https://jdcr.birjand.ac.ir/ Journal of Drought and Climate change Research en daily 1 Sun, 09 Mar 2025 00:00:00 +0330 Sun, 09 Mar 2025 00:00:00 +0330 https://jdcr.birjand.ac.ir/article_3365.html Modeling suspended sediment is a crucial subject for decision-makers at the watershed level. Accurate and reliable modeling of suspended sediment load is essential for planning, managing, and designing water resource structures and river systems. In this research, a new hybrid intelligent approach based on the Support Vector Regression (SVR) model has been developed for estimating river sediment. For this purpose, two optimization algorithms, namely Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO), were employed to model the amount of suspended river sediment. For modeling, statistical data from the Babolroud River hydrometric station, located in Mazandaran province, were used as a case study. Data from 19 input parameter combinations were used in the years 1382 to 1402 (solar calendar years) which is 2003-2023 Gregorian calendar. To evaluate the performance of the models, the evaluation criteria of correlation coefficient, root mean square error, mean absolute error, and Nash-Sutcliffe efficiency coefficient were used. The results showed that combined scenarios in the investigated models improve the performance of the model. The results obtained from the evaluation criteria also showed that the Wavelet-SVR model has a correlation coefficient of 0.962, a root mean square error of 0.344 ton/day, a mean absolute error of 0.158 ton/day, and a Nash-Sutcliffe efficiency coefficient of 0.970 in the validation phase. Overall, the results showed that the use of intelligent models based on the SVR approach can be an effective approach in river engineering stability. https://jdcr.birjand.ac.ir/article_3639.html AbstractWater resources management in agriculture is one of the main components of sustainable economic development and food security. In terms of growing season, crop plants are divided into two groups: spring and winter plants. Winter plants are generally called dryland crops. Dryland crops use winter rainfall to meet water needs. At the beginning of the growing season, several irrigation stages are recommended to provide soil moisture, and at the end of the growing season, supplementary irrigation is planned to fill the seeds. The production of dryland crops is less than the potential, but water productivity is higher. In this study, the issue of agricultural water transfer in the Handijan plain of Khuzestan province was first addressed in order to evaluate water resources for dryland cultivation, and then the optimal amounts of water required for dryland cultivation of four crops, wheat, barley, rapeseed, and lentils, were calculated. The results showed that the water crisis situation in the studied plain will be increasingly serious and the need for dryland cultivation and supplementary irrigation is recommended. In addition, in water deficit conditions, it is essential to reduce autumn irrigation and meet the plant's water needs during the stages of maximum plant coverage. Maximum plant coverage for the studied plants is from late March to mid-May. After this stage, the plant canopy gradually wears out and the final crop is harvested.Keywords: Irrigation optimization, rainfed cultivation, bargaining, Hendijan https://jdcr.birjand.ac.ir/article_3700.html Nanotechnology offers a promising approach for improving the physical and hydraulic properties of saline soils. To evaluate this potential, a study was conducted to assess the effects of both organic and inorganic amendments, applied at nano and conventional scales, on saline loamy soil. Soil samples were collected from a depth of 5 to 30 cm, and various treatments were applied, including pomegranate peel biochar and nanobiochar, nanobentonite, zeolite, microsilica, and nanosilica. These treatments were tested using soil columns (35 × 10 cm) under a completely randomized design with three replications. The results indicated that all applied amendments improved soil structural and moisture characteristics. Specifically, they increased the mean weight diameter of soil aggregates, enhanced the water stability index of aggregates, and optimized key moisture parameters such as field capacity, permanent wilting point, and available water content. Among all treatments, nanosilica showed the highest improvement in aggregate stability, increasing it by 35% compared to the control. This treatment also resulted in the lowest bulk density (1.5 g/cm³). The lowest permanent wilting point was observed in the conventional silica treatment, which was significantly different from other treatments at the 5% level. Except for conventional biochar, all other nano and non-nano amendments significantly increased field capacity at the 5% significance level. Similarly, the available water content was significantly improved by these amendments, excluding the conventional biochar treatment. Overall, the findings confirm the effectiveness of soil amendments, particularly nanosilica, in enhancing soil structure, aggregate stability, and hydraulic properties of saline loamy soils. https://jdcr.birjand.ac.ir/article_3813.html The lack of coverage of meteorological stations and the spatial-temporal heterogeneity of data have made accurate assessment of drought in metropolitan cities such as Tehran uncertain. This study aims to provide an integrated framework for drought monitoring by integrating remote sensing indices VCI from MOD13A2, TCI from MOD11A2, PCI from satellite precipitation product, and SMCI from soil moisture along with meteorological-hydrological indices (SPI, SPEI, and PDSI) for Tehran and its suburbs during the period 2000 to 2022. Preprocessing and extraction of indices were performed in Google Earth Engine and then correlation between indices at monthly, seasonal, and annual scales was analyzed using Mann-Kendall and age-slope tests, trends, and Pearson coefficient. The results showed that precipitation-based indices (SPI and PCI) have the greatest ability to