جستجوی مقالات مرتبط با کلیدواژه « downscaling » در نشریات گروه « آب و خاک »

Soil erosion and its impacts on the earth's resources are significant concerns in many countries. The most important effects of soil erosion are loss of soil fertility, water pollution, reduction of agricultural productions and reduction of dam’s useful life. The present study aims to predict the future effects of climate change and land use change on the soil erosion intensity and potential in the Gorganroud watershed. The erosion rates were compared with the RUSLE model in three different scenarios: future climate change, future land use change, and a combination of climate and land use changes for erosion rates in base period.

Initially, weather data (temperature and precipitation) from existing stations in the catchment area were collected for a 20-year statistical period (2001-2020). The normality, homogeneity, and randomness of the data were examined using the Kolmogorov-Smirnov and run tests, respectively. To address statistical deficiencies, a regression method was employed in SPSS software. The statistical downscaled data from the general circulation model and synthetic data were generated for the future period (2021-2040) using the AIB, A2, and B1 scenarios (optimistic, pessimistic, and moderate, respectively) in the LARS-WG model based on the fifth report of the Intergovernmental Panel on Climate Change, and two models, HADCM3 and GFCM21. Additionally, the land use map of the catchment area was prepared using Landsat 7 and 8 satellite images for the years 2001, 2010, and 2020 and evaluated through Google Earth. Finally, the CA-Markov model in the IDRISI Selva software was used to simulate future land use changes. Soil loss values ​​for the current period and under climate and land use change scenarios were also calculated based on the RUSLE model.

The results showed that soil erosion rates increase under climate change scenarios compared to the base period. Land use changes and coverage will also shift towards a decrease in dense and semi-dense forest areas and an increase in pastures and residential areas. The results indicated that the average annual soil erosion rate in the base period is 41.96 tons per hectare per year. With the consideration of A2, A1B, and B1 scenarios, the erosion rate will increase by 2-4% compared to the base period. By considering the simulated land use in 2040 and the A2, A1B, and B1 scenarios, the erosion rate will increase by 7.5%, 25.5%, and 73.1%, respectively, due to the reduction in natural coverage.

The results showed that land use changes have the most significant impact on soil erosion rates, and therefore, proper management of cover can mitigate the increasing trend of soil erosion in the Gorganroud catchment area. The largest share of land use in creating erosion is related to semi-dense forest use with an average of 115.04 ton/ha/year and dense forest use without considering residential areas has the lowest share with an average value of 51.39 ton/ha/year.

Precipitation is the most important source of water supply for the agricultural sector, which is unevenly distributed across the country based on the long-term average. In this research, the MPI-ESM1.2-HR Earth System Model boundary condition data has been used for dynamically downscaling by RegCM4.7 regional climate model to project the country's average precipitation in the future period of 2026-2075 based on the SSP scenarios. Dynamical downscaling was performed according to SSP2-4.5 and SSP5-8.5 Shared Socio-economic Pathway scenarios from the primary horizontal resolution of 100×100 to 30×30 km. Precipitation projected by the regional climate model was bias corrected using the Linear Scaling (LS) method. In bias correctoion process, CRU gridded observation data and dynamically downscaled precipitation values in the historical period of 2000-2014 were used. The results showed that the annual precipitation in the southern third of the country will be increased by 7 to 15% in all future periods and SSP scenarios, when comparing to the mean observation period. However, in the northern and central third of the country, both increasing and decreasing changes in precipitation (between -2% and +7%) were projected. In the spring and summer seasons, the precipitation is mainly increasing, with the largest increase in the spring in the southern regions and in the summer in the south and east of the country. On the other hand, in the western regions of the country, the precipitation is projected to decrease.  In the autumn season, the country's precipitation is decreasing (increasing) based on SSP2-4.5 (SSP5-8.5). In the winter season, in all model-scenarios, the direction of precipitation change is increasing in the southwest of the country, decreasing in the west, and in other areas within the range of normal fluctuations.

Analysis of the spatial and temporal trends of precipitation is pertinent for the future sustainable management of water resources. Urbanization and climate change affected local rainfall and intensity. As rainfall characteristics are often used to design urban drainage systems, so watershed modeling, and estimate of the flood properties, updating and reviewing rainfall characteristics is necessary. The Intensity-Duration-Frequency (IDF) curves are a suitable tool to estimate the threshold values of precipitation in different return periods. IDF curves should be prepared based on the long-term rainfall statistics in each region and can change under the influence of climate change. These curves which indicate the frequency and maximum intensity of annual rainfall in the different return periods are suitable tools for planning and managing the water resources. IDF curves are broadly used for different purposes, such as the design of flood control and diversion structures in the cities. The main purpose of this research is to investigate the effect of climate change on temperature, precipitation, and IDF curves in the different return periods in an arid environment.

