nozar ghahreman

Evapotranspiration (ET) as a major component of the hydrological cycle and an important variable in the calculation of the earth's surface water and energy balance has a key role in agricultural water management. Under limited meteorological data, ET estimations using empirical models are less accurate and require adjustments based on water balance approach or lysimetric measurements. Combining ground observations with remote sensing information (such as satellite images) can improve the accuracy of these estimations. In this study, SENTINEL-2 satellite products were used to estimate actual evapotranspiration values in 8 stations of the Gorganrud-Gharehsoo basin, north of Iran, during the period 2016 to 2018. Considering the number of required images and significate variations of ET during warm season, a four months period of April to June was selected for comparisons. The Penman Monteith equation estimations using observed data of study stations was chosen as an evaluation metric of satellite products.Comparisons were evaluated using statistical indices of R2, RMSE. The coefficient of determination (R2) values between Penman-Monteith equation (PM) estimations and SENTINEL-2 outputs for the study stations of Rezvan, Bandar Turkman, Aliabad-e-katol, Gonbadkavos, Kalaleh, Gorgan, Gorgan Hashem-Abad, and Minoodasht, were 0.95, 0.76, 0.86, 0.87, 0.80, 0.86 and 0.84, respectively. The highest correlation of SENTINEL-2 and PM equation was obtained in Rezvan station (RMSE: 0.21, R2 = 0.95), while the lowest correlation was observed in Ghonbad kavoos stations, (RMSE = 0.37, R2 = 0.87). According to results, the SENTINEL-2 evapotranspiration estimations can be used in regions with in adequate observed data. The finer spatial resoulution of SENTINEL-2 improves the ET estimations accuracy. This satellite products may be recommended for regions with inadequate weather stations especially in semi arid regions

The evapotranspiration , as a major component of hydrological cycle, is quite important in agricultural water management and cropping pattern. The aim of this study is to asses two different climate datasets namely CRU and CFSR for estimation of potential evapotranspiration using WEAP model, in Karkheh basin southwest of Iran. Potential evapotranspiration estimated by Penman Monteith equation using observed data were considered as the evaluation criteria for WEP model outputs Potential evapotranspiration in Penman-Monteith method with observed data, was used as a criterion for evaluating the estimated values of the WEAP model. Comparisons were made using statistical indices of RMSE, R^2 and NSE between the potential evapotranspiration simulated by the WEAP model with two climate datasets CFSR and CRU and those obtained by PM equation. The corresponding values were 0.76, 0.98, 0.87, 1.08, 0.97 and 0.7 respectively, which shows that model generates better ET estimations by using the CFSR data. Comparison between study stations using RMSE, R^2 and NSE, revealed that using the CFSR dataset in Howeyzeh station with metrics of 0.44, 0.98 and 0.97, respectively and CRU data in Howeyzeh station with corresponding values of 1.19, 0.99 and 0.80, provided best model estimations. Considering the climate of station based on extended de-Martone classification, the accuracy of CRU and CFSR datasets in estimation of potential evapotranspiration were acceptable in temperate Mediterranean, moderate semi-arid and temperate arid desert climates in the study basin.

Climate change has a significant impact on evapotranspiration (ET) as the one of the main components of the hydrological cycle. The main purpose of this research is to project the amount of potential evapotranspiration (ETp), under RCP scenario comparing to the baseline period using the WEAP model in the selected stations of Karkhe basin, Iran. The 1999-2019 and 2020-2100 years were considered as the baseline and future periods. The projected climatic variables by KIOST-ESM and MPI-ESM1-2-LR climate models under two climate change scenarios of SSP2-4.5 and SSP5-8.5 and the observed data of the selected stations in Karkheh basin were used to estimate the  for the future and. According to the outputs of the WEAP model, the potential evapotranspiration will increase by 2100. The highest increase was projected under SSP5-8.5 scenario by the MPI-ESM1-2-LR model, with amount of 89 mm in June and by the KIOST-ESM model, 73 mm in May. The lowest values were equal to 26 and -0.5 mm in the months of December and September, respectively. Correspondingly, these values under SSP2-4.5 scenario were estimated as 85.4, 64.3, 23.3 and -4.6 during June, May, December and September compared to the base period, which the latter indicated a decrease for September comparing to the baseline. Also, the amount of potential evapotranspiration in regions with temperate Mediterranean climates will experience more variations comparing to those with arid temperate climates.

The pan evaporation is one of the major components of hydrological cycle. It is quite important in agricultural water management and water balance estimations. The current research was performed with two main goals. First, to study the trend of pan coefficient during baseline period of 1993-2018 and second, projection of pan evaporation during three future periods under RCP climate change scenarios in 5 selected stations across Iran, namely Mashhad, Bushehr, Ahvaz, Kerman. In part one; the monthly trend of Kp values were studied using Man-Kendal and Sen’s slope estimator in warm season (Spring and Summer).During baseline period a significant decreasing trend in pan evaporation was observed Mashhad,Bushehr and Ahvaz stations.In second part, for projection of pan evaporation under RCP scenarios, the PenPan model, a modified form of P-M equation, was used. The required projected climate data were retrieved from CNRM-C5 model outputs under RCP 4.5 and RCP 8.5 scenarios. The trend analysis of Kp values using Man-Kendal test during the baseline period showed a significant decreasing trend except for the Yazd station with the least coefficient of -1.88 mm. The greatest decreasing value based on Sen’slope estimator was observed in Bushehr station. The results of Man-Kendal test revealed a decreasing trend in pan evaporation in Ahvaz, Mashhad, Bushehr and increasing trend in Kerman stations. For future periods of 2020-2049, 2050 to 2079 and 2080 to 2100, an increasing trend of pan evaporation in Kerman station and decreasing trend in 3 other stations was detected. According to Sens’s slope estimator test, during the baseline period the most increasing trend was observed in Kerman station. For future period the most increasing trend was observed in Mashhad station (+0.64). It is projected that mean pan evaporation values during near, middle and far future periods would decrease 4.7, 6.2 and 8.8%, respectively. Despite of projected increase of air temperature in Mashhad, Bushehr and Ahvaz stations, a reduction in pan evaporation was observed which might be attributed to reduced received radiation, confirmed in previous studies as evaporation paradox.

