نشریه مهندسی اکوسیستم بیابان
پیاپی 32 (پاییز 1400)

Changes in the climate system's components, and in particular the severity and frequency of extreme events, are more effective on society and the natural environment than changes in climate averages. Therefore, it is important to examine the variability of limit indices. To this end, this study used the temperature and precipitation data collected from six meteorological stations in Ahvaz, Khorramabad, Dezful, Kermanshah, Hamedan, and Sanandaj Karkheh synoptic basins to investigate the spatial and temporal trends of temperature and precipitation limit values, which were then measured using R-climdex software. Moreover, the trends of all ten precipitation indices and sixteen temperature indices were determined individually via the Mann-Kendall test. The study's results indicated that the trend of precipitation indices was decreasing in most of the watershed stations, leading to a significant decrease in the trend of precipitation indices on wet, heavy, and very heavy precipitation days, and consecutive wet and/or dry days. The results also suggested that temperature indices such as frost days, ice days, cold days, cold nights, and the range of daytime temperature variations revealed negative trends at most stations at 95% and 99% confidence levels. Furthermore, increasing trends were found for the indices of summer days, hot days, and hot nights at all stations studied at the catchment level.

This study used the data collected from six Karkheh watershed's synoptic stations, including Kermanshah, Khoramabad, Sanandaj, Hamedan, Dezful, and Ahvaz to study the spatial and temporal trends of temperature and rainfall in terms of maximum temperature, minimum temperature, and rainfall at a daily scale during a thirty-year period (1980-2010). It should be noted that the data such as quality control and their homogeneity was pre-processed using the RH Test program. Then, the data was converted to the R-climdex model format to identify extreme indices.The output of the model was used to determine the trend analysis using MATLAB2014 software, the Mann -Kendall test, and Sen's slop. It is worth mentioning that the output included ten precipitation extreme indices, including maximum 1-day precipitation amount, maximum 5-day precipitation amount, simple daily intensity index, the number of heavy precipitation days (over 10 mm), the number of very heavy precipitation days (greater than or equal to 10 mm), the maximum number of consecutive dry days (sum of precipitation less than 1 mm), the maximum number of consecutive wet days (the sum of precipitation rate is greater than or equal to 10 mm), days with a total precipitation rate of more than 95 percentile of rainy days (very wet days), days with precipitation rate greater than the 99th percentile of rainy days (very wet days), the ratio of precipitation to very wet days to the sum of rainfall rate. The output also included sixteen temperature indices including the number of below-freezing days, the number of summer days, the number of ice days, the number of increasing tropical nights, season length, the monthly maximum value of daily maximum temperature, the monthly minimum value of daily maximum temperature, the monthly maximum value of daily minimum temperature, the monthly minimum value of daily minimum temperature, cold nights, percentage of cold days, warm nights, warm days, heatwave duration index, cold wave duration index, and diurnal temperature range.

The results suggested a decreasing trend for precipitation indices in most of the studied stations. Accordingly, there found a significant decrease at a 99% confidence level for precipitation indices, including the number of consecutive wet and/or days and heavy and very heavy precipitation days at Kermanshah and Sanandaj stations, respectively. Moreover, the results indicated negative trends for extreme indices such as the number of below-freezing days in Khorramabad, Hamedan, and Sanandaj, cold days, and cold nights at Ahwaz, Sanandaj, and Hamedan, ice days at Hamedan and Kermanshah, and diurnal temperature range at Ahwaz and Sanandaj stations. Also, increasing trends were found for summer days, warm days, and warm nights in all the stations located at the watershed.

The investigation of the trend of temperature indices in the Karkheh watershed during the period of 1980-2010 indicated an increase in the frequency of warm events, such as warm days and nights and growing season length, and a decrease in the frequency of cold events, such as cold days and nights, and the number of below-freezing and ice days. However, while most of the obtained results were consistent with the results found by the Intergovernmental Panel and other national and international studies, a completely unified pattern cannot be made for changing the indices across the basin. On the other hand, it can explicitly be stated that the minimum quantities of the minimum temperatures changed more than those of the maximum temperature rates. Moreover, the analysis of the precipitation indices revealed a decreasing and negative trend in all the studied stations.Furthermore, the comparison of the rainiest and the least rainy years showed that the range of precipitation fluctuations varied greatly from year to year and that the precipitation distribution was different at different stations. Therefore, considering large dispersion and low precipitation in most stations, a uniformed regional pattern cannot be offered for precipitation. The results also indicated that the temperature points increased with a positive trend and that the precipitation peaks represented a negative precipitation trend.

