جستجوی مقالات مرتبط با کلیدواژه « lars-wg » در نشریات گروه « جغرافیا »

Determining the water requirement of crops in the upcoming period and having an overview of it is essential in water resources management planning and using it we will be able to determine critical areas. The purpose of this research is to estimate the water requirement of wheat using general circulation model data (HadGEM2-ES) by the latest Climate Change Report (Fifth Report) as Representative Concentration Pathway scenarios (RCP), which is scaled using LARSWG model under RCP85 scenario. To this end, 30-year data (1990–2019) were entered into the model, and climatic data were predicted daily for the next 40 years in two time periods (2021–2040) and (2041–2060).The results show that over the 40-year period, all three parameters of maximum, minimum temperature and precipitation will have an increasing trend.In the next step, the data generated to calculate the water requirement of wheat in Pars Abad plain were entered into CROPWAT 8.0 software and it was observed that increasing temperature leads to increase evapotranspiration and increasing water requirement of wheat by 3 to 5% under climate changing.

In this research, the precipitation data needed for the stations of the study area during the statistical period of 1981-2015 was obtained from the Meteorological Statistics Center. To predict and produce statistical data from 2026 to 2065, the LARS-WG and CLIMGEN models were used. In this study, bootstrap method was used to assess precipitation uncertainty. The correlation between the observed and simulated monthly rainfall data shows that the CLIMGEN model simulates the synthetic rainfall data more accurately. The lowest error of the RMSE and MAE in both of these models is at Jolfa Station And the highest error in both models was measured at Sardasht station. Estimation of precipitation output (mean precipitation) by LARS-WG model with bootstrap method indicates higher uncertainty of LARS-WG model than CLIMGEN model. The variance of precipitation observations also indicates a significant change in confidence intervals in the spring and autumn months. Therefore, with regard to the above, it can be said that in the case of rainfall, the LARS-WG model shows more uncertainty than the CLIMGEN model in most of the studied stations in the study area. The absolute magnitude of annual precipitation error with the output of the CLIMGEN model is less than the magnitude of the error with the output of the LARS-WG model. This indicates a lower error value of the CLIMGEN model in the simulated precipitation spatial uncertainty than the LARS-WG model in the northwestern region of Iran.

Late spring frosts and early autumn are a detrimental climate phenomenon that affects the economy, agriculture especially crop production and it is certain this phenomenon will change with climate change and global warming, therefore, it is important to study the timing of their occurrence and predict their future changes. The purpose of this study evaluates changes in frost start and end data using the general circulation of the atmosphere model data (HadGEM2-ES) based on the latest Climate Change Report (Fifth Report) as RCP Scenario using LARSWG model under two scenarios RCP85 and RCP45. For this purpose, 20-year daily data (1981-2010) including minimum and maximum temperature, precipitation and solarization were entered into the model and minimum daily temperature data were predicted for the next 20 years (2021-2040). First and last frost data were extracted at three slight, moderate, and severe temperature thresholds. The results showed that in all three temperature thresholds and under both scenarios the mean minimum temperature would increase in all months and the duration of the frost would decrease and the date of occurrence of the first autumn frost and the last spring frost would be earlier than the ground state.

Gradual changes in climate systems and patterns such as temperature, humidity, cloudiness, and wind and precipitation patterns affect type of climate in different areas. The climate of lagoons is especially sensitive and easily affected by climate changes. Critical ecosystems of lagoons protect biodiversity and have various tourism, economical, ecological, environmental and social values. Located in JazMurian basin, JazMurian wetland is frequently affected by wet years and droughts. However, repeated droughts and loss of freshwater have resulted in the wetland drying out. The dried out water body not only affects plant and animal life, but also acts as a dust emission center.

Three scenarios of emission (A2, A1B, B1) in medium-term (2046-2065) and long-term (2080-2100) are used in the present study to evaluate climate changes in JazMurian basin. Related data have been collected from Iranshahr synoptic station in the east and JiroftMianDeh in the west during 1990-2010 statistical period. The output of HADCM3 model has been downscaled using LARS-WG statistical model and minimum/maximum temperature, and monthly precipitation of the synoptic stations were analyzed.

