Comparison of Soil Moisture Accounting model and Neuro-fuzzy for Rainfall-Runoff Modeling (Case study: Zola Chay watershed)

Subject Areas : climate

Ebrahim  Yousefi Mobarhan ¹ (Soil Conservation and Watershed Management Research Institute, Semnan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Semnan, Iran.)

Received: 2021-02-06 Accepted : 2021-07-06 Published : 2021-05-22

Keywords: Calibration, Neuro-fuzzy, Zola Chay watershed, Rainfall-runoff process, Soil Moisture Accounting model,

Abstract :

Hydrological simulation of watersheds applies for estimating peak discharge and runoff volume from rainfall, flood routing in rivers and flood hydrograph analysis. The purpose of this study is application of soil moisture accounting (HMS SMA) and Neuro-fuzzy models in daily flow, The purpose of this study is application of soil moisture accounting (HMS SMA) and Neuro-fuzzy models in daily flow. runoff volume and hydrograph analysis of the simulated rainfall - runoff in the Zola Chay watershed. In this study after of Zola Chay watershed modeling with HEC-GeoHMS Extension, In this study after of Zola Chay watershed modeling with HEC-GeoHMS Extension. the model entered to HEC-HMS program and by parameters estimating of soil moisture accounting model, the rainfall- runoff simulation in other scales has been done. By analysis of time scales for calibration and optimization of HMS SMA model parameters we can claim that the monthly time scale rainfall - runoff simulation accurate than annual, seasonal, semiannual and annual time scales can be better than the other time scales of flow to estimate peak. Comparing the calibration and optimization soil moisture and Neuro-fuzzy methods revealed that fuzzy method can simulate rainfall- runoff relationship better than SMA model by best statistical coefficients (E= 0.76 and RMSE= 0.18).