جستجوی مقالات مرتبط با کلیدواژه « wg » در نشریات گروه « آبخیزداری، بیابان، محیط زیست، مرتع »

Climate is a complex system that changing mostly due to increased greenhouse gases and global warming, leading to intensification of change in climatic factors such as precipitation amount and intensity of extreme precipitation events. In effect of climate change in the future, change amount and volume of the soil erosion is expected which the most important sensitive factor will be the rain fall erosivity. The aim of this study was to determine the effect of climate change on rainfall erosivity factor. For this purpose the HadCM3 model from A1B scenario was used and downscaling with LARS-WG model was used. So monitoring of rainfall erosivity factor for three periods of 2011-2030, 2045-2065, and 2080-2099 in north of Iran was simulated. Results show that rainfall erosivity factor in Sangdeh, Babol, Korkorsar, Anzali, Behshar and Gorgan stations will be increasing during the 2011-2030 period but for stations in Babolsar, Hashtpar, Rasht and Gorgan in the period 2045-2065 and 2080-2099 decreased. According of calculations, maximum changes of the rainfall erosivity factor in future will be occurring during the 2011-2030 and it’s minimum will be occurring period the 2080-2099. So largest rainfall erosivity factor was simulated about 42.6 MJ mm ha-1 h-1 for Hashtpar station during the 2011 to 2030 period. The obtained results show that the erosivity factor increase will be during the current century in the north of Iran.

Predicting the temperature and precipitation changes is necessary for assessing the change in the future and to mitigate the harmful impacts of climate change on water resources and agriculture, environmental, economic and social issues. Accordingly atmospheric general circulation models (GCMs) have been developed for simulation of climatic parameters. In this research, output data of HadCM3 general circulation model (with three climate change scenarios A1B, A2 and B1) were downscaled using LARS-WG Statistical Model in selected stations of the Urmia Lake Basin and Results has been evaluated and analyzed from three selected synoptic stations, including Saghez, Tabriz and Urmia stations in the base period (1961-1990) and the 2050 (2046-2065) for three variables minimum temperature, maximum temperature and precipitation. In LARS-WG model analysis, evaluation of the amount of MSE, RMSE, MAE indexes were done and correlation coefficients were determined. The conclusion can be presented herein that: the model was fit for the region. The overall results for 20 years of 2050s, indicate a decrease of 12.1 percent in precipitation and also 1.3 Celsius degrees would be increased in temperature compared to the base period. In the study area, the highest maximum temperature increase would be in Tabriz synoptic stations and the highest minimum temperature and highest decries precipitation would occur in Urmia station. The results of this study would help enormous the policymakers and planners for water resources in the future.

Climate change impacts are very dependent on regional geographic features and local climate variability. Impact assessment studies on climate change should therefore be performed at local or at most at the regional level for the evaluation of possible consequences. However, climate scenarios are produced by Global Circulation Models with spatial resolutions of several hundreds of kilometers. For this reason, downscaling methods are needed to bridge the gap between the large scale climate scenarios and the fine scale where local impacts happen. A stochastic weather generator, however, can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability. In paper, LARS-WG model were used to downscale GCM outputs and then assessment of the performance were done for generated daily data of precipitation, minimum and maximum temperature and sunshine hours. Study area is Ghare-su basin in Gorgan and the station is called Gorgan synoptic station. The first step is running the model for the 1970-1999 period. Then mean of observation and synthetic data were compared. T-test was used in the 95% significance level, and the difference between observation and synthetic data was not significant. Finally monthly mean of observation and synthetic data were compared using Statistical parameters such as NA, RMSE & MAE. As a final result, it is found that performance of model is appropriate for generating daily above listed data in Ghare-su basin. So, it is possible to predict the climatic parameters from GCM output using LARS-WG model. Also minimum and maximum temperatures have highest and sunshine hours have lowest correlation.

In arid and semiarid regions, water demands of different sectors are related to groundwater resources. Therefore, the assessment of its changes is very important to achieve sustainable development and optimal management of aquifers in the future periods. The aim of this study was to evaluate the effects of climate change on Islamabad aquifer. For this purpose, the output of general circulation climate models was used. For the generation of future weather data in the region, LARS-WG model was calibrated using meteorological data and then, HADCM3 model was applied and its results was downscaled using LARS-WG model in Islam Abad synoptic station for period of 2011 to 2030. The impacts of climate change on groundwater resources were analyzed based on A2 scenario in the period of 2011 to 2030. Then, optimization of these resources was done using LINGO software. Results of LARS-WG model indicated that the monthly rainfall, minimum and maximum temperatures and radiation will change to 2.04, 2.31, 13.66 and 0.4 %, respectively, compared to the baseline period. These conditions affect water demand and available water volume. Moreover, the benefits of optimizing agricultural production in terms of climate change to the current situation dropped to 3.9%.

