Long Lead Runoff Simulation: Application of Downscaling Techniques

Runoff simulation is a vital issue in water resource planning and management, due to its determinative role in water resources state. Different models with various levels of accuracy and precision are developed for this purpose considering different prediction time scales. In this paper, two long term runoff simulation methods including IHACRES and ANN (Artificial Neural Network) models are employed in the south eastern part of Iran and the results are compared. These models have been utilized to simulate 5-month runoff in the wet period of December-April. In IHACRES application, first the rainfall is predicted and then transformed to runoff using climatic signals. For this purpose the daily precipitation is downscaled using two models called SDSM and LARS-WG. The best results of these models are selected as IHACRES model input to be used for simulating the corresponding runoff. In application of the ANN model, effective large scale signals on rainfall and runoff variations in the study region such as SLP, SST and SLP difference are considered as model inputs. The performances of the considered models in real time planning of water resources is evaluated through calculating the SWSI drought index and comparing it to the observed data. According to the obtained results, the simulated runoff using the IHACRES model are more likely to be observed values and they could be employed with more certainty.