Copyright ⓒ2015 Korean Society of Civil Engineers
DOI 10.1007/s12205-015-0354-8 pISSN 1226-7988, eISSN 1976-3808
www.springer.com/12205
Conjunctive Simulation of Surface Water and Groundwater using SWAT and MODFLOW in Firoozabad Watershed
Sepideh Dowlatabadi
*and S. M. Ali Zomorodian
**Received June 28, 2014/Revised November 12, 2014/Accepted January 5, 2015/Published Online March 27, 2015
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Abstract
One of the most essential groundwater model components is accurate information about the recharge values within the input data, often introduced to a groundwater model as a percentage of rainfall on aquifers. Recharge values are influenced by many temporal and spatial factors. This paper suggests the use of a SWAT model for surface water simulation and the estimation of recharge rates. In this research, sensitivity analysis, calibration, validation and uncertainty analysis of results were performed by SWAT-CUP software.
Due to the semi-distributed features of SWAT and the difficulty of calculating groundwater distributed parameters, recharge values estimated by SWAT were used in a MODFLOW model for groundwater simulation at steady and unsteady states. This method was applied in the Firoozabad basin, which is one of the most suitable agricultural basins for modeling surface water and groundwater in Iran. After MODFLOW model calibration, hydrodynamic coefficients of the aquifer were determined and the sensitivity of the model was checked for hydraulic conductivity and discharge rate of wells. In order to prove confidence, the model was validated.
SWAT and MODFLOW models were successfully tested and the results of the combination of the two models were found to be acceptable.
Keywords: surface water, groundwater, SWAT, MODFLOW, firoozabad, recharge rate
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1. Introduction
In recent decades, water demand has increased due to the rapid growth of population. Water resources supply has become a major concern in arid and semi-arid countries such as Iran.
Traditionally, management of water resources has focused on surface water or groundwater as if they were separate entities (Winter et al., 1998). Nevertheless, surface water and ground- water are not separate components in the hydrological cycle.
Nearly all surface water features (streams, lakes, reservoirs, wetlands, and estuaries) interact with groundwater. These inter- actions take many forms. Pollution of surface water, for example, can cause the degradation of groundwater quality and the pollution of groundwater can conversely degrade surface water.
Thus, effective land and water management requires a clear understanding of the linkages between groundwater and surface water as applied to any given hydrologic setting. Surface water is commonly hydraulically connected to groundwater, but the interactions are difficult to observe and measure and have been frequently ignored in water-management considerations and policies. Even if a surface-water body is separated from the groundwater system by an unsaturated zone, seepage from the surface water may recharge groundwater (Winter et al., 1998).
Simulation models are the most suitable methods for simulating
surface water, groundwater and their interactions. These models can be helpful for water managers to overcome hydrological and water management problems. Groundwater models, when calibrated, may be used to simulate the long-term behavior of an aquifer under various management schemes. Accurate quantification of recharge rates is imperative to the proper management and protection of valuable groundwater resources. Without a good estimate of recharge and its spatio-temporal distribution, these models become unreliable (Sphocleous, 2005).
Several methods have been developed to estimate recharge (see e.g., Scanlon et al. 2002; Sophocleous, 2004). Previous research studies have highlighted the following three common methods for quantifying groundwater recharge; namely, base flow method, lumped conceptual modeling and water- table fluctuation method. MODFLOW model is a three-dimensional model of groundwater flow that has difficulty computing the distributed groundwater recharge, which is a major input for groundwater modeling. On the other hand, the SWAT model is particularly limited in computing distributed parameters such as hydraulic conductivity and storage coefficient, due to its ground- water module lumped nature. The SWAT model, which takes into account the effective factors on recharge, can estimate the distribution of groundwater recharge in the basin. Therefore, in the present study, a surface water model is created using the TECHNICAL NOTE
*M.Sc. Student, Water Engineering Dept., Shiraz University, Shiraz 71441-65186, Iran (E-mail: [email protected])
**Associated Professor, Water Engineering Dept., Shiraz University, Shiraz 71441-65186, Iran (Corresponding Author, E-mail: [email protected])
SWAT model to estimate groundwater recharge. Afterwards, recharge values of the SWAT model are used in a MODFLOW model for the simulation of basin groundwater at steady and unsteady states. This approach is applied to the Firoozabad basin in Fars province, Iran. In this basin, the utilization of groundwater resources is forbidden since 2002-2003 due to groundwater table drawdown and negative water balance.
Siavashhaghighi (2004), simulated the groundwater flow in the Firoozabad aquifer by numerical model of PMWIN. After verifying the accuracy of the proposed model, it is used to predict and manage 6 different strategic manners in the aquifer.
The SWAT and the MODFLOW were linked into a compre- hensive basin model known as SWATMOD by Sophocleous et al. (1999). For the Rattlesnake Creek basin in south-central Kansas, they used SWATMOD, which is capable of simulating surface water, groundwater, and stream-aquifer interactions on a continuous basis. Using this system, Perkins and Sophocleous (1999) examined the relative contributions of stream yield components and the impact of administrative measures on stream yield and base flow to restrict irrigation water use during droughts in the Lower Republican River basin in Kansas. This system was modified to a two-way coupling system by Sopho- cleous and Perkins (2000), who applied it to evaluate irrigation effects on groundwater levels and streamflow in the Lower Republican River basin. Within the Rattlesnake Creek watershed, their system was also used on streamflow and groundwater
declines. Conan et al. (2003) too investigated the fate of N by using coupled modeling of SWAT with MODFLOW for the Coët-Dan watershed in Brittany (western France). Another integrated SWAT-MODFLOW model was also developed to evaluate and predict the surface and subsurface water quality and quantity as affected by anthropogenic activities in the Bonello watershed (Italy) by Galbiati et al. (2006). The model application gave good results in predicting water and nutrients leaching from the surface to the aquifer, groundwater dynamics, aquifer inter- actions with the stream system, and surface water and nutrient fluxes at the watershed outlet. But this model is appropriate for watersheds where hydrology is not topography driven and pumps heavily condition water flow.
