Hydrology modelling in Taleghan mountainous watershed using SWAT

• Autorzy: Noor H. , Vafakhah M. , Taheriyoun M. , Moghadasi M.

Warianty tytułu

PL

Modelowanie hydrologii górskiej zlewni rzeki Taleghan z zastosowaniem modelu SWAT

• Języki publikacji: EN

Abstrakty

EN

Mountainous regions in Iran are important sources of surface water supply and groundwater recharge. Therefore, accurate simulation of hydrologic processes in mountains at large scales is important for water resource management and for watershed management planning. Snow hydrology is the more important hydrologic process in mountainous watersheds. Therefore, streamflow simulation in mountainous watersheds is often challenging because of irregular topography and complex hydrological processes. In this study, the Soil and Water Assessment Tool (SWAT) was used to model daily runoff in the Taleghan mountainous watershed (800.5 km²) in west of Tehran, Iran. Most of the precipitation in the study area takes place as snow, therefore, modeling daily streamflow in this river is very complex and with large uncertainty. Model calibration was performed with Particle Swarm Optimization. The main input data for simulation of SWAT including Digital Elevation Model (DEM), land use, soil type and soil properties, and hydro-climatological data, were appropriately collected. Model performance was evaluated both visually and statistically where a good relation between observed and simulated discharge was found. The results showed that the coefficient of determination R² and the Nash-Sutcliffe coefficient NS values were 0.80 and 0.78, respectively. The calibrated model was most sensitive to snowmelt parameters and CN₂ (Curve Number). Results indicated that SWAT can provide reasonable predictions daily streamflow from Taleghan watersheds.

PL

Górskie regiony Iranu są ważnymi terenami zasilania wód powierzchniowych i podziemnych. Z tego powodu dokładna symulacja procesów hydrologicznych w dużej skali ma znaczenie dla gospodarki zasobami wodnymi i planowania zarządzania zlewnią. Śnieg odgrywa ważną rolę w hydrologii górskich zlewni. Symulacja przepływów w tych zlewniach stanowi więc wyzwanie z powodu nieregularnej rzeźby terenu i skomplikowanych procesów hydrologicznych. W badaniach zastosowano system oceny gleby i wody (SWAT) do modelowania dobowego odpływu z górskiej zlewni Taleghan (800,5 km²) położonej w Iranie na zachód od Teheranu. Większość opadów na obszarze badań stanowi śnieg, dlatego modelowanie dobowego przepływu rzeki jest złożone i obarczone znacznym stopniem niepewności. Optymalizację modelu przeprowadzono metodą roju cząstek (PSO). Zebrano odpowiednie dane wejściowe do symulacji SWAT: cyfrowy model deniwelacji (DEM), dane o użytkowaniu gruntów, typie i właściwościach gleby oraz dane hydrologiczne i klimatyczne. Działanie modelu oceniano zarówno wizualnie, jak i statystycznie. W tym drugim przypadku stwierdzono ścisłą zależność między obserwowanym i symulowanym przepływem wody. Współczynniki determinacji R² i Nasha-Sutcliffa NS wynosiły odpowiednio 0,80 i 0,78. Wykalibrowany model był najbardziej wrażliwy na parametry topnienia śniegu i CN₂. Wyniki badań wykazały, że model SWAT może zapewnić wiarygodne prognozy dobowego przepływu wody w zlewni rzeki Taleghan.

Słowa kluczowe

EN

auto-calibration; particle swarm optimization (PSO); snow hydrology; soil and water assessment tool; Taleghan Dam

PL

autokalibracja; hydrologia śniegu; optymalizacja rojem cząstek (PSO); system oceny gleby i wody; zapora Taleghan

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2014
• Tom: no. 20
• Strony: 11--18
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Noor H.

• Department of Watershed Management Engineering, TarbiatModares University, Tehran, Iran

autor

Vafakhah M.

