Abstract

In this study, the predicted monthly temperature and rainfall data from HadCM3 model (base period, ۱۹۷۲-۲۰۰۱) and next period (۲۰۱۱-۲۰۴۰) under A2emission scenario were used to investigate the impacts of climate change on runoff variations in the Kor river basin. HadCM3 model output was downscaled based on a temporal downscaling approach (Change Factor) and spatial downscaling approach (Proportional) for the basin. The time series of monthly temperature and precipitation were produced in future period. The results showed that temperature and rainfall will increase and decrease in the future period relative to the base period, respectively. Then, the calibrated IHACRES model was used to model rainfall-runoff relationships in the Kor river basin. Then, the downscaled monthly temperature and rainfall time series in next period were employed to rainfall-runoff model and the monthly runoff time series were simulated for period ۲۰۱۱- ۲۰۴۰. The comparison of future period runoff relative to observed period showed that the annual runoff in the basin for period ۲۰۱۱-۲۰۴۰ will decrease about ۹٫۴۳% with respect to base period.

There are numerous methods to estimate missing values of which some are used depending on the data type and regional climatic characteristics. In this research, part of the monthly precipitation data in Sarab synoptic station, east Azerbaijan province, Iran was randomly considered missing values. In order to study the effectiveness of various methods to estimate missing data, by seven classic statistical methods and M5 model tree as one of efficient data mining methods, hypothetical missing values were estimated using precipitation data from neighbor station. The results showed that multiple imputation, Delta Learning Rule, and Multivariable Linear Regression (MLR) yield relatively more accurate results with fewer errors. The results also indicate the fact that, developing if-then rules, M5 tree model, as one of modern data mining methods, has been able to give the most accurate results among the mentioned methods with four simple linear relationship and statistical values including correlation coefficient (0.974), Nash-Sutcliffe model efficiency coefficient (0.948), RMSE (5.11), and MAE (4.189). Therefore, taking simple modeling process, functionality, comprehensibility, and high accuracy of this method into account, this method is proposed to estimate monthly precipitation missing values.

The purpose of this study is erosion and sediment is estimated in whole of Lay watershed, located in the North Wast of Iran.In this study, the model of WEPP, was provided for simulation and forecast of erosion and sedimentation in the study watershed. The data required for WEPP model are entered in six files, including soil file, management, climate, slope, channel and pounds files. In this study, after determining of work units, the information for developing file were collected. In this region there are, 3 types of soil, 2 managements  and 5 channel types. After making the files, WEPP model was run via Geowepp software. It is notable that in this software water erosion and sediment amount was estimated by three methodes of hillslope, watershed and fow path. In these methods sediments estimated were 0.308, 0.215, 0.491 tha^-1 y^-1, respectively. According to estimated results, the hillslpe with 0.308  tha^-1 y^-1 was in good agreement with actual amount 0.319 and is suitable for the erosion and sediment of  Lay watershed.

Air temperature is one of the most frequently used parameters in the assessment of climate change at global and regional scale. So researchers have tried to modeling and predicting it with different models. This study also aims to model and predict the country's monthly minimum and maximum temperature. Investigates of temporal temperature changes is done by Sen’s estimator and Pettit method and predicting made by Holt-Winters model. The results indicated that the minimum temperature during 1961 to 2010 increased by 2.9º C . This rate is more in stations located in the warm and dry regions (3.1°C) than any other stations (1.8°C). While the maximum temperature gradient changes are lower and is about 2.1°C. The results also confirmed the performance of Holt-Winter's forecasting model. Beside a few exceptions, the minimum and maximum temperature will be increased until 2020. The highest increase of temperature will occur in Khoy, so that the minimum and maximum temperature will be increased about 0.6°C and 0.28°C, respectively.