Kim et al. (2008) maintained that the groundwater model used in previous analyses was not adequately linked to surface water analysis. Therefore, they presented a fully integrated SWAT- MODFLOW model capable of simulating the spatio-temporal distribution of groundwater recharge rates and groundwater evapotranspiration. This combined modeling was applied to the Musimcheon basin in Korea by Kim et al. (2008). Chung et al.
(2010) applied this method to the Mihocheon watershed in South Korea, estimated spatio-temporal groundwater recharge distribution.
Their modeling results demonstrated that annual average recharge rates should be estimated by long-term continuous simulations using a distributed hydrologic modeling technique. The presented methodology is generally applicable to humid regions with a
Fig. 1. Firoozabad Watershed in Fars Province, Iran. In this Figure, Firoozabad River and Alluvial Aquifer are Shown
relatively shallow water table. In arid/semiarid regions with deep water tables this time delay-based methodology may run into difficulties due to the extremely long time delays.
This study was designed to first investigate the conjunctive modeling of surface water and groundwater using SWAT and MODFLOW in the Firoozabad watershed, and to assess the performance of the MODFLOW model by using recharge values of SWAT.
The differences of this study with previous researches are: use of ArcSWAT 2009 for the basin surface water simulation;
performance of sensitivity analysis, calibration, validation and uncertainty analysis of surface water model with SWAT-CUP;
use of the aquifer boundary for the groundwater model (MODFLOW) and the watershed boundary for the surface water model (SWAT).
2. Material and Methods
2.1 Description of the Study Area
Firoozabad watershed is suitable for agriculture in Iran. This basin is located between the longitudes of 5219'E to 5238'E and latitudes of 2836'N to 2845', in the southeast of Fars province (Fig. 1). The area of this basin is approximately 723 km2, of which 240 km2 and 483 km2 constitute plains and mountains, respectively. The basin elevation changes from 2891 to 1300 m.
The highest and lowest elevations are situated in the northeast and southeast of the basin. The mean elevation is 1960 m in the mountainous region and 1327 m in the plain. The Firoozabad River enters from the northeast of the plain, where geological settlements form an alluvial plain. Firoozabad River divided the plain into two parts, East and West. This river recharges plain at beginning of entrance to the plain and drains plain in output areas. Thirty-two observation wells exist in this region to measure groundwater levels. According to the latest statistics, there are 1805 wells in this basin, which are mostly used for agriculture.
The changes of Firoozabad plain water resources in different years are shown in Table 1. According to research performed by the Ministry of Power, Fars branch, aquifer recharge components are as following: the direct infiltration of rainfall, infiltration of runoffs and surface flows, subsurface inflows from hillsides or adjacent basins, and the return water from the uses (agriculture, industry and drinking). The outflows of the aquifer are also discharge from wells and springs and qanats, subsurface
discharge or drainage to adjacent basins, evaporation of groundwater table. Due to the reduction of rainfalls and the growing discharge of groundwater, a significant decline in basin groundwater tables has occurred since 1999 and continues today. Wheat, alfalfa, corn and rice are dominant crops in this region. The flooding irrigation in all watershed and methods of sprinkler irrigation and drip irrigation in some parts of the basin used by farmers for crops irrigation. The average annual rainfall of basin was 403 mm during the 1995-2010 periods. The 64% of the annual rainfall occurs in winter. Only one percent of annual rainfall occurs during months of June to September. The rapid and high intensity rainfalls usually happen within seven months, from December to May. Based on the De Martonne expanded method, the Firoozabad watershed has a semi-arid and moderate climate.
3. Overview of SWAT and MODFLOW Models SWAT is a basin-scale, continuous-time model that operates on a daily time step and is designed to predict the impact of water management, sediment, and agricultural chemical yields in un- gauged watersheds. The model is physically based, computationally efficient, and capable of continuous simulation over long periods of time. Major model components include weather, hydrology, soil temperature and properties, plant growth, nutrients, pesticides, bacteria and pathogens, and land management. In SWAT, a watershed is divided into multiple sub watersheds, which are then further subdivided into Hydrologic Response Units (HRUs) that consist of homogeneous land use, management, and soil characteristics. The HRUs represent percentages of the sub watershed area and are not identified spatially within a SWAT simulation. Alternatively, a watershed can be subdivided into only subwatersheds that are characterized by dominant land use, soil type, and management (Gassman et al., 2007).
MODFLOW is a three-dimensional finite-difference groundwater flow model (McDonald and Harbaugh, 1988) widely used in groundwater modeling studies. Darcy’s law governs the flow rate. It can simulate steady and non-steady flows in a saturated system, in which aquifer layers can be confined, unconfined, or a combination of confined and unconfined. Three dimensional groundwater flow is described by the following partial differential equation:
∂ (1)
∂x--- Kxx∂h --- + ∂x ∂
∂y--- Kyy∂h
∂y--- + ∂
∂z--- Kzz∂h
---∂z − w = Ss∂h ---∂t Table 1. Plain Water Resources in Different Years
Year
Well Qanat Spring
Total of discharge (MCM) Number Discharge
(MCM) Number Discharge
(MCM) Number Discharge
(MCM)
1984 145 21.95 12 36.226 10 71.206 129.422
1992 240 - 16 - 10 - 128.4
1996 910 - 15 - 8 - 194.133
1997 917 - 18 - 9 - 194.466
2003 1784 184.681 2 0.622 7 17.566 202.909
2009 1805 187.188 2 0.622 7 17.566 205.416
where, Kxx, Kyy and Kzz are the hydraulic conductivities along the x, y, and z coordinate axes parallel to the major axes of hydraulic conductivities; h is the potentiometric head; W is a volumetric flux per unit volume representing sources (W is negative) and/or sinks (W is positive) of water; Ss is the specific storage of the porous medium; and t is time. Kxx, Kyy, Kzz, and Ss are functions of space (x, y, z) and W is a function of space and time (x, y, z, t) (Todd and Mays, 2005).