explain short-term changes and show a high correlation with the soil moisture status index SMCI; in contrast, VCI responds to moisture fluctuations with a time lag, and TCI has an inverse and significant relationship with drought severity at long-term scales (PDSI/SPEI). From a temporal perspective, summers, with a combination of rainfall deficiency and heat stress, recorded the highest vulnerability, and the years 2008, 2010, and 2016 were the most severe drought periods. Convergence of evidence shows that the proposed multi-attribute approach, while reducing uncertainty, provides a more accurate picture of urban drought dynamics and can be the basis for water demand management planning, early warning, and urban greening model modification. https://jdcr.birjand.ac.ir/article_3702.html Abstract: Climate change, particularly through heat stress, presents a significant threat to agricultural production in Qazvin Province, a vital agricultural hub in Iran. This study aims to analyze the temporal patterns of heat stress—encompassing frequency, intensity, and duration—affecting strategic crops such as wheat, maize, and barley, and to evaluate its impact on their yields under the SSP2-4.5 and SSP5-8.5 climate scenarios. To this end, historical data (1997–2014) from the Qazvin synoptic station and projections from five global climate models (CNRM-CM6-1, CanESM5, GFDL-ESM4, HadGEM3-GC31-LL, MIROC6) were employed, utilizing an optimized ensemble model approach. The findings indicate that, particularly under the SSP5-8.5 scenario, the frequency of heat stress will rise substantially, with maize identified as the most vulnerable crop, experiencing nearly 100% heat stress frequency during its growing season by 2099. Furthermore, both the intensity and duration of heat stress are projected to increase, resulting in significant yield reductions: wheat from 4.20 to 3.00 ton/h, barley from 3.80 to 2.18 ton/h, and maize from 37.9 to 33.1 ton/h. These results highlight the urgent need for adaptation strategies in Qazvin Province, such as adjusting planting schedules and developing heat-resistant crop varieties. Additionally, meticulous planning for water resource management and infrastructure development is critical to bolster agricultural resilience, as neglecting these measures could jeopardize regional food security and economic stability. https://jdcr.birjand.ac.ir/article_4050.html Meteorological drought, as the initial stage of the broader drought process, is a significant climatic hazard with far-reaching implications for environmental sustainability. This study aims to evaluate the trend of meteorological drought in Qaen City, located in South Khorasan Province, over a 37-year period (from the 1988 to 2024 water years). For this purpose, annual precipitation and temperature data, along with two indices—the Percent of Normal Index (PNPI) and the Standardized Precipitation Index (SPI)—were used to analyze drought conditions in the region. According to the results, the average annual precipitation of the region was 161.30 mm (with a coefficient of variation of 37%), indicating high fluctuations in yearly precipitation and the occurrence of years with unusually high or low precipitation. Analyzing the trend in annual precipitation using linear regression, the Mann–Kendall test, and Sen’s slope method revealed no significant trend in precipitation values. However, the annual average temperature showed a significant upward trend, increasing at a rate of 0.078(°C)⁄year. Based on the PNPI and SPI indices, approximately 41% and 16% of the years, respectively (especially after 1999), experienced drought conditions. Wet periods were more frequently observed during the years 1990–1999, as well as in 2019 and 2020. The comparison of two drought indices reveals general agreement during extreme conditions but differences in normal years, as SPI provides a more conservative description of conditions. The study’s overall findings indicate a rising temperature trend, unstable precipitation patterns, and frequent drought occurrences in the region, highlighting increasing climate variability. https://jdcr.birjand.ac.ir/article_3696.html The present study aimed to optimize water allocation from the Qarnaku Dam for the future period (2070–2099) and enhance the resilience of the water supply system. Among CMIP5 climate models, GFDL-ESM2G was selected as the most suitable option for temperature, and NorESM1-M was selected for precipitation. Subsequently, monthly runoff was simulated using Long Short-Term Memory (LSTM) networks, yielding satisfactory accuracy. Agricultural water demand was estimated using the Cropwat model, indicating a 20% increase in future net irrigation requirements. The performance of the Horse Herd Optimization Algorithm (HHOA) and Genetic Programming (GP) was evaluated using the Rastrigin and Rosenbrock benchmark functions, revealing superior results for HHOA. Further analysis showed that HHOA reduced water deficits by up to 22% and improved reservoir resilience from 67% to 79%. Additionally, the LSTM model achieved a Nash–Sutcliffe Efficiency (NSE) coefficient of 0.85, confirming its reliability in simulating monthly runoff. Overall, the integration of advanced metaheuristic techniques with intelligent runoff prediction models provides an effective framework for reservoir management under climate change conditions. https://jdcr.birjand.ac.ir/article_3764.html Objective: This study aimed to analyze the impact of climate-induced migration on the expansion of informal settlements (marginalization) in the metropolitan city of Mashhad, Iran.Methods: This mixed-methods (quantitative-qualitative) study was conducted with a statistical population of 384 migrants residing in informal settlements in Mashhad (selected via cluster sampling) and 20 semi-structured interviews with experts. Data were collected using a researcher-made questionnaire (Total Cronbach’s α = 0.84) and interviews, and were analyzed using path analysis, regression, and thematic analysis.Findings: Quantitative findings revealed that climatic