Document Type : Research Article

Authors

Ferdowsi University of Mashhad

Abstract

Introduction: Determining the amount of watershed sedimentation and its spatial distribution by using field measurements in practice faces a serious challenge. In recent decades, hydrological models have been widely used by hydrologists and water resource managers as a tool for analysing water resource management systems. The SWAT model is one of the semi-physical and semi-distributed hydrological models that have been widely used in recent years. Despite the wide use of the SWAT, simulation of the sediment has been associated with a large error in comparison to flow. These errors may come from using empirical methods such as the sediment rating curve for estimating sediment based on measured data. Therefore, in this research, the capabilities of the genetic algorithm (GA) were used to optimize the relationship between discharge and sediment and further optimal equation used for calibration and validation of the model.
Materials and Methods: The studied area is Fariman dam watershed with an area of 278.8 km2 which is located at latitude of 35 ˚ 33' to 35˚ 41' and longitude of 59 ˚ 34' to 59 ˚ 44' in Razavi Khorasan province. In this study, SWAT model was used to simulate runoff and sediment yield of Fariman dam watershed. In order to run the model, meteorological and hydrometric data including daily rainfall and maximum and minimum temperatures and sediment yield and discharge data, soil and land use maps of the watershed were achieved from relevant resources. The capabilities of the genetic algorithm were used to optimize the discharge -sediment relationship and were compared with sediment rating curve. For this purpose, optimization problem was defined for the genetic algorithm in MATLAB software as a search space of continuous values of the discharge –sediment coefficients. After that, sediment yield was extracted based on discharge data and calculated monthly sediment for SWAT calibration and validation. Sensitivity analysis, calibration and validation of the model were performed using the SUFI-2 algorithm using SWAT-CUP software. For this purpose using high sensitive parameters, the model was calibrated and validated for the period of 1991 to 2000.
Results and Discussion: Optimal coefficients extracted by GA indicate a better performance of the genetic algorithm in estimating the sediment yield. The comparative results of the sediment estimation models, revealed better performance of the genetic algorithm with RMSE = 70.9, NSE =0.46 and R2= 0.72 than the sediment rating curve. According to senetivity analysis of SWAT model, twelve parameters for stream flow and seven parameters for sediment yield were found to be sensitive. The most sensitive parameters for stream flow were SCS runoff curve number (CN2), effective hydraulic conductivity in tributary channel (CH_K1) and base flow alpha factor for bank storage (ALPHA_BNK) and the most sensitive parameters for sediment yield were peak rate adjustment factor for sediment routing, USLE equation soil erodibility factor (USLE_K), sediment concentration in lateral flow and groundwater flow (LAT_SED) and exponent parameter for calculating sediment reentrained in channel sediment routing (SPEXP). The SWAT calibration and validation results showed that the Nash-Sutcliffe efficiency index for monthly sediment and discharge for calibration period was 0.75 and 0.73, respectively and in the validation period was 0.85 and 0.76, respectively. Calibration and validation of the SWAT model was done with genetic algorithm model as an optimal method for deriving sediment data from measured daily discharge. The Nash-Sutcliffe efficiency coefficient for monthly discharge was 0.75 and 0.85 in the calibration and validation periods. Nash-Sutcliffe efficiency coefficients for monthly sediment yield were 0.86 and 0.81 for the same periods. SWAT evaluation results indicate that the model simulation is acceptable for predicting sediment yield and river flow. The performance of SWAT model in predicting of sediment in low flow is poor, which can be due to the effect of the parameters and model simplifications in the simulation of the sediment load.
Conclusions: In this research, simulation of runoff and sediment flow for Fariman dam watershed was performed using SWAT model. For this purpose, the capabilities of the genetic algorithm were used to optimize the relationship between discharge and sediment yields; then the results were used to calibrate and validate the SWAT model. The results indicate that genetics algorithm can be used for optimizing coefficient of sediment discharge equation and the result is better than sediment rating curve. Simulation of watershed hydrology using SWAT shows that the capability of the model in prediction of sediment yield and water flow is good. Using genetic algorithm to optimize the relationship between discharge and sediment has an important role in extracting daily sediment yield and simulation accuracy of the model. Also, the use of evolutionary algorithms can have a significant role in extracting the discharge -sediment relations, which usually is accompanied with a large error in experimental models such as a sediment rating curve.

