Simulation of runoff and sediment yield using SWAT in Gawshan dam watershed

Aminkhah, Sahar; Mahmoodi, Mohammad Ali; Bahmani, Aref; Ghaffari, Golaleh

doi:10.22059/ijswr.2023.348170.669348

Simulation of runoff and sediment yield using SWAT in Gawshan dam watershed

Document Type : Research Paper

Authors

Sahar Aminkhah ¹

Mohammad Ali Mahmoodi ²

Aref Bahmani ³

Golaleh Ghaffari ³

¹ Department of Soil Science, Collage of Agriculture, University of Kurdistan, Sanandaj, Iran

² Department of soil science, Collage of Agriculture, University of Kurdistan, Sanandaj, Iran

³ Department of Watershed Management, Islamic Azad University, Sanandaj branch, Iran

10.22059/ijswr.2023.348170.669348

Abstract

Soil erosion by water is a global threat to the environment which negatively affects soil and water quality. Soil erosion modeling is an efficient method to simulate soil erosion, to identify sediment source areas, and to evaluate soil conservation measures. In this study the Soil and Water Assessment Tool (SWAT) was used to model runoff and sediment in Gawshan dam watershed in west of Iran. For this purpose, climatic data obtained from a period of 11 years, from January 1, 2005 to the end of December 2015, from the stations of Bavale, Ravansar, Sanghor, Ghorve, Sanandaj, Kermanshah, Kangavar and Kamiyaran were used. We used two different approaches for estimating of model parameters. In the forward deterministic approach model parameters were independently derived from digital elevation model (DEM), soil and land use maps and climate data, whereas in the inverse stochastic approach model calibration were performed by sensitive model parameters using Sequential Uncertainty Fitting (SUFI-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP (SWAT Calibration Uncertainty Programs). Model performance was evaluated using the coefficient of determination (R2) and Nash-Sutcliff efficiency (NS) for forward approach and P factor and R factor for inverse approach. Results showed that in the forward approach, based on monthly data (2008-2013), the model performance was satisfactory for runoff (R2=0.66; NS=0.63) and fair for sediment (R2=0.42; NS=0.3). However, model prediction improved for both runoff (R2=0.94; NS=0.83) and sediment (R2=0.61; NS=0.53) when they were averaged on monthly basis. Calibration of the model parameters improved its performance for runoff (P factor>0.6; R factor<1) and sediment (P factor>0.4; R factor <1). These results demonstrated that the SWAT has the potential to be used as a decision making tool for Gawshan dam watershed management.

Keywords

Gawshan

Runoff

Sediment

SWAT

20.1001.1.2008479.1402.54.3.2.6

Subjects

soil erosion and conservation

EXTENDED ABSTRACT

Introduction

Soil erosion by water is a global threat to the environment that negatively affects soil and water quality. Soil erosion modeling is a promising method to simulate soil erosion to identify sediment source areas and to evaluate soil conservation measures. The objective of this study was to use the Soil and Water Assessment Tool (SWAT) to model runoff and sediment in Gawshan dam watershed in west of Iran.

Materials and Methods

The input data required to implement the SWAT include digital elevation map (DEM), soil map, land use/land cover map and climate data. DEM was used to delineate the watershed boundary, divide it into different sub-basins, and prepare slope and flow network maps. It had a resolution of 30 meters which obtained from the United States Geographic Survey. Soil map was prepared from land survey and Landsat ETM remote sensing data. The soil physical and chemical parameters required to run SWAT were measured in the different soil units, and its spatial distribution was coincident with the soil unit boundaries. The climatic data obtained from a period of 11 years, from January 1, 2005 to the end of December 2015, from the stations of Bavale, Ravansar, Songhor, Qurveh, Sanandaj, Kermanshah, Kangavar and Kamyaran were used. The two different approaches for estimating the model parameters have been used. In the forward deterministic approach model, parameters were independently derived from digital elevation model (DEM), soil and land use maps and climate data, whereas in the inverse stochastic approach model, calibration were performed by sensitive model parameters using Sequential Uncertainty Fitting (SUFI-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP (SWAT Calibration Uncertainty Programs). Model performance was evaluated using the coefficient of determination (R²) and Nash-Sutcliff efficiency (NS) in the forward approach and P and R factor in the inverse approach.

