Runoff simulation using SWAT model and SUFI-2 algorithm (Case study: Shafaroud watershed, Guilan Province, Iran)

Runoff simulation using SWAT model and SUFI-2 algorithm

(Case study: Shafaroud watershed, Guilan Province, Iran)

E. Taghvaye Salimi^{1, 5*}, A. Nohegar², A. Malekian³, M. Hosseini⁴, A. Holisaz¹

1. Department of Range and Watershed Management, University of Hormozgan, BandarAbbas, Iran

2. Department of Environmental PlanningManagement and Education, University of Tehran, Tehran, Iran

3. Department of Range and Watershed Management, University of Tehran, Karaj, Iran

4. Soil Conservation and Watershed Management Research Institute (SCWMRI), Tehran, Iran

5. Department of Range and Watershed Management, University of Guilan, SowmehSara, Iran

* Corresponding author’s E-mail: edristaghvaei@guilan.ac.ir

ABSTRACT

Reliable estimates of runoff are required as a part of the information sets that help watershed managers make informed decisions on water resources planning and management. This study was carried out in Shafaroud watershed located in the north of Iran. In order to achieve the best runoff simulation in the study area, first rainfall data of four stations during 1998 to 2011 were collected and combined with other maps of the study area, such as Digital Elevation Model (DEM), land use and soil as input data in the form ofSoil and Water Assessment Tools (SWAT) model. After running the model, the Sequential Uncertainty Fitting (SUFI-2) algorithm in SWAT calibration and uncertainty program (SWAT-CUP) were used to evaluate the data uncertainty and the most accurate simulation. The first three years (1998-2000) of rainfall data for warm-up and the next 7 years (2001-2007) for the calibration and final 4 years (2008-2011) were used for the validation period. Finally, with multiple simulations, the uncertainty of the parameters was assessed with P-factor, R-factor,  and NS coefficients. The results of validation period ( =0.85, NS=0.74) confirmed the potential of SUFI-2 algorithm of SWAT-CUP program for simulating runoff data in the study area.

INTRODUCTION

More detailed information on the status of rainfall runoff also facilitate decisions on future programs for watershed managers, a step towards the preservation of natural resources for sustainable development. Recently, rainfall-runoff models are widely used with hydrologists to simulate watersheds runoff and play a key role in water resources management (Bilondi et al. 2013). In other hand, several programs and techniques have been developed to reduce parameters uncertainty and achieve to best fit of parameters in the hydrological modeling (Singh et al. 2013).

The SWAT model (Arnoled et al. 1998) is a continuous-time semi-distributed hydrological model for application at the watershed scale (Krysanova & Srinivasan 2015). This model has been widely used to land use change effect assessment (Shen et al. 2010; De Girolamo & Lo Porto 2012; Yang et al. 2012; Du et al. 2013; Huang et al. 2013; Niu & Sivakumar 2014; Lin et al. 2015), sediment prediction(Shen et al. 2012; Rostamian et al. 2013), climate change (Andersson et al. 2006; Zhang et al. 2012; Huang et al. 2015), water quality (Debele et al. 2008; Zhang et al. 2011) and simulation of evapotranspiration (Wang et al. 2006). Many computer programs have been developed by hydrologists for parameters uncertainty analysis in river basin model, such as, generalized likelihood uncertainty estimation (GLUE; Beven & Binley 1992), sequential uncertainty fitting (SUFI-2; Abbaspouret et al. 2004), parameter solution (ParaSol; Van Griensven & Meixner 2006) and Markov chain Monte Carlo (MCMC; Kuczera & Parent 1998; Vrugt et al. 2008).

The SWAT-CUP (Abbaspour et al. 2007b) is a computer program that links the Sequential Uncertainty Fitting (SUFI-2) algorithm to SWAT model.

