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As a result of inappropriate management and rising levels of societal demand, in arid and semi-arid regions water resources are becoming increasingly stressed. Therefore, well-established insight into the effects of climate change on water resource components can be considered to be an essential strategy to reduce these effects. In this paper, Iran's climate change and variability, and the impact of climate change on water resources, were studied. Climate change was assessed by means of two Long Ashton Research Station-Weather Generator (LARS-WG) weather generators and all outputs from the available general circulation models in the Model for the Assessment of Greenhouse-gas Induced Climate Change-SCENario GENerator (MAGICC-SCENGEN) software, in combination with different emission scenarios at the regional scale, while the Providing Regional Climates for Impacts Studies (PRECIS) model has been used for projections at the local scale. A hydrological model, the Runoff Assessment Model (RAM), was first utilized to simulate water resources for Iran. Then, using the MAGICC-SCENGEN model and the downscaled results as input for the RAM model, a prediction was made for changes in 30 basins and runoffs. Modeling results indicate temperature and precipitation changes in the range of ±6 °C and ±60%, respectively. Temperature rise increases evaporation and decreases runoff, but has been found to cause an increased rate of runoff in winter and a decrease in spring.

The need for accurate rainfall prediction is readily apparent when considering many benefits in which such information would provide for river control, reservoir operation, forestry interests, flood mitigation, etc.. Due to importance of rainfall in many aspects, studies on rainfall forecast have been conducted since a few decades ago. Although many methods have been introduced, all the researches describe the study as complex because it involves numerous variables and still need to be improved. Nowadays, there are various traditional techniques and mathematical models available, yet, there are no result on which method provide the most reliable estimation. AR (auto-regressive), ARMA (auto-regressive moving average), ARIMA (auto-regressive integrated moving average) and ANNs (artificial neural networks) were introduced as a useful and efficient tool for modeling and forecasting. The conventional time series provide reasonable accuracy but suffer from the assumptions of stationary and linearity. The concept of neurons was introduced first which then developed to ANNs with back propagation training algorithm. Although certain ANNs) models are equivalent to time series model, but it is limited to short term forecasting. This Paper presents a mathematical approach for rainfall forecasting for Iran on monthly basic. The model is trained for monthly rainfall forecasting and tested to evaluate the performance of the model. The result shows reasonably good accuracy for monthly rainfall forecasting.

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