In MODFLOW, an aquifer system is replaced by a discretized domain consisting of an array of nodes and associated finite difference blocks (cells) (Chiang and Kinzelbach, 1998). In this study, groundwater flow is simulated using the MODFLOW-96 model in PMWIN package. MODFLOW-96 can simulate the effects of wells, rivers, drains, head-dependent boundaries, recharge and evapotranspiration.
4. Theoretical Structure of Coupling SWAT and MODFLOW Models
One of the most essential components of an efficient ground- water model is the accuracy of recharge rates within the input data. Groundwater recharge rates show spatial-temporal variability due to climatic conditions, land use, soil characteristics and hydrogeological heterogeneity. Most of these factors are not considered for the estimation of recharge rates in groundwater models. However, recharge rates are an input to the groundwater model and have therefore been determined by trial and error during calibration. Altogether, the MODFLOW model has difficulties in computing distributed groundwater recharge.
On the other hand, since SWAT has its own module for groundwater components (Arnold et al., 1993), the model itself is lumped and therefore distributed parameters such as hydraulic conductivity distribution could not be represented.
In this study, SWAT and MODFLOW models were set up and run individually. Subsequently, they were coupled together with recharge rates. By considering the advantages of the two models, the SWAT model estimates the recharge rates using flow data, and presents groundwater recharge values in HRUs levels. In the coupling method, the recharge rate of the HRU should be exchanged with cells and used as input data for the MODFLOW from SWAT.
Due to the semi-distributed features of SWAT, spatial locations of each HRU within subbasins are not determined, which is a key weakness of the model. Hence, to reflect HRU locations, one HRU is created for each subbasin by dominant land use, soil, and slope option.
5. Construction of Input Data for Models
5.1 SWAT
The SWAT model requires basic information about topography, land use, soil, and climate data to accurately simulate the discharge.
The spatial data sets include a Digital Elevation Model (DEM), data on land use and soil maps. Arc SWAT 2.3.3a was applied for
this study.
Data on land use and soil maps were obtained from the GIS center of Shiraz University and the global soil map of the Food and Agriculture Organization of the United Nations (FAO, 1995), which provides data for 5000 soil types comprising two layers (0-30 cm and 30-100 cm depth) at a spatial resolution of 10 km, respectively. For model calibration, a hydrometric station should be leastwise located in the outlet of the basin. However, the only hydrometric station of this basin is Tangab station located in the inlet of the basin. With studying hydrometric stations at the environs of the basin, Dehrood hydrometric station was selected for two reasons: being the closest station to the outlet of the basin and being established on Firoozabad’s river. Therefore, the area of the basin was enlarged to the Dehrood hydrometric station located in the outlet. Based on the 30 meter resolution DEM and the stream network, the study area was automatically delineated and divided into 79 subbasins. Daily precipitation was collected from three gauging stations including: Tangab, Firoozabad Climatology and Dehrood. The data of the Tangab and Firoozabad Climatology stations was used for daily maximum and minimum temperatures (Fig. 2). The climatic inputs used in SWAT included:
daily precipitation, maximum and minimum temperature, solar radiation data, relative humidity, and wind speed data, which can be used as input from either measured and/or generated records.
The WXGEN weather generator model (Sharpley and Williams, 1990), incorporated in SWAT, was used to generate climatic data or to fill gaps within the already measured records. This information (climatic data) is recorded in synoptic stations of Iran and therefore, for use of WXGEN weather generator model, the synoptic station of Shiraz was used in present study.
SWAT provides two methods for estimating surface runoff: the SCS curve number produce (USDA Soil Conservation Service, 1972) and the Green Ampt infiltration method (Green and Ampt, 1911) (Neitsch et al., 2005). In this study, Surface runoff is calculated using a modified SCS curve number method.
Fig. 2. Enlarged Basin Boundary with Subbasins and Rain, Tem- perature, Hydrometric Stations
The model offers three options for estimating potential evapo- transpiration: Hargreaves (Hargreaves and Samani, 1985), Priestley-Taylor (Priestley and Taylor, 1972), and Penman- Monteith (Monteith, 1965) (Neitsch et al., 2005). The selection of potential evapotranspiration method depends on data availability. In this study, potential evapotranspiration was calculated using the Hargreaves method, which only requires the minimum and maximum temperature. In this model, channel routing is simulated using the variable storage or Muskingum methods. The Muskingum method was selected for the simulation of water routing. The catchment was classified into nine classes based on land use (Table 2). Fig. 3 shows land use map with location of observation wells. The soil map includes 4 types of soils (Table 3).
