water resources management, the most important of which are [6]: the automation of data input and output in the preand post-processing stage of model development, as well as the ability to develop interactive post-processing tools that provide the opportunity for easier understanding of hydrologic system function; and, the continuous increase in data availability and quantity, which gives the opportunity to investigate important hydrological variables. This paper presents the combined application of hydrological model SWAT with GIS technology as a management tool for a medium-sized Mediterranean basin located in Northeastern Greece. The study aimed to assess the SWAT model performance in the area, and evaluate the current management practices and several management scenarios. The suitability of SWAT in the development of a River

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Basin Management Plan for Mediterranean basins has been evaluated in terms of: 1) model performance; and 2) ability of the model to simulate relevant management scenarios for the region. 2. Materials and methods 2.1. Study area description Kosynthos river basin is located in Thrace District, in north-eastern Greece. Kosynthos river total length is approximately 52 km, it originates from Rhodope Mountains and after traversing a basin of about 440 km2 that includes mountain terrain, agricultural plains and urban areas, it discharges to Vistonis lagoon (Fig. 1). The major sources that can affect its water quality originate from agricultural, urban and industrial activities taking place in the lower reaches of the basin. Water needs of the area are satisfied mainly by groundwater abstracted from numerous wells. The natural environment of the study area is still relatively unaffected in the greatest part of the basin. Geologically, Kosynthos catchment belongs entirely to the Rhodope massif, consisting of old metamorphic rocks (gneisses, marbles, schists), observed mainly in the northern part of the study area. Moreover, igneous rocks (granites, granodiorites) have intruded Rhodope massif, through magmatic events in tertiary times, and outcrop in the southern part of the area, together with quaternary sediments. Precipitation averages 791 mm annually in the plain area, ranging from 368 to 1307 mm, while in the mountainous area it averages 1044 mm annually, ranging from 539 to 1828 mm. Kosynthos river water quality is an important aspect, as water is used for irrigation purposes and also recharges the Xanthi's plain aquifer, which constitutes the potable and irrigation water supply of about 50,000 inhabitants. Moreover, Kosynthos river discharges into Vistonis lagoon which is one of the most significant ecosystems of Greece, protected by the Ramsar Convention, and is considered as a first priority site under EU Natura 2000 network [7]. Over the past

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20 years, the lagoon has suffered severe impacts due to point and nonpoint sources of pollutants [8,9]. Although several measures have been taken over the last decade to reduce point source pollution, such as sewage treatment and diversion from the watercourses, proper solid waste management and industrial waste elimination, there are still pollutants entering the lagoon, mainly associated with agricultural land use practices [8,9]. For this, the different crop categories and the area each crop covers in the watershed was determined based on information collected from the local authorities. Then, the application rates of nitrogen and phosphorus fertilizers for the most significant crops were estimated based on a manual compiled by the local authorities for the farmers of the study area, indicating the application rates and the application periods of the fertilizers. As shown in Table 1, major crops for the study area are wheat, corn, cotton and tobacco. There are significant quantities of N and P entering the watershed from fertilizers (about 1190 tons N and 162 tons P), indicating that agricultural activities significantly affect the nutrient budget. The watershed was ungaged. For this, river flow and water quality monitoring was undertaken at four monitoring sites (MSs) along Kosynthos river and its tributaries (Fig. 1). Monitoring site 1 (MS1) was located in tributary Gerakas, at the northern of the city of Xanthi. Monitoring site 2 (MS2) was situated in the city of Xanthi. Monitoring site 3 (MS3) was located in a significant tributary (Kimmeria Creek) of Kosynthos river near Kimmeria village, which confluences with Kosynthos upstream of monitoring site 4 (MS4), located downstream of the city of Xanthi. This study focuses on data collected between October 2003 and November 2006 for river flow, and between November 2004 and November 2006 for nitrate and soluble phosphorus concentrations. River flow measurements were conducted using a Valeport model 801 flowmeter, while nitrates and soluble phosphorus concentration were determined by spectrophotometry according to standard methods [10]. A detailed study about water quantity and quality characteristics of Kosynthos river has been presented by Pisinaras et al. [11].