Drought is a long-term natural phenomenon when the average precipitation rate is less than that of the normal periods. As a natural phenomenon, drought has turned into a  natural disaster occurring throughout Iran in recent decades with all its associated consequences. On the other hand, although rainfall is considered as the main indicator of water availability, the temperature is also an important factor in this regard, as it controls the evapotranspiration rate. Therefore, parameters such as precipitation and temperature can be used as indicators for analyzing the drought. Moreover, identifying drought trends based on previously recorded data, noting their occurrence in different times and places, and studying their variation over time play a significant role in managing water resources. It should be noted that the purpose of trend analysis is to determine the decreasing or increasing nature of trends in a series of observations performed for a random variable over time.

The Standardized Precipitation Evapotranspiration Index (SPEI) is a meteorological drought index that considers the variability of both precipitation and temperature in predicting drought conditions in a region. Enjoying a multi-scale ability to monitor and analyze drought in different scientific disciplines, the index fulfills the requirements of a drought index, being recently used in a variety of drought analyses. This study measured SPEI using precipitation and temperature data from eleven meteorological stations in Sistan and Baluchestan Province, Iran, during the study period.  The Drought events were then identified via the Standardized Precipitation Evapotranspiration Index over both 3 and 12-month timescales. The total geographical area of Sistan and Baluchestan Province is approximately 187,000 km2, located at 25° 03 N to 31° 28 N (latitudes), and 58° 47 E to 63° 19 E (longitudes). The province has an arid and semiarid climate with a mean annual rainfall of about 100 mm. The monthly precipitation and temperature data recorded at the stations were obtained from the Sistan and Baluchestan's Meteorological Organization. One of the commonly used tools for detecting changes in climatic and hydrologic time series is trend analysis. Mann-Kendall is a non-parametric trend test commonly used to assess the significance of trends in time series. This study used both nonparametric trend tests (Mann-Kendall) and graphical Mann-Kendall model trend analysis (statistical significance at 95% confidence level) to explore the drought trends in each station. The purpose of trend analysis is to determine whether the time series of a random variable's observations generally increases or decreases over time. While parametric trend tests are more powerful, it is non-parametric trend tests are widely used, as they can accommodate outliers in the data and require independent data.

The study's results indicated no negative trend in precipitation but revealed a significant trend in temperature. The SPEI values measured for the short-term time scale (SPEI 3) showed a statistically significant downward trend for all stations, which corroborates the occurrence of more critical drought periods in recent years. Moreover, the study of the frequency of drought occurrence in the stations during 10-year periods suggested an increasing trend in the occurrence of drought in recent years. As for the maximum drought index, it could be said that the index's value has typically increased in recent years, that is, more severe droughts have occurred. According to the results, the most severe drought belonged to the Khash station, and the wettest period was found in the Chabahar station.

This study sought to identify possible drought trends in Sistan and Baluchestan province using the Mann-Kendall test, considering the fact that detecting such changes offer valuable information for future water resources management. Accordingly, the analysis of previous drought events showed that more severe droughts are expected to occur in the years to come. Although this study did not seek to find possible causes of decreasing trends, the results presented herein could be used as a benchmark for further analysis of the consequences of climate change. It could be argued that low precipitation and high potential evapotranspiration (PET), especially the PET caused by rising temperature, are the main factors that could influence drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis, and it is suggested that more comparative studies of different drought indices be conducted on analyzing future climate change-induced droughts.

As one of the most important national and international capitals, wetlands have a great impact on their surrounding microclimate and ecosystems. Therefore, they need to be greatly protected as the most important natural habitats. Supporting a wide range of ecosystem services, these dynamic systems can modify the temperature of urban areas by acting as a cooler in areas close to highly populated cities. This study used temperature and vegetation to investigate the impact of the Gavkhuni wetland on its surrounding areas. To this end, the temperature rates of four areas, including wetland, vegetated areas around the wetland, the whole area around the wetland, and the surrounding bare lands were examined, using the Surface Energy Balance Algorithm for Land (SEBAL) to illustrate those changes.

This study sought to investigate the effect of the Gavkhuni wetland on regional microclimate, using the Surface Energy Balance Algorithm for Land (SEBAL). To this end, the data collected from MODIS and LANDSAT were used. In general, this research can be divided into five general stages. First, the required meteorological data and satellite images were collected and the necessary corrections were made. It should be noted that the meteorological data (minimum and maximum temperature, relative humidity, precipitation, evaporation, wind speed) were obtained from Naein Synoptic Station, which is the nearest station to the wetland, and records accurate information. In the second stage, after analyzing the satellite images and due to the fact that the required images were not available for the study period, MODIS images were used together with the Landsat one, considering the cloudy conditions. In the third stage, the wetland's water and dry surfaces were identified.Accordingly, ten Landsat and MODIS extracted images belonging to the 2000-2019 period were selected, out of which five images belonged to the wet years (2004-2005-2006-2007-2008) and five images belonged to the drought years (2000 -2013-2015-2016-2019). In the fourth stage, the land surface temperature was measured using the SEBAL algorithm, which is a relatively new algorithm that uses remote sensing to estimate the Land surface temperature, calculating the rate of evapotranspiration via satellite images with the minimum required ground data based on the energy balance. The algorithm finally examines temperature variations and their impact on microclimate.Obtained from TLST  equation, the temperature variation Index of a region represents the relationship between humidity and drought. For this index, the average temperature rate of the Naein Synoptic Station, and land surface temperature were used, using the SEBAL algorithm. In the last stage, the wetness index was used to investigate the moisture content of the area. Then, two images belonging to the wet and dry years were selected. Landsat Image No. 8 belongs to 2016 which represents zero value for the wetland's humidity index, and Landsat image No.6 belongs to 2008 when the wetland's humidity index was 56.7.