Climate change has a critical impact on water resources, especially in arid regions. In the first part of the study, the LARS-WG was used for downscaling of climatic variables including rainfall, solar radiation, minimum and maximum temperature over the Ghareh-Chay basin in Markazi province for a 31 year historical period (1983-2013). Results showed that LARS-WG can be applied successfully to downscale the climatic variables. By using the three emission scenarios, A1B, A2 and B1 in different future horizon, decreasing and increasing trends were seen for rainfall and temperature, respectively. In the second part of study, adaptive inference neuro fuzzy system (ANFIS) was applied for run-off simulation over the basin for three different horizons, 1400, 1435 and 1470. Two different identification methods namely, grid partitioning and subtractive clustering were used for ANFIS model. Performance of the subtractive clustering was slightly better than the grid partitioning in run-off modeling process. The results indicated that in comparison to the baseline period, the maximum discharge will be reduced by14, 27 and 43 percent in 2020, 2055and 2090 horizons, respectively. Based on the occurrence of climate change over the basin and intensification during the next years, it seems necessary to consider some ways for adaptation and coping with climate change for protecting the water resources.

Climatological parameters are among the most important ecological capability evaluation criterias, on other hand, climate changes which has intensified in the past decades and has the consequences such as; increases in climatic threshold phenomena like occurrence of horrific floodings, destructive hurricanes, abnormal and sudden colds and heats, untimely rainfalls and heavy snows, widespread droughts and etc., has shown the necessity of studding the impacts of climate change on various parts of economy and social more than before. But due to low spatial and temporal resolution of global climate models, regional experts must increase the resolution of this model's data using downscaling methods. This study has evaluated the power and precision of LARS-WG model in climatic data generating and future climate forecasting for Guilan province of Iran. Accordingly the daily data collected from synoptic stations in Guilan, with a minimum of 15 years of daily data, between 1995 to 2009 has been used. The parameters used included; rainfall, minimum temperature, maximum temperature and solar radiation. The results show that, the highest calculated value of Mean Absolute Error (MAE) for rainfall modeled data was 14.48 in Astara station, and the highest bias calculated value for rainfall parameter was -4.35 and in Astara station too. The model precision for modeling the minimum and maximum temperature was desired for modeling this parameters, and the highest MAE and bias values for minimum temperature were 0.17 and 0.065 in sequence and both in Anzali station. Also, the highest MAE and bias calculated values of maximum temperature were 0.26 and 0.23 in sequence and both in Rasht station. And in LARS-WG precision in modeling the solar radiation parameter, the Rasht station had the highest calculated MAE and bias values, with the amount of 0.31 and 0.08 in sequence. Results analysis show that the LARS-WG model, has the proper power and precision for climate modeling and data generating in Guilan province of Iran.

In the study of climate changes, Prediction of the future climatic parameters is performed by general circulation models (GCMs) and emissions scenarios of greenhouse gases. However, Global Circulation Models have very course spatial resolutions. For this reason, downscaling methods are needed to bridge the gap between the large scale climate scenarios and the fine scale where local stations exist. Downscaling methods are divided into two categories: 1) statistical models and 2) Dynamic models. Among these methods, statistical methods are much more popular which is due to low expenses and less time consuming procedures. Lars-WG and SDSM models are among the most concise methods of statistical tools for downscaling. Herein this research these two models were used in simulating precipitation and temperature changes in Urmia lake basin located in the north west of Iran. Four synoptic stations including Saqez, Tabriz Khoy and Urmia were considered. These four stations had a good and long data especially in base period (1961-1990). In order to assess the models, MSE, RMSE & MAE indexes along with regression and bias were used. Results show that both models were good in simulating temperature but SDSM was better in simulating precipitation according to statistical performance measures and has less uncertainty. But it has more complex and time consuming procedures. While Lars-WG is simpler and faster comparing with SDSM. In general, none of the models has absolute superior in

Increasing greenhouse gases on earth especially after the Industrial Revolution that led to sensible changes by human’s activities is called climate change. Assessment of the effects of the climate change on different parts of the water cycle for the better managing is very important. In this research, the hydrological model has been studied to assess the effects of climate change on water resources in Beheshtabad catchment. The effects of climate change are investigated in future periods. In this study, SWAT-2009 model has been used to simulate the watershed and it has calculated the amount of runoff. The results of simulation (in daily step) and existing hydrometric data have been used for calibration and validation by SUFI-2 algorithm and the 15-year period (2013-1998). The output of hydrometric stations have been used, the output of general circulation models, and LARS-WG model (HadCM3) in order to assess the effect of climate change on runoff under the climate scenarios A1B, A2 and A1B for the period (2020s), (2050s) and (2080s). The results of climate simulations have been introduced to SWAT model in change factors method. Then by re-running the model for the three above scenarios. The results showed that the annual mean temperature has been increased in the coming period in all scenarios except of B1 and runoff scenarios for the near and distant future generations get dropped. So that under the most difficult conditions the amount of runoff of Beheshtabad catchment will be decreased by 45 percent until 2100 and for the A2 scenario.