Fig. 1. Map of the study area, including the monitoring sites (MSs).

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Table 1 Crop distribution in the watershed, fertilizer application rates and total nutrient quantities entering the watershed due to agricultural activities. Crop type

Cultivated % of the N fertilizer area (ha) cultivated application rate area (kg/ha)

Wheat 2146.0 Corn 2490.3 Sunflowers 4.6 Rice 0.5 Alfalfa 122.2 Cotton 385.2 Tobacco 947.9 Tomatoes 72.8 Other 376.9 Total 6546.5

32.1 37.3 0.1 0.0 1.8 5.8 14.2 1.1 7.6 100.0

130 300 140 95 35 125 40 167.5 169.1 Total N applied: 1193.05 tons

P fertilizer application rate (kg/ha) 15.3 39.2 37.1 19.6 48.0 21.8 19.6 65.4 2.5 Total P applied: 162.22 tons

2.2. Model description The AVSWATX [12] version of the SWAT model was used. This version integrates the latest version of Soil and Water Assessment Tool (SWAT2005) [13,14] with ArcView. In AVSWATX, the pre-processing of the data is done by applying some of the ESRI ArcView GIS functionalities. SWAT constitutes a river basin or watershed-scale, distributed model, which simulates the rainfall-runoff process, sediment transport and nutrient loads in large watersheds, where complex soil, land use and management patterns are observed [13]. The SWAT model has been developed by the USDA Agricultural Research Service (ARS), it incorporates features of several previous ARS models and is the evolution of the Simulator for Water Resources in Rural Basins (SWRRB) model [15]. There have also been several other models that contributed to the development of SWAT, such as the Chemicals, Runoff and Erosion from Agricultural Management Systems model (CREAMS) [16], the Groundwater Loading Effects of Agricultural Management Systems model (GLEAMS) [17], and EPIC [18]. The water budget equation is the basis for the simulation of the hydrologic cycle in SWAT [13,19]. Total runoff hydrographs are computed based on runoff calculated separately at each sub-basin, and then routing through several channels. A modified version of SCS curve number method [20] is used for surface runoff computation, while the Modified Universal Soil Loss Equation (MUSLE) [21] is used for erosion and sediment yield calculation. Nutrient load and concentration prediction is based on a modification of the code in EPIC model [18,22]. Finally, soil surface and plant data are used to calculate evapotranspiration in the watershed, while precipitation and temperature data can be either provided as time series data, or simulated using a firstorder Markov chain model in the case when meteorological data time series are not available. The distributed SWAT model with the use of AVSWATX interface is parameterized three-dimensionally by spatial and relational databases [5]. Horizontal variability of input parameters, such as land use, is provided by grid data, stored and operated by ArcView. Relational databases of soil properties serve for parameterization of a vertical model structure, because they are linked to spatial modeling units. The watershed discretization in the SWAT model is approached through subwatersheds defined by the watershed digital elevation model, and Hydrologic Response Units (HRUs), which comprise similar land use and soil type combinations within the subwatershed. The Watershed Delineation module of AVSWATX is based on some elementary raster functions provided by ArcView and the Spatial Analyst extension [5], in combination with the standard methodology based on the eight-pour point algorithm with steepest descent [23].