The LST maps generated for the four intended areas over a 20-year period through the SEBAL algorithm indicated that the average estimated index for the whole study area was 0.86 and 0.73 in the drought and wet periods, respectively. Therefore, it could be argued that the LST is lower in wet periods than in drought ones. Moreover, the wetness maps prepared by the TASSELED CAP index showed that the average variation index was -0.05 and -0.04 in wet and drought periods, respectively. In other words, increases in moisture content during the wet period and its decreases during the drought period make the index negative in the wet period. The study's results revealed that vegetation was denser around the wetland during the wet period. Therefore, it could be argued that the microclimate of the area around the wetland was directly affected by the wetness or dryness of the Gavkhuni wetland.

Considered as one of the most important natural ecosystems, wetland habitats exert a significant influence on the temperature, vegetation density, and development of their surrounding areas. This study's results indicated that the higher the average moisture of a wetland is, the lower the temperature variation index would be, and the lower the average moisture index of the wetland is, the higher the temperature of the surrounding areas and the greater its influence on the region's temperature would be, leading the decline of the surrounding area's desirability.

Covering an area of 45490 square kilometers, the East Azerbaijan province is located in the northwest corner of the Iranian plateau at the range of 45 degrees 7 minutes to 48 degrees 20 minutes’ east longitude and 36 degrees 45 minutes to 39 degrees 26 minutes north latitude, being considered the 10th largest Iranian province. In general, the East Azerbaijan province is a mountainous region, about 40% of whose surface is mountainous, 2.28% of its surface is hilly, and 8.31% of its surface comprises of plains (including mountainous plains). Moreover, the province generally enjoys a cold and dry climate. However, the region has different climates due to its diverse topography. Affected by the cold northern and Siberian winds and the Mediterranean and Atlantic seas' humid winds, the province is a cold and mountainous region that is classified as a semi-arid region in terms of climate, whose average annual precipitation rate is 250-300 mm.

Introduction of SDSM Exponential Micro Scale Model  Weibel et al. (2005) used a multivariate regression model called SDSM to examine the effects of climate change on statistical downscaling, in which the station's daily forecast data (predicted), large-scale NCEP variables (predictor), and large-scale variables of general circulation models under various diffusion scenarios serve as inputs for future study periods. The predictor outputs have many variables. This study used the meteorological data collected from synoptic stations, NCEP data, and CANESM2 data under two RCP 2.6 and RCP 4.5 scenarios. The calibration of the model was performed using the NCEP data. Moreover, the temperature and precipitation rates of Tabriz, Mianeh, and Sarab stations were predicted for the three periods of 2020-2050, 2051-2080, and 2080-2100, which were then compared with the base period. It should be noted that the SDSM model performs better than other models because it combines both regression and probabilistic methods to produce meteorological data, and it is, therefore, one of the best models in this regard compared to other models. Application of the SDSM model to the study basin This study used the SDSM 5/3 model for statistical downscaling. It also used the data regarding the temperature and precipitation collected from three synoptic stations (Tabriz, Mianeh, Sarab), each of which contained 31 statistical years. The SDSM model is performed in several stages, including selecting predictor variables; calibration and validation; model performance review; developing climate scenarios for RCP 2/6 and RCP 4/5 and calibration. Assessing the Performance of the SDSM Model There are various statistical indices for evaluating the performance of observational data, prediction, and error values, including the Index of Agreement (d), Nash-Sutcliffe Performance Index (NSE), Second Root Mean Square Error (RMSE), and Mean Error Average (MAE). In Nash-Sutcliffe Index (NSE) whose variations range from infinite to minus one, the closer the data are to 1, the higher their accuracy would be (Nash et.al, 1970). On the other hand, the value of d ranges from one to zero; Accordingly, the closer the d values are to one, the higher the accuracy and agreement of the predicted and observed data would be.  The average error values ​​between the predicted and observed data are shown by RMSE and MAE. Each of the aforementioned variables is calculated through the following equations. represents monthly precipitation data of Cordex (Ghonchepour et. al., 2019).                                                                                                                                                       

The results obtained from the three meteorological stations indicated that compared to the observational period, the precipitation rate would increase in Tabriz and Mianeh stations and decrease in Sarab station in all three periods under both RCP 2.6 and RCP 4.5 scenarios. Moreover, the temperatures would decrease in Tabriz and Sarab stations in all three periods under both the RCP 2.6 and RCP 4.5 scenarios compared to the observational period, while it would increase in Mianeh station during the same period and under the same scenarios.

