Long Lead Runoff Simulation Using Data Driven Models

Runoff simulation is a vital issue in water resource planning and management. Various models with different levels of accuracy and precision are developed for this purpose considering various prediction time scales. In this paper, two models of IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) and ANN (Artificial Neural Network) models are developed and compared for long term runoff simulation in the south eastern part of Iran. These models have been utilized to simulate5-month runoff in the wet period of December-April. In IHACRES application, first the rainfall is predicted using climatic signals and then transformed to runoff. For this purpose, the daily precipitation is downscaled by two models of SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station-Weather Generator). The best results of these models are selected as IHACRES model input for simulating of runoff. In application of the ANN model, effective large scale signals of SLP(Sea Level Pressure), SST(Sea Surface Temperature), DSLP and runoff are considered as model inputs for the study region. The performances of the considered models in real time planning of water resources is evaluated by comparing simulated runoff with observed data and through SWSI(Surface Water Scarcity Index) drought index calculation. According to the results, the IHACRES model outperformed ANN in simulating runoff in the study area, and its results are more likely to be comparable with the observed values and therefore, could be employed with more certainty.