2.3. Model parameterization For the SWAT simulations the available topography, land use, soil types and meteorological data had to be aggregated. AVSWATX gives the opportunity for pre-processing the data by applying some of the ArcView GIS functions. This involves the creation of the river network, the basin area, and the sub-basins. The latter step is crucial, since it creates the boundaries for the following simulation [24]. It is well known that the quality of the DEM will have a strong influence on the final output of the hydrological model [25]. A 50 × 50 m resolution DEM was used in this study. The CORINE Land Cover 1:100,000 vector map was used in this study. The CORINE land cover consists of a geographical database describing vegetation and land use in 44 classes, grouped in three nomenclature levels. The CORINE land cover classification codes were converted to the SWAT land cover/plant codes, so a reclassified and aggregated land use data set was made indicating that 0.16% of the whole basin belongs to the “Residential-High Density” class (URHD), 1.25% belongs to the “Range-Brush” class (RNGB), 0.31% belongs to the “Pasture” class (PAST), 7.45% belongs to the “Range-Grasses” class (RNGE), 0.11% belongs to the “Wetland-Mixed” class (WETL), 0.34% belongs to the “Commercial” class (UCOM), 76.07% belongs to the “Forest-Mixed” class (FRST), 0.32% belongs to the “Transportation” class (UTRN), 12.90% belongs to the “Generic Agricultural Land” class (AGRR) and 1.09% belongs to the “Residential-Med/Low Density” (URML) class. Unfortunately, a detailed crop type map was not available. For this an average N and P fertilizer application was applied for all agricultural (AGRR) HRUs. Data on soil attributes were obtained from soil maps provided by the Greek Department of Agriculture and a significant previous work for the study area [8,26]. For each sub-basin, the soil percentage in clay, silt, sand, as well as percent of organic matter, were estimated for up to six soil layers from soil section data. Then, the dominant soil type was determined by using the USDA-SCS soil texture classification with the largest coverage in the HRU. A hydrologic category (A to D) was assigned to each HRU according to USDA-SCS [20]. All this data was entered to the Soil Database of AVSWATX manually or in dbf format. Weather data from three meteorological stations was collected for the simulation period. One of the meteorological stations is located in the mountainous part of the basin, while the other two are located in the lowlands. The weather data collected includes the daily precipitation rate, daily maximum/minimum temperature, daily values of wind speed, daily values of solar radiation and daily relative humidity values. Evapotranspiration is one mechanism by which water is removed from a catchment. Three methods are provided within the SWAT model for potential evapotranspiration (PET) estimation; the Penman–Monteith method [27], the Priestley–Taylor method [28] and the Hargreaves method [29]. The Hargreaves method was used in the calculation of PET in Kosynthos River catchment. In SWAT model application, the watershed is discretized into subwatersheds, whose size depends on the threshold value (CSTV). CSTV is defined by the user and constitutes an important parameter for the definition of the HRUs, which allows watershed discretization in more detail [30,24]. The model developer proposes a series of predefined relative CSTVs for the watershed size, and the model user selects the appropriate CSTV from several possible relative threshold values, as described by Romanowicz et al. [24]. According to FitzHugh and Mackay [30], significant attention has to be paid when defining the CSTV. CSTV assignment is followed by the watershed disaggregation into homogeneous subwatersheds and HRUs, where the various hydrological attributes are assigned [24]. The classification of the Kosynthos River catchment resulted in 32 sub-basins. With a threshold value of 10% for land use and for soil types the number of HRUs is 135.

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2.4. Model calibration and verification SWAT input parameters are physically based and are allowed to vary within a realistic uncertainty range for calibration. The SWAT model contains many difficult to measure or not measurable parameters [31]. Calibration techniques are generally referred to as either manual or automated. Santhi et al. [32] suggested a generalized manual calibration procedure, indicating the most sensitive input parameters, acceptable model evaluation results and sensible ranges of parameters uncertainty. A manual calibration procedure has also been presented by Gikas et al. [8]. Coffey et al. [33] recommended using the R2 and modeling efficiency objective functions while Gikas et al. [8] recommended using the R2, the root mean square error (MSE), the normalized objective function (NOF) and scattergrams for calibration of daily streamflow, sediment and nutrient data. The following modeling evaluation indices were used in this study: 1. The root mean square error (RMSE) and the normalized objective function (NOF) [34,35] were computed based on the following equations: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3ffi u2 N u 2 u6 ∑ ðPi  Oi Þ 7 u6 7 i=1 ð1Þ RMSE = u 7 u6 5 N t4

NOF =

RMSE ― O

ð2Þ

where Pi are the model predicted values, Oi are the observed values — for the N observations, and O is the mean of observed values. According to Kornecki et al. [34], the ideal value of NOF is 0.0. However, a model is acceptable for NOF values in the range from 0.0 to 1.0 when site specific data are available for calibration. In that case, the model can be used to test scenarios associated with management practices. 2. The Nash–Sutcliffe coefficient [36], which is calculated by the following equation: n

2

∑ ðOi  Pi Þ

NSC = 1 

i=1 n

ð3Þ

2 ∑ ðOi  OÞ

i=1

The optimal statistical value occurs when the NSC value is closer to 1.