5.2 MODFLOW
The aquifer system in this study was set up as a one-layer, unconfined aquifer. To simplify the interaction between the HRUs of SWAT and the cells in MODFLOW, the model divides the cells into 300 m × 300 m (coefficient of DEM cell size). Therefore, the aquifer was discretized into a grid of 62 rows and 105 columns. The topographical surface assigned as the top layer of the model was interpolated from the Digital Elevation Model (DEM). Apart from the southern border section, the total borders of the model are defined as General-Head boundaries. The initial values of the hydraulic properties of the aquifer, namely, hydraulic conductivity and specific yield have been estimated by means of pumping test data. The river-aquifer relationship was simulated using the River package for MODFLOW. The initial value of riverbed conductance is defined as:
(2) where,CRIV= Hydraulic conductance of the riverbed [L2/T]
K= Hydraulic conductivity of the riverbed sediment [L/T]
L= Length of the river within a cell [L]
W= Width of the river within a cell [L]
M= Thickness of the riverbed [L] (Chiang and Kinzelbach, 1998).
15% of the wells’ discharge was considered for the return water. Recharge was distributed according to SWAT simulation outputs for each day. Since the depth to water table was very high, the evaporation from groundwater was negligible. Therefore, evaporation was not considered in groundwater modeling.
6. Results and Discussion
6.1 SWAT Model Calibration and Validation
The SWAT model was operated from December 22nd 1994 to August 31st 2010 on a monthly basis. In this study, SUFI-2 program (the Sequential Uncertainty Fitting, version 2) in the SWAT-CUP software was used for sensitivity analysis, calibration, validation and uncertainty analysis. In SUFI-2 algorithm, para- meter uncertainty accounts for all sources of uncertainties such as uncertainty in driving variables (e.g., rainfall), conceptual model, parameters, and measured data. The degree to which all uncertainties are accounted for is quantified by a measure referred to as the P-factor, which is the percentage of measured data bracketed by the 95% prediction uncertainty (95PPU). Another measure quantifying the strength of a calibration/uncertainty analysis is the R-factor, which is the average thickness of the 95PPU band divided by the standard deviation of the measured data (Abbaspour, 2011). Ideally, one would like to bracket most of the measured data (plus their uncertainties) within the 95PPU band (P−factor→1) while having the narrowest band (R−
factor→0).
With hydrological study of the basin and also, review of previous CRIV = K.L.W
---M Table 2. Land Use Classes in Study Area
Land Use classes Code in model Area (%)
Forest-low density FRSD 49.91
River bed WATR 1.15
Arid (sterile) lands BSVG 0.5
Forest-Semi dense FRST 7.2
Irrigated farming CRIR 17.98
Dry farming CRDY 2.45
Pasture-low density RNGB 19.04
Residential URBN 1.4
Prairie grove and shrubbery SHRB 0.39
Table 3. Types of Soils in Study Area
Texture in model Texture Area (%)
Xh31-3a-3288 clay-loam 33.56
I-Rc-Yk-c-3508 loam 7.41
Rc37-3c-3552 loam 57.51
Yk36-2-3a-3606 clay-loam 1.52
Fig. 3. The Land use Classes and Observation wells in Enlarged Basin
studies, 31 parameters related to discharge were initially selected for sensitivity analysis in SUFI-2 program. Global sensitivity analysis in SWAT-CUP software was applied. In this method, average changes in the objective function resulting from changes in each parameter be estimated, while all other parameters are changing. More detailed descriptions can be found in Abbaspour (2011).
The sensitivity analysis showed that the nine parameters were sensitive to river discharge. Parameters such as CN2 (initial SCS runoff curve number for moisture condition II), depth from soil surface to bottom of layer (SOL_Z), available water capacity of the soil layer (SOL_AWC) and deep aquifer percolation fraction (RCHRG_DP) were most sensitive between nine sensitive para- meters. The simulation period for calibration of discharge was 1998-2006.
The years (1994 to 1997) were chosen as a warm-up period in which the model was allowed to initialize and then approach reasonable starting values for model state variables (Omani et al., 2007). Surface water model validation was performed for the years 2007-2010.
In order to compare the measured and simulated monthly discharges we used a slightly modified version of the efficiency
criterion defined by Krause et al. (2005):
(3) Where R2 is the coefficient of determination between the measured and simulated signals and b is the slope of the regression line.
Table 4 shows the calibrated parameters. Calibration statistics for Dehrood hydrometric station are shown in Table 5. An R- factor of less than 1 generally shows a good calibration result.
This is obvious for Dehrood station. But the P-factor is rather small indicating that the actual uncertainty is likely larger, which could, however, be improved at the expense of a larger R-factor.
As reported by Schuol et al. (2008a), each hydrological model suffers from uncertainties of conceptual models and this is especially true for models of watersheds where many processes (natural or artificial) may not be represented in the models.
So far, special criterions have not been presented for the suitable values of Nash-Sutcliff and R2, which should be between 0 and 1 to be generally viewed as acceptable levels of performance.
In general, model simulation can be judged as satisfactory for the monthly streamflow if NS > 0.5. R2 values greater than 0.5 are
φ = b R2 if b≤1 b–1R2 if b>1
⎩ ⎭
⎨ ⎬
⎧ ⎫
Table 4. List of SWAT’s Parameters that were Fitted and their Final Calibrated Values
Sensitive parametersa Description Final range
v__ESCO.hru Soil evaporation compensation factor [0.272553,0.323033]
v__CH_N1.sub Manning's “n” value for the tributary channel [0.096167,0.121713]
v__GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm) [0.105616,0.318588]
v__CH_N2.rte Manning's “n” value for the main channel [0.105271,0.139967]
v__ALPHA_BF.gw Base flow alpha factor (days) [0.000003,0.051465]
v__RCHRG_DP.gw Deep aquifer percolation fraction [0.121251,0.186935]
r__SOL_AWC().sol Available water capacity of the soil layer (mmH2O/mmsoil) [-0.205651,-0.056209]
r__SOL_Z().sol Depth from soil surface to bottom of layer (mm) [0.575107,1.031945]
r__CN2.mgt Initial SCS runoff curve number for moisture condition II [-0.427031,0.285207]
av__ means the existing parameter value is to be replaced by the given value, r__ means the existing parameter value is multiplied by (1+ a given value).