Up to now, researchers used SUFI-2 algorithm for model calibration and uncertainty analysis of parameters of SWAT model. Narsimlu et al. (2015) in Kunwari River basin applied SUFI-2 algorithm in 19-year period (1987-2005) for model calibration, sensitivity and uncertainty analysis. Fukunaga et al. (2015) investigated application of the SWAT hydrologic model to a tropical watershed at Brazil. Nyeko (2015) assessed the capabilities and limitations of SWAT model in modeling watershed that has limited field and hydrologic data for possible use in water resources management. Romanowicz et al. (2005) investigated Sensitivity of the SWAT model to the soil and land use data in the Thyle catchment of Belgium country. Schuol & Abbaspour (2006) used SWAT to simulate water quantity of the four million  area in West Africa and applied Sufi-2 algorithm on parameters uncertainty. Defersha & Melesse (2012) applied SWAT to evaluate the impacts of land use changes on runoff and sediment yield in the Mara River basin, Kenya. Krysanova & Srinivasan (2015) assessed five projects of different applications of SWAT covering the following themes: impacts of climate change, impacts of land cover change and combined impacts of climate change and human intervention in water management. Bossa et al. (2012) applied the SWAT model in the Republic of Benin, West Africa to evaluate the effects of different soil databases on modeling of hydrological processes and sediment yield. Vilaysane et al. (2015) applied SWAT model to test the capability of the model for predicting stream flow and also used SUFI-2 algorithm for calibration and uncertainty analysis in Xedone river basin. Singh et al. (2013) used GLUE and SUFI-2 algorithms to simulate daily and monthly streamflow for the period 1993–2002 in the Krishna River basin. Their study revealed excellent correlation during monthly calibration, and good model match between the observed and simulated streamflows. Lin et al. (2015) in their study investigated the effects of land use and land cover changes on runoff response using SWAT model.

They used two different landuse scenarios (1985 and 2006, with reduced forest and increased cropland and urbanized area) in Jinjiang catchment. Shen et al. (2012) used SWAT model to simulate sediment and streamflow in Three Gorges reservoir basin. Their research showed that sediment simulation presented greater uncertainty than streamflow. Yang et al. (2008) tried to find the best uncertainty analysis techniques in Chaohe basin. They compared five algorithms (e.g. GLUE, ParaSol, SUFI-2, MCMC and PSO) to a distributed watershed model (SWAT) in north China. In this study, we focused on application of SUFI-2 algorithm for prediction of stream flow and uncertainty analysis in the Shafaroud watershed.  The main objective of this study is to test feasibility and capability of the SUFI-2 algorithm for runoff simulation of the study area, which will contribute to the preservation of natural resources in the Shafaroud watershed and thereby is useful for sustainable development.

MATHERIALS AND METHODS

Study area

Shafaroud watershed is located in Guilan Province at north of Iran, between longitudes 48˚ 39´ 34˝ and 49˚ 8´ 11˝ East and latitudes 37˚ 24´ 58˝and 37˚ 34´ 18˝ north with a drainage area of 336.89  (Fig. 1). The altitude of the catchment ranges from 168 m to 2895 m. The main river with a total length about 40.95 km and located in the north of the catchment.

The numbers of meteorology stations were four stations and discharge data was measured at one gauge, located at the outlet.

The majority of land is used for forest, agriculture and pasture.

SWAT and SWAT-CUP

Soil and water assessment tools (SWAT) is a semi-physically based model for assessing the impact of management and climate on water supplies, sediment, and agricultural chemical yields in catchments (Narsimlu et al. 2015). In SWAT, a catchment is divided into multiple sub-catchments whit hydrologic response units (HRUs) that consist of homogeneous land use, management, topographical, and soil characteristics (Abbaspour et al. 2007a). Each sub-catchment is split into multiple hydrological response units (HRUs) based on topography, management, land use and soil types (Wang & Kalin 2011).

SWAT-CUP is a computer program for calibration of SWAT models. It enables sensitivity analysis, calibration, validation, and uncertainty analysis of SWAT models (Abbaspour et al. 2007b).

Fig. 1. Location of Shafaroud Watershed.

SUFI-2 Algorithm

Uncertainty in Sequential Uncertainty Fitting (SUFI-2) algorithm is defined as the difference between simulated and observed variables (Rostamian et al. 2013). The uncertainty is determined by the 95% prediction uncertainty band calculated at the 2.5% and 97.5% levels of the output variables (Abbaspour et al. 2004, 2007b).