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3. Another way to assess the calibration is through the use of scattergrams [37,38] where predicted quantities are plotted against observed ones. In a scattergram, a regression straight line of the following form is also fitted through the data: Pi = γOi

ð4Þ

and its slope γ is compared to the 1:1 slope (perfect match). The value of the slope γ is a measure of the over- (γ > 1.0) or under-prediction (γ < 1.0) of the model compared to the observed data. In addition, the square of the correlation coefficient R2 of the regression line is computed. The lower the value of R2 falls below 1.0, the worse the data correlation is, i.e., the greatest is the scatter of the data around the line. Therefore, best calibration requires that values for both slope γ and R2 be as close to 1.0 as possible. The calibration of the model was conducted for the period from November 1st, 2003 to October 31st, 2005, using the field measurements collected at the four monitoring stations. The calibration procedure was conducted first for water volume and flow rate, and then for nutrient quantities, separately for each monitoring station. One important rule for proper calibration is to begin calibration from monitoring stations located upstream and then proceed to monitoring stations located downstream. Thus, the calibration sequence for Kosynthos River basin was the following: MS1, MS2, MS3 and MS4. Total water volume and discharge in each monitoring station was calibrated in two steps: first, a curve number value was selected using standard SCS tables. The default curve number value assigned by the AVSWATX database, according to the land use and soil hydrologic group of each HRU was varied within the range from ±5 of this value until predicted and observed values at each monitoring station approached [39]. Then the soil available water capacity (SOL-AWC) was calibrated; this is estimated as the difference between the in situ water field capacity and the permanent wilting point, and represents the water volume that should be available to plants, if the soil, inclusive of rock fragments, was at field capacity. These steps were repeated until an acceptable fit to observed water volume at the outlet was obtained. Further agreement of observed and predicted values was achieved by adjustment of the groundwater parameters GW_REVAP, REVAPMN, GWQMN and RCHRG_DP. Finally, better adjustment of the shape of the hydrograph was achieved by varying the base flow alpha factor ALPHA_BF. Through this process the hydrologic budget was continuously checked in order to avoid serious errors. Because of this ESCO and EPCO values were properly adjusted. Nutrient loadings were calibrated at each monitoring station according to the following procedure: first, the groundwater contribution to stream nitrate concentrations was adjusted using GWNO3 and NPERCO parameters in order to calibrate the nitrate loadings.

Table 2 Several parameter values used for calibration of the SWAT model. Variable name

Model processes

Description

Normal range

Actual value used

CN2 ESCO EPCO SOL_AWC GW_REVAP GWQMN

Flow Flow Flow Flow Flow Flow

−5 to + 5 (from SCS table values) 0–1 0–1 0–1 0.02–0.20 0.0–300.0

− 3 to + 3 (from 65 to 78) 0.95 1 0.11–0.14 0.02 200

RCHRG_DP N ALPHA_BF NPERCO GWNO3

Flow Flow Flow Nitrate nitrogen Nitrate nitrogen

0.0–1.0 0–1 0.0–1.0 0.0–1.0 –

0–0.5 0.05–0.1 0.024–0.048 0.2 0.10

PPERCO GWSOLP

Soluble phosphorus Soluble phosphorus

Curve number Soil evaporation compensation factor Plant uptake compensation factor Soil available water capacity Groundwater revap coefficient Threshold depth of water in shallow aquifer for percolation to occur Deep aquifer percolation fraction Manning's n for channel Base flow alpha factor Nitrogen percolation coefficient Concentration of nitrate in groundwater contribution to streamflow from sub-basin Phosphorus percolation coefficient Concentration of soluble phosphorus in groundwater contribution to streamflow from sub-basin

10.0–17.5 –

10 0.08

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Fig. 2. Observed and simulated flow, and corresponding scattergrams at each monitoring station for the calibration period.