Keywords

1- Abbaspour K. C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., and Srinivasan R. 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333 (2): 413-430.
2- Abbaspour K.C. 2009. User manual for SWAT-CUP2, SWAT calibration and uncertainty analysis programs. Swis Federal Institute of Aquatic Science and Technology, Eawag, Duebendorf, Switzerland, 95 Pages.
3- Abbaspour K. C. 2011. SWAT calibration and uncertainty programs–a user manual. Swiss Federal Institute of Aquatic Science and Technology, Eawag.
4- Abbaspour K.C., Rouholahnejad E., Vaghefi S., Srinivasan R., Yang H., and Klve 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
5- Arnold J.G., Srinivasan R., Muttiah R.S., and Williams J.R. 1998. Large area hydrologic modeling and assessment part I: model development. Journal of the American Water Resource Association, 34 (1): 73–89.
6- Arnold J. G., Moriasi D. N., Gassman P. W., Abbaspour K. C., White M. J., Srinivasan R., and Kannan N. 2012. SWAT: Model use, calibration, and validation. Transactions of the ASABE, 55(4):1491-1508.
7- Alansi A. W., Amin M. S. M., Abdul Halim G., Shafri H. Z. M., and Aimrun W. 2009. Validation of SWAT model for stream flow simulation and forecasting in Upper Bernam humid tropical river basin, Malaysia. Hydrology and Earth System Sciences Discussions, 6 (6): 7581-7609.
8- Arabi M., Frankenberger J. R., Engel B. A., and Arnold J. G. 2008. Representation of agricultural conservation practices with SWAT. Hydrological Processes, 22 (16): 3042-3055.
9- Altunkaynak A. 2009. Sediment load prediction by genetic algorithms. Advances in Engineering Software, 40 (9): 928-934.
10- Asselman N.E.M. 2000. Fitting and interpretation of sediment rating curves. Journal of Hydrology, 23(4): 228-248.
11- Abdi Dehkordi M. 2012. Intelligent Estimation of suspended sediment discharge using modern technologies. MS.c Thesis. Gorgan University of Agricultural Sciences and Natural Resources. Faculty of Water and Soil Engineering. Iran. 91 P. (In Persian)
12- Bahmanesh J., Mohammadpour M M., and Bateni M. 2017.Comparison of River Suspended Sediment Load Estimation, using Regression and GA Methods, Journal of Watershed Management Research, 8(16), 132-141. (In Persian)
13- Baffaut C., and Sadeghi A. 2010. Bacteria modeling with SWAT for assessment and remediation studies: A review. Transactions of the ASABE, 53(5): 1585-1594.
14- Bayramin I., Dengiz O., BAŞKAN O., and Parlak M. 2003. Soil erosion risk assessment with ICONA model; case study: Beypazarı area, Turkish Journal of Agriculture and Forestry, 27(2): 105-116.
15- Bouzeria H., Ghenim A., and Khanchoul K. 2017. Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria. Journal of Water and Land Development, 33(1):47-55.
16- Briak H., Moussadek R., Aboumaria K., and Mrabet R. 2016. Assessing sediment yield in Kalaya gauged watershed (Northern Morocco) using GIS and SWAT model, International Soil and Water Conservation Research, 4(3): 177-185.
17- Dowlatabadi S., and Zomorodian M.A. 2013. Hydrological simulation of Firoozabad basin using SWAT model. Journal of Irrigation and Water, 4(14): 38-48. (In Persian with English abstract)
18- Duan Z., Song X., and Liu J. 2009. Application of SWAT for sediment yield estimation in a mountainous agricultural basin, In Geoinformatics, 2009 17th International Conference on IEEE. (pp. 1-5).
19- Emam Gholi Zadeh S., Karimedemaneh R., and Ajdari KH. 2016. Comparison of common methods for estimating suspended sediment load of Karkheh River with the method of gene expression programming. Geography and Development Quarterly, (45): 121-140. (In Persian)
20- Fleming G. 1979. Deterministic model in hydrology. IRRIGATION and Drainage paper.32 FAO.Rome, 80p.
