جستجوی مقالات مرتبط با کلیدواژه "تغییراقلیم" در نشریات گروه "اکولوژی"

The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.

The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.

The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively. 

Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.

The impact of dust storms on the Sistan and Baluchistan region is one of the most important current issues in this region. The current research is an attempt to investigate the effects of climate change on the trend of changes in dust storms in Sistan and Baluchistan province in the future perspective (until the end of 2100) compared to the base climate period (2010-2020). The study method consists of two parts, namely, analyzing the trend of dust storm events changes from the past to the present and projection the future situation. In the first step, using the multiple linear regression method, the correlation and relationship between the variable of days with dust with other climatic parameters (temperature, rainfall, relative humidity, and wind speed (during the statistical period of 2010-2020 was obtained. Then, the modeled data of the CMIP6 global model in three scenarios and 8 decades from 2020 to 2100 were used as inputs of significant regression relationships during the period 2010 to 2100 to investigate the future status of dust storms. Analysis of the results showed that, the trend of dust event change based on three scenarios of optimistic, moderate, and pessimistic in Sistan and Baluchestan provinces is increasing. As expected, the least dusty days are seen based on the optimistic scenario (SSP1-2.6). The dust trends based on the middle (SSP3-7.0) and pessimistic (SSP5-8.5) scenarios have almost the same trend and are increasing. Therefore, we can expect to have more frequent dust events in the future as climate change continues. As a result, the necessary investments for adaptation measures and mitigation against the harmful effects of dust need to be put on the agenda from now on.

Predicting the effect of climate change on the distribution of valuable and endangered plant species is essential for their conservation and management. In this study, the MaxEnt model and 10 environmental variables were used to predict the current and future distribution of E. amoenum and E. italicum in response to climate change. Also, to predict the effect of climate change in the future (the 2050s and 2070s), two climate scenarios of RCP 2.6 and RCP8.5 were used under the CCSM4 general circulation model. Evaluating the accuracy of the models based on the AUC index indicates their excellent performance (<0.9). Resutls  of this study reveal that the most important variables affecting the distribution of E. amoenum species are slope (38.8%), annual temperature range (11.5%), and precipitation in the driest season (31.9%) are. Also, the solar radiation (37.6%), slope (36.4%), and the average temperature of the coldest season (11.5%) are the most important environmental factors affecting the distribution of E. italicum. In addition, the results show that the distribution of the studied species will decrease in response to climate change and under RCP2.6 and RCP8.5 in the 2050s and 2070s. Therefore, the results of this study emphasize the need to develop  conservation strategies to prevent the extinction of these species.

It is accepted that the global annual average temperature has been increased during recent decades. The climate of Iran is also affected by this global warming. Some studies indicated the increasing of mean annual temperature of Iran between 3.5-4.5○C by 2050. Crop phenology is directly related to temperature, so climate change could significantly change the phenology and yield of crops. Modelling phenology is a way to simulate the timing of phenological stages based on climatic factors. Amongst models, WOFOST is known as a powerful model for simulation of phenological stages and yield of wheat. In this study climate warming of Iran was evidenced. Based on this fact, trends of potential grain yield and time of flowering and maturity of wheat were studied in different climatic zones of Iran during 1992-2012. It is expected that understanding the thermal induced of phenological changes, can lead us to better field management decision making.

Weather data of 18 cities from 4 climatic zones of Iran (northern warm and humid (Zone 1), southern warm and dry (Zone 2), temperate (Zone 3) and cold and high elevation (Zone 4)) were analyzed during 1992-2012 and the long-term trends of air temperature were detected by linear regression. The crop growth simulation model WOFOST, was used to simulate the time of flowering and maturity and also the potential grain yield of winter wheat. Calibration and validation of model was conducted in 4 selected cities from each zone using statistical measures. The consequences of temperature on duration of grain filling period and also yield of wheat were determined.

Results indicated that the mean annual air temperature was significantly increased in all cities during 1992-2012. The highest increasing rate (regression line slope) belonged to Ardabil (0.159○C.y-1) and the lowest rate was observed in Pars Abad Moghan (0.06○C.y-1). Validation of WOFOST in 4 selected cities from each climatic zone, showed the perfect ability of model in simulating the flowering and maturity time of wheat. Regression analysis showed that the grain filling period was increased in accordance with temperature rise in zone 1 and 4 whereas it was shortened in zone 2 and 3. Grain yield showed the same trends predicted for grain filling period in different climates. Slop of linear regression between temperature and yield was significant except in Gorgan (Zone 1). In all cities the relation between grain filling period and yield was direct and positive.

Although temperature has increased in all climatic zones of Iran, flowering and maturity time and grain yield of wheat has showed different responses. There was a nonsignificant slope of regression line in the north humid climate, which means that the humidity of northern part of Iran (south of Caspian Sea) enhanced stability for this area. Annual mean temperature in southern warm and dry zone of Iran ranged between 24.3-27.9 ○C that could be stressful for wheat, and hence resulted in shorter grain filling period and less grain yield. It seems that in temperate zones (such as Mashhad) increasing temperature caused yield reduction due to faster GDD accumulation and lack of time to complete remobilization of photosynthetic materials. On the other hand, in cold areas increasing temperature could reduce the risk of cold stress in flowering time, resulting in longer grain filling period and higher grain yield. According to WOFOST simulation results, during 20 years study (1992-2012), mean potential yield of wheat increased 6.25 and 11.42 percent in northern warm and humid (Zone 1) and cold and high elevation (Zone 4), respectively and decreased 12.17 and 13.11 percent in southern warm and dry (Zone 2) and temperate (Zone 3), respectively. Total mean potential yield of wheat, by consideration of proportion of each zone in total wheat production of Iran, showed reduction of 1.8% and 3.28% during 1992-2001 period (10 years) and 1992-2012 period (20 years), respectively.          

General circulation models (GCMS) are only tools to predict future climate condition under climate change scenarios. An outstanding issue with the use of GCM output for regional and local application is the coarse spatial resolution. So there are various methods to predict، climate variables at regional، local and a station scale that all these methods are known downscaling. The Statistical Downscaling Model (SDSM) is one of the most used methods that identifies relationships between variable predictors (output GCM) and variable predictands (Temperature، precipitation، etc. in particular station) using multiple linear regression. Intergovernmental Panel on Climate Change (IPCC) has determined two periods (1961-1990) and (1971-2000) as baseline to compare the effects of climate change in future periods. As well as reanalysis data، that produced by the National Center for Environmental Prediction (NCEP) are important components for the structuring of the SDSM as they supply the predictor values for thecalibration and validation of the model. Type and period of reanalysis data can be effective in model accuracy. In this study for downscaling temperature and precipitation variables the sensitivity of the SDSM model was examined to type and reanalysis dada of NCEP in Kerman meteorological station. The mean absolute error (MAE) was used to determine the sensitivity of the model. Result showed that the model is sensitivity to both type and base period reanalysis data. The mean absolute error of the reanalysis CGCM model data، for the average maximum، minimum and mean temperature variables equal to 11، 4. 5 and 4. 7 times the case that theHadCM3 model data is used respectively. In the case of the base period، when data of (1961-1990) is used، MAE for the mentioned variables and daily precipitation equal to 3. 5، 1. 4، 3. 5 and 1. 4 times that the state which is used for the base period (2000-1970)، respectively.