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Fig. 3. Observed and simulated nitrate loadings, and corresponding scattergrams at each monitoring station for the calibration period.

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Fig. 4. Observed and simulated soluble phosphorus loadings, and corresponding scattergrams at each monitoring station for the calibration period.

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Then, groundwater contribution to steam phosphorus concentration (GWMINP) was adjusted using GWMINP and PPERCO parameters in order to match the observed soluble phosphorus loadings with the predicted ones at all monitoring stations. After model calibration was finished, model verification followed. Model verification is the process of performing the simulation, using a different time-series of input data, without changing any parameter values that may have been adjusted during calibration. The purpose of model verification is to establish whether the model can estimate output for locations, time periods or conditions other than those that the parameter values were adjusted to fit. Model verification was performed using meteorological and field data collected from November 2005 until November 2006. 2.5. Development of land use change and crop management scenarios The ability of a model to perform different management scenarios is a powerful tool for the decision-making process. For this, several management scenarios were tested after calibration and verification of SWAT model in Kosynthos watershed in order to assess the impact of land use change or crop management change, both in water flow and nutrient loadings. Three land use change scenarios were applied in order to evaluate the land use change impact on water flow. The first scenario, which is the “100% Deforestation” scenario, is a pessimistic and extreme scenario, and assumes the deforestation of the whole watershed possibly by a forest fire. The “20% Expansion of Urban area” scenario assumes an expansion of the urban area by 20%. This change is small in relation to the area of the whole watershed as urban area covers only 2% of the total watershed area. Finally, an expansion of the agricultural land by 20% was assumed in order to assess the impact of an increase of agricultural activities. The last two land use change scenarios were developed through GIS by buffering the urban area polygons for the “20% Expansion of Urban area” scenario and the agricultural polygons for the “20% Expansion of Agricultural land” scenario. In order to evaluate the impact of alternation of different crops in Kosynthos watershed, four different scenarios of crop management were applied. For each alternative scenario, only one crop was considered covering the entire arable area: wheat, corn, cotton or tomato. The fertilizer application rates shown in Table 1 were applied according to each crop type for the whole agricultural part of the watershed. 3. Results and discussion 3.1. Model calibration results Table 2 presents an overview of the SWAT2005 parameter changes applied during the model calibration. These changes reflect, to a large degree, the special characteristics of this Mediterranean watershed. The default curve numbers set by the AVSWATX user interface, i.e., the values recommended by the SCS Handbook [20] were sometimes reduced by 3 units in the subwatersheds where runoff volumes needed to be reduced, reflecting better soil drainage than the conditions assumed in the default SWAT database. In an opposite situation CN was increased. The runoff lag coefficient ALPHA_BF started from 0.024 and ended to 0.048 when storm recessions needed to be less steep. The soil evaporation compensation factor and plant uptake compensation factor were kept at their default value of 0.95 and 1, respectively. ESCO and EPCO decrease resulted in very high evapotranspiration values thus affecting water balance. The relatively homogeneous soils which are mainly sandy clays and sandy loams resulted in little variance for soil available water capacity values. The groundwater coefficient that controls the amount of water that moves from the shallow aquifer to the root zone, i.e., the SWAT revap parameter, was kept to the default value of 0.02 to allow more movement of water from the shallow aquifer to the unsaturated root zone. High values (up to 0.5) for the deep aquifer