pressures (β = 0.59) and spatial inequality (β = 0.44) had a direct and significant impact on marginalization. Furthermore, climate migration indirectly affected this phenomenon through reducing socio-economic resilience (β = -0.32). Qualitative findings identified five main themes: Migration as a Response to Livelihood Crisis, Reproduction of Inequality, Weak Resilience, Policy Vacuum, and An Uncertain Future.Conclusion: The results confirm that climate migration, in the absence of supportive policies, is not only an emergency response but also a factor in the reproduction of poverty and spatial inequality. Managing this phenomenon requires the development of integrated, justice-oriented policies and planning to enhance resilience in both sending and receiving areas. https://jdcr.birjand.ac.ir/article_3728.html Drought is a recurring natural climatic phenomenon that typically occurs in all parts of the world. This study was conducted to investigate the intensity and duration of drought in the two counties of Ferdows and Tabas in South Khorasan Province. In this study, daily precipitation time series data for the selected stations were collected over a 25-year statistical period (1990–2014), and the Standardized Precipitation Index (SPI) was analyzed for the two studied stations using two GCM models, IPSL and MPI, from CMIP6. The SSP2-4.5 scenario was used to estimate SPI for the period 2030–2059, and the BCSD downscaling method was applied to predict meteorological data. The results indicate that the overall SPI trend for both models show significant fluctuations between positive and negative values, posing challenges for water resource management in the two counties. However, the IPSL model predicts drought with greater intensity, highlighting the need to develop water management plans based on various climate scenarios for the studied counties. According to the study’s findings, an increase in the average intensity and duration of drought in the future period compared to the baseline indicates an intensification of drought. Nevertheless, the number of normal periods in both the baseline and future periods was higher than other periods at the stations studied. https://jdcr.birjand.ac.ir/article_3817.html The present study aimed to investigate the effects of foliar application of zinc oxide nanoparticles (ZnO-NPs) on the morphological and physio-biochemical traits of the ornamental–medicinal plant periwinkle [Catharanthus roseus (L.)] under water deficit stress. The experiment was conducted as a factorial arrangement based on a completely randomized design) with four replications. The first factor was water deficit at three levels (80%, 50%, and 20% of available water content), while the second factor consisted of foliar application of ZnO-NPs at four concentrations (0, 50, 100, and 200 µM), applied as a pre-treatment at the four-leaf stage. The experiment continued until full flowering (two months), after which morpho-physiological parameters (plant height, stem length and diameter, leaf number, root volume and length, fresh and dry weights of stems, leaves, and roots, gas exchange parameters, relative water content (RWC), and electrolyte leakage (EL)) as well as biochemical traits (malondialdehyde (MDA) content, proline content, photosynthetic pigments concentration, and the activities of catalase, peroxidase, and ascorbate peroxidase) were measured. The results demonstrated that water deficit stress significantly reduced plant height, leaf area, RWC, photosynthetic pigments concentration, fresh and dry biomass, and gas exchange, while increasing EL, MDA content, proline accumulation, and antioxidant enzyme activities. In contrast, foliar application of ZnO-NPs enhanced antioxidant enzyme activity, RWC, photosynthetic pigments, and gas exchange, thereby improving plant tolerance to drought stress. Among the tested concentrations, 50 and 100 µM ZnO-NPs were the most effective in alleviating the adverse effects of water deficit and promoting plant growth under stress conditions. https://jdcr.birjand.ac.ir/article_3827.html Composite droughts, which simultaneously affect both meteorological systems and water resources, pose a serious threat to arid and semi-arid regions. This study aimed to model droughts under future climate change scenarios using copula functions. Historical data (1989–2020) and statistically downscaled outputs from the CanESM5 climate model under SSP126, SSP370, and SSP585 scenarios for the future period (2021–2040) were utilized. Watershed runoff was simulated using the IHACRES model. P-12 and R-12 were calculated, and a joint hydro-meteorological drought index (JDHMI) was developed using copula functions. Drought characteristics (frequency, intensity, duration, and magnitude) and trends were analyzed for both historical and future periods. Results indicated that the composite JDHMI exhibited less fluctuation compared to univariate indices, providing a more stable and comprehensive depiction of drought conditions. Under climate change scenarios, the drought pattern is projected to shift from fewer but longer and more severe droughts to more frequent, shorter-duration droughts, particularly under the SSP585 scenario. Although average duration and intensity decreased in some scenarios, the drought "magnitude" index—integrating intensity and duration—increased across all future scenarios, indicating an overall intensification of drought content. Furthermore, the frequency of "severe" droughts significantly increased under all scenarios. Trend analysis confirmed a significant and accelerating decline in the JDHMI index in the future. This framework can serve as a scientific basis for early warning systems, and sustainable water resource management in the face of climate change. https://jdcr.birjand.ac.ir/article_3701.html Climate change is currently a global phenomenon that has affected all aspects of the lives of organisms and their environment. Climate change, especially the recent droughts in the country, has visibly affected production in the agricultural sector. Adaptation