P-factor

The P-factor (percentage of measured data bracketed by the 95% prediction boundary) often named 95PPU (Percentage Prediction Uncertainty). The 95PPU is calculated at the 2.5% and 97.5% levels of the cumulative

Distribution of an output variable obtained through Latin hypercube sampling (Abbaspour 2011). The range of the P-factor varies from 0 to 1, with values is close to 1 indicating good fitness between simulated and observed values (Yang et al. 2008).

R-factor

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. The calibrated parameter ranges can be generated with an acceptable value of the R-factor and P-factor.

 The R-factor is given by Eq. (1) (Yang et al. 2008; Narsimlu et al. 2015):

Where  and  are the upper and lower boundaries of the 95UB and  is the standard deviation of the observed data.

NS objective function

Nash-Sutcliffe function has been used for assessment of model performance. This

Function is calculated by using the following equation Eq. (2) (Nash & Sutcliffe 1970):

Where  is the ground-based measurements;  is the model predicted data and  is the mean of the ground-based measurements.

Coefficience

The range of determination coefficient ( ) is 0 to 1 that explain the relationship between

Observed variance and simulated values. The  is given by Eq. (3) (Pluntke et al., 2014):

Where  and  are the observed and simulated values, respectively.

RESULTS

Setup SWAT Model

According to the Soil and Water Assessment Tools (SWAT) model, the following main data was used: landuse, soil characteristics, topography and climate data. First, the raster maps (e.g. topography, landuse, soil) were imported in ArcSWAT 2012 interface.

 In the next step, soil and landuse characteristics were overlaid for each sub-catchment. In addition, the weather data were defined. Finally, it was ran and simulated a 14-year period with 3 years warm-up from 1998 through 2011.

Calibration and Sensitivity Analysis  

For calibration model we used SWAT-CUP program with SUFI-2 algorithm which can read output data from ArcSWAT interface. In this section, fourteen parameters were selected for calibration that influence streamflow. Sensitively analysis was performed and its results indicated the most sensitive parameters that illustrated in Table 1. According to Table 1, the most sensitive parameters are soil bulk density (SOL_BD) and SCS curve number for moisture condition II (CN2) because of P-value close to 0 and t-stat bigger than other parameters.

In the next step, model simulated and compared monthly simulated and observed streamflows using SUFI-2 algorithm. We calibrated a 7-year period from 2001 to 2007 and validated a 4-year period from 2008 to 2011. Analysis of hydrographs indicates that the calibrated model slightly underestimate the peak runoff (Fig. 2). The size of uncertainty band (95PPU) is shown in Fig. 2 which confirms the uncertainty is very high. After defining the initial values of the fourteen parameters, it was specified for selecting appropriate parameters ranges. It could be reduce the band of uncertainty. Furthermore, after three iterations with 500 model runs, the best calibration illustrated in Fig. 3, where  value was 0.86, P-factor of 0.51, R-factor of 0.54 and NS was 0.77. With this calibration, the best ranges of parameters were obtained (Table 2). According to the last calibration, the best parameters values were imported (Table 2) in SWAT model and validated using data set for the period of 2008 to 2011and compared the plot of observed and simulated data.

Table 1. Sensitively analysis of parameters.

*dimensionless

Fig. 2. 95% probability of uncertainty plot and comparing observed and simulated streamflow before calibration.

Table 3 illustrates the values of P and R factors,  and NS in calibration (2001 to 2007) and validation (2008 to 2011) periods. Taking an analysis of the catchment at the outlet had a positive correlation with surface runoff, with  of 0.85, while P-factor, R-factor and NS were 0.63, 0.49 and 0.74 respectively (Fig. 4).

In other words, the evaluation of the hydrograph plot showed good model match in validation period. Also coefficient of determination ( ) value of calibration and validation period showed a good correlation between observed and simulated values (Fig. 5).

Fig. 3. 95% probability uncertainty plot and comparison observed and simulated streamflow after calibration (2001-2007).

Fig. 4. 95% probability uncertainty plot and comparison observed and simulated streamflow in validation period (2008-2011).

Fig. 5. Scatter plot of river streamflow for (a) calibration period (2001-2007) and (b) validation period (2008-2011).

Table 2. Optimum ranges of parameters.

Table 3. Statistical Analysis of runoff simulation.