Evaluation of LARS-WG model proved that monthly and annual average of minimum and maximum temperature in all modeling scenarios are higher than the observation period. The highest and lowest temperature increase will occur in A2 (business as usual) and B1 (the most environmental) scenarios. The highest increase in monthly average of maximum temperature will occur in Iranshahr station during the long-term A2 scenario (4.3 for maximum temperature in April and 4.6 for minimum temperature in May). In Iranshahr station, the highest increase in monthly average of maximum temperature predicted for the medium-term A2 scenario will equal 2.5 ° in April. In both scenarios, the lowest increase in monthly average of maximum temperature in Iranshahr will occur in October.According to all scenarios, precipitation will decrease in January and December. An increase in precipitation is recorded during March and October in Iranshahr station, and during February in Jiroft. Thus, changes will mainly occur in Mediterranean winter precipitation in thestudy area, while negligible changes will occur in monsoon precipitation during summer. Precipitation modeling shows higher precipitation fluctuation in long-term scenario compared to the basic statistical period. Long-term A 1B and B1 scenarios have predicted a small increase in precipitation of both stations compared to the basic statistical period. A2 scenario has shown a small decrease in precipitation during the same period. Modelling based on B1 scenario has indicated that the region will experience a higher increase in precipitation in long-term future (2080-2100) compared to the medium-term future (2046-2065), while the other two scenarios have predicted a lower increase in precipitation of the same period. Medium-term B1 scenario has predicted a lower annual precipitation average compared to the observation period. Results indicated that in accordance with the B1 scenario, Iran will experience the lowest level of precipitation during the 21 century in the medium-term period.

Investigating various meteorological parameters in western and eastern borders of JazMorian basin has predicted 1.5° to 2.1° increase in average temperature during the future 50 years, and 2.4° to 3.9° increase in average temperature during the future 100 years. Moreover, results indicated that changes will mostly occur in winter precipitation, and summer time changes will be negligible. All things considered, all scenarios have predicted lower precipitation for eastern parts of JazMurian compared to the western parts.

The Study of the spatial and temporal variations of groundwater quality plays an important role in the management of watershed fields in arid and semi-arid regions. Future climate parameters were predicted using HADCM3 and LARS-WG models under B1 and A2 scenarios and the data of minimum temperature, maximum temperature, precipitation and sunny hours of the base period (1992-1993) in the Silakhor -Borujerd agricultural plain. Dry and wet years were determined Using SPI drought index during the base period and future. Using the water quality data of 18 wells in the study area, the distribution of TDS, EC, PH, SAR parameters was studied using four main methods of geostatistic zoning (IDW, RBF, Kriging and Cokriging). The best model was selected using the root mean square root mean square error (RMSE). Finally, the effects of climate change and droughts on the geochemical parameters of water were investigated using factor analysis and analysis of variance. The results showed that for pH, TDS, SAR and EC parameters, conventional cokriging, simple coccygeal, simple cokriging and simple cokriging were more suitable. Among the studied parameters, TDS and EC had the highest correlation with mean temperature and SAR had no high correlation with the climate parameters. The pH parameter has a strong negative and significant negative temperature. The findings indicated that drought periods have no significant effect on water quality except PH. Climate change studies shows that by increasing the minimum and maximum temperatures, TDS and EC will increase in the future.

In recent years, attention has been paid to climate change, which could be the result of economic, social, and financial losses associated with extreme weather events. The purpose of this study is to investigate the variation of extreme temperature and precipitation in Kurdistan province. For this purpose, daily rainfall data, minimum temperature and maximum temperature of 6 stations were used during the statistical period (1990-1990). And their changes during the period (2041-2060) using the universal HadGEM2 model under two scenarios RCP2.6 and RCP8.5 and the LARS-WG6 statistical downscaling were investigated. In order to study the trend of climatic extreme indexes, rainfall and temperature indices were analyzed using RClimdex software. The results showed that during the period (2016-1990), hot extreme indicators have a positive and incremental trend. This trend is significant for the "number of summer days" and "maximum monthly of maximum daily temperature" indicators. This is while the cold extreme indexes had a decreasing and negative trend. This trend was significant only for the "cold days" index. Extreme precipitation in Kurdistan province has a negative trend in most stations. ،this trend is significant at most stations, that indicates a reduction in the severity, duration and frequency of precipitation during the study period. The results of the climate change outlook also indicate that the temperature will increase over the next period and rainfall will decrease.