to climate change is one of the most basic and important strategies for managing climate change in agriculture. This research was conducted to investigate the use of different plant varieties on the acceptance of adaptation to climate change in the agricultural sector in 2019. The present study is applied in terms of its purpose and descriptive-inferential in terms of its method. Research data were collected through a questionnaire whose validity and reliability have been evaluated (Cronbach's alpha is 9.76), using a random sampling method from 247 people. The method used in this research was the logit model, which was selected as the appropriate model after examination. The results indicate that factors such as variables, extension classes, use of different plant varieties, agricultural input subsidies, modern irrigation methods, and agricultural credits have a significant effect on the acceptance of climate change adaptation in the agricultural sector. Since water scarcity is one of the most important factors affecting the acceptance of climate change adaptation, and according to the results obtained, the use of different plant varieties will create a kind of confidence in farmers and ultimately lead to a reduction in farmers' risk aversion and will lead farmers to use different types of water-poor plants. https://jdcr.birjand.ac.ir/article_3426.html In this study, using the modified reservoir index in the Dez catchment basin located in the west of the country, the peak discharge values will be simulated and checked on an annual scale for the Dezful hydrometric station built after the Dez dam reservoir. The annual peak discharge values of the Dezful hydrometric station and the average discharge values of this station have both changed trends and failed in the annual series in 2016. Examining the trend of changes in the reconstructed flow peak values of Dezful hydrometric station in the statistical period of 1398-1357 showed that the Mann-Kendall statistic for the series is -2.48, which does not indicate significant changes. To investigate the best-detailed function according to the evaluation criteria for the peak discharge values of Telezang station with log-normal statistical distribution and the reconstructed peak discharge values of Dezful station with generalized limit statistical distribution in comparison with the best-detailed function for the observed peak values of Telezang and Dezful hydrometry stations with The statistical distribution of both normal log, the detailed function of a type called Galambos was obtained and theta parameter was calculated for the modified peak flow of Dezful and Telezang 9.8841 and for the observed peak flow of Dezful and Telezang 8.6981. In addition to the mentioned cases, in this study, the detailed function was used to estimate the probability of occurrence of the examined data, which led to the presentation of type diagrams for frequency analysis. https://jdcr.birjand.ac.ir/article_3595.html To investigate the effects of brassinosteroid and melatonin on antioxidant enzymes and yield of quinoa under drought stress, a split-plot experiment based on a randomized complete block design was conducted during the 2019–2020 and 2020–2021 growing seasons at the Research Farm of Shahid Bahonar University of Kerman. Treatments included three irrigation levels (100, 75, and 50% of field capacity) as the main plots, and nine foliar applications as subplots. Drought stress, foliar application, and their interactions significantly affected physiological traits and yield. Increasing drought enhanced malondialdehyde (MDA) content and polyphenol oxidase, while reducing leaf protein. The highest MDA obtained under no foliar application with 50% field capacity irrigation (36.52% increase), whereas foliar application under 75 and 50% irrigation significantly reduced it. The lowest leaf protein was also observed under no foliar application at 50% field capacity (22% reduction). The maximum catalase occurred under 75% irrigation with foliar application of 0.5µM melatonin × 0.5µM brassinosteroid, while the minimum was under 50% irrigation without foliar application. Combined foliar spraying increased peroxidase activity (up to 0.83%) compared with the control. Drought stress significantly enhanced polyphenol oxidase, with the highest activity (25% increase) recorded at 50% field capacity. Grain yield was strongly affected, with the highest yield under 100% irrigation and the lowest under 50% in the second year. Combined foliar application of melatonin and brassinosteroid, particularly under stress (75 and 50%), significantly improved yield by up to 65% compared with the control, with higher concentration being more effective under severe drought. https://jdcr.birjand.ac.ir/article_3621.html Climate change and its effects on water resources and agriculture have made the accurate prediction of climatic parameters at the local scale even more necessary. General Circulation Models, due to their low spatial resolution, require downscaling for regional analyses. This study investigates the performance of the XGBoost machine learning model in the statistical downscaling of monthly mean temperature and relative humidity at the synoptic station of Qaen during the period from 1991 to 2015. In this research, the output of the GCM model, after correcting structural errors using the Chunk Mapping method,was used as input for the XGBoost model. The model's performance was evaluated using statistical criteria KGE, NSE, NRMSE, and R² in two phases: training and testing. The results indicated that the XGBoost model exhibited very good performance in downscaling mean temperature (with R² and NSE values close to one and low NRMSE in both phases) and acceptable performance for relative humidity. The model's stability in temperature simulation was evident, although there is a need for improvement in the model training process for relative humidity. The analysis of the distribution of simulated data showed that the model faces limitations in reproducing extreme temperature values (less than -10 