In this research, the effect of climate change on IDF curves was investigated in Kashan City as a dry region. Kashan city with an area of 9647 km2 is located in the northern part of the Isfahan Province. In this study, RCP8.5, RCP4.5, and RCP2.6 scenarios (which are introduced as optimistic, intermediate, and pessimistic scenarios) reported in the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), were used to predict the impacts of climate change on temperature and precipitation changes. Then, IDF curves were calculated evaluated, and compared for the basic (1993-2017) and future periods (2011-2070). To examine the impact of climate change on the rainfall intensity patterns, it is necessary to extract IDF curves in different climate conditions. Bell's method is a usual method for extraction of the IDF curves, which was promoted by Gharehman-Abkhezr in Iran. In this study, the latest promoted relationships developed were for the desert and southern regions (e.g;Nizar-Salfachgan, Kuhpayeh, Herat, and Jiroft basins in the central Provinces, Qom, Isfahan, Kerman, and Yazd) have been presented and used to extract IDF curves. To investigate the effects of climate change on IDF curves, first, the base period curves were extracted via rainfall data measured at the Kashan synoptic station. Then, using the new climate scenarios (RCP8.5, RCP4.5, and RCP2.6) and the output data of the LARS exponential microscale model, the IDF curves were extracted for different climate scenarios in the future (2011-2030), (2031-2050), and (2051-2070). Finally, the results of these curves were compared with the base period.

According to the results, for the base period, the precipitation and temperature data were predicated with an acceptable accuracy by the LARS model. The accuracy of the model has been higher in estimating the minimum and maximum temperature. the Nash and explanation coefficients for the maximum and minimum temperatures were 0.99 and 0.99, respectively. While, the explanatory and Nash coefficients for precipitation data were 0.95 and 0.93, respectively. Based on the results, the rainfall intensity will have fewer changes compared to the base period for long-term duration rainfall compared to short-duration rainfall (less than four hr. The maximum changes are related to rainfalls with a duration of less than one hour, whereas the minimum changes are predicted for 24- hr rainfalls. For all studied scenarios, a significant difference (P<0.05) was predicted between the average rainfall intensity of 0.17 to 24 hr in the region. As in the two-year return period and under the 2.6 scenario, rainfall intensity was increased from 10.75 to 30.54 mm hr-1 for the duration time of 0.17 hr.

An increase in air temperature, decrease in rainfall, change in the rainfall pattern, decrease in river discharge, and increase in sudden floods, followed by an increase in soil erosion, and a decrease in the amount of agricultural products are the results of climate change. Therefore, the long-term analysis and monitoring of climatic conditions can be very effective for crisis management caused by climate changes such as floods and droughts. Based on the results, the effect of climate change on the intensity of short-term rainfall is greater than long-term duration rainfall, as the intensity of short-term rainfall will increase more than long-term rainfalls.

Greenhouse gases and the occurrence of climate change have occurred with the development of technology and the industrialization of human societies. long-term forecasting of climate parameters has always been interesting due to the importance of climate change for the earth and its inhabitants. General Circulation Models (GCMs) are one of the most widely used methods for evaluating future climate conditions. In the present study, the results of three general circulation models including the American model of GFDL-CM3, the Canadian model of CanESM2, and the Russian model of inmcm4ncml for the study area were evaluated and the CanESM2 model was selected as the superior model. The RCP scenarios 2.6, 4.5, and RCP 8.5 were used with the CanESM2 model to assess climate change conditions across the Hablehroud River basin for the period 2020-2051. According to the results, the total monthly precipitation shows an increasing trend in the coming decades 2020-2051 period compared to the period 1986-2017. The results of the study of temperature changes in the period 2020-2051 in the Hablehroud River basin also indicate an increase in the monthly average of maximum and minimum temperatures in the coming decades. The consequences of these conditions are of great hydrological importance in the study area, this condition necessitates the adoption of climate change adaptation policies in this watershed.