Increasing global warming is one of the major global issues of the current century. For this reason, it is important for humans to study and evaluate its trend, so simulating this climatic variable can be a way to understand the human future. There are several methods for simulating and predicting heat waves, the most valid of which is the use of the general circulation model of the atmosphere and GCM. One of the most widely used models for GCM data miniaturization is the LARS WG statistical model. In the present study, the efficiency of this model for exponential micro-scale of heat waves in Julfa station was evaluated. To achieve this goal, daily statistical average maximum temperature above 35 ° C in a given statistical period has been used. Also, to predict the heat waves in the coming years, the maximum temperature data of the models (CESM CAM, CMCC CMS, CAN ESM, MPI ESM) under the RCP8.5 scenario for the next period 2011-2016 were performed. The results showed that heat waves in the future period (2011 2065) have a normal distribution compared to the base period. . Therefore, these results show the very good performance of the LARS WG model in simulating heat waves. Jolfa station heat waves in the base periods (1990-2020), with severe irregularity and variability of annual heat wave events, indicate a strong variability of the average annual temperature and a variety of factors affecting the temperature of the region. On the other hand, according to the forecast of MPI ESM, CESM CAM and CMCC CAM climate models, the temperature of the annual heat waves that go towards more heat in the warm period;

Increased atmospheric anomalies such as sudden rainfall, wind storms, droughts and rising temperatures in some parts of Iran there a few years. Snow in the cities of the south and center after 50 years, extreme cold and hail in the blooming trees, increasing the average temperature of some cities in recent years are examples of tangible changes in the climate of Iran. General circulation models(GCM) of the atmosphere by greenhouse gas emissions scenarios predict future climate of the Earth. But very low spatial resolution of these models and their results to evaluate the impact of climate change does not apply in the sciences. Thus Downscaling GCM data should be used. But the accuracy of these models depends on the geographic and climatic conditions of each region. In this study the possible effects of climate change on climate parameters daily rainfall, minimum and maximum temperature synoptic stations north of Khuzestan in statistical period (2014-1985) were studied. Forecast using the A2 and B1 and HadCM3 model for the period (2030-2011) and (2050-2030) was carried out. Downscaling GCM data software was used for LARS-WG. The results show that annual rainfall in the region has come down trend. The highest temperature rise in the minimum temperature. And this increase is more pronounced in winter. And the maximum temperature rise was observed in the A2 scenario for 2050.

Climate change is one of the greatest challenges facing humanity in the 21st century. Therefore, prediction of climate change is very important to predict the future situation and to consider the necessary measures to adjust and adapt to climate change. Therefore, in this study, climate change was predicted in the Dez watershed. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios RCP2.6, RCP4.5 and RCP8.5 and the application of two downscaling models of LARS-WG and SDSM and the climate changes in the next three periods compared to the base period (1989-2018) were examined. The results showed that both downscaling models have good accuracy in simulating climate change in the study watershed. The results of prediction the studied models also showed that in the future periods the amount of precipitation in the watershed will change between -6.3 to 15.7% compared to the base period. The most decreasing and increasing changes will be related to the eastern and southwestern areas of the watershed, respectively. Also, the maximum temperature of the watershed will fluctuate between 1.3 to 3.9 oC and the minimum temperature will fluctuate between 1.5 to 3.5 oC. The highest and lowest changes will be related to the southeastern and northwestern areas of the watershed, respectively. Therefore, due to the increase in temperature and precipitation, as well as the mountainous nature of the watershed under study, it is necessary to consider flood control and containment and management strategies.

Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd. The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours. The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results. To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall. By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period. The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data. The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5. The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.

Climate change is one of the most important challenges of this century. The maximum and minimum temperatures and precipitation were predicted for the intermediate future (2046- 2065) and distant future (2100- 2080) by statistical downscaling outputs of HadCM3 model under emission scenarios shows average of the mean temperatures will increase in the range of 1.5 to 2.1 °C in the next 50 years (20-year average of the intermediate future) and 2.4 to 3.9 °C in the next 100 years (20-year average of the distant future) compared to the base periods. According to the results of this study, drought is one of the most serious future crises in the country, which if not addressed, will threaten Iran's future in various aspects.