degrees Celsius) and very high relative humidity (more than 80 percent), showing a greater tendency to simulate median temperature values. These findings are consistent with similar research in other parts of the world and confirm the high potential of XGBoost as an efficient tool in climate change studies, especially in arid regions. https://jdcr.birjand.ac.ir/article_3733.html Sustainable crop production requires forward-looking strategies and thorough analysis of future conditions. Rice, as a staple cereal, plays a critical role in global caloric supply. However, its high water demand, coupled with diminishing water resources, presents a significant challenge. This study investigates the impacts of climate change on rice cultivation, both transplanted and direct-seeded methods, in SarkhonKolah, Gorgan County, during the period 2031–2090, compared to the baseline period of 1990–2020, under three climate scenarios (SSP126, SSP245, SSP585). Meteorological data analysis indicates that, under the most adverse conditions, minimum and maximum temperatures are projected to increase by 2 to 5°C. Additionally, trends in precipitation and reference evapotranspiration show an upward trajectory relative to the baseline. Crop growth simulations were conducted using the CERES-Rice model for both cultivation methods. Results revealed that grain yield generally increased compared to the baseline. Under the SSP585 scenario, the lowest yield increases for direct-seeded systems were observed with flood irrigation (6%), sprinkler irrigation (14%), and drip irrigation (7%). Yield reduction in direct-seeded systems with modern irrigation was less pronounced than in transplanted systems with flood irrigation, indicating superior water-use efficiency. The findings also highlight that elevated temperatures induce spikelet sterility, resulting in grain abortion. Consequently, strategic interventions such as promoting heat-tolerant cultivars, expanding direct-seeded systems with modern irrigation technologies, educating farmers on climate change impacts, and investing in climate monitoring and modeling infrastructure are essential to ensure the long-term sustainability of rice production. https://jdcr.birjand.ac.ir/article_3804.html Flooding, as a severe hydrological consequence of climate change, is a recurrent and destructive threat to UNESCO World Heritage sites such as the historical city of Shushtar. Climate change and urban development have intensified the risk of recurrent floods in this region. The study’s objective is to provide a risk assessment framework to enhance resilience and support adaptation strategies against these climate hazards. In this research, a framework based on the Analytic Hierarchy Process (AHP), utilizing 15 independent indicators, was integrated within a GIS environment to identify Flood Hazard, Vulnerability, and Risk Zones. Results indicate that more than 32% of the region’s area is at a high or very high-risk level. The model’s accuracy and reliability were confirmed with an accuracy exceeding 90%. Sensitivity analyses further highlight the critical role of the indicators. The validity of this model provides valuable insights for decision-makers to strengthen sustainable climate risk management strategies and protect cultural heritage against the impacts of climate change. https://jdcr.birjand.ac.ir/article_3828.html Groundwater represents a primary source of water supply in arid and semi-arid regions where surface water availability is severely limited. Increasing demand driven by rapid population growth, agricultural expansion, and industrial development requires robust and cost-effective assessment strategies for groundwater resource planning. This study delineates groundwater potential zones in the Nayband Plain, Tabas County, eastern Iran. The analysis integrates Geographic Information Systems (GIS) with the fuzzy Analytical Hierarchy Process (Fuzzy-AHP). Seven hydro-environmental factors—slope, geomorphology, soil texture, lineament density, land cover, precipitation, and proximity to drainage networks—were selected and weighted based on expert knowledge within a fuzzy multi-criteria decision-making framework. The resulting groundwater potential map was classified into five categories: very good, good, moderate, poor, and very poor. Findings indicate that approximately 36% of the region exhibits good to very good groundwater potential, predominantly in the northern and northwestern sectors of the plain. Model validation using 10 wells, 3 qanats, and 3 springs demonstrated a strong spatial agreement between high-potential zones and existing groundwater extraction structures. The results confirm that coupling GIS with fuzzy-AHP provides a reliable and transferable methodology for groundwater resource evaluation, especially in data-scarce arid regions. This approach supports informed decision-making for sustainable groundwater exploitation and artificial recharge projects in environments with high water scarcity. https://jdcr.birjand.ac.ir/article_3832.html This study aimed to assess the resilience of a native rice cultivar (Tarom Damsiah) to climate change in Golestan Province. Field data were collected during the 1400 crop year and used to calibrate and validate the CERES-Rice model. Baseline climate data (1990–2020) were extracted from the Minodasht synoptic station and projected using the HadGEM3-GC31-LL model under three emission scenarios SSP126, SSP245, and SSP585 for future periods (2031–2090). The calibration results showed that the CERES-Rice model has a high ability to simulate phenological stages, biomass growth and grain yield (Normalized Root Mean Square Error (NRMSE) <10 and Wilmot's Agreement Index (d) >0.9). (Analysis of yield sensitivity to temperature and precipitation changes showed that increasing temperature, especially at the grain filling stage, causes a significant change in paddy yield (in the optimistic and pessimistic cases, between +18.6 and -80.7 percent compared to the yield in the base period, respectively); so that in the SSP585 scenario, a sharp