The results of this study obtained from the SDSM outputs can be used in hydrological and environmental sectors. They can also be used for predicting climate parameters and assessing climate change, considering the fact that climate change affects water resources, human health, food, and agriculture. Therefore, the study's results could also be used in all of these sectors. On the other hand, as any decline in precipitation rate and increase in temperature would lead to quantitative and qualitative changes in water resources, the water uses plans need to be revised so that they can minimize the harmful effects of such variations.

Rapid population growth, increasing water demand, decrease in precipitation, and occurrence of drought may increase the use of water resources, especially the extraction of groundwater resources, leading to a drastic decline in groundwater level, and consequently the occurrence of land subsidence phenomenon. There are various methods for monitoring land subsidence. However, from among ground and space-based methods for the detection of earth crust deformations, the application of Interferometric Synthetic Aperture Radar (InSAR) on the collected data is considered as the most proper method in terms of accuracy and continuous spatial coverage. 

Located in central Iran in the west of Yazd Province, Abarkouh plain is a part of the Abarkouh – Sirjan basin, covering an area of 1390 km2. The area consists of alluvial fans and plains, surrounded by mountains on the west, south, and southwest and bounded on the east by Abarkouh Playa. This study used 46 satellite images taken from 2014 to 2018 to measure the amount of land subsidence in the Abarkooh plain. Moreover, the Shuttle Radar Topography Mission (SRTM) DEM was applied with 30 m resolution to remove the topography effect. A small Baseline Subset (SBAS) time series analysis was also performed to examine the short-term and long-term behavior of the subsidence.Decline in groundwater level and the subsurface sediment thickness are the two most important factors affecting the subsidence. The data used in this study were collected from 34 piezometric wells and 77 geologic logs. Finally, the most effective factors involved in subsidence and their relationship with other factors were investigated by comparing the output of the subsidence map and other existing maps.

The study's results indicated that the subsidence occurred in the east, northeast, and north of the area with the maximum accumulated value of 21, 10, and 6 cm, respectively, over four years. Moreover, the study of groundwater level and the thickness of fine-grained sediments showed that despite the decline in water level throughout the whole plain, subsidence is observed only in regions with clay subsurface sediments. According to different trends of decline in the groundwater level of the study area, groundwater level variations are changed during three periods. Accordingly, the water level declines during the first period in the east, northeast, and north of the area, while it increases in the west and southwest of the region. However, the decline in water level occurs throughout the whole region during the second period, and it is decreased at a lower rate in the east, north, and northeast during the third period.

In the first period, the comparison of the location of areas with increase or decrease in their water level with their corresponding areas on the Landsat showed that the water level declined in those residential and agricultural areas where there are more water wells, and, therefore, the subsidence rate is much more than other areas. On the other hand, the study of areas with an increase in water level suggested that the aquifer of these areas was recharged by mountains and alluvial fans.In the second period, those areas whose water had declined in the previous period experience more decline. Therefore, it can be concluded that the aquifer had not sufficiently been recharged in wet periods. In other words, the increase in the decline of the areas' water level occurred due to the decrease in the recharging of the underground waters because of several years of drought, and the increased groundwater withdrawal caused by the development of agricultural lands. However, despite the sharp decline in the areas' water level, no subsidence was found in the region.In the third period, some piezometric wells were dried, and the water level decline was significant in the west and southwest areas, which could be attributed to factors such as increased acreage, creation of new industrial centers, etc. Therefore, it could be argued that the subsidence rate of this four-year period will certainly return to the hydraulic conditions before this period. Thus, it can be concluded that in addition to the decline in groundwater level, other geological and hydrogeological factors play an important role in causing subsidence.

Ninety percent of Iran's surface is under arid and semi-arid climates. However, low precipitation and low permeability of soils are considered as some of the most important natural problems of rangelands in such climates that prevent the successful establishment of plants. Therefore, it is necessary to perform a series of mechanical operations to make optimal use of wastewater and to store the rainfall.

a factorial experiment was performed via a completely randomized design with three replications and three treatments, including the rainfall storage structure (in five levels), soil depth (in two levels), and sampling season (in two levels) to compare the two methods of contour furrow and catchment crescents. Accordingly, a total of 60 soil samples were collected, which were immediately weighed after harvest and transferred to the soil science laboratory where their weight moisture content was measured individually. Moreover, a randomized systematic plot method was used to measure forage production and compare it to structural type treatments. In each of the treatments, two 100-meter transects were used at a distance of 100 meters from each other, and the total forage was harvested in 50 plots, whose weights were measured as grams per square meter.