In recent years, the impact of climate change and drought forecasting on water resources planning and management has received much attention. In the present study, probable climate change on Malayer basin temperature and precipitation over the period 2014-2014 was investigated and monthly, seasonal and annual forecasts for the near future (2030-2011) under three scenarios A2, B1 and A1B using HadCM3 general circulation model The LARS-WG model was used for performing and exponential micro-scale.
Data and

ARIMA multiplication time series and AIC and SBC criteria and Pert-Manto test in predicting precipitation and SPI and SDI indices have been used to predict drought for the period (1397-1418) of Merville, Pihan and Wasjeh hydrometric stations.The results show an increase in precipitation and temperature in all three monthly, seasonal and annual scales in the coming period, and Shows that the largest meteorological drought for the base period in 1998-1999 is -1/96 and In the coming year 1418-1418 there was adecrease of -2/4. Surveys show that moderate and severe droughts will increase in the coming statistical period at the Mervil, Peyhan and Vasge stations.

Drought occurrence reduces discharge and hydrological drought. The results show that due to variability of precipitation and mean air temperature, the trend of drought changes is not the same in different months. Therefore, the duration, severity and frequency of droughts vary from month to year.

Investigation of correlation (r) and mean error (MSE) values ​​between observed and calculated values ​​of discharge and precipitation at the stations under study indicate the high capability of ARIMA model in simulating monthly discharge. And it can be used in other parts of the country.

Today, pre-risk awareness has become an integral part of the national development management and planning system in many countries (Civiacumar et al., 2005). Agriculture is inherently sensitive to climatic conditions. The minimum temperature, which has been identified as the most vital determining factor in the distribution of plant species on the planet, can be both a limiting factor and a factor in the spread and species distribution (Rodrigo, 2000: 155). Therefore, in this study, we examined the changes in minimum temperatures in the statistical period (1980-2010) and predicted these changes in the 2050s (2065-2046) in the Northwest of the country using the LARS-WG microscale method and model output. Atmospheric pairings of HadCM3 and MPEH5 were addressed. The prediction of minimum temperature variations to determine the extent of its future changes and considering the necessary measures to minimize the adverse effects of climate change on agricultural products were of great importance. In this regard, general atmospheric circulation models (GCMs) are designed that can simulate climatic parameters. 

In the present study, the output data of two HadCM3 and MPEH5 general circulation models based on two scenarios A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in the Northwest of the country. The results were monthly and periodic on the base period (1980-1999) and the 2050s (2046-2065), thereby the minimum temperature was evaluated and analyzed. In assessing the LARS-WG model, the observational and simulation error data were evaluated using MSE, RMSE, MAE and R2, and the model was evaluated for the appropriate region. The results showed that the minimum temperature in the future period will increase compared to the base period in the study area. This increase in air temperature at the study area is based on the HadCM3 and MPEH5 models, on average, 1.9 and 1.7 degrees Celsius to 2065 horizons compared to the base period. The north-eastern part of the northwestern region of Iran will have higher temperatures than the semi-southern regions. In fact, the cooler regions of the high latitudes will face more incremental changes in the amount of minimum temperatures. The results and achievements of this research are important for long-term plans for adaptive measures in the management of fruit gardens, agricultural products and water resources management. In order to calibrate and ensure the accuracy of the LARS-WG microscale model, the model was first implemented for the basic statistical period (1980-2010); then the minimum temperature output and its standard deviation were compared with the observational data of the studied stations, which indicated a small difference between the observed and simulated values ​​and also deviated from their criteria. 

The results of evaluation of observational and simulated data by LARS-WG microscale model using RMSE, MSE and MAE error measurement indices for the studied stations indicate that there is a significant difference between the simulated values ​​and the values ​​of the observed observations. There is no critical 0.05 significance levels, and Pearson correlation values ​​between simulated and real data are acceptable at the significance level of 0.01. The obtained results show that the accuracy of the model varies in different stations. In general, the results of error measurement indices indicate that the LARS-WG model is of good accuracy for micro-scaling the parameters under study. In order to better represent and ensure the accuracy of the prediction as well as to investigate the uncertainties in the studied models, the simulated values ​​were compared and observations were made on a long-term average during the base period in the studied stations using comparative graphs. As can be seen, the observed and generated values ​​in the base period at all stations are very close to each other and the LARS-WG model has been successful in simulating the studied parameter. After evaluating the LARS-WG model and ensuring its suitability, the data predicted by the model for two scenarios A2 and B1 using HadCM3 and MPEH5 models and were examined on a monthly and long-term basis. The study of the status of minimum temperature changes of the studied stations in the future period (2065-2056) shows that the minimum temperature is based on both scenarios and in all months and stations compared to the period, the base has increased. Due to the large number of study stations, only stations located in provincial centers of this study are listed. 