In this study, the Frequency and the spell of rainy days was analyzed in Lake Uremia Basin using Markov chain model. For this purpose, the daily precipitation data of 7 synoptic stations in Lake Uremia basin were used for the period 1995- 2014. The daily precipitation data at each station were classified into the wet and dry state and the fitness of first order Markov chain on data series was examined using Chi-square test at a significance level of 0.01 and was approved. After computing transition probability matrix, the persistent probability, average spell of dry days and rainy days and weather cycle was calculated. By calculating the frequency of 1-10 rainy, the spell of this periods and 2-5-days return period were calculated. The results show that in this study period the average of rainy days is 25% and the probability of P_dd is more than other states (P_ww ، P_dw و P_wd). The average spell of rainy days in the study area was estimated at about two days. Generally, in all stations the persistent probability of wet state is more than rainy state. Estimation of frequency and spell of rainy days and 2-5-days return period show that with increasing duration, the frequency of rainy days decreases. Also with increasing duration of rainy days, their spell is reduces and return period increases.

The aim of this study was to investigate the effects of global warming on where the slope changes when the monthly temperature in Iranian territory over the coming decades (2050-2015). The simulated temperature dynamic model EH5OM subset Hybrid Models atmospheric circulations (GCM) selection and data model of the Center for Theoretical Physics Salam (Italy) were derived from emission scenarios A1B scenario was chosen given the scenario of 2100 -2001 found that from 2050 to 2015 were used in this study data is then output the data in the fourth edition of the regional climate model (RegCM4) Linux environment was fine scale output data Downscaling model with dimensions of 27/0 * 27 / Degrees latitude is where the dimensions of 30 x 30 km area of ​​approximately cover the average temperature of the matrix deals 13140 2140 * was extracted. Finally, the slope of the average monthly temperature during the period under study by Mann-Kendall slope age and matrix computation in MATLAB software 13140 * 12 respectively. Results show rising temperatures in March and April to June, more than 90% of the country, that it will be spring's warmer. Increasing the temperature in the winter months and spring mountainous parts of the western half of the country is warming the cold regions of Iran. Temperature negative trend in October and November in the northern part of the eastern half of the region's countries could be indicative of colder temperatures in the northern West.

The aim of this study was to provide a reliable estimate of the amount of solar radiation in Kermanshah province by using “Liu and Jordan” model in order to develop solar sites. The amount of atmospheric elimination in each month was calculated using an index called clearness index () and the results were applied on different slopes, aspects and heights. Then, according to the obtained results, amounts of direct, diffuse and total radiation were calculated in different slopes of the region and the relevant maps were consequently drawn. The results showed that the minimum amount of atmospheric elimination and thus the maximum amount of received radiation in the province occurs in late spring and summer due to the increase of clearness index () with a maximum of 1360 cal/cm²/day in May. the least amount of received radiation occurs in Janury equal to 386.3 cal/cm²/day. Radiation variations at the provincial level are high between lowlands and highlands in Janury and December, according to the angle of radiation and significant elevation changes in the region. However, in summer, distribution of surface radiation is almost identical because of high solar radiation, and thus standard deviation amount of received radiation is reduced. The highest amount of radiation is received in Javanrood city as much as 528.1 cal/ cm²/day due to greater heights, and the lowest amount is received in Qasr-e Shirin city as much as 443.6 cal/ cm²/day due to lower heights.

Landslide mapping is one of the approaches (which can be) used to define the slope stable critical areas and the maps prepared based on this demarcating are used in desining sustainable development. The purpose of this study is to compare the landslide susceptibility mapping models based on Fuzzy logic, Information value and area Density applied in the Chamsangar watershed. According to the tectonic and Iran old basins divisions, this basin is a part of folded Zagros. To study the stability of slopes in the Chamsang field, in first step the sliding point and subsequent distribution maps of landslide were prepared using satellite images and field visits (recording the sliding points by GPS). in this study, Landslide-related factors such slope, aspect, altitude, lithology, rainfall, landuse and distance from the road and drainage used in the landslide susceptibility analyses. To assess and classify was there models outputs the Sum of Quality (QS) index used. Results show that area Density model with QS=1.85 is the more suitable than Information value model with QS=1.60 and Fuzzy logic with QS=0.554 based on the out put of the area Density model, the 36.31, 44.78, 16.62, 1.65, and 0.63 percent of the area is located in very low, low, medium, high and very high risk classes, respectively.