Temperature is a key variable in climate and agriclutiral studies especially crop models, water requirement estimation and climate change. Despite of ease of measurement and large number of recording stations, data gaps in remote areas and the need for downscaling the grided climate model output has led to development of weather generators. In this study, the skill of Stochastic Harmonic Autoregressive Parametric (SHArP)SHArP weather generator in simulation of the daily maximum and minimum air temperature on a daily scale in 4 weather stations was evaluated. For this purpose, maximum and minimum temperature data as well as CNRM CMIP5 climate model projections were used in four synoptic stations of Kerman, Ahvaz, Karaj and Tabriz during the period of 2000-2015. The results of Pearson correlation coefficient showed that there is a significant correlation between observed data (0.78 to 0.93) and climatic model outputs.Comparing the observed and simulated temperature data generated by the SHArP model showed a good agreement and significant correlation which confirms the skill of this generator. The correlation coefficient in the studied stations varies between 0.80 to 0.95. The highest value of this coefficient belonged to the maximum temperature. The SHArp model also less simulates the temperature. In general, the findings of this study revealed that the SHArP model is capable to generate temperature data and can be used for filling the gaps.

The study of climate change effects on crop growth and irrigation water requirement is crucial in maintaining food security. As a direct consequence of warmer temperatures, the hydrologic cycle will undergo significant impact with accompanying changes in the rates of precipitation and evaporation. Water scarcity and climate change pose a big threat for Iran’s food security. Climate change is projected to increase temperatures, spatio-temporal variability in rainfall pattern, and water stresses to crops. Due to the significant role of water in crop yield, inadequate soil water in crop producing regions can result in substantial yield drops. Agriculture, which accounts for 80 percent of freshwater consumption worldwide and the researchers, examined trade through the lens of what they call “virtual water” — a measure of how much water goes into the production of a bushel or a kilogram of a given crop. Upon release of new scenarios based on Radiative Forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of 42 global climate models (GCMs) have been proposed for future climate projectionsThe current study was aimed to investigate the possible impacts of climate change effects on evapotranspiration and virtual water of several major crops of Wheat, barley, potato and tomato in there region in Kerman province, south of Iran.

Study consists two sections. In part one for climate change detection, trend analysis of temperature, rainfall and evapotranspiration variables were performed using Mann-Kendall and Sen’s slope estimator test in three study stations namely Bam, Jiroft, Kerman during three future periods (i.e. 2018-2039,2040- 2069, 2070-2100). In second part, for projection of virtual water of selected crops under RCP4.5 and RCP8.5 climate change scenarios (IPCC Fifth Assessment Report) during period of 2018-2100 in three study stations, crop evapotranspiration were worked out using downscaled outputs of CNRM-C5 climate model.The first step in impact assessment studies is selecting suitable climate models from those recommended by IPCC for obtaining required climatic data under certain scenarios. The results of uncertainty analysis performed in previous studies by authors were used choosing different models of CMIP5 project which are approved in fifth assessment report (AR5) . Also crops yield were simulated using Aqua Crop model. By choosing new date of sowing, temperature, rainfall and evapotranspiration during projected growing season were determined. Based on the maximum simulated yield for the study crops, the optimum date of sowing for future periods were chosen. Finally the crops virtual water (evapotranspiration divided by yield) was calculated.

The results of study showed that the air temperature, in all month in study stations, would increase comparing to baseline period 1990-2017, such that in three study stations under RCP 8.5 scenario the air temperature will increase 0.66,0.77,0.68 ◦C, respectively. Similarly under RCP 4.5 scenario, the corresponding values are 0.42, 027, 0.45 ◦C. The Maize crop yield would decrease in all three stations (with highest decrease in Jiroft station with 52 and 56 percents under RCP 4.5 and RCP 8.5  in 2018-2039 period comparing to baseline period, respectively.) The virtual water of all selected crops is projected to increase, but this increase would be higher for wheat and barley crops. The lowest increase in virtual water was observed in tomato crop during the future period of 2018-2100.In Bam station, the highest amount of virtual water belongs to barley crop during the period of 2040-2069, i.e. 4853 and 5153 cubic meter per ton under two RCP scenarios, respectively. In Jiroft wheat crop has the highest virtual water during the period 2040-2069 projected to be 4984 m3/ton. In case of Kerman station, largest amount of virtual amount under RCP4.5 belongs to wheat during the period 2040-2069 and under RCP 8.5 corresponds to barley with amount of 4256 m3/ton.

