Document Type : Research Paper
Authors
Abstract
Introduction In order to achieve sustainable management of water resources, integrated knowledge of water resources and modeling is essential, especially in arid and semi-arid regions where water resources have become scarcer with increasing demands from socioeconomic development and population growth. In recent years, utilization of hydrological models has been increased to simulate watershed processes for cost saving purposes. Various hydrological models such as soil and water assessment tool (SWAT) have been developed to simulate runoff in the watersheds. In this study, SWAT was used to simulate monthly runoff in Bazoft watershed and the impact of springs discharge on the simulation accuracy was evaluated.
Materials and Methods Bazoft is one of the watersheds in Karun basin, (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E) located in northern part of the Karun river basin in southwestern Iran. The area of the watershed is 2168 km2. The main river in the watershed is Ab Bazoft which is joined by the Karun River at the outlet of the watershed. The elevation ranges from 880 m in the south of the watershed to 4200 m on Zardkuh Mountain in the north eastern area. Because the topography is very high in the watershed, the rainfall distribution is different, the average of rainfall in the northern part of the watershed is 1400 mm, while the southern part is 500mm. SWAT was used because the model is a continuous time, spatially and semi-distributed and basin-scale model, in which hydrological processes and water quality are coupled with crop growth and agricultural management practices. Input data include digital Elevation Model (DEM), land use, soil type, meteorological and hydrological observed data were provided. After running the model, a sensitivity analysis was done using the one at time method and SUFI-2 program. For calibration and uncertainty analysis in this study, we used the PSO (particle swarm optimization) algorithm. SUFI-2 and PSO are linked to SWAT in the environment of SWAT-CUP software. We used about two-third of the observed data for calibration and the remaining for validation. The simulation period was from 1992 to 2008. The study period was 1998–2008 for calibration and 1992–1997 for validation. The calibration and validation period results were analyzed at monthly time scale. ). The objective function was the Nash– Sutcliffe coefficient. Two indices, the P-factor and the R-factor, are used to quantify the goodness of calibration performance. The P-factor is the percentage of data bracketed by the 95PPU band, and ideally we would like to bracket all measured data, except the outliers, in the 95PPU band, and the R-factor is the average thickness of the 95PPU band divided by the standard deviation of the corresponding measured variable. Theoretically, the value of P-factor ranges between 0 and 100%, while that of R-factor ranges between 0 and infinity. In ideal conditions when the uncertainty model is perfect, P-factor will be 1 and the R-factor will be 0.
Results and Discussion The results showed that the simulated base flow, peak flow, and hydrograph trend by entrance of spring discharge data to the model were more in agreement to the observed runoff data than the model with no spring discharge data. Therefore, the constructed model with the spring discharge data was selected to calibrate the particle swarm optimization (PSO) algorithm. In the sensitivity analysis, the parameters of curve number for moisture condition II (CN2), groundwater delay time (GW_DELAY), deep aquifer percolation fraction (RCHRG_DP), snow pack temperature lag factor (TIMP), the average monthly precipitation during the prediction period (PCPMM), temperature and precipitation parameters and surface runoff lag time coefficient (SURLAG) were the most sensitive parameters in the watershed.
Conclusion The calibration and validation results for the base period (1992-2008) showed that the accuracy of the simulations was satisfactory for the discharge and sediment values. The obtained evaluation criteria r-factor, p-factor, and R2 for the calibration period were 1.01, 76% and 0.79 and for the validation period were 0.76, 72% and 0.57, respectively. Therefore, due to the noticeable effects of spring discharge data and the input parameters on the runoff simulation in the study area, it appears that it is essential to consider these factors for the runoff simulation using SWAT in similar mountainous watersheds with high topography.
Keywords
Abbaspour, K.C. 2012. User manual for SWAT-CUP, SWAT calibration and uncertainty analysis program. Swiss Federal Institute of Aquatic Science and Technology, Ewag, Duebendorf, Switzerland. 103 pp.
2. Bakker, M.M., Govers, G., and Rounsevell, M.D.A. 2004. The crop productivity–erosion relationship: an analysis based on experimental work. Catena, 57:55-76.
3. Besalatpour, A.A., Hajabbasi M.A., Ayoubi, S., and Jalalian, A. 2012. Identification and prioritization of critical sub-basins in a highly mountainous watershed using SWAT model. Eurasian Journal of Soil Science, 1:58-63.
4. Basalatpour, A.A. 2012. Modelling of soil erosion hazard in the Bazoft watershed using fuzzy logic algorithm, SWAT model, and Fuzzy Clustering - Genetic algorithm. PhD thesis in soil science. Faculty of agriculture, Isfahan University of Technology (in Persian with English abstract).
- 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.
- Heppner F., and Grenander, U. 1990. A stochastic nonlinear model for coordinated bird flocks. In: Krasner, S. (ed.), the Ubiquity of Chaos, ISBN: 0871683504, AAAS Publications, Washington, DC. 233-238.
- Kennedy, J., and Eberhart, R.C. 1995. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks IV, Piscataway, NJ: IEEE Press. 1942-1948.
- Morgan, R.P.C., and Nearing, M.A. 2011. Handbook of erosion modelling. John Wiley & Sons, Ltd.
- Neitsch, S.L., Arnold, J.G., Kiniry, J.R., and Williams, J.R. 2011. Soil and Water Assessment Tool: Theoretical Documentation: Version 2009. Texas Water Resources Institute, Technical Report No. 406, Texas A&M University System, Collage Station, Texas 77843-2118.
10. Oeurng, C., Sauvage, S., and Sanchez-Perez, J.M. 2011. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model. Journal of Hydrology, 401:145-153.
11. Phan, D.B., Wu, C.C., and Hsieh, S.C. 2011. Impact of climate change on stream discharge and sediment yield in Northern Viet Nam. Water Resources, 38(6):827-836.
12. Rostamian, R. 2007. Estimation of Runoff and Sediment in Beheshtabad Watershed in Northern Karun Basin Using SWAT2000. Master thesis in irrigation engineering. Faculty of agriculture, Isfahan University of Technology (in Persian with English abstract).
13. Yang, Q., Meng, F.R., Zhao, Z., Chow, T.L., Benoy, G., Rees, H.W., and Bourque, C.P.A. 2009. Assessing the impacts of flow diversion terraces on stream water and sediment yields at a watershed level using SWAT model. Agriculture, Ecosystems and Environment, 132:23-31.
)