21- Gassman, P.W., Reyes, M.R., Green, C.H., Arnold, J.G. 2007. The soil and water assessment tool: historic development, applications, and future research directions. Trans. ASABE, 50 (4): 1211–1250.
22- Goldberg D. 1989. Genetic algorithms in search optimization and machine learning, Journal of Hydrology Research, 8:354-361.
23- Golshan M., Esmaeli Ouri A., Shahedi K., and Jahanshahi A. 2016. Evaluation of the Efficiency of SWAT and IHACRES Models in Runoff Simulation of Khoramabad Basin. Journal of Water and Soil Science, 26(1/2): 29-42. (In Persian)
24- Goodarzi M.R., Zahabioun B., Masah Boani A., and Kamal A. 2012. Comparison of performance of three hydrological models SWAT, IHACRES and SIMHYD in simulation of Ghareh Sou basin runoff. Journal of Water Management and Irrigation, 2(1): 25-40. (In Persian with English abstract)
25- Gupta H.V., Kling H., Yilmaz K.K., and Martinez G.F. 2009. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, Journal of Hydrology, 377(1-2), pp.80-91.
26- Hayat Zadeh M., Chezgy J., and Dastoorani M.T. 2015. Evaluation of Sediments Using Rating Curve and Artificial Neural Network Methods by Combining Morphological Parameters of Basin (Case Study: Bagh Abbas Basin) Journal of Science and Technology of Agriculture and Natural Resources, Water and Soil Sciences, 19 (72): 217-227. (In Persian)
27- Holland J. H. 1975. Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. Ann Arbor, MI: University of Michigan Press.
28- Hosseini M., Ghafouri A. M., M Amin M. S., Tabatabaei M. R., Goodarzi M., and Abde Kolahchi A. 2012. Effects of land use changes on water balance in Taleghan catchment, Iran. Journal of Agricultural Science and Technology, 14(5): 1161-1174.
29- Kavian A., Bahrami M., and Rouhani H. 2014. Evaluation of the Efficiency of SWAT Model in Estimating Surface Runoff in kachik Watershed of Golestan Province. Watershed Research (Research and construction). 103. (In Persian)
30- Kavian A., Golshan M., Rouhani H., and Esmaeli Ouri A. 2015. Runoff and sediment simulation of the watershed of the Haraz River of Mazandaran using the SWAT pattern. Natural Geography Research, 47(2): 197-211. (In Persian)
31- Kliment Z., Kadlec J., and Langhammer J. 2008. Evaluation of suspended load changes using AnnAGNPS and SWAT semi – empirical erosion models. Catena, 73:286-299.
32- Li Q., Yu X., Xin Z., and Sun Y. 2012. Modeling the effects of climate change and human activities on the hydrological processes in a semiarid watershed of loess plateau. Journal of Hydrologic Engineering, 18(4), 401-412.
33- Mahzari S., Kiani F., Azimi M., and Khormali F. 2016. Using SWAT Model to Determine Runoff, Sediment Yield and Nitrate Loss in Gorganrood Watershed, Iran. ECOPERSIA, 4(2): 1359-1377.
34- Mohammad Reza pour O., Haghighat-ju P., and Zainali M J. 2015. Compression of Genetic Algorithm and Particle Swarm Algorithm models for Optimizing Coefficients of Sediment Rating Curve in estimation of Suspended Sediment in Sistan River ;Case Study Kohak station. Journal of Irrigation and Water Engineering, 6(22): 76-89. (In Persian)
35- Mosaedi A., Zanganeh M.A., and Farazjoo H. 2010. Estimation of suspended sediment discharge based on the sediment curve equation and its affected factors in the Gorganroud watershed. Proceedings of the First National Conference on Applied Resources of Water Resources of Iran. Kermanshah Regional Water Company. Iran. 12 P. (In Persian)
36- Muhammadi A., Akbari G., and Azizzian G. 2012. Suspended sediment concentration estimation using artificial neural networks and neural-fuzzy inference system case study: Karaj Dam. Indian Journal of Science and Technology, 5(8): 3188-3193.
37- Moriasi D.N., Arnold J.G.,VanLiew M.W., Bingner R.L., Harmel R.D., and Veith T. L. 2007. Model evaluation guidelines for systematic quantification ofaccuracy in watershed simulations. Transactions of the ASABE, 50(3): 885–900.
