xxxxHydrological and water quality modeling in a medium sized basin usign the SWAT

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

Hydrological and water quality modeling in a medium-sized basin using the Soil and

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 b s t r a c t

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

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 veryflexible and reliable tool for water decision-making, 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

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

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

ment, providing a decision-making framework for setting

environ-mental 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

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 op-tions 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 pre-and post-processing stage of model development, as well as the ability

to develop interactive post-processing tools that provide the opportu-nity 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 manage-ment scenarios The suitability of SWAT in the developmanage-ment of a River

☆ 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: tsihrin@otenet.gr , tsihrin@env.duth.gr (V.A Tsihrintzis).

0011-9164/$ – see front matter © 2009 Elsevier B.V All rights reserved.

Contents lists available atScienceDirect

Desalination

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

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 km2that includes mountain terrain, agricultural plains and

sources that can affect its water quality originate from agricultural,

urban and industrial activities taking place in the lower reaches of the

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

Rho-dope 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

Greece, protected by the Ramsar Convention, and is considered as a

20 years, the lagoon has suffered severe impacts due to point and non-point 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 inTable 1, major crops for the study area are wheat, corn, cotton and tobacco

from fertilizers (about 1190 tons N and 162 tons P), indicating that agricultural activities significantly affect the nutrient budget

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 upstream of monitoring site 4 (MS4), located downstream

of the city of Xanthi This study focuses on data collected between

November 2004 and November 2006 for nitrate and soluble

soluble phosphorus concentration were determined by

water quantity and quality characteristics of Kosynthos river has been presented by Pisinaras et al.[11]

2.2 Model description

version integrates the latest version of Soil and Water Assessment Tool

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

trans-port and nutrient loads in large watersheds, where complex soil, land

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

contributed to the development of SWAT, such as the Chemicals,

Runoff and Erosion from Agricultural Management Systems model

budget equation is the basis for the simulation of the hydrologic cycle

runoff calculated separately at each sub-basin, and then routing

Modi-fied Universal Soil Loss Equation (MUSLE)[21]is used for erosion and

sediment yield calculation Nutrient load and concentration

Finally, soil surface and plant data are used to calculate

evapotrans-piration in the watershed, while precipitation and temperature data

can be either provided as time series data, or simulated using a

first-order 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

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

model, and Hydrologic Response Units (HRUs), which comprise

similar land use and soil type combinations within the

subwa-tershed The Watershed Delineation module of AVSWATX is based

on some elementary raster functions provided by ArcView and the

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

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

aggregated land use data set was made indicating that 0.16% of the

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

“Forest-Mixed” class (FRST), 0.32% belongs to the “Transportation” class (UTRN), 12.90% belongs to the“Generic Agricultural Land” class

(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

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 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 Pen-man–Monteith method[27], the Priestley–Taylor method[28]and 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)

for the definition of the HRUs, which allows watershed discretization

selects the appropriate CSTV from several possible relative threshold values, as described by Romanowicz et al.[24] According to FitzHugh

the CSTV CSTV assignment is followed by the watershed disaggre-gation 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

Table 1

Crop distribution in the watershed, fertilizer application rates and total nutrient

quantities entering the watershed due to agricultural activities.

Crop type Cultivated

area (ha)

% of the cultivated area

N fertilizer application rate (kg/ha)

P fertilizer application rate (kg/ha) Wheat 2146.0 32.1 130 15.3

Corn 2490.3 37.3 300 39.2

Sunflowers 4.6 0.1 140 37.1

Rice 0.5 0.0 95 19.6

Alfalfa 122.2 1.8 35 48.0

Cotton 385.2 5.8 125 21.8

Tobacco 947.9 14.2 40 19.6

Tomatoes 72.8 1.1 167.5 65.4

Other 376.9 7.6 169.1 2.5

Total 6546.5 100.0 Total N applied:

1193.05 tons

Total P applied:

162.22 tons

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

para-meters[31] Calibration techniques are generally referred to as either

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]

objective function (NOF) and scattergrams for calibration of daily

evaluation indices were used in this study:

1 The root mean square error (RMSE) and the normalized objective

equations:

RMSE =

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

∑N

i = 1ðPi OiÞ2

N

2

6

6

3 7 7

v

u

u

where Piare the model predicted values, Oiare the observed values

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

following equation:

n

i = 1ðOi PiÞ2

∑n

i = 1ðOi OÞ2 ð3Þ

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

3 Another way to assess the calibration is through the use of

against observed ones In a scattergram, a regression straight line

of the following form is alsofitted through the data:

and its slopeγ is compared to the 1:1 slope (perfect match) The value of

(γ<1.0) of the model compared to the observed data In addition, 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 slopeγ and