Analysis of variance of the collected data indicated that the main variables' (structure, depth, and sampling season) effect and the interaction effects (structure × depth, structure × sampling season, and structure × depth × sampling season) on the percentage of soil moisture were very significant. Moreover, there was a significant difference (P% 1) between the percentage of moisture's weight in the catchment crescent treatment with contour furrow, and between these two treatments with the other three ones (i.e., between catchment crescents, contour furrow, and control). The analysis of the data also suggested that when measured by the contour furrow method, the soil's moisture was 13.3% and 66.2% higher than the control treatment at the depth of 20-50 cm in the first and second sampling seasons, respectively. on the other hand, when measured by the catchment crescent method, the soil's moisture was 115.9% and 183% greater than that of the control treatment, respectively, indicating that the use of both catchment crescent and contour furrow methods increased moisture storage in deep soil.Furthermore, the study's results showed that the catchment crescents method played a more effective role in soil water storage. It was also found that compared to the contour furrow method, moisture content was increased in the catchment crescents method by 90.5% and 70.4% in deep soil during the spring and autumn, respectively. Moreover, the results suggested that the amount of forage production was significantly different (P≥ 1%) in the catchment crescent treatment, the contour furrow treatment, and the other three treatments, and that the use of both precipitation storage methods increased forage production in the rangeland, with the production of herbaceous plants being increased by 126% in the contour furrow method and 378% in the catchment crescent method, compared to that of the control. Taking the obtained results into consideration, it could be said that the catchment crescent method was 110% more effective in increasing forage production than the contour furrow method.

considering the direct relationship between the percentage of soil moisture and the amount of forage production in rangeland plants, it could be argued that increased moisture storage in the soil induced by the use of rainfall storage methods could have a great effect on increasing rangeland plant production, especially perennials. Taking this study's results into account, it can be said that an increase in moisture storage in deep soil because of the application of the catchment crescent method, and, therefore, its added effect on vegetation characteristics, indicates the higher efficiency of the method over the contour furrow one, for which two reasons can be offered: 1- In the catchment crescent method, the runoff is collected from a larger surface on the upstream slope of the crescents, which is then stored in a small area inside each catchment crescent; 2- The depth of the catchment crescent is more than that of the contour furrow (minimum 50 cm vs. maximum 25 cm), and therefore it stores more runoff inside, giving ample opportunities to the water stored in the catchment crescent so that it can penetrate deep into the soil. Therefore, it can be suggested that the relevant organizations use the catchment crescent method instead of the contour furrow one to store precipitation in the soil so that they can improve and rehabilitate the rangelands.

Drought is a long-term natural phenomenon when the average precipitation rate is less than that of the normal periods. As a natural phenomenon, drought has turned into a  natural disaster occurring throughout Iran in recent decades with all its associated consequences. On the other hand, although rainfall is considered as the main indicator of water availability, the temperature is also an important factor in this regard, as it controls the evapotranspiration rate. Therefore, parameters such as precipitation and temperature can be used as indicators for analyzing the drought. Moreover, identifying drought trends based on previously recorded data, noting their occurrence in different times and places, and studying their variation over time play a significant role in managing water resources. It should be noted that the purpose of trend analysis is to determine the decreasing or increasing nature of trends in a series of observations performed for a random variable over time.

The Standardized Precipitation Evapotranspiration Index (SPEI) is a meteorological drought index that considers the variability of both precipitation and temperature in predicting drought conditions in a region. Enjoying a multi-scale ability to monitor and analyze drought in different scientific disciplines, the index fulfills the requirements of a drought index, being recently used in a variety of drought analyses. This study measured SPEI using precipitation and temperature data from eleven meteorological stations in Sistan and Baluchestan Province, Iran, during the study period.  The Drought events were then identified via the Standardized Precipitation Evapotranspiration Index over both 3 and 12-month timescales. The total geographical area of Sistan and Baluchestan Province is approximately 187,000 km2, located at 25° 03 N to 31° 28 N (latitudes), and 58° 47 E to 63° 19 E (longitudes). The province has an arid and semiarid climate with a mean annual rainfall of about 100 mm. The monthly precipitation and temperature data recorded at the stations were obtained from the Sistan and Baluchestan's Meteorological Organization. One of the commonly used tools for detecting changes in climatic and hydrologic time series is trend analysis. Mann-Kendall is a non-parametric trend test commonly used to assess the significance of trends in time series. This study used both nonparametric trend tests (Mann-Kendall) and graphical Mann-Kendall model trend analysis (statistical significance at 95% confidence level) to explore the drought trends in each station. The purpose of trend analysis is to determine whether the time series of a random variable's observations generally increases or decreases over time. While parametric trend tests are more powerful, it is non-parametric trend tests are widely used, as they can accommodate outliers in the data and require independent data.

