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A methodology is presented for the stochastic generation of daily rainfall which accounts for changes to the climatic inputs. The focus of the study is an example catchment in Iran. The methodology addresses the inability of GCMs to provide suitable future scenarios for the time and space scales required for a water resource impact assessment for a small catchment. One stochastic model for rainfall (Neyman-Scott Rectangular Pulses, NSRP, model) is used to generate daily rainfall sequences and then validated using historic records. For present climate conditions, the NSRP model is fitted to observed rainfall statistics. GCM outputs are then downscaled using regressions between atmospheric circulation indices (ACIs) and rainfall statistics. The relationships are then used to predict the rainfall statistics for future conditions using GCM outputs. In this respect, climate change impacts are studied and assessed in this paper. Generated rainfall scenario can then be used as inputs to a rainfall-runoff model in order to generate daily streamflow data which is not investigated here.

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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.