The virtual water of all selected crops is projected to increase; this increase would be more for Barley and Wheat. According to results it is recommended to estimate the virtual water of currently cultivated crops in the region for coming years. Continuous cropping of wheat and barley is not advisable Alternative low-water-use crops may be an option for producers. Further studies on major crops’ virtual water in the agricultural regions of the country are recom

Evapotranspiration is an important component of water balance and a key element in water resources management, especially in arid and semi-arid regions such as Iran. The purpose of this study is to investigate the uncertainty of actual evapotranspiration (ETa) estimates derived from a remote sensing-based model, i.e. Priestley–Taylor Model (PT-JPT), and two global climate datasets namely GLEAM and ERA-Interim in Karkheh basin southwest of Iran during  the 2013-2017 period. The three cornered hat (TCH) method was used to analyze the uncertainty for each spatial cell (0.25× 0.25) in the basin. The results of uncertainty analysis showed that ETERA-Interim, ETGLEAM, and ETPT- JPT data have the lowest relative uncertainty in 38%, 12.6% and 49.4% of cells, respectively. The highest percentage of cells with lowest uncertainty in Seimare, South Karkhe and Gamasiab sub-basins was correspond to ETPT-JPT model (54.4%, 72.3%, and 50%, respectively). In Gharehsoo and Kashkan sub-basins the ETERA-Interim estimations were found as the method with least uncertainty, (55.5% and 53.4%, respectively). The highest number of cells with lowest relative uncertainty belongs to ETERA-Interim. Considering the lowest uncertainty, variation of actual evapotranspiration with elevation in Karkhe basin showed that the two databases and PT-JPT model perform well at 1400 to 1800 m above sea level. ETPT-JPT model did a better job in warm dry climates. ETERA-Interim and ETGLEAM data estimations were selected as the best methods in semi-humid temperate and hyper-humid-cold climates, respectively. In cells with farm-garden and forest land use, ETGLEAM have the lowest uncertainty. Similarly, in rangelands, both ETPT-JPT and ETERA-Interim databases, and for drylands, ETERA-Interim data can be recommended. Further feasibility studies in other climates are required for more scrutiny.

Regional estimation of evapotranspiration (ET) is crucial in agricultural water management, cropping pattern and crop yield forecasting.The main purpose of this study is evaluation of actual evapotranspiration estimations of Global Land Evaporation Amsterdam (GLEAM) model of Karkhe basin, southwest of Iran. GLEAM outputs include different set of RS-based estimations, i.e. transpiration, interception, bare soil evaporation, open water evaporation and snow sublimation. In current research, the GLEAM estimation of actual evapotranspiration were compared by water balance approach (assuming a watertight basin) using coefficient of determination (R2). The corresponding values of R2 for the each study stations across the Karkhe basin namely Aran-e-Gharb,Pirsalman,Nazarabad,Polchehr,Paypol,Poldokhtar,Tangsazbon, Pole-e-kaskhan were 0.66, 0.62,0.77,0.84,0.59,0.49,0.56 and 0.66 Similarly, the RMSE values were 0.7, 0.62, 0.74, 0.25 0.45, 0.87, 0.73 and 0.51. The MAE of same comparisons were found to be 0.52, 0.43, 0.58, 0.18 0.3, 0.71, 0.53 and 0.36. Besides the relative deviation of GLEAM dataset estimations and those obtained from water balance method were calculated as -21,-3.73,-4.96,-4.19,-19.61,-2.39,-3.64,-25.18. The best agreement by all statistical indices was observed in Polchehr sub-basin. The results of this study indicated that RS-based estimations of GLEAM model can be used in Karkhe basin, especially in regions with lack of sufficient observed data.

Data mining approaches are designed for classification problems in which each observation is a member of one and only one class. In this study, a non-deterministic approach based on C5.0 data mining algorithm has been employed for discriminating the phenological stages of maize from emergence to dough, in a field located in Karaj, Iran. Two readily-available predictors i.e. accumulated growing degree days (AGDD) and multi-temporal LANDSAT7-extracted normalized difference vegetation index (NDVI) was used to build the decision tree. The AGDD was calculated based on three cardinal thresholds of temperature i.e. effective minimum, optimum, effective maximum. The NDVI was compared with two recently developed indices namely, enhanced vegetation index2 (EVI2) and optimized soil adjusted vegetation index (OSAVI) using the signal to noise ratio (SNR) criterion. Findings confirmed that these three remotely sensed indices do not have significant differences, therefore, the smoothed time series of NDVI was used in the C5.0 algorithm. The precisions of classification by C5.0 data mining algorithm in partitioning of training and testing data were approximately 90.51 and 81.77%, respectively. The mean absolute error (MAE) values of the onset of maize phenological stages were estimated about 2.6–5.3 days for various stages by C5.0 model. While corresponding values for the classical AGDD model were 3.9–10.7 days. This confirms the skill of data mining approach in comparison with commonly-used the classical AGDD model in applications of real time monitoring.

The temperature humidity index (THI) is one of the measures which describe the climatic discomfort as a result of combined effects of temperature and humidity. This index provides a rapid and accurate evaluation of heat stress effect on animal’s health and productions. Considering the sequences of global warming and climate change, it is expected that THI values would vary due to changes in temperature and humidity in coming decades. As the soft computing skills increased in recent decades, more number of climate models has been developed for weather and climate predictions which have significantly improved the quality and resolution of projections. This remarkable increase in number of climate models has enabled the scientists to estimate a wide range of main climate variables such as humidity and temperature in fine temporal and spatial resolution. Upon release of new scenarios based on Radiative Forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of climate models (GCMs) have been proposed for future climate projections. The main aim of this study is to investigate the temporal and spatial variations of THI values in selected weather stations across Iran as a result of climate change under RCP climatic scenarios proposed in fifth assessment report (AR5) of IPCC.

Crop phonological stages are commonly predicted by using accumulated growth degree days(AGDD).In this study a combined model of AGDD and remotely sensed NDVI has been developed for prediction of maize (cv. K407) phenology in Karaj using a nine year (2002 to 2010) dataset. For smoothing the existing noises of image processing, a combination of double logistic and weighing average (DL-WLS) approaches was employed. The results of combined phenology model were compared by two frequently used methods based on AGDD and date of sowing. The findings showed that in general, the developed model predicted the first 7 phenological stages of emergence to milky, more accurately comparing to other approaches (with average 2 and 2.5 days difference with observed dates, respectively) but was inaccurate for maturity stage. Our study highlights the need for further improvements in observations in the region.