The study's results indicated no negative trend in precipitation but revealed a significant trend in temperature. The SPEI values measured for the short-term time scale (SPEI 3) showed a statistically significant downward trend for all stations, which corroborates the occurrence of more critical drought periods in recent years. Moreover, the study of the frequency of drought occurrence in the stations during 10-year periods suggested an increasing trend in the occurrence of drought in recent years. As for the maximum drought index, it could be said that the index's value has typically increased in recent years, that is, more severe droughts have occurred. According to the results, the most severe drought belonged to the Khash station, and the wettest period was found in the Chabahar station.

This study sought to identify possible drought trends in Sistan and Baluchestan province using the Mann-Kendall test, considering the fact that detecting such changes offer valuable information for future water resources management. Accordingly, the analysis of previous drought events showed that more severe droughts are expected to occur in the years to come. Although this study did not seek to find possible causes of decreasing trends, the results presented herein could be used as a benchmark for further analysis of the consequences of climate change. It could be argued that low precipitation and high potential evapotranspiration (PET), especially the PET caused by rising temperature, are the main factors that could influence drought in the future. Therefore, the influence of the PET should not be ignored in drought analysis, and it is suggested that more comparative studies of different drought indices be conducted on analyzing future climate change-induced droughts.

Due to the low annual precipitation rate and, therefore, the existence of a weak and fragile ecosystem, arid regions are more subject to drought. Moreover, significant fluctuations in the temporal and spatial distribution of precipitation make drought forecasting in these areas a complicated task. Having a dry and fragile climate, Yazd Province has experienced numerous droughts within the past few decades. Therefore, it is highly important to monitor and forecast drought in this region. Population growth has increased water demand. Moreover, the increase in the concentration of greenhouse gases due to rising fossil fuels consumption has caused global warming and climate change, changing the hydrological cycle components including precipitation patterns (snow and rain). Therefore, climate change has changed the frequency, severity, and duration of droughts in many areas, especially in arid and semi-arid regions. So far, several indices have been developed to assess drought. This study used the RDI, which is based on precipitation (as system input) and potential evapotranspiration (as output), to estimate the drought. Furthermore, there are several methods for forecasting drought, among which projections of global climate models derived from greenhouse gas emission scenarios are quite common.

This study sought to analyze the efficiency of global climate models in predicting drought in arid regions (Yazd Synoptic Station, Yazd, Iran). To this end, 1961 to 2005 was selected as the base period, RDI values of which were imported to downscale the model (SDSM). Moreover, 2006 to 2018 was selected as the forecast period whose data were not imported to the model. Finally, the predictions of the model for the period 2006 to 2018 were compared with the observed RDI values of the same period for time scales of one, three, and six months. Accordingly, first, the climatic data collected from Yazd Synoptic Station (minimum and maximum temperature, relative humidity, sunshine hours, and wind speed) were prepared, and then the potential evapotranspiration (PET) was calculated for the period 1961 to 2018 via FAO-Penman-Monteith method. RDI value for the period 1961 to 2018 was then estimated on a monthly basis.Moreover, to predict drought through global climate models, CanESM2 global model forecasts were obtained based on the RCP8.5 greenhouse emission scenario from 2006 to 2018 for monthly precipitation, minimum temperature, maximum temperature, average temperature, sunshine hours, wind speed, and relative humidity. Using SDSM statistical downscaling model, the observed data for the period 1961 to 2005 and the projections of the CanESM2 model for the period 2006 to 2018 were downscaled. On the other hand, the mean surface temperature predictor was used to a downscale minimum and maximum temperatures and sunshine hours. Wind speed and surface-specific humidity predictors were also used to downscale wind speed and relative humidity outputs, respectively. Furthermore, to increase the accuracy, three predictors of precipitation, surface specific humidity, and mean surface temperature were used to downscale precipitation outputs. Finally, as for the high spatial and temporal variability of precipitation in arid regions, possible biases in precipitation data were corrected using the Linear Scaling bias correction method, which is based on the average difference between monthly observed time series and GCM historical simulations time series over the same period of the observed series. These differences were then applied to the future GCM simulated climate data to get bias-corrected climate variables.