In this research, two categories were used to investigate the effect of the change of clarion surface water of Khash plain. The data on 2010 to 2020 obtained from the Liz Hydrometer Station on the Syanjah River Khash Plain, on the other hand, the base rainfall data was obtained from the Khash Station. Based on the measured regression model, the base period of the relationship between Dubai and precipitation at the Khash basin level was modeled. With the implementation of the SDSM statistical microscale model, the simulated rainfall values were obtained in the statistical period of 2020-2040, under two lines of release 4.5 and 8.5, which was used as an input to the regression model for remodeling in the climate change period. The results showed that in the period of climate change 2040-2020, despite the fact that the rainfall of the Khash basin has increased in the cold months of the year, the simulated runoff of the period 2040-2020, in both RCP4.5 release trajectories and RCP8.5, has had a decreasing trend. The results indicated that the climate change period 2020-2040, in the RCP4.5 release, had reduced about 0.02 cubic meters per second to the base period, and in the RCP8.5 release track, this was a decrease of 0.016 cubic meters per second.

Climate change means any specific change in the long-term average weather state that occurs for a given location or for the entire globe (Goudarzi and Koupaei, 2020). Climate change is one of the most critical factors threatening food security, and it is expected to make food and nutrition security more challenging in the future (Carpena et al., 2019). It will affect the agricultural sector by changing the irrigation water requirements, crop yield, and water productivity (Boonwichai et al., 2018; Liu et al., 2019). Rice is the third most important crop in the world, following wheat and maize (Kapela et al., 2020). The occurrence of water shortages and droughts have raised concerns about the sustainability of rice production, including the main rice cultivation production region of Mazandaran in Iran (Yosefian 2018). Most studies have reported that rice production will decrease in the future due to a projected increase in temperature and a projected decrease in precipitation) Basak et al. 2010; Boonwichai et al. 2018; Nasir et al. 2020; Nicolas et al. 2020). Although many farmers, particularly in Iran, feel that permanent flooding conditions for rice farming are inevitable, climate change forces the use of water-saving technologies to ensure the long-term viability of irrigated rice production in paddy fields (Yosefian 2018; Mirfenderski 2022). Accordingly, it is necessary to find new methods for rice cultivation that reduce water use and make optimal use of the available water for irrigation while maintaining yield under climate change. The goal of this study was to examine the water requirement, water productivity, and risk of rice yield for different irrigation levels under various climate change scenarios using a crop simulation model.

Nowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied, compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature. 

Today, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and Kling-Gupta Efficiency (KGE) were used.

Before using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the algorithms used the important factors including F1, F2, F15, F16, F18, F20 and F26 by more than 50% and the first variable (mean pressure at the ocean surface) was the most important parameter in downscaling of maximum temperature. Also, the highest importance was for P1_v and the lowest value related to P5_u, as 73.2% and 15%, respectively. Violin plots of downscaled maximum temperature in validation step of different algorithms along with the observed maximum temperature in Birjand synoptic station in each of the algorithms showed that the values of the first and third quartiles in the output data of SPSA algorithm compared to other algorithms were closer to the observed data. According to the evaluation criteria, SPSA algorithm has a higher performance than other algorithms in reproducing the maximum monthly temperature values in Birjand synoptic station. Also, based on the volumetric efficiency evaluation criteria and relative Nash-Sutcliffe, GBM algorithm was more successful in selecting predictors than Ridge and LASSO algorithms. It is also observed that SPSA algorithm shows different results than other algorithms. In comparison of mean and variance of downscaled and observed maximum temperature, the results of t-test and F-test showed that SPSA algorithm has higher efficiency than other algorithms in regenerating mean and variance of observed maximum temperature in Birjand synoptic station at the 5% significance level.

The data used in this study included large scale atmospheric variables and the maximum observed temperature at Birjand station. The algorithms were used to select important predictors and the performance of these methods in the validation part. According to the results of this study, the highest importance among large-scale variables is related to P1_v and the lowest value is related to P5_u, the values of which were 73.2% and 15%, respectively. The SPSA algorithm also performs better than other algorithms in selecting predictors and consequently the maximum temperature.