As a direct consequence of warmer temperatures, the hydrologic cycle will undergo significant impact with accompanying changes in the rates of precipitation and evaporation. Climate change will cause changes in climate variable such as precipitation, temperature, sunshine hours, wind speed and etc. So as a result of climate variable change, the related variable such as potential evapotranspiration will change too. As the soft computing skills increased in recent decades, more number of climate models has been developed for weather and climate predictions which have significantly improved the quality and quantity of projections. This notable increase in number of climate models has enabled the scientists to estimate a wide range of main climate variables such as precipitation and temperature in fine temporal and spatial resolutions. Although the uncertainty in model outputs still remains a main challenge. Upon the release of new scenarios based on radiative forcing which are known as Representative Concentration Pathway scenarios (RCP scenarios), by Intergovernmental panel on climate change (IPCC) in fifth assessment report (AR5), a new set of 42 global climate models (GCMs) have been proposed for future climate projections. Apart from increased number of available models, three main sources of uncertainty including: measurement error, variability, and model structure, that have been explained and studied in AR5.The aim of the current study is to investigate of changes of potential evapotranspiration (ET) over Mashhad plain, Northeast of Iran in future period 2021-2070 under two RCP scenarios i.e. RCP4.5 and RCP8.5. The main synoptic station in the region is Mashhad Station located at 59◦ 38ﹶE, 36◦ 18ﹶN, with elevation of 990 m. above M.S.L. The required meteorological data including maximum and minimum temperature, sunshine hours,wind speed for period of 1991 to 2005 were obtained from Iran Meteorological Organization for ET calculation using FAO Penman-Monteith (hereafter, FAO-PM) equation. Besides, the downscaled historical data of potential evapotranspiration provided by Swedish Meteorological and Hydrological Institute (SMHI) have been retrieved for the baseline period of 1991-2005.Then these historical estimated data were compared with those estimated using FAO-PM equation. The historical ET values were post-processed using a statistical proposed method for more accuracy. By completion of this part, the accuracy of historical dataset provided by SMHI was confirmed and used for further comparisons. In the second section the ET variations for future period of 2021 to 2070 under two RCP scenarios of 4.5 and 8.5 was studied. The results showed better estimation of ET during warm months. Statistical comparisons using T-test revealed significant differences between historical and estimated values of ET in months of February, March and December. The correlation coefficient between post processed and observed data showed similar results as in T-test. Since the historical dataset of potential evapotranspiration provided by SMHI was acceptable, it was used for the analysis during future period (2021-2070) under RCP4.5 and RCP8.5 scenarios compared to baseline observed data. The result of this part showed that the highest increase of potential evapotranspiration would be for January by 15.4% and 16.4% under RCP4.5 and RCP8.5 scenarios respectively and October would experience lowest decrease by -12.5% and -10.0% decrease, respectively. In general ET increase will be more under RCP 8.5 scenario comparing to RCP 4.5

The seasonal forecasts of precipitation play a major role in agricultural and water resource management and also in monitoring extreme events such as drought and flood. The Earth Systems Physics (ESP) group of the Abdus Salam International Centre for Theoretical Physics (ICTP) maintains and distributes a stateof- the-science regional climate model called the ICTP Regional Climate Model (RegCM), which has been successfully used in different regions of the world for a diverse range of climate-related studies. This study was performed with two aims: (1) to evaluate the performance of the RegCM4 dynamic model in forecasting monthly, seasonal and annual precipitation in four selected stations in the northwest of Iran, i.e. Tabriz, Ardabil, Khouy and Urumia; and (2) to examine the accuracy of a stepwise regression technique for post processing of the outputs of the model for a 30-year period from 1982 to 2011. In order to run the RegCM4, the required observed weather data of the study stations were collected from the Iran Meteorological Organization (IRIMO) archive, while the rest of the data were collected from the ICTP database including three sets of the weather data: NCEP/NCAR Reanalysis Product version 1 (NNRP1) with a 6-hour time step and a horizontal resolution of 2.5°×2.5° on the reanalysis data from the National Center of Environmental Prediction (NCEP) of the United States, Sea Surface Temperature (SST) of the Optimum Interpolation Sea Surface Temperature (OISST) type, retrieved from the National Oceanic and Atmospheric Administration (NOAA) database and surface data (SURFACE), which were consisted of three topographic features: Global Topographic (GTOPO), vegetation or land use Global Land Cover Characterization (GLCC), and soil type Global Zobler (GLZB) data, with a horizontal resolution of 30×30 seconds from the United States Geological Survey, for the period 1982–2011. To determine a suitable rainfall scheme, the normal year of 2009 was chosen for running the model using different schemes. Accordingly, the Kuo scheme with a minimum bias compared to the observed precipitation amounts in the entire 36 synoptic stations of the region was selected as the best scheme. The time step was set to 100 seconds, with a spatial resolution of 30×30 km2, and the number of grid points were 152 in longitude (iy) and 168 in latitude (ix) during the study period. The geographical area center was placed at 30.5° N and 50° E. Nine significant variables (excluding, total precipitation; tpr) having the highest correlation with precipitation were determined as q2m, t2m, ps, v1000, v500, u1000, u500, omega1000, and omega500. For post-processing of the outputs of the model, the multiple linear regressions (MLR) approach was used. Except for the warm months, the output of the RegCM4 showed a wet bias, and overestimation. Applying the multivariate linear regression equation (and sometimes two-variables) to the output of the model led to a better agreement between the observed and simulated values of precipitation, such that in 75% of the cases, the bias and relative error decreased for the monthly, seasonal and annual forecasts. At all stations, except for Urumia, performing the post processing improved the accuracy of the RegCM4 output at all time scales. Further scrutiny is recommended for explaining the variations among the stations.