Cold and frost are one of the most significant climatic hazards on fruit trees. This type of climate risk affects different parts every year, especially the cold regions of the northwest of the country. Studies show that in recent years, the rate of economic damage to fruit trees in this region has increased, so in this study, the outlook for changes in minimum temperatures in this region using the LARS-WG statistical microscale model and output two HadCM3 global model and MPEH5 were introduced in the 2050s (2065-2046). For accuracy and precision of the models, error measurement indices and coefficients of determination and correlation were used. The results showed that the LARS-WG model has a good ability to simulate the studied variables in the study area. The results of long-term prediction of the studied models show that the minimum temperature values ​​will increase in all study stations, which is based on HadCM3 and MPEH5 models on average. In the 2050s, and it will be 1.9 and 1.7 respectively, compared to the base period. The results of the studies of Kayo et al. (2016), Sharma et al. (2017), Khalil Aghdam et al. (2012), Qaderzadeh (2015), Sobhani et al. (2015) and Khalili et al. (2015) were confirmed. In general, based on the studied scenarios and models, the minimum temperatures are expected to increase in the study area in the future. By increasing it, the yield of some crops that need cold during the growing and productive period would decrease. It can also reduce snowfall, followed by frost on crops and lack of water in dry seasons. Therefore, due to the fact that following the climate changes, the conditions of the agricultural climatology are also changing, it is necessary for the relevant officials and planners in the agricultural sectors to adopt the necessary strategies to reduce the consequences and adapt to the new climate.

Rising global warming is one of the greatest challenges facing humanity in the 21st century. Therefore, it is very important to predict the maximum temperatures in order to know the amount of changes and, as a result, to take the necessary measures to adapt and moderate the adverse effects caused by it. Therefore, in this study, maximum temperatures were predicted in three provinces of Kurdistan, Kermanshah and Ilam in the west of the country. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios: RCP2.6, RCP4.5 and RCP8.5, as well as two microscale models of LARS-WG and SDSM, and changes in maximum temperatures on a monthly and annual basis in the future 2050-2021) compared to the base period (2018-1989) were examined in 17 meteorological stations. MAE, MSE, RMSE and R2 indices were used to calibrate and validate SDSM and LARS-WG models. The results showed that both models have a high ability to simulate the maximum temperatures of the study area. However, the SDSM model is more accurate than the LARS-WG model, with the lowest and highest accuracy for Bijar and Tazehabad stations with RMSE of 0.02 and 0.18, respectively. The results of maximum temperature forecasting also showed that according to both models, the maximum temperature in the future period will increase compared to the base period, which is the average of the studied models between 0.8 to 1.9 degrees Celsius in the region. Will be studied. The highest rate is estimated based on the RCP8.5 scenario. Spatially, the most changes are related to the northern and eastern areas of the study area and the least changes are related to the western areas of the study area.

Iran's location on the arid and semi-arid belt of the world, as well as the mismanagement of water resources, has created a warning situation of water shortage in many parts of the country. The present research evaluates the effects of climate change on temperature, rainfall and runoff in future periods with the help of LARS-WG statistical model and SWAT hydrological conceptual model for Lar Basin. To estimate the flow rate of the river, the performance of Bayesian network and the combined wavelet-neural network model are also examined. After entering the rainfall and temperature information of the region, runoff was simulated for two hydrometric stations of Gozeldareh and Plour and the outflow runoff of Plour station between 1979 to 2018 was calibrated and validated as a control point. In order to evaluate the efficiency of the models, the criteria of Nash-Sutcliffe and explanation coefficient are used. According to climate models, the highest temperature increase in the final period and under the RCP8.5 climate scenario shows about 10% increase in temperature in spring and winter. Finally, among these models, the physical model with an average annual prediction of 6.04 cubic meters per second according to the observation period, showed a decrease in runoff

Estimation of the rivers sediment load has high complexity due to effecting different parameters in this aspect. Regarding the power relationship between discharge data and suspended sediment load use of sediment rating curves is one of the most common methods for determining the sediment yield in ungauged watersheds. Sediment condition shows the upstream characters and using of the obtained data makes a relation between erosion and sediment load. The different parameters such as climate, land use, data accuracy and the applied methods have the effect on the sediment rating curve shape. Agriculture activities such as tillage in the direction of slope lead to accelerated erosion in the watersheds, especially in the Mediterranean area. These decades many studies assessing the effects of climate changes in the future period and it affects on runoff. In this study, the main objective is to obtain sediment changes during the future decade (2011-2030) using the curve rating in sediment estimating. For this purpose, the IHACRES hydrologic model and the LARS_WG climate model were used.