decrease in yield and an increase in water requirement were observed. Evaluation of physical resilience indices showed that in the optimistic SSP126 scenario, the cropping system is able to maintain relative productivity, but in the pessimistic SSP585 scenario, resilience is reduced and yield fluctuations increase. The use of strategies such as changing the planting date and stocking density showed, Planting on May 15 or June 25 had significantly higher yields and lower yield fluctuations than late planting. These results indicate that the adaptive approach can play an important role in the country's food security. https://jdcr.birjand.ac.ir/article_3834.html This article was aimed at assessing vulnerability of the urban water resources system (UWRS) in Birnin Kebbi, Northwestern Nigeria. Multi-dimensional data obtained from various organisations were input into a modified Driving-Force, Pressure, State, Impact and Response System (DPSIR) framework. The Model generated consists of fifteen (15) evaluation factors/indicators that are relevant to the study area’s peculiarities. UWRS vulnerability grades were categorized. Vulnerability intensities of factors were computed. Patterns of how each pair of the model’s factors behaved (from 1990 to 2020) were generated in PAST3.2 environment. Results showed an increase in resilience trends. Resilience has been shown to have more pronounced negative correlations between the sub-systems’ indicators in the post-2000 years. This suggests vulnerability transitions as not all parts of the system moved in concurrence across the decades. In the early 1990s, vulnerabilities were rising together (some positive correlations for short distances), but over time, divergences appeared. The overall vulnerability indicated resilience transition because correlations across the board (red to blue and other cells' colours in the heat map) appeared, including strong negatives implying internal dynamics that push back against uniform change in the system. We conclude that this implies evidence of adaptive management and resilience from the authorities' Responses. The study is crucial because it successfully modelled study area’s UWRS’s resilience/vulnerability patterns. https://jdcr.birjand.ac.ir/article_3836.html Predicting the effects of climate change on rainfall and drought in the coming years is of great importance in planning and policy-making in the agricultural sector and water resources management, and implementing measures to reduce the negative effects of drought. In this study, the results of rainfall forecasting over the 40-year period 2021-2060 were downscaled using the outputs of the HadGEM3-GC31, MRI-ESM2-0, and ACCESS-ESM1-5 general circulation models using the LARS-WG8 statistical downscaling method under three scenarios: pessimistic, continuation of the current trend, and optimistic. After selecting the appropriate general circulation model, the annual SPI drought index was used to estimate the probability of drought occurrence at the stations under study. The results showed that the rainfall trend in most stations and scenarios was decreasing, and this will lead to an intensification of drought in the coming years; In addition to the numerous adverse socio-economic effects, this will also have a negative impact on the region's agricultural performance and future cropping patterns. https://jdcr.birjand.ac.ir/article_3838.html This quantitative-survey study aimed to assess the security consequences of climate change and drought in the Karun and Zohreh Jarahi watersheds, and was conducted with a sample of 226 experts and managers in the water and security sectors. The data were analyzed using a researcher-made questionnaire and advanced statistical techniques including exploratory factor analysis, correlation, and structural equation modeling. The research findings identified three key dimensions of the water crisis: socio-legal justice (factor loading 0.87), political governance (0.79), and bio-economic resilience (0.72), which explain a total of 71% of the variance. The results of Pearson correlation (0.47 to 0.72) and structural equation modeling (CFI=0.93) confirmed the direct impact of climate change on the five dimensions of the water crisis and the ground-breaking role of the political index (β=0.52-0.62).The findings of this study indicate that addressing the security implications of climate change requires a comprehensive approach focused on reforming water governance and achieving water justice https://jdcr.birjand.ac.ir/article_3855.html Climate change poses a significant challenge to agricultural production, and corn yield, a strategic crop in the Qazvin Plain, is highly sensitive to climatic variability. This study investigated the impacts of climate change on forage corn yield under the SSP2-4.5 and SSP5-8.5 scenarios for the periods 2026–2050, 2051–2075, and 2076–2100. To reduce climatic uncertainty, outputs from multiple General Circulation Models (GCMs) were integrated using a linear-weighted ensemble approach. In the second stage, machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest (RF), were combined within an ensemble framework based on weighted averaging to improve yield simulation accuracy. Climatic variables, including precipitation, minimum and maximum temperature, and evapotranspiration, derived from the CNRM-CM6-1, GFDL-ESM4, MIROC6, and HadGEM3 models, were evaluated individually and collectively against station observations for the baseline period of 1986–2014. The evaluation results showed that the ensemble climate model outperformed individual GCMs, achieving a high coefficient of determination (R² = 0.95) and a low RMSE, thereby reducing simulation errors. Future projections indicate increasing minimum and maximum temperatures, as well as evapotranspiration, along with decreasing precipitation across the study area. Yield simulations using the ensemble machine learning models revealed a decline in forage corn yield under both scenarios, with reductions of approximately 4.72% under SSP2-4.5 and 8.72% under SSP5-8.5 during