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percolation fraction (RCHRG_DP) were chosen to simulate the significant groundwater recharge [11] across the alluvial cone of Kosynthos river at the south part of the study area. The lowest values of both nitrogen and phosphorus percolation coefficients were determined as most appropriate for Kosynthos river watershed. Finally, the concentrations of nitrate and soluble phosphorus in groundwater contribution to streamflow from its sub-basin were 0.1 and 0.08 mg/L, respectively. Typical comparisons of observed and predicted values are presented in Figs. 2–4 for each monitoring station and for the three parameters, i.e., flow rate, nitrate nitrogen and soluble phosphorus quantities. One can see that all predicted values at all stations, for the three parameters for the entire simulated period show quiet good agreement with measured values. Calibration statistics results are presented in Table 3 for all sites and the three parameters. One can see that the NOF values are less than 1.0 in all cases, thus the model can safely be used for estimating and determining results associated with management practices [34]. Scattergrams for each parameter at three sampling sites, one in each sub-basin, are presented in Figs. 2–4. Values for the slope γ of Eq. (4) are close to 1.0 for all parameters, particularly for flow rate (between 0.981 and 1.071). Except for MS2 (γ = 0.987) where nitrate quantities were slightly underpredicted, for all the other monitoring stations nitrate quantities were slightly overpredicted (γ between 1.139 and 1.292). Soluble phosphorus was generally slightly underpredicted (γ between 0.765 and 0.960). Correlation coefficient values are close to 1.0 for all the simulated parameters (R2 between 0.617 and 0.958). Similarly to R2, NSC values were close to 1.0 for the three simulated parameters ranging 0.617 and 0.915 (Table 3). 3.2. Model verification Model verification was performed using meteorological and field data collected from November 2005 until November 2006. Calibrated parameter values were retained the same for the verification period as summarized in Table 3. Typical visual comparison of observed and predicted values for the verification period and for all monitoring stations is shown in Figs. 5–7. Similarly to calibration, these figures show that all predicted values at all stations, for the three parameters, for the entire verification period show good agreement with measured values. Accuracy of the predictions from verification runs was determined with the three methods also used in calibration, i.e., NOF computation Eqs. (1), (2), use of scattergrams and Eq. (4), and the Nash–Suttclife coefficient determination Eq. (3). NOF, NSC, γ and R2 values for the verification period are presented in Table 3. One can see that the NOF values are less than 1.0 in all cases (and in most times less than 0.5) (Table 3), thus the

Table 3 Goodness-of-fit criteria used for calibration and verification of SWAT model. Station

MS1

MS2

MS3

MS4

Parameter

NOF NSC γ R2 NOF NSC γ R2 NOF NSC γ R2 NOF NSC γ R2

Calibration

Verification

Flow

Nitrate

Sol. phosphorus

Flow

Nitrate

Sol. phosphorus

0.402 0.815 1.046 0.839 0.266 0.915 1.000 0.898 0.479 0.741 0.981 0.790 0.351 0.858 1.071 0.865

0.157 0.904 1.193 0.943 0.150 0.897 0.987 0.884 0.196 0.873 1.292 0.958 0.178 0.861 1.239 0.922

0.218 0.781 0.931 0.779 0.207 0.784 0.786 0.931 0.327 0.616 0.931 0.617 0.217 0.764 0.785 0.909

0.274 0.679 1.103 0.772 0.279 0.859 0.944 0.825 0.208 0.906 0.990 0.923 0.259 0.889 1.010 0.867

0.331 0.727 0.768 0.682 0.263 0.765 0.841 0.653 0.286 0.767 1.175 0.793 0.226 0.815 1.186 0.767

0.254 0.751 0.960 0.724 0.303 0.608 0.765 0.750 0.290 0.727 0.824 0.800 0.227 0.785 0.8900 0.791

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Fig. 5. Observed and simulated flow, and corresponding scattergrams at each monitoring station for the verification period.

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Fig. 6. Observed and simulated nitrate loadings, and corresponding scattergrams at each monitoring station for the verification period.

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Fig. 7. Observed and simulated soluble phosphorus loadings, and corresponding scattergrams at each monitoring station for the verification period.