The IHACRES model for seven hydrometric stations was calibrated and validated. This model is a rainfall and runoff erosion that require a little data for running including minimum and maximum temperature, rainfall, discharge and study are. This model defined as a lumped model and highly common in watersheds with scarce data. With running this model in all of the models the model parameters were calibrated. Also, the LARS_WG model was used for determining the weather changes that are occurring in the Samian watershed. This watershed has near to 4 thousand square kilometers that have many sub-watersheds. In this study, the watersheds in the west of the Samian watershed were selected for modeling. The average of rainfall in this area is between 220 and 457 mm, and the weather temperature changes in this region are high and that is between -32 to 34 C°. The results of LARS_WG showed the weather changes in each part of the hydrological model inputs that these changes were applied to the IHACRES model and the discharge flow rate was estimated for the future. On the other hand, using the observed discharge and sediment yield were calculated the sediment curve rate. By changes in flow discharge at the study stations, were calculated the suspended sediment discharges for the future period.

The results of the LARS_WG model showed that the amount of precipitation decreased to 3.68 percent and the minimum and the maximum temperature increased by 16.48 and 5.39 percent, respectively. Decreasing of the input precipitation in most part of the world particularly in Iran watersheds mentioned in many studies. One of the other the most important effect of the climate change in this area is minimum and maximum temperature increase that leads to evapotranspiration increasing and soil moisture loss. The results of the IHACRES model showed that this model has the suitable capability for simulation runoff in the study area, therefore, it was used for estimating the future runoff regarding climate changes. The model output showed that during the next decades the average flow rate in the hydrometric stations will decrease by a total of 16 percent and the number of peak flood events will increase, that the highest increase between the study watersheds observed in the Yamchi hydrometric station with a mean of 2.09 m3s-1 and 16 peak events with over 6 m3s-1. Using the obtained results of the climatic model, hydrological model and the sediment rating curve the suspend sediment changes were estimated for the future period. The result shows that these climatic changes will lead to a 47 percent reduction in the average of suspended sediment load at study stations.

The consequences of the climate changes have the significant effect on water resources quality and quantity. The aims of this study were calculating the weather changes and it's ruling on discharge and sediment yield changed. the results of this study indicate the effect of climate change on the Ardabil province watersheds is remarkable. Considering the environmental impacts of climate change and dependence on human life on the environment it is necessary to implement an appropriate approach for decent management in Watersheds.

 Considering that water resources are at risk from climate change, the study of temperature and precipitation changes in the coming years can lead to droughts such as droughts, sudden floods, high evaporation and environmental degradation. To this end, global climate models (GCMs) are designed to assess climate change. The outputs of these models have low spatial accuracy. In order to increase the spatial accuracy of this data, downscaling methods are used which are divided into statistical and dynamic methods. One of the reasons for using these models is their quick and easy operation compared to other methods. Our study area consists of Kurdistan, Kermanshah and Hamedan provinces in the west of the country. In this study, observational data of minimum temperature, maximum temperature, precipitation and radiation of 6 synoptic stations in the studied area in the statistical period of 1961 until 2005. In this study, the LARS-WG model was used for downscaling of HadCM3 global model data. The LARS-WG model is one of the most popular weather generator models that which to generation for maximum and minimum temperature, rainfall and radiation are used daily under current and future climate conditions. This model as a downscaled version of the same process less complex and simulated data input and output, high ability to predict climate change. The HadCM3 model is also a type of atmospheric- oceanic circulation model developed at the Hadley Center for Climate Prediction and Research, which has a 2.5 degree latitude network at 3.75 degrees longitude. Also, three climate change scenarios A1B, A2 and B1 have been used, each of which reflects the characteristics of the world's economic growth, the world's population and social awareness. The methodology is that the model receives the monitored data of the basic course; by examining them the statistical characteristics of the data are extracted. Then, in order to validate and ensure the model's capability for the basic statistical period, the model is implemented to re-establish a series of artificial data in the base period. Then the outputs to evaluate the performance of the model in the reconstruction of the data, the statistical characteristics of observations to test and compare various criteria. MAE, MSE, RMSE and R2 criteria were used to evaluate and analyze the performance of the downscaling model.

