In this study, two general circulation models (GCMs) (Can-ESM2, BNU-ESM) were used to simulate the future precipitation of Tabriz city. The weakness of GCMs is the coarse resolution of climate variables in which the different methods of Downscaling is about to solve this deficiency. In this study, the Artificial Intelligence (AI) models i. e., Artificial Neural Network (ANN) and Adaptive neuro fuzzy inference system (ANFIS) were used to statistically downscale the climate variables of GCMs. Without any doubt, the most important step during the use of these models, is selecting of the dominant inputs among huge of large-scale GCM data. So in this study for the selection of dominant inputs, decision tree and mutual information (MI) feature extraction methods were used. Also, the ensemble techniques were used to evaluate the efficiency of Downscaling models and to decrease the uncertainties. Comparison the result of Downscaling models indicated that the ensemble technique (i. e., hybrid of ANN and ANFIS) with dominant inputs based on decision tree feature extraction method presents better performance. In both GCMs, the application of the ensemble Downscaling couple with dominant predictors based on decision tree model in precipitation Downscaling showed 10%-38% increase in DC in versus the individual ANN and ANFIS Downscaling models. The projection precipitation of Tabriz synoptic station for future (2020-2060) by proposed ensemble AI-based model indicated 30%-40% precipitation decreases under RCP4. 5 and RCP8. 5 scenarios.

General Circulation Models (GCMs) have been identified as a suitable tool for studying climate change. But these models simulate climatic parameters in the large-scale which has poor performance in the simulation of processes such as rain fall-run off. Therefore, several of Downscaling methods were developed. This research is presented Downscaling model based on k-nearest neighbor (K-NN) non-parametric method. The model is used to simulate daily precipitation data in Ahvaz station for the next period (2015-2044) under climate change scenarios based on out puts of three General Circulation Models, including HADCM3, NCARPCM and CSIROMK3.5. The results indicate that the model has a high capacity for down scaling data. It is predicted that the frequency of storm is increased with high intensity on future period in Ahvaz station while dry spells will be prolonged.

Climate change causes the change in temperature, rate of evapotranspiration, soil moisture, wind speed, and temporal and spatial variation in precipitation. These factors will lead to changes in hydrological parameters, such as groundwater level. According to the important role of climate parameters in water resources management, in this study HADCM3 model and A2, A1B and B1 scenarios are used to predict the climate parameters. For the statistical Downscaling of atmospheric general circulation model data, LARS-WG model is used as one of the most famous random weather generator models. Also prediction of groundwater levels changes in Tasuj basin was done by time series models in R software for the period of 2013-2022. The results revealed a decrease in rainfall as well as higher temperatures in the A2 scenario compared to the other scenarios. Changes in temperature and precipitation are similar in A1B and B1 scenarios. In all three scenarios, maximum rising temperatures and the highest percentage of precipitation decrease will occur in the months of June, July, August and September which coincides with the peak of groundwater use for drinking, agricultural and environmental purposes. Also the cross-correlation showed that the impact of rainfall on groundwater levels has a 2 months lag. Due to the climate change and assuming the persistence of the existing conditions of exploitation from groundwater in Tasuj basin, the cumulative decline of groundwater level in a 10-year period is predicted as 7.85 meter below the baseline in 2002. These forecasts should be taken as a serious warning for water management in this region so that to prevent human and environmental disasters.

In the statistical Downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers from be uncertainty. The outputs of Downscaling models are used as inputs to agriculture and water resources models; therefore, identifying the models inputs’ error or uncertainty is essential to realize the reliability of obtained results. In this research, an attempt is made to investigate the uncertainty of Artificial Neural Network (ANN) as a Downscaling model in a case study in the northwest of Iran. For this purpose, precipitation, minimum and maximum temperature variables were used in the designed ANN model, and the NCEP data was employed for its calibration and validation. The HadCM3 was the selected AOGCM in this study. Observed daily time series were gathered at all stations in the study period and on the basis of bootstrap method the 99% confidence interval was calculated for all the variables. In the next step, the simulated (downscaled) mean and variance of the variables by the ANN model, compared to the calculated confidence interval. To compare the results, the criterion of the number of station-month was used. The results showed that the average maximum temperature at 14 station-months were within the confidence interval. The results of monthly analysis showed that the accuracy of ANN model in summer was low and its uncertainty is more than the other seasons. In the simulation of minimum temperature based on this criterion, 18 station-months were within the confidence interval. The accuracy of ANN to estimate the minimum temperature in summer was low with high uncertainty in almost all the stations. Moreover, in June and August in any of the stations estimated values were not within the confidence interval. Due to the high variability of rainfall in relation to temperature, confidence range was very high, and in some stations was more than 50% of average monthly precipitation. Because of the high confidence rang of precipitation, in 53 Stations-month cases were within the confidence interval.