Results and Discussion

Results showed that in the first approach, based on monthly data (2008-2013), the model performance was satisfactory for runoff (R²=0.66; NS=0.63) and fair for sediment (R²=0.42; NS=0.3). However, model prediction improved for both runoff (R²=0.94; NS=0.83) and sediment (R²=0.61; NS=0.53) when they were averaged on the monthly basis. Calibration of the model parameters improved its performance for runoff (P factor>0.6; R factor<1) and sediment (P factor>0.4; R factor <1). One of the reasons of the model weakness in simulating runoff is its uncertainty in simulating snow melting or the lack of information about water withdrawal from aquifers in the study area. Also, the lower efficiency of the model in simulating the sediment compared to the runoff can be attributed to the greater uncertainty of the measured data of the sediment compared to the runoff. Overall, these results demonstrated that the SWAT has the potential as a decision-making tool in the Gawshan dam watershed management.

Abbaspour, K. C. (2015). SWAT-CUP: SWAT Calibration and Uncertainty Programs- A User Manual. EAWAG. Swiss.

Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H. and Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752.

Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., Srinivasan, R. and Reichert, P. (2007). Modelling of hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology. 333, 413–430.

Ahl, R. S., Woods, S. W. and Zuuring, H. R. (2008). Hydrologic calibration and validation of SWAT in a snow-dominated Rocky Mountain watershed, Montana, USA. Journal of the American Water Resources Association, 44(6), 1411–1430.

Ahmadi Ilkhchi, A., Hajabbassi, M. A. and Jalalian, A. (2003) Effects of converting range to dry-farming land on runoff and soil loss and quality in Dorahan, Chaharmahal and Bakhtiari Province. Journal of Science and Technology of Agriculture and Natural Resources, 6(4),103–114.

Aminkhah, S. (2017). Simulation of runoff and sediment yield using SWAT in Gawshan dam watershed. MSc. thesis, University of Kurdistan, Sanandaj.

Arabi, M., Govindaraju, R. S., Engel, B. and Hantush, M. (2007). Multiobjective sensitivity analysis of sediment and nitrogen processes with a watershed model. Water Resources Research, 43(6), 1–11.

Bagnold, R. A. (1977). Bed load transport in natural rivers. Water Resources Research, 13, 303–312.

Behera, S. and Panda, R. K. (2006). Evaluation of management alternatives for an agricultural watershed in a sub-humid subtropical region using a physical process based model. Agriculture, Ecosystems and Environment, 113(1–4), 62–72.

Blake, G. R. and Hartge K. H. (1986). Particle density. In A. Klute (Ed.), Methods of soil analysis. Part 2. 2^nd ed. Agronomy Monograph. 9. (pp. 377-381). ASA and SSSA, Madison, WI.

Borrelli, P., Alewell, C., Alvarez, P., Anache, J. A. A., Baartman, J., Ballabio, C., Bezak, N., Biddoccu, M., Cerda, A., Chalise, D., Chen, S., Chen, W., De Girolamo, A. M., Gessesse, G. D., Deumlich, D., Diodato, N., Efthimiou, N., Erpul, G., Fiener, P., . . . Panagos, P. (2021). Soil erosion modelling: A global review and statistical analysis. Science of the Total Environment, 780, 146494-146512.

Dregne, H. E. (1992). Erosion and soil productivity in Asia. Journal of Soil and Water Conservation, 47, 8–13.

FAO, UNDP and UNEP (1994) Land degradation in south Asia: its severity, causes and effects upon the people. World Soil Resources Report no. 78, FAO, Rome.

Fohrer, N., Haverkamp, S., Eckhardt, K. and Frede, H. G. (2001). Hydrologic response to land use changes on the catchment scale. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7–8), 577–582.