Estimation of the rivers sediment load has high complexity due to effecting different parameters in this aspect. Regarding the power relationship between discharge data and suspended sediment load use of sediment rating curves is one of the most common methods for determining the sediment yield in ungauged watersheds. Sediment condition shows the upstream characters and using the obtained data makes a relationship between erosion and sediment load. The different parameters such as climate, land use, data accuracy, and the applied methods have an effect on the sediment rating curve shape. Agriculture activities such as tillage in the direction of slope lead to accelerated erosion in the watersheds, especially in the Mediterranean area. These decades many studies assessing the effects of climate changes in the future period and it affects runoff. In this study, the main objective is to obtain sediment changes during the future decade (2011-2030) using the curve rating in sediment estimating. For this purpose, the IHACRES hydrologic model and the LARS_WG climate model were used.

The IHACRES model for seven hydrometric stations was calibrated and validated. This model is rainfall and runoff erosion that require a little data for running including minimum and maximum temperature, rainfall, discharge and study are. This model defined as a lumped model and highly common in watersheds with scarce data. With running this model in all of the models the model parameters were calibrated. Also, the LARS_WG model was used for determining the weather changes that are occurring in the Samian watershed. This watershed has near to 4 thousand square kilometers that have many sub-watersheds. In this study, the watersheds in the west of the Samian watershed were selected for modeling. The average of rainfall in this area is between 220 and 457 mm, and the weather temperature changes in this region are high and that is between -32 to 34 C°. The results of LARS_WG showed the weather changes in each part of the hydrological model inputs that these changes were applied to the IHACRES model and the discharge flow rate was estimated for the future. On the other hand, using the observed discharge and sediment yield were calculated the sediment curve rate. By changes in flow discharge at the study stations, were calculated the suspended sediment discharges for the future period.

The results of the LARS_WG model showed that the amount of precipitation decreased to 3.68 percent and the minimum and the maximum temperature increased by 16.48 and 5.39 percent, respectively. Decreasing the input precipitation in most parts of the world particularly in Iran watersheds mentioned in many studies. One of the other the most important effect of climate change in this area is minimum and maximum temperature increase that leads to evapotranspiration increasing and soil moisture loss. The results of the IHACRES model showed that this model has a suitable capability for simulation runoff in the study area, therefore, it was used for estimating the future runoff regarding climate changes. The model output showed that during the next decades the average flow rate in the hydrometric stations will decrease by a total of 16 percent and the number of peak flood events will increase, that the highest increase between the study watersheds observed in the Yamchi hydrometric station with a mean of 2.09 m3s-1 and 16 peak events with over 6 m3s-1. Using the obtained results of the climatic model, hydrological model and the sediment rating curve the suspend sediment changes were estimated for the future period. The result shows that these climatic changes will lead to a 47 percent reduction in the average suspended sediment load at study stations.

The consequences of climate change have a significant effect on water resources quality and quantity. The aims of this study were to calculate the weather changes and it's ruling on discharge and sediment yield changed. the results of this study indicate the effect of climate change on the Ardabil province watersheds is remarkable. Considering the environmental impacts of climate change and dependence on human life on the environment it is necessary to implement an appropriate approach for decent management in Watersheds.