R2be as close to 1.0 as possible

The calibration of the model was conducted for the period from

measure-ments collected at the four monitoring stations The calibration

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

was calibrated; this is estimated as the difference between the in situ

the water volume that should be available to plants, if the soil,

repeated until an acceptablefit 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_RE-VAP, REVAPMN, GWQMN and RCHRG_DP Finally, better adjustment of

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

contri-bution 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 Flow Curve number −5 to +5 (from SCS table values) −3 to +3 (from 65 to 78) ESCO Flow Soil evaporation compensation factor 0–1 0.95

EPCO Flow Plant uptake compensation factor 0–1 1

SOL_AWC Flow Soil available water capacity 0–1 0.11–0.14

GW_REVAP Flow Groundwater revap coefficient 0.02–0.20 0.02

GWQMN Flow Threshold depth of water in shallow aquifer for

percolation to occur

0.0–300.0 200 RCHRG_DP Flow Deep aquifer percolation fraction 0.0–1.0 0–0.5

ALPHA_BF Flow Base flow alpha factor 0.0–1.0 0.024–0.048

NPERCO Nitrate nitrogen Nitrogen percolation coefficient 0.0–1.0 0.2

GWNO3 Nitrate nitrogen Concentration of nitrate in groundwater contribution

to streamflow from sub-basin

PPERCO Soluble phosphorus Phosphorus percolation coefficient 10.0–17.5 10

GWSOLP Soluble phosphorus Concentration of soluble phosphorus in groundwater

contribution to streamflow from sub-basin

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

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

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

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

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

output for locations, time periods or conditions other than those that

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

of SWAT model in Kosynthos watershed in order to assess the impact

and nutrient loadings

Three land use change scenarios were applied in order to evaluate

scenario, and assumes the deforestation of the whole watershed

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

to each crop type for the whole agricultural part of the watershed

3 Results and discussion

3.1 Model calibration results

Table 2presents 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

reduced by 3 units in the subwatersheds where runoff volumes needed

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

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

percolation fraction (RCHRG_DP) were chosen to simulate the signif-icant groundwater recharge[11]across the alluvial cone of Kosynthos river at the south part of the study area The lowest values of both

most appropriate for Kosynthos river watershed Finally, the concentra-tions 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 inFigs 2–4for each monitoring station and for the three

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 inTable 3for 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

Scattergrams for each parameter at three sampling sites, one in each sub-basin, are presented inFigs 2–4 Values for the slopeγ of Eq (4) are close to 1.0 for all parameters, particularly forflow rate (between

were slightly underpredicted, for all the other monitoring stations

between 0.765 and 0.960) Correlation coefficient values are close to

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

predicted values for the verification period and for all monitoring stations

is shown inFigs 5–7 Similarly to calibration, thesefigures show that all predicted values at all stations, for the three parameters, for the entire

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 R2values for the verification period are presented inTable 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 Parameter Calibration Verification

Flow Nitrate Sol.

phosphorus

Flow Nitrate Sol.

phosphorus MS1 NOF 0.402 0.157 0.218 0.274 0.331 0.254 NSC 0.815 0.904 0.781 0.679 0.727 0.751

γ 1.046 1.193 0.931 1.103 0.768 0.960

R 2

0.839 0.943 0.779 0.772 0.682 0.724 MS2 NOF 0.266 0.150 0.207 0.279 0.263 0.303 NSC 0.915 0.897 0.784 0.859 0.765 0.608

γ 1.000 0.987 0.786 0.944 0.841 0.765

R 2

0.898 0.884 0.931 0.825 0.653 0.750 MS3 NOF 0.479 0.196 0.327 0.208 0.286 0.290 NSC 0.741 0.873 0.616 0.906 0.767 0.727

γ 0.981 1.292 0.931 0.990 1.175 0.824

R 2

0.790 0.958 0.617 0.923 0.793 0.800 MS4 NOF 0.351 0.178 0.217 0.259 0.226 0.227 NSC 0.858 0.861 0.764 0.889 0.815 0.785

γ 1.071 1.239 0.785 1.010 1.186 0.8900

R 2

0.865 0.922 0.909 0.867 0.767 0.791

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

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