38- Nash J. E., and Sutcliffe J. V. 1970. River flow forecasting through conceptual models, Part I - A discussion of principles, J. Hydrol, 10: 282–290.
39- Neitsch S. L., Arnold J. G., Kiniry J. R., and Williams J. R. 2005. SWAT theoretical documentation version 2005. Grassland. Soil and Water Research Laboratory, Agricultural Research Service, Temple, Texas, USA.
40- 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.
41- Ndomba P.M., Mtalo F.W., and Killingtveit A. 2007. Sediment yield modeling using SWAT model at a large and complex catchment: Issues and approaches. A case study of Pangani River catchment, Tanzania, 4th International SWAT Conference, Institute for Water Education Delf, The Netherlands, July 4-6.
42- Osmani H., Motamedvaziri B., and Moeini A. 2013. Flow Simulation, Calibration and Validation of SWAT Model, Case Study: Upper Basin of Latyan Dam, Tehran. Journal of Engineering and Watershed Management, 5(2): 134-143. (In Persian with English abstract)
43- Parajuli P.B., Nelson N.O., Frees L.D., and Mankin K.R. 2009. Comparison of AnnAGNPS and SWAT model simulation results in USDA-CEAP agricultural watersheds in south-central Kansas, Hydrol. Process, 23: 748–763.
44- Phomcha P., Wirojanagud P., Vangpaisal T., and Thaveevouthti T. 2011. Suitability of SWAT model for simulating of monthly streamflow in Lam Sonthi Watershed, Journal of Industrial Technology, 7(2): 49-56.
45- 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.
46- Roshangar K., Aalami M.T., and Vojudi-Mehrabani F. 2015. Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River). Journal of Water and Soil, 29(2): 1416-1426. (In Persian)
47- Saleh A., and Du B. 2004. Evaluation of SWAT and HSPF within BASINS program for the upper North Bosque River watershed in central Texas. Transactions of the ASAE, 47(4): 1039.
48- Tabatabaei M., and Salehpour Jam A. 2017. Optimization of sediment rating curve coefficients using evolutionary algorithms and unsupervised artificial neural network. Caspian Journal of Environmental Sciences, 15(4), 387-401.
49- Teshager A. D., Gassman P. W., Secchi S., Schoof J. T., and Misgna G. 2016. Modeling agricultural watersheds with the soil and water assessment tool (SWAT): Calibration and validation with a novel procedure for spatially explicit hrus, Environmental Management, 57(4): 894-911.
50- Vafaei Nejad A., Chatr Simab Z., Boolori A., and Mirdar Harijani F. 2017. Optimization Coefficients of Curve Equation Sediment Measurement in Estimating Sediment Flow Using Particle Swarm Algorithm (PSO) and Simulated Refrigeration Algorithm (SA) Case Study: Bijar Station. Journal of natural ecosystems Iran, 8(3), 69-82. (In Persian)
51- Vilaysane B., Takara K., Luo P., Akkharath, I., and Duan W. 2015. Hydrological stream flow modelling for calibration and uncertainty analysis using SWAT model in the Xedone river basin, Lao PDR. Procedia Environmental Sciences, 28: 380-390.
52- Williams J. R. 1975. Sediment routing for agricultural watersheds. JAWRA Journal of the American Water Resources Association, 11(5): 965-974.
53- Williams J.R., and Berndt H.D. 1977. Sediment yield prediction based on watershed hydrology. Trans. ASAE 20 (6), 1100–1104.
54- Wishmeier W.H., and Smith D.D. 1978. Predicting rainfall erosion losses. USDA Agricultural Research Service (USDA-ARS) Handbook 537.
55- Yang J., Reicher P., Abbaspour KC., Xia J., and Yang H. 2008. Comparing uncertainty analysis techniques for a SWAT application to the Chao he Basin in China. Journal of Hydrology, 358 (1–2):1-23.
56- Yesuf H. M., Melesse A. M., Zeleke G., and Alamirew T. 2016. Streamflow prediction uncertainty analysis and verification of SWAT model in a tropical watershed. Environmental Earth Sciences, 75(9): 806.
CAPTCHA Image