According to the importance of snowfall in supplying water of different regions especially mountainous areas, accurate estimation of snow water equvallent and changes of its coverage would be effective in agriculture, energy, management of reservoir and flood warnings. In this study runoff orginated from snow melting in ShahrChay basin under the terms of climate change calculated. For this purpose, snow cover for water year of 2012-2013 were extracted in ENVI software by using daily images of Modis satellite.Then, GIS software the physiographic specification of the basin was obtained. In the next step, data of snow cover, meteorological variables and other necessary parameters to SRM model provided as an input of model and run_off from snow melt was simulated. Then output of the 6 models of atmospheric general circulation with title of 3 scenarios nomely A1B , A2 and B1 converted to a downscaleing by using LARS-WG model. By comparing the output of 6 models in the future period to period based on monthly statistical, the best model and scenario for generation of air temperature and precipitation data in the period 2030-2011 were selected. As a result the HADCM3 model under the scenario A1B was used for generation of precipitation and the MPEH5 under scenarios A2 was used for generation of temperature data. In order to estimate the rate of change of runoff orginated from snowmelt rate of change of monthly data of air temperature and precipitation of the base time period as well as future time period under selected model and scenarios was entered to SRM model in simulation time period. Results for all of the scenarios show that runoff orginated from snowmelt in late spring will be reduced. The peak flow appeared earlier in comparison with base time period and its value would be larger than base time period.

Extended  Abstract Cold and frost is one of the most important climatic parameters in the agricultural climate, and the damage caused by them reduces the possibility of producing many agricultural and horticultural products in vulnerable areas. Cold and frost is one of the climatic hazards that annually causes damage to various activities. The agricultural sector is the most important part of the damage that is most seriously damaged by frost. Cold and frosty weather for many crops and gardens results in harmful and destructive consequences, in some years billions of rials damage farmers, farmers and, ultimately, the national interests of the country. Considering that the northwest region of Iran suffers a lot of financial losses each year due to atmospheric hazards especially cold and frost. Identification and zoning of areas with high potential of cold and frost hazard and prediction of their occurrence can provide valuable and valuable information for preventing and mitigating damages. In this study using HadCM3 global model under two scenarios A2 and B1 and The LARS-WG microscope model is dealt with this. It is important to check the time of occurrence and predict their future changes. For this purpose, general atmospheric circulation (GCM) models are designed that can simulate future climate parameters. In this study, the output data of the HadCM3 general circulation model under two scenarios of A2 and B1 were analyzed by LARS-WG statistical method in 21 synoptic stations located in northwest of Iran. The results of this study were based on the base period (1980-1989) and The 2020 decade (2030-2011) was evaluated for two climate variables: minimum temperature and maximum temperature. Then the history of the first and last frost and cold of autumn and spring was extracted and their date of occurrence was calculated in the future. The monthly average of the minimum temperature of the stations studied in the course of the 2020s and the base period shows that the temperature has been increased according to both scenarios and increased in all months and at most study stations compared to the base period. The maximum changes in the minimum temperature in the study area are based on the average scenarios in this decade related to Abhar, Ardebil, Khoy and Urumieh stations at 0.8 degrees Celsius; In fact, the minimum temperatures that occurred at these stations during the base period have not been observed in the next period and the heating process has shown that its rate in the region of the study area in the 2020s is between 0.4 and 0.8 It will be in the base period. The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 degrees Celsius. The results of the first and last glacial survey in the decade of 2020 indicate that the first glacial precipitate of autumn occurs between 2 and 9 days later, with the least change in the history of frost occurring in two stations of Qazvin and Meshkinshahr each with 2 The change day is relative to the base period. The last frost of late spring also will be 3-10 days earlier on the surface of the region. However, the duration of the ice free period will be reduced at all stations, which is the highest decrease for Khoy station with 16 days, then the stations of Urmia and Ardebil each Two with 14 days and the lowest decrease is due to Meshkinshahr station for 6 days. Based on the results of changes in the date of early ice ages, changes are less than the late frost. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. The least changes were observed in the south-east of the study area, Meshkinshahr and Sarab regions, and the most changes in the glacial period were related to Khoy, Urmia, Tabriz and Ahar areas. According to the results of most studied areas, averaging between 10 and 12 days decrease in length The ice age will experience the base course. The results indicate an increase in the monthly average of the minimum and maximum daily temperatures in the upcoming period to about 0.8 C. Based on this, the study of the condition of glaciers and serma in most of the studied stations shows that the first frost and autumn frost in the coming period will start earlier and the cold and the frostbite will end sooner. Also, the length of the cold and freezing period is decreasing, which may reflect the consequences of climate change at study stations. The results of this study are based on the studies of Grasick and Dodwilich (2015) in Poland, Medella et al. (2016) in Texas, Hosseini and Ahmadi (1395) in Saqez, Aqa Shariatmadari et al. (1395) in West Iran, Sobhani et al. (1396). ) In Ardebil and Khalili et al. (1396) in Iran.