• Department of Watershed Management Engineering, College of Natural Resources and Marine Sciences, TarbiatModares University, Noor 46414-356

autor

Taheriyoun M.

• Civil Engineering Department, Isfahan University of Technology, Isfahan, Iran

autor

Moghadasi M.

• Department of Water Engineering, College of Agriculture, Arak University, Arak, Iran

Bibliografia

• [1.] ABBASPOUR K.C. 2011. SWAT-CUP user manual. Duebendorf. Eawag pp. 105.
• [2.] ABBASPOUR K.C., YANG J., MAXIMOV I., SIBER R., BOGNER K., MIELEITNER J., ZOBRIST J., SRINIVASAN R. 2007. Modelling hydrology and water quality in the prealpine/ alpine Thur watershed using SWAT. Journal of Hydrology. Vol. 333. Iss. 2–4 p. 413–430.
• [3.] AHL R.S., WOODS S.W., ZUURING H.R. 2008. Hydrologic calibration and validation of SWAT in a snow-dominated rocky mountain watershed, Montana, USA. Journal of American Water Resource Association. Vol. 44. Iss. 6 p. 1411–1430.
• [4.] AKHAVAN S., ABEDI-KOUPAI J., MOUSAVI S.F., AFYUNI M., ESLAMIAN S.S., ABBASPOUR K.C. 2010. Application of SWAT model to investigate nitrate leaching in Hamadan– Bahar Watershed, Iran. Agriculture, Ecosystems and Environment. Vol. 139 p. 675–688.
• [5.] ARNOLD J.G., SRINIVASAN R., MUTTIAH R.S., WILLIAMS J.R. 1998. Large area hydrologic modeling assessment. P. I. Model development. Journal of American Water Resources Association. Vol. 34. Iss. 1 p. 73–89.
• [6.] BANASIK K., WOODWORD D. 2010. Empirical determination of runoff Curve Number for a small agricultural watershed in Poland [online]. 2nd Joint Federal Interagency Conference, Las Vegas, NV, June 27 – July 1, 2010. [Access 10.01.2014]. Available at: http://acwi.gov/sos/pubs/2ndJFIC/Contents/10E_Banasik_28_02_10.pdf
• [7.] BELYAEH A., ADAMOWSKI J. 2013. Drought forecasting using new machine new machine learning methods. Journal of Water and Land Development. Vol. 18 p. 3–12.
• [8.] BEVEN K., BINLLEY A. 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes. Vol. 6. Iss. 3 p. 279–298.
• [9.] BEVEN K. 1989. Changing ideas in hydrology – the case of physically-based models. Journal of Hydrology. Vol. 105 p. 157–172.
• [10.] BEVEN K.J. 2001. Rainfall-Runoff Modeling: the Primer. New York. John Wiley and Sons. ISBN 978-0471985532 pp. 372.
• [11.] CHAU K.W. 2006. Particle Swarm Optimization training algorithm for ANNs in stage prediction of ShingMun River. Journal of Hydrology. Vol. 329 p. 363–367.
• [12.] FAUT 1993. General investigation of Taleghan Basin: Hydrometeology and climatology report. Tehran pp. 67.
• [13.] GILL M.K., KAHEIL Y.H., KHALIL A., MCKEE M., BASTIDAS L. 2006. Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resources Research. Vol. 42. Iss. 7. W07417 doi: 10.1029/2005WR004528.
• [14.] HOSSEINI M., AMIN M.S.M., GHAFOURI A.M., TABATABAEI M.R. 2011. Application of soil and water assessment tools model for runoff estimation. American Journal of Applied Sciences. Vol. 8 (5) p. 486–494.
• [15.] KENNEDY J.,EBERHART R.C. 2001. Swarm intelligence. San Mateo; CA. Morgan Kaufmann. ISBN 978-1558605954 pp. 512.
• [16.] KUCZERA G., PARENT E. 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm. Journal of Hydrology. Vol. 211. Iss. 1–4 p. 69–85.