Table 5. Final Statistics from Calibration (Validation) Results of the SWAT Model for Firoozabad Watershed
Station P-factor R-factor R2 NS φ
Dehrood 0.43(0.41) 0.48(0.35) 0.77(0.01) 0.7(-0.57) 0.7733(0.0003)
Fig. 4. Calibration (Left Graph, 1998-2006) and Validation (Right Graph, 2007-2010) Results for Discharge at Dehrood Hydrometric Sta- tion. The Blue and Red Lines are Observation and Best Simulation, respectively, and Shaded Regions Indicate the 95% Predica- tion Uncertainty (95PPU): (a) Calibration Results, (b) Validation Results
considered acceptable (Moriasi et al., 2007).
As shown in Fig. 4(a), flow dynamics are simulated quite well for Dehrood station during the calibration period (R2= 0.77).
Peak discharges are critical values for river studies and this model has well identified the time occurrence of the peaks. The simulated flow of April 1998, January and March 1999, January 2002 is less than the observed flow. The reasons for such difference between the observed and simulated discharges includes: the low number and unsuitable distribution of stations in compared with large-scale basin; human errors in data recorded of meteorological and hydrometric stations due to lack of Data- Logger devices.
Validation results for the surface water model of Firoozabad watershed are shown in Table 5 and Fig. 4(b). P-factor and R- factor statistics are similar to calibration results, indicating con- sistency in model simulation for the calibration and validation periods. The accuracy of model results is slightly reduced in the validation period, because the Tangab dam, which is located in the upstream of the watershed, was not considered in this model.
The impoundment time of Tangab dam occurred during this period, so management qualifications of this dam were certainly affected by the discharge of the Firoozabad’s river. Comparing Fig. 4(a) and Fig. 4(b), it is evident that the measured discharge of the river has decreased during the validation period in comparison with the calibration period. The watershed rainfall declined during this period compared to the calibration period.
Generally, simulation models of the watershed show weak performance at the estimation of low flow rates. This problem can be pertained to the simplification of watershed models in the simulation and the complex interaction between runoff and subsurface flows in low height rains (rains with low height).
Hantush and Kalin (2005) have also experienced the same issue.
These reasons and the factors mentioned in the calibration
process were among the factors affecting the decline of the quality of the results.
The simulated annual hydrological components by SWAT in Firoozabad watershed are described in Table 6. Results show that the mean annual evapotranspiration is 66% of the annual precipi- tation and the mean annual surface runoff is 22% of that. The groundwater recharge shows a higher temporal variability with annual values (41.113-7554.297 mm/year), which corresponds to approximately 0.21-26% of the annual mean precipitation.
6.2 Extraction of Recharge Values by SWAT
The final range of sensitive parameters determined in the last stage of calibration was applied to the SWAT model. After that, the SWAT model was run again from December 22nd 1994 to August 31st 2010 on a daily basis. Between the nine parameters specified in the sensitivity analysis, only CH_N1 was effective on the recharge rate in the steady state. In addition to CH_N1, ESCO, SOL_Z, CN2 and SOL_AWC parameters affected recharge rates in the unsteady state.
The groundwater recharge rates were provided at the 79 sub- basins (HRUs). These values were extracted from the GW_RCHG in the “output.hru” file. The basin was inevitably enlarged because of the SWAT calibration and lack of hydrometric stations in its outlet. Taking into consideration the objectives of this work, recharge of subbasins applied in the MODFLOW model that were in the Firoozabad basin confined no enlarged basin (Fig. 5).
For subbasins located in both basin and aquifer, the number of cells was counted inside each subbasin. The recharge values of the SWAT model (mm) were divided by the number of cells.
These values were then converted to millimeter/day and exerted to each cell of the subbasin.
Hydrological and alluvial aquifer boundaries were used for Table 6. Estimated Annual Hydrological Components During 1995-2010
Year Precipitation (mm)
Evapotranspiration (mm)
Surface runoff (mm)
Recharge (mm)
Recharge rate, percentage of precipitation (%)
1995 33277.8 16201.436 7809.367 758.075 2.27802
1996 29351.3 17992.524 10714.76 7554.297 25.73752
1997 27615.2 16518.023 2732.446 247.513 0.896293
1998 22519.2 15971.768 8150.342 5162.946 22.92686
1999 23381.1 13667.472 6091.476 2301.256 9.842377
2000 22477 11791.508 4038.013 216.236 0.962032
2001 9416 11841.23 518.145 73.83 0.784091
2002 19721 16524.44 3232.537 1066.03 5.405558
2003 21883.5 15448.296 3222.456 588.166 2.687714
2004 42649.5 15332.12 16646.14 3520.492 8.254474
2005 24531.528 15401.75 7605.991 5045.277 20.5665
2006 19086 15534.957 2456.182 290.797 1.523614
2007 14662.5 15380.416 1800.18 818.915 5.585098
2008 11011 10327.767 1306.806 142.172 1.291182
2009 19122 10977.068 1667.374 41.113 0.215004
2010 5227 11057.498 446.879 186.667 3.571207
Average 21620.72675 14373.01706 4902.443 1750.861 7.032972
Surface Water (SWAT) and groundwater (MODFLOW) models, respectively. The surface water model boundary was extended sufficiently outwards so that the groundwater model boundary was included. Therefore, some subbasins were located out of the aquifer boundary (Fig. 5). The following steps were applied for recharge rates in these subbasins: 1. drawing the flow lines of the groundwater, 2. taking into consideration the groundwater flow direction, the recharge distributed in the suitable subbasins’
boundary of the inside aquifer. According to the results obtained, the annual groundwater recharge is concentrated in winter which confirms the precipitation pattern in the basin. Forest areas and irrigated farming lands have highest values of groundwater recharge between land uses.