Gee, G. W. and Bauder, J. W. (1986). Particle size analysis. In A. Klute (Ed.), Methods of soil analysis. Part 2. 2^nd ed. Agronomy Monograph. 9. (pp. 383-409). ASA and SSSA, Madison, WI.

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. 50(6),1151:1171.

Klute, A. and Dirksen C. (1986). Hydraulic conductivity and diffusivity: Laboratory methods. In A. Klute (Ed.), Methods of soil analysis. Part 2. 2^nd ed. Agronomy Monograph. 9. (pp. 687-734). ASA and SSSA, Madison, WI.

Mohammadi, Sh., Karimzadeh, H., and Alizadeh, M. (2018). Spatial estimation of soil erosion in Iran using RUSLE model. Echohydrology, 5(2), 551-569.

Mahmoodi, M. A. and Aminkhah, S. (2018). Providing land use and land cover maps using remote sensing data and artificial neural network. Iranian Journal of Soil and Water Research, 49(5), 1171-1180. (In Persian)

Moore, A. D., McLaughlin, R. A., Mitasova, H. and Line, D. E. (2007). Calibrating WEPP model parameters for erosion prediction on construction sites. Transactions of the ASABE, 50(2), 507-516.

Morgan, R. P. C. and Nearing, M. A. (2011). Handbook of erosion modelling (2th ed.). Chichester: Wiley-Blackwell.

Neitsch, S. L., Arnold, J. G., Kiniry, J. R., Williams, J. R. and King, K.W. (2005). Soil and water assessment tool theoretical documentation. Texas Water Resources Institute Technical Report No. 406. Texas A&M University System, Texsas.

Panagos, P., & Katsoyiannis, A. (2019). Soil erosion modelling: The new challenges as the result of policy developments in Europe. Environmental Research, 172, 470-474.

Peterson, J. R. and Hamlet, J. M. (1998). Hydrologic calibration of the SWAT model in a watershed containing fragipan soils. Journal of the American Water Resources Association, 34, 531–544.

Quinton, J. N. (1997). Reducing predictive uncertainty in model simulations: A comparison of two methods using the European Soil Erosion Model (EUROSEM). Catena, 30(2), 101-117.

Refahi, H. Gh. (2015). Water Erosion and Conservation. Tehran: University of Tehran Press.

Rhoades, J. D. and Oster J. D. (1986). Solute Content. In A. Klute (Ed.), Methods of soil analysis. Part 2. 2^nd ed. Agronomy Monograph. 9. (pp. 985-1006). ASA and SSSA, Madison, WI.

Rostamian, R. )2006(. Assessment of runoff and sediment in Beheshtabad watershed, Northern Karun by SWAT 2000. M.Sc. Thesis, Isfahan University of Technology, Iran.

Rostamian, R. Jaleh, A., Afyuni, M., Mousavi, S. F., Heidarpour, M., Jalalian, A. and Abbaspour, K. C. (2008). Application of a SWAT model for estimating runoff and sediment in two mountainous basins in central Iran. Hydrological Sciences Journal, 53(5), 977-988.

Shirmohammadi, A., Chu, T. W. and Montas, H. J. (2008). Modeling at catchment scale and associated uncertainties. Boreal Environment Research, 13(3), 185–193.

Tolson, B. A. and Shoemaker, C. A. (2004). Watershed modeling of the Cannonsville basin using SWAT2000: model development, calibration and validation for the prediction of flow, sediment and phosphorus transport to the Cannonsville Reservoir. Technical Report, School of Civil and Environmental Engineering, Cornell University, Ithaca, New York.

Walkley, A. and Black I. A. (1934). An examination of the degtjareff method for determining soil organic matter and a proposed modification of the chromic acid titration method. Soil Science, 37:29–38.

Williams, J. R. (1975). Sediment-yield prediction with universal equation using runoff energy factor. p. 244–252. Proceedings of the Sediment-Yield Workshop, 40, 244-252.

Williams, J. R. (1980). SPNM, a model for predicting sediment, phosphorus, and nitrogen yields from agricultural basins. Water Resources Bulletin, 16, 843–848.