In this study, precipitation simulated annual and seasonal in East and North-East of Iran ,in 1987-2011, by using RegCM4 dynamic model in two case; with and without using post-processing technique. The required data for RegCM4 model with NetCDf format, received from ICTP center. For the implementation of the main dynamic model, Convective precipitation test scheme and the horizontal resolution, performed for 2007. According to the test, Kuo Schema had less error than Emmanuel and Gurl schemes in Precipitation and region temperature modeling. Horizontal resolution selected 30 Km. After model implementation with Gurl schema and 30 Km horizontal resolution, Precipitation and temperature output post- processed using MA model. According to results, in the study area, during 2006-2011 verification period, average annual rainfall raw bias of RegCM4 model was calculated and post-processed equal to 8.3 millimeter and 61.04 respectively. Briefly in the annual time scale, in 75% of studied stations, post-processing is effective and MA model is more efficient. In seasonal scale, bias error of average precipitation is equal to 54.99 millimeter in the winter, 27011 millimeters in the spring, -3.6 millimeter in the summer and 7.21 millimeter in the fall. Simulation of the temperature data in the stations using RegCM4 and MA model in north-east of Iran, revealed high performance. Bias error of average temperature is equal to -2.78 for RegCM4 model and post-processed equal to -0.05. In all stations, modeled Annual temperature and observational data has difference less than 0/1 ° C. In seasonal scale, the mean bias error range according ° C is equal to -4.1 in the winter, -4.09 in the spring, -1.8 in the summer and -1.5 in the fall.
 

In recent years, the severe fluctuations in precipitation have affected various parts of the country. On the southern coasts of the Caspian Sea, precipitation as one of the important climatic parameters has undergone changes due to global climate change. In the present study, we tried to evaluate the effect of climate change on rainfall in this region by applying a suitable model. In this study, observational period rainfall (1961-2001) was analyzed. the output of the HadCM3 model was used. At first, seven synoptic stations were selected and their data were analyzed in terms of accuracy, and length of statistical period, and lost data was restored. The AOGCM data were simulated using the SDSM model and the rainfall values were simulated for the observation period. After confirming the matching of the simulated data with observational data, the values of the Future (2039-2011) is estimated. The estimation errors of the SDSM model were calculated monthly by MBE and MAE criteria, and then compared. The output of the SDSM model was used to study the total annual precipitation in days with rainfall of more than 1 mm in the observation period and the upcoming period (2011-2039) by the R-Climdex model and the values of the PRCPTOT index Became zoning in the Future. The results showed that the model error in season with high rainfall is more than seasons with low rainfall. On a monthly scale, the maximum error occurred in the months of September, October, November and December. The maximum error in the fall and the minimum error was calculated in the spring and April and May months. According to the results, the total annual rainfall in the period of 2039-2011 will decrease in Anzali, Babolsar, Gorgan and Noshahr stations and rainfall will increase in stations of Astara, Ramsar and Rasht. Geographical distribution of selected were 5 sites in the Khuzestan, 20 sites in Bushehr, 24 sites in Hormozghan and 12 sites in Sistan and Baluchistan provinces. In total, 9000 sites were selected with a 2 km2 were suitable for large scale microalgae cultivation. The total area of these sites were estimated to be 18000 km2. The highest number of proper sites were found in Hormozghan province and lowest numbers of sites were found in Khuzestan province. The availability of technical service, carbon dioxide point resources from oil and gas units are an advantages for microalgae related activities in the Bushehr and Khuzestan provinces. The higher quality of water in the Sistan and Baluchistan province is an advantages for development of microalgae biomass production in the area.