6.3 MODFLOW Model Calibration and Validation
After the recharge of the aquifer was calculated via the calibrated
model in the SWAT software, this aquifer was modeled using MODFLOW96 at the PMWIN software for two conditions:
steady and unsteady states. The observed water levels in the Firoozabad aquifer were used for calibration purposes. Because the hydraulic heads were recorded monthly in the Firoozabad aquifer, the time step in groundwater modeling was determined monthly in Persian months. The period of study from December 22nd 1994 to January 20st1995 (corresponding to month Dey in the Persian calendar) was selected as the steady state based on the availability of observed groundwater data. MODFLOW requires initial hydraulic heads at the beginning of a flow simulation (Chiang and Kinzelbach, 1998). For a steady state flow simulation, the measured heads of observation wells were used as the initial head. For unsteady state flow simulations, the measured heads of observation wells in the previous month were considered as initial heads in the future month. According to the information recorded by the Ministry of Power, Fars office, Firoozabad plain’s groundwater was discharged by 817 wells in steady state (Fig. 6). Observation well No.14 was eliminated during the simulation of steady and unsteady states. The position of this observation well is showed in Fig. 6 with the green arrow.
Fig. 5. The Aquifer, Basin and Development of Basin Boundaries with Subbasins and Grids
Fig. 6. The Distribution of wells in Steady Time and Position of Observation wells Over the Firoozabad Plain
Fig. 7. Scatter Diagram of Calculated and Observed Heads (Left) and water Budget (Right) in Steady State
In the steady state modeling, the hydraulic conductivity values in various zones were estimated by using an iterative process and UCODE subroutine.
In numerical solution techniques, the system of equations solved by a model actually consists of a flow continuity statement for each model cell. Continuity should also exist for the total flows into and out of the entire model or a sub-region. This means that the difference between total inflow and total outflow should equal 0 (steady state flow simulation). Figure 7 shows the scatter diagram of the calculated heads against the observed heads and water budget in a steady state. In this Figure, the variance value is less than 1 and the percent discrepancy of inflows and outflows for the model is 0. These are the accuracy indicators of the simulation results in the steady state.
The hydraulic conductivity and wells’ discharge were used for performance of aquifer sensitivity analysis. The hydraulic con- ductivity values in all zones were equally increased and decreased (Fig. 8). According to Fig. 8, the model had minimal error variance in the optimum values of hydraulic conductivity. For sensitivity analysis of the wells’ discharge, the six blocks including the observation wells were considered with the same size in the various regions of the model (Fig. 9). The hydraulic head descended by increasing the wells’ discharge located inside each block (Fig.
10). In the aquifer’s east blocks (p16, p21), increasing the wells’
discharge resulted in the reduction of error variance but the variance increased in blocks p26, p31. By increasing the wells’
discharge, the variance first decreased and then increased in blocks p5 (near the river) and p22 (outlet of plain).
The groundwater model of the aquifer was calibrated since Fig. 8. Model Sensitivity Analysis to Changes of Hydraulic Con-
ductivity in All Plain
Fig. 9. Position of Selected Blocks for Sensitivity Analysis of wells’
Discharge
Fig. 10. Sensitivity Analysis in Blocks of Observation wells No.16, 31, 22
Table 7. The Estimated Values of Hydraulic Conductivity and Spe- cific Yield
Zone No
Piezometers in zone
Hydraulic Conductivity (m/day)
Specific Yield
1 p25-p26 1.29 0.009
2 p27 38 0.228
3 p28 16.5 0.1078
4 p23 16.5 0.09
5 p24 13.7 0.09
6 p29-p30 16.5 0.196
7 p32 16.4 0.196
8 p2 2.5 0.03
9 p4 35 0.2
10 p31 5.23 0.055
11 p1-p11 7.2 0.045
12 p2 9.96 0.08
13 p3 6 0.069
14 p5 20.1 0.15
15 p6 30.5 0.16
16 p7 1.42 0.025
17 p8-p10 17.4 0.109
18 p9-p21 20.13 0.151
19 p12-p13 18.8 0.12
20 p15-p16-p17-p18-pp19-p20 8 0.077
21January to 21December 1995 as unsteady state with 11 stress periods based on Persian months. After the determination of K, the specific yield was estimated by unsteady modeling (Table 7).
The scatter diagram of the calculated heads against the observed heads was illustrated at the 4th and 6th stress periods of the unsteady state in Fig. 11. Fig. 12 presents the hydrographs of two piezometers located in the east and west of the aquifer at unsteady modeling. Studying the hydrographs of all piezometers,
it is observed that the hydrograph of most piezometers is properly simulated in the unsteady stress periods. In some hydrographs, the simulated groundwater tables are upper or lower than the measured groundwater tables, both states occurring in some other hydrographs. The main reasons for these differences consist are 1. Error in recording the wells’ discharge associated with over- looking illegal wells, 2. Human nonchalance in recording the Fig. 12. Hydrographs of Observed and Simulated Heads in Piezometers No. 16, 22 for Unsteady State
Fig. 11. Scatter Diagram of Calculated and Observed Heads in 4 and 6 Stress Periods of Unsteady State (21 Jan to 21 Dec 1995)
Fig. 13. Scatter Diagram of Calculated and Observed Heads in 1995-1996
Fig. 14. The Difference between Isopotential Lines of Simulated and Observed Heads in 1995-1996
observation wells’ groundwater level, 3. The unavoidable error in the estimation of hydraulic conductivity due to the use of the finite difference method in the MODFLOW96 model. The finite difference method requires a rectangular element shaped discretization of the aquifer but the size and shape of the finite elements are arbitrary, being typically triangular or quadrilateral.