In this research, the trend of spatial changes in extreme indices of temperature related to the health and agriculture sectors such as the number of frost days, number of summer days, number of icing days, number of tropical nights, growing season length, diurnal temperature range, cold spell duration index, and warm spell duration index were investigated for 54 synoptic stations throughout Iran for observational (1976-2005) and future (2025-2054) periods. Daily maximum and minimum temperature data of three regional climate models namely, CCSM4, MPI-ESM-MR, and NORESM1-ME from the CORDEX project under RCP4.5 and RCP8.5 scenarios were downscaled for each station using a developed multiscale bias correction method. Then, trends and changes of extreme temperature indices were investigated using Mann-Kendall and Sen’s trend line slope methods. The results indicated that the warm indices such as the number of summer days and tropical nights indices have had a positive trend at most stations in both observational and future periods. In contrast, cold indices like the number of frost days have had a decreasing trend in most stations. The results of cold and warm spell duration indices showed that most stations have had no trend for both periods. The growing season length has increased in more than 60% of stations (45% having a significant trend) mainly located in the northern, northwestern, and western regions of the country. Based on the results, it can be concluded that without considering thoughtful climate adaptation measures, some parts of the country may face health risks and limited habitability and agriculture in the future.

With the development of technology and the industrialization of human societies, the increase of greenhouse gases, the occurrence of climate changes on the surface of the earth and its harmful effects (floods and droughts) on human life, and resources have been confirmed. There, obtaining information about the possible effect of climate change on meteorological parameters is of particular importance and necessity. In this study, an attempt was made to determine the potential effect of climate change on the meteorological parameters of Shiraz the synoptic station in a comprehensive way by using the evaluation of General Circulation Models (12 models) and downscaling of their output (with the help of Multilayer Perceptron Neural Network method). The evaluation results (based on MSE, RMSE, and R) in the base period (1986-2005) proved the superiority of the CanESM2 and HadGEM2CC models. As a result, under the two RCP4.5 and RCP8.5 scenarios, HadGEM2CC outcomes during 2045-2026 and 2046-2065 showed a decrease in precipitation (11-19 and 21-36%, respectively). Also, it depicted an increase in minimum temperature (0.4-1 and 0.7-2°C), an increase in maximum temperature (0.5-1 and 0.9-1.8°C), and an increase in solar radiation (0.35-0.7 and 0.6-1.1 kWh per m2 per day). The HadGEM2CC showed a decrease in precipitation (7-16 and 16-35 %, respectively), an increase in minimum temperature (0.3-0.9 and 0.7-1.7°C), in maximum temperature (0.4-1.1 and 0.9-1.8°C) and in solar radiation (0.3-0.8 and 0.8-1.3 kWh per m2 per day).

Today, changing temperature and precipitation parameters are the major challenges of water resources. In this study, meteorological data of Birjand station have been used. Data on minimum daily temperature and maximum daily temperature and daily precipitation values of Birjand station during the years 1990 to 2014 were prepared as baseline data from Birjand regional water and then arranged. three models of IPSL-CM6A-LR, MIROC-ES2L, and MRI-ESM2-0 from the set of sixth report models (CMIP6) were used to evaluate the Projection of the effect of climate change on the maximum and minimum temperature and precipitation parameters. Using correlation coefficient (R), root mean square error (RMSE),and Kling-Gupta (KGE) evaluation tests, the MIROC-ES2L model was selected as the most suitable model, and using the latest diffusion scenarios that have been introduced as Socio-Economic Trajectory (SSP), the Projection of the effect of climate change on temperature and precipitation parameters of Birjand station was studied. To reduce the scale of the data, the CMhyd model was used and three scenarios, SSP1-2.6, SSP2-4.5, and SSP5-8.5 (2022-2050), were used for the next period. Then, using Mann-Kendall test and Sen’s Slope, the trend of observational data parameters was determined. In this testm each of the parameters of maximum temperature, minimum temperature and precipitation in the observation period and the future, there is a significant trend in some months and some months do not have a significant trend. The results of this study showed that the maximum and minimum temperature changes in the next period (2022-2050) compared to the observational data have an increasing trend and the precipitation parameter has a sinusoidal trend and is increasing in some months and decreasing in some months, but In general, It can be said that the average total monthly precipitation is increasing in the future under all three scenarios for the MIROC-ES2L model.

 Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.

 In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.

By visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.

 Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.