the 2026–2050 period. Overall, the results suggest that effective adaptation strategies and improved water resource management are crucial for mitigating the impacts of climate change on corn production in the Qazvin Plain. https://jdcr.birjand.ac.ir/article_3863.html Introduction: Climate justice, as a fundamental pillar of sustainable urban development, addresses the equitable distribution of environmental risks and benefits among different social groups. This study was conducted with the aim of analyzing climate justice in the informal settlements of Mashhad city, based on an integrated and interdisciplinary approach.Materials and Methods: This study employed a combination of quantitative and qualitative data, including the analysis of satellite imagery, questionnaire data, semi-structured interviews, and focus group sessions.Results and Discussion: The findings revealed that informal settlements, such as Qaleh-Sakhteman and Sidi, exhibit the highest land surface temperatures and the lowest vegetation cover. Residents of these areas suffer from low environmental satisfaction and inadequate access to services. Furthermore, a significant gap was identified between the perceptions of residents (who focused on service inequality) and urban managers (who emphasized institutional and financial constraints).Conclusion: This research confirms the existence of climate injustice in the informal settlements of Mashhad and demonstrates that achieving climate justice requires the simultaneous integration of distributive justice (equitable distribution of cooling resources and services), procedural justice (citizen participation in policymaking), and restorative justice (compensation for climate-related damages). https://jdcr.birjand.ac.ir/article_3872.html Climate change has become one of the greatest threats to human life. Gaining sufficient information about climate variations helps policymakers and regional managers—especially in arid and semi-arid regions—make better and more informed decisions. In this study, daily and monthly precipitation data from the Saravan meteorological station for the period 1987–2014 were used to simulate rainfall. Among the sixth assessment report (AR6) climate models evaluated for future precipitation prediction, the ACCESS-CM model was selected as the most suitable for the study area due to its high correlation (0.99) and low error (0.002) compared to other models. For downscaling the data, the Linear Scaling method was chosen because of its higher accuracy (RMSE = 0.002) and strong correlation (R = 0.99). Statistical tests indicated that only April exhibited a statistically significant rainfall trend at the 95% confidence level. Rainfall projections for future periods under different SSP scenarios suggest an increase in precipitation, particularly during winter and summer at the Saravan station. Overall, despite a general upward trend in precipitation under various SSP scenarios, the monthly and seasonal rainfall variations across the watershed area do not show statistically significant trends for future periods. https://jdcr.birjand.ac.ir/article_3877.html Considering global warming trends, climate change, and fluctuations in precipitation and temperature, surface water resources and sustainable water management in Iran particularly in arid and semi-arid regions are under serious threat. In this context, the present study investigates the impacts of climate change on surface water resources in the Meymeh watershed, Ilam Province. The SWAT hydrological model was employed to simulate the watershed’s hydrological processes, coupled with the CanESM5 climate model and the LARS-WG statistical downscaling model under SSP1-2.6 and SSP2-4.5 scenarios for future climate projection. The SWAT model demonstrated good performance in hydrological simulation, with NSE and R² values of 0.72 and 0.74 for the calibration period (2010–2016), and 0.84 and 0.90 for the validation period (2017–2020), respectively. Future climate projections indicated an increase in temperature ranging from 2.5 to 2.9 °C and a decrease in annual precipitation of approximately 13% under the intermediate scenario. Hydrological analysis revealed that the mean annual river discharge is expected to decline from 5.63 m³/s to 1.61 m³/s in future periods, while summer runoff is projected to approach zero. These findings underscore the urgent need for adaptation strategies, including runoff storage, artificial groundwater recharge, modification of cropping patterns, and the development of flood early warning systems. By integrating LARS-WG and SWAT models with the latest IPCC scenarios, this study provides a scientific basis for smart water resource management and strategic regional decision-making. https://jdcr.birjand.ac.ir/article_3937.html Climate change is widely recognized as one of the most critical global challenges, directly influenced by human activities, particularly the increasing generation and improper management of solid waste. The growing volume of municipal and industrial waste, especially in developing countries, contributes significantly to the emission of greenhouse gases such as carbon dioxide and methane, thereby threatening environmental sustainability. This issue not only exacerbates pollution across air, soil, and water resources but also intensifies public health risks and accelerates climate change impacts. Consequently, effective and sustainable waste management has emerged as a key strategy for mitigating greenhouse gas emissions. This study explores the role of artificial intelligence (AI) in enhancing waste management systems and reducing the environmental and climatic impacts associated with waste generation. Intelligent algorithms have the capability to predict waste generation trends, optimize transportation routes, automate material sorting, and evaluate climate-related policies, all of which contribute to reducing emissions and energy consumption. AI systems have achieved prediction accuracies of up to 98.5% in specific applications, such as monitoring bin fill levels. So, machine