Also, it is easy to define the boundaries in the finite elements’
method.
With the recent droughts and the growing discharge of water resources, using long periods for the model validation and its results for the aquifer future prediction is not rational. Therefore, the groundwater model was validated at 1995-1996 and 2007- 2008. Figures 13 and 14 show the scatter diagram and the groundwater isopotential lines of simulated and observed heads in 1995-1996, respectively. According to Figs. 13 and 14, the model was validated as appropriate and acceptable in 1995-1996.
The error variance becomes 3.811621, because the model met with difficulties in the validation of observation wells No. 22, 24 and 25 (marked with the dashed line in Fig. 15) at 2007-2008.
The tributary of Khergheh river and floodways are located in the aquifer’s northwest, especially piezometer No. 25 confine, which are not incorporated in the modeling process due to their seasonal discharge and lack of data.
7. Conclusions
The groundwater recharge rate and its spatio-temporal variability are the main components in groundwater simulation. In this study, SWAT and MODFLOW models were applied for combined simulation of surface water and groundwater in the Firoozabad watershed. The SWAT and MOFLOW models were run individually and coupled together with recharge rates. The recharge values extracted the HRUs (Hydrologic Response Units) of SWAT and were used in the cells of MODFLOW. Using inverse modeling and the SUFI-2 method, the SWAT model’s suitably was calibrated and validated in Dehrood station. By applying recharge rates in both steady and unsteady states, the results of MODFLOW model calibration and validation were quite satisfactory. The sensitivity
of the groundwater model was checked with the hydraulic conductivity and discharge rate of wells. The model had more sensitivity towards hydraulic conductivity. This integration reduced the number of parameters in the groundwater model calibration due to the use of recharge values on SWAT. The SWAT ground- water module for the model was lumped and therefore distributed parameters could not be represented. In this method, the distributed parameters such as hydraulic conductivity and specific yield were determined by the fully-distributed groundwater model, MODFLOW. Overally, the results of the combination of the two models were successful.
The following suggestions can improve the results of SWAT and MODFLOW models:
−Increasing the number of the rainfall and temperature gages and establishing hydrometric stations along the river, espe- cially at the basin outlet.
−The use of sediment, crops and irrigation information in the basins such as Firoozabad which do not have the hydromet- ric station at the outlet of the basin.
−Specifying the location of HRUs in SWAT model. Because the groundwater recharge values are presented in HRU lev- els. To solve this problem in this study, one HRU is created for each sub basin by selection dominant land use, soil, and slope option. This is a limitation for simulating a large basin with different land uses and soil types.
−Access to map and detailed information of basin soil.
−Enter Tangab dam and its management parameters to the SWAT model.
References
Abbaspour, K. C. (2011). User manual for SWAT-CUP4, SWAT calibration and uncertainty analysis programs, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland.
Arnold, J. G., Allen, P. M., and Bernhardt, G. (1993). “A comprehensive surface-groundwater flow model.” Journal of Hydrology, Vol. 142, Nos. 1-4, pp. 47-69, DOI: 10.1016/0022-1694(93)90004-S.
Canon, C., Bouraoui, F., Turpin, N., de Marsily, G., and Bidoglio, G.
(2003). “Modeling flow and nitrate fate at catchment scale in Brittany (France).” Journal of Environmental Quality, Vol. 32, No. 6, pp.
2026-2032, DOI:10.2134/jeq2003.2026.
Chiang, W. H. and Kinzelbach, W. (1998). Processing modflow: A simulation system for modeling groundwater flow and pollution, Humburg, Zürich, p. 325.
Chung, I. M., Kim, N. W., Lee, J., and Sophocleous, M. (2010). “Assessing distributed groundwater recharge rate using integrated surface water- groundwater modelling: Application to Mihocheon watershed, South Korea.” Hydrogeology Journal, Vol. 18, No. 5, pp. 1253-1264, DOI:
10.1007/s10040-010-0593-1.
Food and Agriculture Organization (FAO). (1995). The digital soil map of the world and derived soil properties, version 3.5 [CD-ROM], Rome.
Galbiati, L., Bouraoui, F., Elorza, F. J., and Bidoglio, G. (2006).
“Modeling diffuse pollution loading into a Mediterranean lagoon:
development and application of an integrated surface-subsurface model tool.” Ecological Modelling, Vol. 193, Nos. 1-2, pp. 4-18, DOI: 10.1016/j.ecolmodel.2005.07.036.
Fig. 15. Scatter Diagram of Calculated and Observed Heads in 2007-2008
Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G. (2007).
“The soil and water assessment tool: Historical development, applications, and future research directions.” American Society of Agricultural and Biological Engineers, Vol. 50, No. 4, pp. 1211- 1250.
Green, W. H. and Ampt, G. A. (1911). “Studies on soil physics 1: The flow of air and water through soils.” Journal of Agricultural Science, Vol. 4, No. 1, pp. 1-24.