Multi site modeling of rainfall is one of the most important issues in environmental sciences especially in watershed management. For this purpose, different statistical models have been developed which involve spatial approaches in simulation and modeling of daily rainfall values. The hidden Markov is one of the multi-site daily rainfall models which in addition to simulation of daily rainfall values, explores the spatial and temporal pattern of rainfall events. In this study, the winter (January to April) rainfall pattern of 130 rain gauges have been modeled using hidden Markov approach during a 21 years period (1990-2010). The aim of this study was finding temporal and spatial distribution of weather patterns and stochastic simulation of occurrence and amount of rainfall, simultaneously. To achieve this goal, different hidden Markov algorithms including, Viterbi decoding algorithm, Expectation-Maximization (EM) algorithm and a stochastic simulation approach with the probability transformation were applied. It is expected that extracted patterns, using hidden Markov model, are consistent with synoptic patterns and accordingly eight different weather pattern as the definite set of possible cases were recognized. The most frequent rainfall pattern extracted from hidden Markov model was the dry pattern (stable condition) in which the rainfall occurrence probability is low in most of the stations. This pattern has the maximum initial probability of 0.429 and maximum Markov transfer probability of 0.637 Besides, multi-site simulation of winter rainfall keeping the basic statistic of mean, standard deviation of total seasonal rainfall and percentile values in each station and also spatial correlation of occurrence or non-occurrence of rainfall produced reasonable result. In general this approach can be recommended for regional studies.

The accuracy of the classical methods to calculate evapotranspiration of plants for use in large areas may not be sufficient and therefore less useful. Thus, the choice of high-precision approaches, despite of restrictions ahead seems necessary. The purpose of this study is calculation of sugarcane evapotranspiration in Ahvaz, Khuzestan province using two methods. In method one, soil evaporation and crop transpiration were separately calculated using NDVI index extracted from MODIS images in. In the second approach, evapotranspiration was estimated using single crop coefficient approach known as FAO 56 method using available coefficients. Besides, crop coefficient values were calculated using SimDualKc software during entire growing season and FAO Penman-Monteith equation was worked out accordingly.The results were also compared to a lysimetric dataset. Correlation coefficient of ET estimations using single and dual crop coefficient approaches with lysimetric measurements were 0.954 and 0.885, respectively. A. On the other hand, the standard error for single coefficient approach was 67.29 and 29.34 dual approach. The results showed that the remote sensing-based dual coefficient approach provides acceptable estimations of sugar cane in the region. More studies are recommended for further scrutiny.

The main perspective in seasonal prediction of precipitation، is presenting a qualitative prediction for upcoming seasons. Information gained from such predictions can be used for decision making in various deciplines such as agriculture، water management and hydropower production. Besides، it can help for reducing the adverse effects of climatic changes like drought and flood. But General Ciculation Models (GCMs) outputs have coarse resolution (>100 km). Dynamical downscaling is a method for obtaining high-resolution climate data from relatively coarse resolution global climate models which do not capture the effects of local and regional forcing in areas of complex surface physiography. GCMs outputs at spatial resolution of 150-300 km are unable to resolve important sub-grid scale features such as clouds and topography. Many impacts models require information at scales of 50 km or less. As suchm، several statistical and dynamical methods are developed to estimate the smaller-scale information. Dynamical downscaling uses a limited area، high resolution model (a regional climate model: RCM) driven by boundary conditions from a GCM to derive smaller scale information. The aim of this study was application of RegCM4 dynamic model (Reginal climate model) in forecasting rainfall and improving the outputs using post processing techniques in northwest Iran during period 1982-2011. The recorded data of precipitation were collected from Urmia، Tabriz، Ardebil and Khuy Stations. The data required for running the regional climate model RegCM4 were adopted from center ICTP (International Centre for Theoretical Physics)، in the format of NetCDF including three sets of weather data، NNRP1 with a 6-hour-scale and a horizontal resolution of 2. 5°×2. 5° on the reanalysis databases of National Center of environmental prediction of United States، sea surface temperature، (SST) with a horizontal resolution of in 1°×1° from the type of SST belonged to America and National Oceanic and Atmospheric surface SURFACE، which were consisted of three topographic data GTOPO، the vegetation or land use، GLCC، and the soil type data GLZB، with a horizontal resolution of 30×30 seconds from United States Geological Survey، for the period 1982 to 2011. In order to implement the dynamic model، a test was conducted to determine the Convective Precipitation scheme and the amount of horizontal resolution for the year 2009 (as a normal year)، Accordingly، Kuo scheme with minimum mean bias error (MBE)، in comparison with observed precipitation in 36 synoptic stations of the region، was implemented as the main scheme، horizontal resolution of 30 × 30 square kilometers، the number of grid points including 152 in longitude (iy) and 168 in latitude (ix) was conducted during the statistical period of 1982 to 2011. Geographical area center implemented in the intended period was located in 30. 5 degrees north latitude and 50 degrees east longitude respectively. The output of the model included atmospheric data (ATM)، surface cover (SRF) and radiation cover with the format of NetCDF، each containing a large number of meteorological variables among which except precipitation from the Model (tpr)، 9 variables that were associated more with precipitation including q2m و t2m، ps، v1000، v500، u1000، u500،omega1000، omega500 were extracted. For post-processing the output of the model، the Multi-Layer Perceptron (MLP) and in Moving Average (MA) methods were used. For MLP Entering variables were 10 aforementioned variables and the target variable was observatory precipitation in the stationary point. At any one time، 80% of the data at the beginning of the series were for train and final 20 percent of data was used for test. The results of the study demonstrated that، in the study area، the mean bias error of raw annual precipitation outputs of the RegCM4 model was 124. 3 mm in the validation period، which by conducting Post Processing، reduced to 8. 9 mm. In the seasonal and monthly time scales، also، mean bias error of the were 31. 1 and 10. 4 mm، respectively، which were reduced to -0. 3 and zero mm، respectively، after post processing. The MA model was the prefered post processing method، in all time scales. In conclusion، it can be stated that the RegCM4 regional climate model with the said implementing conditions and in the study area، contained، mainly، overestimate in precipitation forecasting. However، the application of post-processing will optimally reduce bias. The appropriate method is also the simple moving average (MA) method.