Due to technical limitations, most satellites cannot simultaneously collect images with high spatial, temporal, and spectral resolution. In this paper, the possibility of using DisTrade and TsHARP downscaling methods and STI-FM method in order to achieve high spatial resolution for MODIS and Landsat8 images was investigated. The RMSE value for sharpened images of 1 km to 30 meters was less than 3.93 ° C. In this study, the surface energy balance algorithm (SEBAL) was investigated to estimate the actual evapotranspiration distribution using a combination of Landsat 7 and MODIS satellite images based on the T-Sharp downscale method. The results showed that the integration of satellite images improved the accuracy of estimating actual evapotranspiration compared to Landsat 7 images. The mean squared error of estimated evapotranspiration during the growth period was determined in comparison with lysimetric data for Modis images of 1.24-2.91 mm / day and for integrated images of 0.81-1.1 mm / day. In general, the results of this study showed that the estimation of evapotranspiration using SEBAL algorithm and based on the combination of images with different temporal and spatial accuracy can provide acceptable results.

Climate change has many impacts on all environmental processes and society. In this study, three models selected from Coupled Model Intercomparison Project Phase 6 (CMIP6) including ACCESS-CM2, HadGEM3-GC31-LL, and NESM3 are validated. The best model (i.e. ACCESS-CM2) is selected to simulate the climatic parameters of the Sari Station using the latest emission scenarios called “shared socioeconomic pathways (SSP).” The LARS-WG is adopted for downscaling, and two emission scenarios SSP2-4.5 and SSP5-8.5 are used for two periods 2041-2060 and 2081-2100, respectively. Several statistical tests are conducted including F-test, T-student, Kolomogrov-Smirnov, coefficient of determination (R2), and root mean square error (RMSE) to validate the LARS-WG model. The verification results indicate the efficiency of the LARS-WG model. The Man-Kendal and Sen’s slope tests are adopted to determine the trend of climatic observational parameters. In general, the results show that the average temperature change increases in the range of 1.16-4.09 °C and also the average annual rainfall increases by 24-36 percent. The Sen’s slope results in terms of maximum and minimum temperatures show an ascending trend in this parameter, but it is descending in the rainfall. Since long-term climate change is one of the factors affecting groundwater and surface resources, it is necessary to develop proper management strategies for the future, preserving ecosystems, and adapting humans to these changes.

Soil moisture is of particular importance in the study of water resources and agriculture. The microwave electromagnetic spectrum does not have the physical limitations of other radiometric spectral in measuring soil moisture, but they often have very large pixel dimensions (more than 10 km). In this study, in order to apply soil moisture remote sensing data at the farm scale, using field soil moisture measurement data in the Nishabour plain during 2017-2019, calibration of AMSR2 retrieval data was performed. It turned out that altitude and vegetation changes are among the key factors affecting calibration accuracy. Based on the theory of thermal inertia and with the help of MODIS sensor images, the interactions of the average daily soil moisture and the daily surface temperature difference between the two MODIS and AMSR2 sensors were investigated. Using it, downscaling linear relationships of soil moisture images were obtained to convert the image dimensions from 25 km to 1000 m. Validation of R2 statistical indices with a minimum of 0.73 and a maximum of 0.84, MAE and RMSE with a range of 1.6 to 4, showed that the algorithm used in the downscaling is well able to reflect the interactions between rainfall, soil moisture, vegetation and changes in canopy temperature profile and this feature reinforces its application in agrometeorological studies.

Downscaling methods seem to be a reasonable solution to solve the problem of having no simultaneous high spatial and temporal satellite data, and it is possible somehow to meet the requirement of having high spatial-temporal resolution satellite data for monitoring the natural phenomena such as evapotranspiration, through these methods. Sentinel-2 satellite launched in 2015 enables to provide 10-m spatial resolution data with a 5-day revisit time; however, its sensor does not acquire data in thermal infrared wavelength. This study aims to generate 10-m daily evapotranspiration maps based on Sentinel-2 and MODIS data fusion for Amir-Kabir Agroindustry farms. For this purpose, STARFM and improved TSHARP methods were applied for downscaling MODIS data to Sentinel-2 data. To achieve this goal, First, MODIS visible and near and middle infrared bands were downscaled by STARFM to 10-m spatial resolution. Then, improved TSHARP was applied for downscaling MODIS thermal band to 10-m spatial resolution and SEBAL algorithm fed by the downscaled bands, was used to produce daily evapotranspiration map with 10-m spatial resolution. Assessing downscaled evapotranspiration maps with those derived from FAO Penman-Monteith equation indicated a RMSE of 0.64 mm/day showing efficient performance of the downscaling framework proposed for 10-m daily evapotranspiration mapping in this study.