The results obtained from the application of the FAO-Penman-Monteith method indicated that the variable trend presented by PET values during the study period was mostly induced by changes in wind speed fluctuations. Moreover, downscaled precipitation values showed a more significant error rate than those of the downscaled temperature outputs, probably due to the fact that precipitation values had more variability than the temperature ones, especially in arid climates. On the other hand, the application of the Linear scanning bias correction model showed that the precipitation values downscaled by SDSM for the base period (1961-2005) were overestimated compared to the real precipitation data over the same period. Therefore, the overestimation was corrected using the Linear scanning bias correction method. After correcting the probable precipitation biases and calculating the RDI index, the results suggested that the use of the Linear scanning bias correction model remarkably increased the accuracy of CanESM2 precipitation outputs.Then, the RDI was calculated by replacing the predicted precipitation and potential evapotranspiration values with the real data in the period 2006 to 2018 by measuring the potential evapotranspiration based on the data mentioned. Accordingly, R2 between real RDI values and the CanESM2-extracted forecasted RDI values for the 2006-2018 period was 0.472 for the 1-month time scale, while R2 was 0.738 and 0.762for for 3 and 6-month time scales, respectively.

The results indicated that the model presented a good performance at 3 and 6-month timescales. Moreover, considerable fluctuations in one-month precipitation values resulted in high variability of the one-month RDI time series, decreasing the model's performance at this timescale. Therefore, for projecting precipitation in arid and hyper-arid zones, a bias correction method should be applied to minimize probable biases. Furthermore, the comparison of actual droughts that occurred from 2006 to 2018 with the values ​​predicted by the model at 1, 3, and 6-month time scales showed that the CanESM2 model predicted the drought patterns with relatively good accuracy at 3 and 6-month timescales. However, as for the one-month timescale, the model presented lower efficiency than the other two time scales due to more fluctuations. Therefore, it can be concluded that GCM forecasts have a relatively acceptable efficiency for long-term drought prediction in arid climates.

In recent decades, considering merely the fodder production in dealing with rangeland ecosystems has led to the destruction of fields and reduced rangeland capacity for direct usages such as forage production. Therefore, proper and stable utilization of rangelands with the aim of improving the rancher's quality of life and stability in such kind of exploitation are among the major goals of Iran's social and economic development programs. Most development scholars have proposed the application of a diversification approach to economic activities within the framework of a stable development model to reduce the negative effects of mono-functional use of rangelands, considering the fact that this approach guarantees rangelands and economic stability of gainers, as the diversity of subsistence can be a replacement for unfavorable living conditions and poverty in such areas.

The present study was conducted in Cheshmekhan rangelands in Jajarm city in North Khorasan province. In this study, the beneficiaries were interviewed to check the capacity of the income sources, and those sources that were capable to be applied in the region were listed to prepare a suitability map and optimally combine the income sources. In addition to determining the suitability of rangelands for multi-purpose usages (livestock grazing, apiculture, and medicinal plants), the optimal combination of income sources for representative gainers was determined using the Linear Mathematical Planning Model in LINDO software.  Moreover, the instructions proposed by Arzani et al (2008) were used to determine the suitability of exploitation, according to which all the relevant factors were identified and scored in each model, and finally, the suitability of different utilizations (livestock grazing, apiculture, and medicinal plants) was categorized in classes S1, S2, S3, and N based on the total obtained scores.  On the other hand, to determine the optimal patterns of income sources for representative beneficiaries, the beneficiaries were interviewed in-person to check the capacity of NGOs' income sources. Then, those income sources that could be operationalized in terms of NGOs were listed to prepare a suitability map and develop their optimal patterns. Then, the required information regarding each NGO was obtained through interviews and questionnaires, based on which, a number of representative beneficiaries were defined in terms of the amount of capital available for each NGO, and their optimal income sources' patterns were determined individually, followed by a comparison between the optimal patterns and the current ones. It should be noted that this study used a linear planning model, which is solved by LINDO software.

the study's results indicated that 44.7% of the area was moderately suitable, 43.66% of the area was hardly suitable, and 11.54% of the area was not suitable for livestock grazing. Moreover, 14.23% of the area was very suitable, 49.38% of the area was moderately suitable, and 24.84% of the area was hardly suitable for apiculture. It was also found that 7.22%, 71.62%, and 9.6% were very, moderately, and hardly suitable for harvesting medicinal plants, respectively, and 11.5% of the region was not suitable for this purpose at all. The best exploitation pattern for different plant types was determined according to the ecological potentials of the region by identifying the optimal pattern of income sources, the results of which suggested that the efficiency of the optimal patterns increased by 1.01%, 2.11%, 3.18%, and 30.25%, respectively, compared to the efficiency of the current pattern for the representative gainer 1, 2, 3, and 4.

rangeland managers are required to manage and maintain the health of the rangeland's ecosystem, which is only possible when plant communities are used according to their suitable potential. The results of suitability studies showed that it was possible to reduce the number of livestock and restrict the use of medicinal plants and apiculture. Therefore, it could be argued that the knowledge concerning the suitability of multi-purpose usage and the economic desirability of any type of utilization could be applied in prioritizing the utilization and conservation of resources by identifying the utilization method as an alternative or complementary option. Therefore, this study determined the best utilization pattern for different plant types according to the ecological potentials of the region by identifying the optimal pattern of income sources.