An accurate estimation of evapotranspiration is a key issue in running climate models, especially for the calculation of surface fluxes. In recent years, by development of global and regional climate models, long-term predictions of weather parameters affecting evapotranpiration have been done more easily. The ability of the RegCM regional climate model (Version 3) in simulating the potential evapotranspiration over Mashhad in Northeastern Iran was evaluated during the base period of 1961 to 1985 and the future period of 2021-2035. Due to lack of a measured amount of evapotranspiration, the Penman–Montith (P-M) equation was chosen to estimate actual values of ET in the base period. For initialization of the RegCM3 model, boundary conditions from EH5OM General Circulation Model (GCM) were used as initial and boundary conditions to feed the RegCM3 regional model. Future predictions of evapotranspiration were done under A1B emission scenario. A horizontal resolution of 50 km was considered for the model. Based on the assumed conditions in running the model, the results showed that the nonpost- processed RegCM model outputs cannot be used for the estimation of potential evapotranspiration. However, after post processing using multivariate regression, results were closer to those calculated by P-M equation. Temperature and radiation parameters were considered as independent variables in the multiple regression models to perform post-processing. Our results showed that the mean annual evapotranspiration in the future period (1075 mm) will be increased by 16.34%, compared to the base period (924 mm). On the whole, the average potential evapotranspiration will be increased in April, May, June, August, September and October, while it will be decreased in January, February, July, November and December, compared to the base period. The outcomes of the present study reveal that the maximum evapotranspiration in the future period will be in June, while in the base period the maximum amount of evapotranspiration occurs in July.

Because of the scarcity of in situ measurements، estimation of soil temperature by other means is very indispensable، as for Irrigation management and scheduling when in different field conditions. So far، many regression models have been developed for an estimation of soil temperature، using meteorological data under bare soil. Throughout this study، the performance of COUP simulation model and multiple regression approach for an estimation of soil temperature within an experimental plot، and under grass (Lolium perenne) canopy (in Karaj climatic conditions has been evaluated. Soil physical parameters، estimated as based on soil analysis (soil texture، bulk density)، and daily meteorological data (including maximum and minimum temperature، wind speed، pan evaporation، sunshine hours and rainfall) as well as vegetation data (crop height، root depth and Leaf Area Index (LAI) were made use of to run the model over the growing period. Soil temperature was measured using standard soil thermometers at depths of 10، 30، 50 and 70 centimeters. Stepwise approach was employed to develop suitable regression models. Following a running of both simulation and statistical models، the observed and simulated data values were compared، making use of statistical indices. The results revealed that، by inclusion of variables affecting incoming radiation i. e. crop height، and leaf area index، the accuracy in the prediction of soil moisture increases.

Evaporation is a fundamental parameter in the hydrological cycle. This study examines the performance of M5 model tree and artificial neural network (ANN) models in estimating potential evapotranspiration calculated by Penman- Monteith and Hargreaves- Samani equations. Daily weather data from two meteorological stations in a semi-arid climate of Iran, namely Kerman and Zahedan, were collected during 1995-2004 and included the mean, maximum and minimum air temperatures, dewpoint, relative humidity, sunshine hours, and wind speed. Results for both stations showed that the performance of the M5 model tree was more accurate (R=0.982 and 0.98 for Penman-Monteith equation and R=0.983 and 0.98 for Hargreaves-Samani equation in Kerman and Zahedan, respectively) than the ANN model (R=0.975 and 0.978 for Penman-Monteith equation and R=0.967 and 0.974 for Hargreaves-Samani equation in Kerman and Zahedan, respectively), but the models’ differences were insignificant at a confidence level of 95%. It also performed better at the Zahedan station using the Penman- Monteith equation. The most significant variables affecting the potential evapotranspiration in the case of the Penman–Monteith equation were found to be mean air temperature, sunshine hours, wind speed, and relative humidity. Similarly, for the Hargreaves-Samani equation, the maximum and minimum temperatures, sunshine hours, and wind speed were determined to be the most significant variables. Further studies in other climates are recommended for further analysis.