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Table 4 Estimated water flow at the outlet of Kosynthos watershed for year 2006, for different land use change scenarios. Land use scenario

Flow

Nitrate

Volume (× 106 m3) Current land use Conversion of 20% of the forested area to agricultural land 20% expansion of urban area 100% deforestation

% change

Soluble phosphorus

Loading (tn)

% change

Loading (tn)

% change

153.86 157.08

0.00 + 2.09

320.90 617.87

0.00 + 92.54

11.30 27.09

0.00 + 139.76

153.90 186.38

+ 0.02 + 21.13

320.81 333.23

− 0.03 + 3.84

11.35 13.82

+ 0.42 + 22.33

Table 5 Estimated nutrient loads to Vistonis lagoon from different crop management scenarios in Kosynthos watershed for 2006. Scenario

Present crop management Only corn Only cotton Only wheat Only tomato

Nitrate

Soluble phosphorus

Fertilizer applied as N (kg/ha)

Nitrate loading (tn)

% change

Fertilizer applied as total P (kg/ha)

Soluble phosphorus loading (tn)

% change

167.7 300.0 125.0 130.0 167.5

320.9 721.3 234.0 242.3 322.0

0.00 + 124.77 −27.08 −24.49 + 0.34

25.2 39.24 21.8 15.2 65.4

11.3 13.1 9.8 8.0 16.0

0.00 + 15.38 − 13.37 −29.28 + 41.12

model can safely be used for estimating and determining results associated with management practices [34]. Scattergrams for each parameter and site are presented in Figs. 5–7. Values for the slope γ of Eq. (4) are close to 1.0 for all parameters, particularly for flow rate (γ between 0.944 and 1.103) and nitrate quantities (γ between 0.768 and 1.186). Soluble phosphorus is again slightly underpredicted (γ between 0.765 and 0.960). Correlation coefficient values are closer to 1.0 for flow rate (R2 between 0.772 and 0.923) and lower for the other parameters (R2 between 0.653 and 0.793 for nitrates, and between 0.724 and 0.800 for soluble phosphorus), indicating some scatter around the straight line of Eq. (4). Similarly to R2, NSC values were close to 1.0 for the three simulated parameters ranging between 0.679 and 0.906 (Table 3). Generally, the agreement between observed and predicted values can be considered good for all parameters used, especially for flow and nitrate, while soluble phosphorus was slightly underestimated in all torrents. Therefore, the calibrated model, according to the input parameters shown in Table 2, reproduces well the measured quantities during the verification time period, and thus, it can be safely used in testing management scenarios.

3.3. Land use change and crop management scenarios Table 4 shows the water flow change at the outlet of Kosynthos river watershed. The “100% Deforestation” Scenario affects the water flow mostly due to the fact that the largest part of the watershed (almost 75%) was altered. Also a significant decrease in soluble phosphorus loadings was observed. The “20% Expansion of Urban area” scenario indicates that the small expansion of urban areas in relation to the total watershed area does not affect significantly the water flow. The agricultural land expansion is also increasing the water flow, but it mainly affects the nutrient loads as they become nearly double and more than double for nitrate and soluble phosphorus, respectively. In Table 5, the nutrient transport from Kosynthos watershed is summarized as a result of the four alternative crop scenarios. The “only cotton” and “only wheat” scenarios predict a considerable reduction of both nutrients. On the other hand, the “only corn” scenario predicts a significant increase of the transport of both nutrients, which for nitrate reaches 124.8%. The “only tomato” scenario predictions showed an almost equal nitrate loading, but also showed a significant increase in soluble phosphorus loading reaching 41.1%. All scenarios indicate significant loadings of nutrients entering Vistonis Lagoon.

4. Conclusions The newest version of SWAT (SWAT2005) assisted by the AVSWATX integrated GIS environment was applied in Kosynthos river watershed located at Northeastern Greece. Both calibration and verification results of SWAT model applied in Kosynthos watershed showed quite good agreement between observed and simulated data. The methodology described proved to be useful in performing watershed-scale simulations under typical Mediterranean conditions. The SWAT model application, supported by GIS technology, proved to be a very useful tool in evaluating management alternatives of both land use change and crop management in rural basins. This fact makes SWAT a flexible and reliable tool for water decision-making, especially under the need for harmonization with the WFD.

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