Hantush, M. M. and Kalin, L. (2005). “Uncertainty and sensitivity analysis of runoff and sediment yield in a small agricultural watershed with kineros2.” Hydrological Sciences Journal, Vol. 50, No. 6, pp. 1151- 1172.
Hargreaves, G. and Samani, Z. A. (1985). “Reference crop evapotrans- piration from temperature.” Applied Engineering in Agriculture 1, Vol. 1, No. 2, pp. 96-99, DOI: 10.13031/2013.26773.
Kim, N. W., Chung, I. M., Won, Y. S., and Arnold, J. G. (2008).
“Development and application of the integrated SWAT-MODFLOW model.” Journal of Hydrology, Vol. 356, Nos. 1-2, pp. 1-16, DOI:
10.1016/j.jhydrol.2008.02.024.
Krause, P., Boyle, D. P., and Bäse, F. (2005). “Comparison of different efficiency criteria for hydrological model assessment.” Advanced Geosciences, Vol. 5, pp. 89-97, DOI: 10.5194/adgeo-5-89-2005.
McDonald, M. G. and Harbaugh, A. W. (1988). A modular three- dimensional finite-difference groundwater flow model: U.S. Geological Survey Techniques of Water Resources Investigations Report, Book 6, Chapter A1, p. 586.
Monteith, J. L. (1965). “Evaporation and environment.” The State and Movement of Water in Living Organisms, Cambridge University Press, Cambridge, Vol. 19, pp. 205-234.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Binger, R. L., Harmel, R. D., and Veith, T. (2007). “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.”
American Society of Agricultural and Biological Engineers, Vol. 50, No. 3, pp. 885-900.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Willams, J. R. (2005). Soil and Water Assessment Tool theoretical documentation: Version 2005, p. 476.
Omani, N., Tajrishy, M., and Abrishamchi, A. (2007). Modeling of a river basin using SWAT model and SUFI-2, Accepted to be published in 4th International SWAT conference, Delft, Netherlands.
Perkins, S. P. and Sophocleous, M. (1999). “Development of a compre- hensive watershed model applied to study stream yield under drought conditions.” Groundwater, Vol. 37, No. 3, pp. 418-426, DOI: 10.1111/j.1745-6584.1999.tb01121.x.
Pooladian, A. (2007). The briefing report for continuation of forbiddance on Firoozabad groundwater resources, Ministry of Power, Fars office.
Priestley, C. H. B. and Taylor, R. J. (1972). “On the assessment of surface heat flux and evaporation using large-scale parameters.” Monthly Weather Rev., Vol. 100, No. 2, pp. 81-92.
Scanlon B. R., Healy R. W., and Cook P. G. (2002). “Choosing appropriate techniques for quantifying groundwater recharge.” Hydrogeology Journal, Vol. 10, pp. 18-39, DOI: 10.1007/s10040-0010176-2.
Schuol, J., Abbaspour, K. C., Sarinivasan, R., and Yang, H. (2008a).
“Estimation of freshwater availability in the West African sub- continent using the SWAT hydrologic model.” Journal Hydrology, Vol. 352, Nos. 1-2, pp. 30-42, DOI: 10.1016/j.jhydrol.2007.12.025.
Sharpley, A. N. and Williams, J. R. (1990). EPIC-Erosion Productivity Impact Calculator, 1, model documentation. U. S. Department of Agriculture, Agricultural Research Service, Tech. Bull. 1768.
Siavashhaghighi, M. (2004). Application of mathematical model of groundwater in management and predict of Firuzabad aquifer, Thesis of M.Sc degree hydrogeology, University of Sistan and Baluchestan.
Sophocleous, M. (2004). Groundwater recharge, Groundwater, Encyclopedia of life support systems (EOLSS), UNESCO, EOLSS, Oxford, p. 41.
Sophocleous, M. (2005). “Groundwater recharge and sustainability in the High Plains aquifer in Kansas, USA.” Hydrogeol Journal, Vol.
13, No. 2, pp. 351-365, DOI: 10.1007/s10040-004-0385-6.
Sophocleous, M. A., Koelliker, J. K., Govindaraju, R. S., Birdie, T., Ramireddygari, S. R., and Perkins, S. P. (1999). “Integrated numerical modeling for basin-wide water management: the case of the Rattlesnake Creek basin in south-central Kansas.” Journal of Hydrology, Vol.
214, Nos. 1-4, pp. 179-196, DOI: 10.1016/S0022-1694(98)00289-3.
Sophocleous, M. and Perkins, S. P. (2000). “Methodology and application of combined watershed and ground-water models in Kansas.” Journal of Hydrology, Vol. 236, Nos. 3-4, pp. 185-201, DOI: 10.1016/S0022- 1694(00)00293-6.
Todd, D. K. and Mays, L. W. (2005). Groundwater Hydrology, Third Edition, John Willey & Sons, Inc. p. 636. USA.
USDA Soil Conservation Service. (1972). National Engineering Handbook Section 4: Hydrology, Chapters 4-10.
Winter, T. C. (1998). “Relation of streams, lakes, and wetlands to groundwater flow systems.” Hydrogeology Journal, Vol. 7, No. 1, pp. 28-45, DOI: 10.1007/s100400050178.
Winter, T. C., Harvey, J. W., Franke, O. L., and Alley, W. M. (1998).
Ground water and surface water a single resource, U. S. Geological Survey Circular 1139, Denver, Colorado, p. 79.
Zare, M., Raeisi, E., and Mirbagheri, S. M. (2001). “Determination of ground water evaporation using pan evaporometer data in different depth to water table and soil texture conditions.” International congress; 31st, International Association of Hydrogeologists; New approaches characterizing groundwater flow, pp. 1319-1323.