• [17.] LEMONDS P., MCCRAY J.E. 2007. Modeling hydrology in a small rocky mountain watershed serving large urban population. Journal of the American Water Resources Association. Vol. 43 .Iss. 4 p. 875–887.
• [18.] MORIASI D.N., ARNOLD J.G., VAN LIEW M.W., BINGNER R.L., HARMEL R.D., VEITH T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.Transactions of the ASAE. Vol. 50. Iss. 3 p. 885–900.
• [19.] NEITSCH S.L., ARNOLD J.G., KINIRY J.R., WILLIAMS J.R. 2011. Soil and water assessment tool theoretical documentation version 2009. TWRI Report TR-406. Texas. Texas Water Resources Institute, College Station pp. 618.
• [20.] PHOMCHA P., WIROJANAGUD P., VANGPAISAL T., THAVEEVOUTHTI T. 2011. Suitability of SWAT model for simulating of monthly streamflow in Lam Sonthi Watershed. Journal of Industrial Technology. Vol. 7. Iss. 2 p. 49–56.
• [21.] PRADHANANG S.M., ANANDHI A., MUKUNDAN R., ZION M.S., PIERSON D.C., SCHNEIDERMAN E.M., MATONSE A., FREI A. 2011. Application of SWAT model to assess snowpack development and streamflow in the Cannonsville watershed, New York, USA. Hydrological Processes. Vol. 25 p. 3268–3277.
• [22.] RAHMAN K., MARINGANTI C.H., BENISTON M., WIDMER F., ABBASPOUR K., LEHMANN A. 2013. Streamflow modeling in a highly managed mountainous glacier watershed using SWAT: The Upper Rhone River watershed case in Switzerland. Water Resources Management. Vol. 27 p. 323–339.
• [23.] TALEBIZADEH M., MORID S., AYYOUBZADEH S.A., GHASEMZADEH M. 2010. Uncertainty analysis in sediment load modeling using ANN and SWAT Model. Water Resource Management. Vol. 24 p. 1747–1761.
• [24.] TEDELA N.H., MCCUTCHEON S.C., RASMUSSEN T.C., HAWKINS R.H., SWANK W.T., CAMPBELL J.L., ADAMS M.B., JACKSON C.R., TOLLNER E.W. 2013. Runoff Curve Numbers for 10 small forested watersheds in the mountains of the Eastern United States. Journal of Hydrologic Engineering. Vol. 17. Iss. 11 p. 1188–1198.
• [25.] VAFAKHAH M., MOHSENISARAVI M., MAHDAVI M., ALAVIPANAH S.K. 2011. Snowmelt Runoff Prediction by Using Artificial Neural Network and Adaptive Neuro-fuzzy Inference System in Taleghan Watershed. Iran-Watershed Management Science and Engineering. Vol. 5. Iss. 14 p. 23–36 (in Persian).
• [26.] VAN GRIENSVEN A., MEIXNER T., GRUNWALD S., BISHOP T., DILUZIO M., SRINIVASAN R. 2006.A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology.Vol. 324 p. 10–23.
• [27.] WOJAS W., TYSZEWSKI S. 2013. Some examples comparing static and dynamic network approaches in water resources allocation models for the rivers of high instability of flows. Journal of Water and Land Development. No. 18 p. 21–27.
• [28.] WOODWARD D.E., SCHEER C.C., HAWKINS R.H. 2006. Curve number update used for runoff calculation. Annals of Warsaw Agricultural University – SGGW, Land Reclamation. Vol. 37 p. 33–42.
• [29.] ZHANG X., SRINIVASAN R., BOSCH D. 2009. Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging. Journal of Hydrology. Vol. 374 p. 307–317.
• [30.] ZHANG X., SRINIVASAN R., ZHAO K., VAN LIEW M. 2008. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrological Processes. Vol. 23. Iss. 3 p. 430–441.