Climate change that the human faces is a somewhat unavoidable phenomenon. Successful management of water resources needs recognition and perception of climate change in order to cope with water scarcity. The water scarcity is created by natural forcings such as drought، which is affected by regional climate. In other words، variation of climate variables as a result of climate change leads to variations in drought severity and frequency. Since climate change scenarios are based on assumption of increasing، decreasing or non-significant trend in climatic means، It is expected that the effects of these assumptions would be reflected in the prediction of meteorological phenomena like drought. In Markov analysis، these variations are determined as change in transfer probability function values or shift in drought severity class، which are both important in management decisions. For instance، by increasing the temperature or decreasing the rainfall it is expected that occurrence of a drought event under certain conditions would be more probable. In this study، the outputs of three General Circulation Models (GCMs) namely; ECHO-G، CGCM3T63 HADCM3 under three climate change scenarios were downscaled using a non-parametric approach for simulation of rainfall and temperature series during 2011-2040 in northwest of Iran. This downscaling approach is combination of two techniques i. e. Kernel probability density function estimator (KDE) and Strategic Re-sampling method by which predicted variations of GCM outputs are extended and transformed to generated time series of a given future period. In KDE method، A probability density function is defined with center value of ith observation from series (xi، i=1،…،n). Contribution of each observation in estimation of probability density function of ith observation is estimated by this Kernel function. The main parameter of this function is the bandwidth which is، by mathematical definition، a distance on x-axis in which the function variation is insignificant. Firstly a random normal kernel is selected and its average is considered as the base vector. Selection probability of each vector is 1/n. Then by calculation of cumulative probability and comparison with a random number between 0 and 1، one of the normal kernels is selected for rest of the simulations. The strategic re-sampling method uses a rule for generating series with specific feature such as increasing frequency of warmer or more rainy days. The criteria for such features are selected by the user based on the outputs of GCMs. Considering its semi random nature، this approach cannot be used alone for regional climate change simulations and should be combined with a weather generator such that the applied rule should be run on observed or historical series. Then، the outputs are feed in weather generator for generating a completely random series coincide with climatic scenario. After simulation of climate، Reconnaissance Drought Index (RDI) was used for monitoring drought during two periods 1971-2000 and 2011 to 2040 in northwest of Iran. This index uses the ratio of precipitation and evapotranspiration (calculated by Thorntwait method)، hence as the index becomes smaller، more severe would be the drought. Thus، the necessary variables for RDI estimation are monthly mean temperature and total rainfall. For RDI calculation، firstly، the precipitation (prec) and potential evapotranspiration (PET) are calculated cumulatively with determination of the moving window value، and then، RDI values are obtained as logarithm of cumulative prec to PET ratio. Four classes are considered for RDI including: normal class (larger than -1)، moderately drought class (-1 to -1. 5)، severe drought class (-1. 5 to -2) and very severe drought (less than -2). Taking into account the length of the dry periods in the arid regions of the country، the Reconnaissance Drought Index in 6-month timescale was used for drought monitoring. Markov analysis was applied for calculation of transfer probability and corresponding drought severity classes with three steps forward to assess the sequences of climatic assumptions on management early warnings. Behavior of a Markov model is determined by a series of probabilities in transition from one state to another namely transition probabilities. These probabilities may vary by climate change. The first-order Markov chain model was employed to predict drought condition up to 3-step ahead. This model was fitted on the RDI series at all stations of interest، and it was identified that it can represent the probabilistic behavior of drought over northwest of Iran. Research findings are presented in three parts of downscaling method implementation، RDI monitoring and Markov analysis. The weather generator model was successful for simulation of monthly normals including means and standard deviation. Also، strategic re-sampling technique as aligning method was successful for simulation of deviations from normal. Drought monitoring with RDI showed a water tension resonance in second 15 years of 1971-2000 periods. Likewise، in part of Markov analysis، findings of this study revealed that under conditions of increasing temperature and decreasing rainfall، as the worst case، the effect of climate change on meteorological drought would appear as the class shift، and in most of the study stations under this scenario، increased duration of extremely drought (class 4) was forecasted، even 2 steps ahead، which is important in water resource management.

Assessment of agricultural drought risk at critical times before and during the growing season can provide sufficient time to policy makers and farmers to implement appropriate strategies to reduce the potential of risk. The purpose of this study is to develop a statistical model to estimate the quality of agricultural drought risk for rainfed barley crop (before planting and during growing season) in East Azarbaijan province، Iran. Model input variables include the amounts of Weekly Standardized Precipitation Index (SPI) in various time windows and the only output variable is the risk of crop yield (with two groups: high risk and low risk). A multivariate technique was used for modeling the relationship between input and output variables during different growth stages. The results showed that the calibrated model can be used to assess real-time agricultural drought risk for barley crop at pre-planting and during crop growth stages by retaining previous، and adding current WSPI data as the crop pass through the various development stages. The accuracy of risk assessment increases as barley crop develops. Model validation revealed that the most appropriate time to asses drought risk for barley crop in the area of study is the flowering stage، but the results for tillering stage is also suitable.

In agricultural meteorology، the growing season is the period of each year when crops can be grown with the view of thermal condition. This period is generally between the first and last frost dates in autumn and spring in which temperature is above a specific threshold. There are two other definitions using a 50C threshold. One of the methods to study future climate is using the general circulation models (GCMs)، but these models have low temporal and spatial resolution and they can’t show local changes. In this study، statistical model LARS-WG has been used for downscaling GCMs outputs under A2 and B1 emission scenario conditions in several climatic regions of Iran during period of 1961-2004 for estimation of growing season length (GSL). The results showed that growing season with 00C threshold under the two scenarios will increase during 2020 to 2050 in Mashhad، Tehran and Rash stations and will decrease Kerman and Gorgan and Tabriz in comparison with their past climates. GSL with 50C threshold under two scenarios will increase in all stations except for Shiraz station. In general GSL for all three definitions under both scenarios would increase in Mashhad، Rasht and Tehran. In case of Esfahan، no significant trend was observed using 00C threshold and B1 scenario، and increasing trend for rest of the conditions.