The loss of biodiversity worldwide and its impact on ecosystems' functions and performance over the past few decades has led to the conduct of many studies in this regard. On the other hand, identifying the reasons behind and the processes involved in changes made in species diversity has remained a challenge in ecological research. However, as vegetation is one of the factors that affect the richness of species, this study sought to investigate the relationship between species richness and vegetation percentage in the middle Taleghan rangelands.

considering the effects of livestock grazing, pasture condition and tendency, soil erosion, soil depth, and the percentage of rocks and pebbles at each site, the researchers of this study identified five levels of turbulence and stress in order of importance, including high turbulence and medium stress, medium turbulence and medium stress, medium turbulence and low stress, low turbulence, and moderate stress and low turbulence and low stress. To this end, random-systematic sampling was performed on the representative areas of each sampling unit to collect the required samples. Moreover, the number of plots was determined via the statistical method of 21 (one square meter) and the sampling was performed along two to three transects (100 to 150 meters long) in the representative areas of the study sites (a total of 735 plots). In each plot, first, the list of plants was recorded and their life forms were identified individually. Furthermore, different regression models were used to investigate the relationship between canopy cover percentage and species richness. These models were developed in such a way that the researchers were able to insert the amount of species richness (species per square meter) as a dependent variable (response) and the percentage of vegetation canopy as an independent variable in the relevant equations. On the other hand, to investigate the relationship between species richness and canopy cover percentage of plant species, first, the matrix of the data regarding the plant species cover percentage in different plots and sites was produced, followed by the modeling of regression equations via vegan and IME 4 packages in R software. Moreover, this study used a hybrid model to model the regression relationships between species richness and vegetation percentage. To this end, the best model was selected based on AIC standard statistics from among different regression models fitted on species richness and percentage of the vegetation canopy. It should be noted that as the results of such a statistic are separately calculated for each effect, and that the best model is selected based on the lowest value of the statistic, two or more models are selected as the best model in cases where the difference of the statistic is less than 2 in two or more models. Finally, R2C and R2m values were calculated for the best model using the MuMIn package in R software.      

The study's results indicated that the logarithm-quadratic model was the best-fitted model for the relationships between species richness and percentage of vegetation canopy for forbs, grasses, and shrubs. It was also found that the highest variance in modeling was explained by the fixed effect (based on R2m values). Therefore, the broad-leaf weeds and the shrubs had the highest and lowest variance, respectively. The results also showed that the relationship between species richness in different vegetative forms and the percentage of vegetation canopy were Gaussian but not complete. Moreover, the Gaussian model was observed more on the left side of the curve. Furthermore, in the broad-leaf weeds, the highest species richness was observed in moderate turbulence and stress and the lowest one was found in low turbulence and stress. On the other hand, in wheatgrass plants, the highest species richness was observed at high turbulence levels and the lowest one was found at low turbulence and stress. According to the study's results, the quadratic model was identified as the best-fitted model for the relationships between species richness and canopy cover percentage. Also, it was found that species richness was increased with increasing vegetation percentage and that the species richness decreased (Gaussian relationship) with increased vegetation percentage. Moreover, the highest amount of species richness was found in low turbulence and moderate stress and medium turbulence and low stress, and its lowest amount was observed in low turbulence and low stress. Furthermore, the highest species richness was found in 60-65% of the vegetation.

The results showed that the relationship between species richness and canopy cover percentage was of Gaussian type and that the amount of species richness increased with an increase in the percentage of vegetation, and this increasing trend continued almost to 60% of the vegetation. Moreover, it was found that the species richness decreased with an increase in the percentage of vegetation (more than 60% of vegetation), which may be attributed to reduced light intake, which, in turn, would lead to a Gaussian relationship. Furthermore, according to the study's results, the highest species richness was observed at moderate turbulence and stress, and the lowest was observed at the level of low turbulence and stress. On the other hand, in wheatgrass plants, the highest species richness was observed at a high turbulence level, which could be due to the effect of grazing intensity and turbulence on species of the wheatgrass family. In general, a Gaussian relationship was observed in the whole study area between species richness and vegetation. Therefore, as measuring vegetation is easier and more accessible than other parameters such as biomass, it can be used as a parameter to predict species richness in rangelands. Moreover, considering the fact that a few studies have so far been conducted on the investigation of such a relationship in Iranian rangelands, and on the threshold at which vegetation is at its maximum species richness, examining such a relationship can help the relevant officials manage and maintain vegetation as much as possible so that that optimal species richness is obtained in the ecosystem.