The frequency and intensity of extreme rainfalls will increase over many areas of the globe due to climate change. So, it is required to revise result of such studies based on the climate change scenarios. One of the most effective tools in such studies is Weather Generators, including LARS-WG. While GCMs predict future changes in the various characteristics of precipitation, usually in Downscaling using LARS-WG, just changes of monthly averages are considered. In this paper, the future climate change impact on extreme precipitation in Gorgan and Khoramabad stations are assessed; while, the results of two methods of applying just change in averages (simple method) or applying changes in various characteristics of precipitation (complete method) in Downscaling are compared. For future, CanESM2 outputs under RCP2. 6, RCP4. 5, and RCP8. 5 scenarios for 2036-2065 period were used. The results showed that for climate change impact assessment on extreme rainfalls, additional to change in averages, change in other precipitation characteristics should be considered. Because the results of the two methods are different. In Gorgan, for example, the annual maximum daily rainfall with a return period of 15 years in the future will increase by 16 to 21 percent according to the more complete method, but between 37 and 49 percent according to the simpler method. Based on the complete, Intensity of the extreme rainfalls at both stations will increase in the future. This increase will be between 23% and 30% for the 2-year return period and between 25% and 29% for the 30-year return period.

In recent decades, the rapid growth of industrial activities has caused imbalance in climate of the earth which is so called climate change. This phenomenon directly affects meteorological parameters such as temperature and precipitation. The objective of this research is investigation of the impacts of climate change on precipitation and maximum and minimum temperatures in Karkheh River Basin during the period of 2010-2039. The representative climate model of the region using AOGCM and observed data period of 1971-2000 was selected. Comparison of performance indicators of few AOGCM models for rainfall and temperature simulation showed that generally HadCM3 model is suitable for the region using synoptic and climatological weather stations of the region. Statistical and regression Downscaling was carried out for the selected AOGCM. Statistical and regression Downscaling was performed using statistically dynamic model of SDSM. The final results for near future, 2010-2039, shows 2% reduction in rainfall for both synoptic stations of Kermanshah and Khoramabad in the north of the basin and 4% reduction in Hamidieh climatological weather station in the south of the basin. The increase in maximum temperature for above stations are estimated as 119 and 3% and increase in minimum temperature are 24, 4 and 1%, respectively. Using HadCM3 and SDSM for near future, period of 2010-2039, simulation shows that as one moves from north to the south of the basin (colder climate to warmer climate) the effects of climate change on maximum and minimum temperature are less pronounced while the trend for rainfall, although small, is opposite and is 2% for the north and 4% for the south.

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate soil moisture. Hence Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many hydrological applications such as agriculture monitoring and drought prediction. Various Downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to propose a statistical assimilative method to downscale European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) Microwave (MW) remote sensing soil moisture products. For this purpose, firstly we used the NOAA images and NDVI, LST and albedo indices in regression process to International Soil Moisture Network (ISMN) in-situ SM. Then we downscaled the ESA products by making proportion between results and ESA products. Because of some limitations, we operated on three study area, Kyeamba Creek catchment area in Australia and two areas in Parsabad in Iran. Validation results of each area were evaluated using ground data and results showed average R2 in 0. 77, for Australia, and 0. 59 and 0. 34 for two case study in Iran. According to the results, it can be said that the proposed method, in addition to scalability and simplicity, has a higher productivity in uniform and unmixed areas such as Kyeamba Creek catchment, than agricultural land such as Parsabad.

Climate change could largely affect the surface water and groundwater sources. This impact should be simulated using appropriate models. In the present study, using LARS-WG and SDSM statistical models, the output of HADCM3 general circulation model under four emission scenarios was downscaled in the Hablehroud Basin in the period of 2018-2047. The SWAT model was calibrated in the basin and utilized to simulate the discharge, groundwater recharge, and soil water content in the mentioned period. Three different methods including Hargreaves, Penman-Monteith and Priestley-Taylor in the SWAT model were used to estimate reference evapotranspiration. Different combinations of the factors effecting the uncertainty, including Downscaling model, emission scenario, and evapotranspiration estimation method were used. The results showed that the method used in Downscaling the output of the general circulation model is the most effective factor affecting the uncertainty of the output of the SWAT model. It was also observed that the different combinations produce more outliers in simulating groundwater recharge, in comparison with simulating the discharge and soil water content. The median of the annual discharges simulated using all combinations was calculated to be 13. 32 cms. The results showed that the combinations of Downscaling model, emission scenario, and evapotranspiration estimation method that simulate values less than 13. 32 cms have less uncertainty than other combinations. In the case of groundwater recharge (with a median of 2. 07 mm/ year) and soil water content (with a median of 112. 4), same results were observed.

Estimation of soil moisture at various temporal and spatial scales is a key to the strategic management of water resources. Satellite-based microwave observations have coarse spatial resolution despite widespread and continuous of the provision surface soil moisture (SSM). In this study, the SSM data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) 25km resolution were used and these products were downscaled by three parameters retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) to 1km resolution. In the next step, the integration of the SSM Downscaling model with SMAR model was used to monitor the root zone soil moisture(RZSM) in the study area (Rafsanjan plain). In order to evaluate the performance of the proposed method, the SSM and the soil profile moisture were measured at 10 points in the Rafsanjan plain. Comparison of AMSR2 25k SSM and downscaled SSM with the field measurement data showed that the mean of total stations for the correlation coefficient(R) was increased from 0. 540 to 0. 739 and the mean absolute error(MAE) and the root mean square(RMSE) were reduced from 0. 039 and 0. 040 to 0. 018 and 0. 020, respectively. Moreover, the results obtained from the validation of the RZSM values showed that the proposed method could estimate the RZSM with high accuracy and indicate the variations.