The outputs of General Circulation Models (GCMs) which are available for the users based on predefined scenarios have a low spatial resolution. Hence، downscaling techniques should be used for regional studies. Since climate is an effective factor in natural phenomena، a time series of future weather data is required for meteorological، agricultural and hydrological prediction or pre-warning applications. It is also important to select and utilize better and more accurate techniques for theses purposes. Generally، downscaling methods are classified into two categories: dynamical and statistical. The statistical downscaling is commonly considered due to its simplicity and wide applicability. As an example، LARS-WG is a parametric or semi-parametric model which has been used widely، but it underestimates monthly variances. Undoubtedly، it is more useful to use an approach having a non-parametric structure so that it does not rely on a statistical structure by default. These models use a set of observations in the simulation process not a certain value namely the «parameter». The aim of this study was to use a nonparametric approach for downscaling the GCM outputs. This approach is composed of one weather generator (WG) and a technique called strategic re-sampling which creates series that match the GCM output. The weather generator itself is based on a Kernel Density Estimator (KDE) and it is a multivariate weather generator. In the KDE method، all of the observations with a definite kernel function، commonly standardized and normalized، are used. Firstly، one of the normal kernels (with probability 1/n) is selected randomly and its mean is considered as the basic vector. Bandwidth (h) is the only parameter that should be estimated. The strategic resampling method includes a stochastic function based on the definition of the «shape parameter»، which determines the tendency of the new series. At the first step، a strategic re-sampling is run and then strategic series are prepared as input to the WG and the simulating climate prospect. The study area is Karkheh Basin in Khuzestan Province، Southwest of Iran. Downscaling was done for two periods، 30 years (2011 to 2040) and 60 years (2011- 2060) for monthly rainfall and air temperature variables based on the A1B scenario of the CGCM3T63 model. The results can be divided into three groups: an estimate of the strategic re-sampling parameter، evaluation of the weather generator applicability and finally، the climate change simulation. Results showed that in case of temperature، by selecting a unit value for the shape parameter، the generated series coincide with the observed or historical series. Substituting the values less or more than one resulted in warmer and colder simulated series، respectively. Similarly، for rainfall series the optimum value was 0. 9. Accoring to the results، the ability of the WG in simulation of moments of different orders (mean، variance and skewness coefficient) was acceptable and the coefficients were cross validated. The applied GCM showed warmer and drier series for both study periods. The findings of the study revealed that future climate would be simulated accurately and non-significantly different from GCM outputs. In general، the suggested non-parametric approach can be recommend due to the following features: non existence of a default pattern in its structure، the least number of parameters for running، coincidence of the high accuracy in downscaling with the GCM outputs and simplicity. More case studies are recommended for further scrutiny.

Wind speed is one of the major parameters required for an estimation of evapotranspiration and determination of crop water requirements. Hence، several models and methods have been developed for a prediction of this needed climatic variable. In recent years، by development of soft computing tools، such intelligent systems as Artificial Neural Network (ANN) approach have been widely employed in agrometeorological studies. In this study، three types of four layers ANN models of different number of neurons were generated and utilized for a prediction of wind speed، using hourly data of Jiroft Agrometeorological Station، during a 6 month period، April to September، 2010. During these months winds are of higher speeds than those during the rest of the year. Statistical indices of RMSE، ME and EF (Efficiency Factor) were utilized for comparisons and as well for models'' evaluations. The results revealed that an ANN model with 20 neurons in each layer is of the most suitable performance in prediction of wind speed with the respective corresponding values of these indices as 1. 1827، 0. 6947 and 0. 9246.

Introduction In absence of long-term time series of meteorological variables required for different type of studies, simulation models of these variables, generally known as weather generators (WGs), have been developed in recently. Taking into account the significant climatic variations defined in climate change scenarios, it seems necessary to define these changes upon simulated series. Since conventional weather generators do not have such a capability, new methods should be taken into consideration. Models that have been used in empirical downscaling studies include multiple linear regression, canonical correlation analysis, nearest neighbor and artificial neural network approaches. These models can account for variability in the surface variables to an acceptable level but extrapolation beyond the historical conditions may be unreliable. The ability of the downscaling models to predict climate change impacts is greatly reduced due to the coarse spatial resolution of GCMs. This paper describes the application of a non-parametric approach called strategic re-sampling, in simulation of series along with the climate change scenarios. Using this method, these changes were conditioned upon observed series and then by using a weather generator, transferred to simulated series. The basic concept of the models is the non-parametric method of Nearest Neighborhood.Material and MethodsThe study was performed in 5 synoptic stations namely Tehran-Mehrabad, Mashhad, Tabriz, Qazvin and Bushehr stations. The target variables were maximum temperature, minimum temperature and rainfall. In this non-parametric simulation method, by defining a moving window with central feature vector and The atmospheric Aerosol Optical Depth (AOD) of the city of Tehran in each station was calculated using MODIS images. The calculated AODs were compared with pollution data collected at the station where a correlation coefficient of about 0.7 through a linear equation of (PM10 (μgr/m3) = 222.78*AOD - 65.091) was achieved. This is shown in figure (1) below: Fig. 1. A Model for extraction of PM10 from MODIS AODResults and Discussion Generally it seems that this amount of correlation for low spatial resolution data such as MODIS is acceptable. It should be noted that the AOD's pixel size are usually 10*10 km2 and therefore the ground underneath is covered only with a few of these pixels in each satellite passage, while the ground based measurement data are only valid in a limited distance around the station. This in many cases renders the accuracy of comparing satellite data and ground-based data difficult. One of the main sources of uncertainties was the MODIS AOD product low spatial resolution of 10 by 10 kilometers. While the results of measurement in the stations can hardly represents 100 square kilometers. ConclusionA positive relationship between aerosol optical depth and particulate matter of less than 10 nanometer size is discovered by many workers. To supply a model for this relationship use has been made from MODIS AOD products as well as in situ measurements of pollutions specially PM10. Despite low resolution of MODIS products, a correlation of about 0.7 was found between AOD and PM10 in Tehran. The results seem acceptable in situation of lack of continuous observation due to the present financial problems.