The large-scale computational network of planetary models are not able to predict climatic variables on a regional scale. In other words, these models are affected by processes with a smaller scale than the model network in providing predictions of regional precipitation. Therefore, the model outputs should convert into a regional scale. The research purpose is to investigate the different configurations of the WRF model in the simulation of 5-days rainfall in March 17 to 22 March 2019 in Golestan province, which has caused devastating floods and heavy damage in the province.

The observation and quality control precipitation data was analyzed in 13 synoptic stations of Golestan province for a 5-days period from March 17 to 22 March 2019 in the form of 24 Hours (From 06 UTC the day before to 06 UTC the next day) and 6 hours (00, 06, 12 and 18 UTC are 3:30, 9:30, 15:30 and 21:30 local time, respectively). Also, two types of input data including initial condition data and boundary condition data were used in the WRF model. The boundary condition data was GFS data with 0.5-degree resolution. Furthermore, two domains were used in WRF model, 1) the large (mother) with a horizontal resolution of 18 km and 2) internal domain, which is the main domain and has 6 km horizontal resolution.

Two configuration was selected which showed better output results. The 5-days cumulative precipitation data which caused the flood show that the maximum 24-hour precipitation during the 5-days period is 06:00 UTC on March 18 to 06:00 UTC March 19 and the maximum cumulative rainfall of 6 hours is related to 06 to 12 UTC on March 18, 2019. Subsequently, by study similar research in Iran, different configurations for precipitation prediction were extracted and modeled. Then, in order to determine the accuracy of the model, the values obtained from the model in different configurations were compared with the values of synoptic stations. To ensure this comparison, MAE, d, R and ENS test statistics were used.

The results showed that the WRF model overestimate the precipitation data in most stations. In both configurations, results convey the precipitation cores well illustrated and the model accuracy was good enough in predicting precipitation. In maximum values of precipitation, the configuration of the first type show better results. Overall, the first type configuration performed more accurate than the second type configuration.

In the present study, precipitation in six stations of Karun3 basin is downscaled by using the hybrid of least squares support vector machine and whale optimization algorithm (LSSVM-WOA), K nearest neighbor (KNN), and artificial neural network (ANN). For downscaling precipitation, first, the days of year are classified into wet and dry days by using MARS and M5 algorithms. Then, the amount of precipitation for wet days is estimated by using each of LSSVM-WOA, KNN and ANN methods. Based on the findings, MARS algorithm is superior over M5 algorithm. Based on the mean precipitation in the six stations, ANN is a little bit better than LSSVM-WOA (0.5 percent more accurate). While, by regarding the mean of standard deviations, the Nash-Sutcliff for Ann is up to 5.04 percent more accurate than LSSVM-WOA. Eventually, the amount of precipitation is predicted based on the CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 scenarios for 2020-2040 and 2070-2100 periods. Based on the results of applying LSSVM-WOA, the precipitation in each three scenarios is decreased compared to the base period. Maximum decrease of precipitation (18%) is calculated by RCP8.5 for 2070-2100 period. Minimum decrease of precipitation (1%) is related to RCP2.6 scenario for 2020-2040 future period. But, the precipitation variation amount that is predicted by ANN is between -43 and 72 percent. Therefore, the results of LSSVM-WOA are more reliable and less uncertain

Bias correction methods are one of the most common statistical post-processing methods which are utilized on the output of climate models. This study evaluates the effect of five bias correction methods on the skill of seasonal precipitation forecast (fall season) from the CFSv2 climate model based on 12 stations located in Gorganrud basin in Iran. Bias correction methods that have been used in this study consists of two non-parametric methods (Linear Scaling (LS), Empirical Quantile Mapping (EQM)), one parametric method (Power Transformation (Ptr)), and two parametric methods based on the statistical distribution (Parametric Quantile Mapping (PQM), Generalized Parametric Quantile Mapping (GPQM)). Various metrics have been used for evaluating the effects of these methods on the skill of seasonal precipitation forecast which consists of bias, Pearson correlation coefficient, ranked probability skill score (RPSS), and the relative operating curve skill score (ROCSS). The Results of this study revealed that most of bias correction methods decreased the biases of the raw forecasts. The effect of each bias correction method on the RPSS and ROCSS (below and above normal events) scores may vary based on location and time, and each method can improve or worsen these two scores based on location and time. The results of this study suggest that the evaluation of various bias correction methods and distinguishing the most suitable method based on the goal of each study would be helpful in the improvement of seasonal precipitation forecast skill.