Two major issues through studies on hydrological impact assessment of climate change are the sufficiency of historical data and selection of the best rainfall-runoff model. Climate models، with the ability to simulate climatic variables، are considered as references for future projections. Therefore، the rainfall-runoff model must be able to simulate streamflow using only these variables. Current study addresses a methodology of choosing the best model from global climate models for impact assessments which require local-scale climate scenarios. The approach is based on the use of a weather generator which can generate the local-scale daily climate scenarios used as an input in many process-based impact models. The results of analysis of Sufichay streamflows، which flow in Urmia lake basin، by the rainfall-runoff model of IHACRES، indicated a considerable difference between the climate models under emission scenarios A2 for the future period 2046-65. There were no noticeable differences under emission scenarios B1 for both future periods (2011-30 and 2046-65) and under emission scenarios A2 for the near period (2011-30) especially in warm seasons. The uncertainty of selecting different emission scenarios was low for the period 2011-30 and high for the mid-century (2046-65).

Studies of climate change and its impacts on the frequency and intensity of future droughts can help programming for the management of water resource and adaptation of the destructive effects of this phenomenon. The aim of this research is trying to assess the climate change in 2011-2030 period and its impact on drought in Eskandari Basin. To achieve this purpose, after validating Lars-WG to generate weather parameters, HadCM3 output downscaled under A2 and B1 scenarios. Next, minimum and maximum temperature and precipitation was predicted. Also, SPI index was calculated. Underlying these scenarios minimum temperature growth dramatically in March, June, July and September 0.3 to 1.6 °C. Additionally, highest and lowest increment in maximum temperature will occur about 0.02 and 1.6 in May and April, respectively. However, the results indicated an increase of 7.2 to 10.9 percent in the average of annual precipitation, but due to the fact that there is no any increase in the number of wet days, extreme rainfall intensity will soar remarkably. Substantial reduction in precipitation belongs to May and April. Regarding this issue, this shortage will affect with several months lag and the most severe agricultural drought will occur in August and hydrological drought will fall out in December based on six and 12 months SPI, respectively. Annual analysis showed that 75 percent of the years are in normal condition. Therefore, in Eskandari Basin, increasing drought frequency will not be expected in 2020s. But, precipitation oriented towards warm seasons.

The exceedingly fast trend of climatic changes in recent decades that are mainly caused by industrial activities along with population growth have taken necessitated prediction of changes for the future. The impact of changes in climate on temperature and precipitation in a sub-basin of Zayandehrud (Eskandari) for the 2020 decade is predicted in this research study. The Lars-WG model is used for downscaling Had-CM3 predictions into station scale. Results of running the models show an annual increase equal to 1 degree centigrade in temperature، while a 7. 2% decrease in precipitation will occur. These changes in temperature and precipitation will be more significant during the summer time. Thus، it is expected that we shall have to find ways to deal with serious water shortages in the next decade during this season. Keywords: climate change، Eskandari، Lars-WG، Downscaling، HadCM3 Model.

AOGCMs predict the future climate based on an increasing Green House Gases (GHG) scenario. The temporal resolution of those models is suitable but, their spatial resolution is very coarse and the output of those models can’t be used in earth sciences such as hydrology, water resources and soil conservation. For this reason, downscaling of their outputs is necessary. At present, Statistical downscaling models mostly used in different sciences and a lot of research was carried out. Those models have different accuracy and their accuracy is depended on geographical and climatic conditions.This research was carried out for accuracy evaluation of LARS-WG on different morpho-climatic conditions in northwest of Iran. Northwest of Iran has complex topography and climate due to intrusion of different rain bearing weather systems to the region. Firstly, daily climate data (precipitation, maximum and minimum temperature) of 7 synoptic stations was collected and their time series were created. Geographic data and climate variable were used in LARS-WG Model for each station, and then the model was calibrated and validated. The error of model was calculated for each climate variable by MAE method. The morpho-climatic data was extracted for synoptic stations and correlated with error of model for each climate variable. The results showed that the error in precipitation has significant relation with distance to grid center, whereas the error in maximum temperature is related to elevation of stations.