learning based predictive models can improve the timing and efficiency of waste collection, thereby decreasing fuel use and pollutant release. The findings indicate that integrating advanced AI technologies with environmental management approaches offers a robust pathway for addressing climate challenges. Such integration can significantly support sustainable development goals by lowering the carbon footprint, improving resource efficiency, and strengthening adaptive capacity to climate change. https://jdcr.birjand.ac.ir/article_3948.html Climate change, as one of the major challenges of the present century, has substantially affected the sustainability of agricultural production—particularly in semi-arid regions—through declining precipitation, rising temperatures, and the increasing frequency of extreme events. In this context, safflower, as a low-water-demand medicinal crop with high tolerance to climatic stresses, exhibits considerable potential for adaptation to future climatic conditions.This study aimed to assess the impacts of climate change on crop yields and to examine the economic adaptation potential of safflower as a drought-tolerant crop in the Hamedan–Bahar Plain. To this end, future changes in precipitation, temperature, and ET0 were projected under three climate scenarios SSPs, and their effects on crop performance and regional cropping patterns were simulated using a PMP model. Baseline data for the 2022–2023 cropping year were collected through farm-level questionnaires and supplementary information obtained from relevant institutions. Climate projections indicated that Hamedan–Bahar Plain will experience decreasing precipitation and increasing min&max-temperatures across all scenarios, leading to yield reductions for most conventional crops. In contrast, safflower responded favorably under all scenarios, with yield increases ranging from 4-7%. Economic analysis further revealed that this yield improvement could support an expansion of approximately 189h in safflower cultivation and generate more than 9.02billion-tomans in additional net agricultural profit. Accordingly, expanding safflower cultivation under climate stress conditions represents a climate-smart and economically viable strategy that can enhance water productivity, increase farmers’ income, and strengthen the resilience and sustainability of agricultural-systems in the study area, as well as in other regions with similar agro-climatic characteristics. https://jdcr.birjand.ac.ir/article_3954.html To evaluate the response of chrysanthemum (Chrysanthemum morifolium) to salinity stress and certain stress modulators, a factorial experiment was conducted based on a completely randomized design with three replications. The first factor comprised salinity stress at four levels (0, 30, 60, and 90 mM NaCl) applied through irrigation water, and the second factor consisted of five levels (control, L-glutamic acid at two concentrations of 300 and 600 mM, and bacterial inoculation with Pseudomonas putida and Curtobacterium spp. strains). The results indicated that increasing salinity stress significantly reduced photosynthesis rate, transpiration, stomatal conductance, maximum quantum efficiency of photosystem II (Fv/Fm), chlorophyll stability index, flower diameter, bud emergence time, and bud opening time, while increasing flower wilting. For instance, salinity at 60 mM caused a 38.07% and 58.9% decrease in chlorophyll stability index and flower diameter, respectively, compared to the control; however, the application of growth-promoting substances led to increases of 41.25% and 29.30%, respectively. Furthermore, increasing stress levels reduced potassium concentration and led to an increase in the sodium-to-potassium ratio in chrysanthemum leaves. The application of both bacterial types and both concentrations of L-glutamic acid significantly improved all measured traits compared to the control. At salinity levels of 0 and 30 mM, L-glutamic acid at 600 mM concentration had the greatest effect; however, at higher salinity levels (60 and 90 mM), L-glutamic acid at 300 mM concentration was more effective. It is noteworthy that no flowering occurred at the 90 mM salinity level. https://jdcr.birjand.ac.ir/article_3959.html Grasping the intricacies of drought dynamics particularly as global warming accelerates atmospheric evaporative demand remains paramount for effective water resource risk management. To achieve this, we developed a multi-model ensemble (CMIP6-MME) aeveraging outputs from the Coupled Model Intercomparison Project Phase 6. Initially, the methodological framework evaluated the performance of the CMIP6-MME in reproducing the 12-month Standardized Precipitation Evapotranspiration Index (SPEI-12) across nine synoptic stations over a historical (1990–2014). Statistical assessments revealed an interesting caveat: while Nash-Sutcliffe Efficiency values fell below zero (NSE<0) due to inherent temporal phase mismatches typical of General Circulation Models, Willmott’s Index of Agreement firmly validated the ensemble’s robust capacity to simulate regional climate behavior. These projections were driven by three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP3-7.0, and SSP5-8.5. The resulting simulations point toward the emergence of a starkly bipolar climate in Iran’s future. In the southern territories and lower latitudes, projections under the SSP5-8.5 indicate that drought intensity will deteriorate to severely critical thresholds (SPEI≤-2). Governed by the Clausius-Clapeyron thermodynamic relationship and a subsequent surge in precipitable water, these northern expanses will likely witness a mitigation in severe drought intensity. Paradoxically, the actual frequency of dry spells in these areas is projected to surge by up to 34%. Ultimately, this spatial migration of the nation’s drought epicenter underscores an urgent reality: mitigating the multifaceted impacts of climate change will demand highly localized and entirely distinct adaptation strategies across different geographic zones. https://jdcr.birjand.ac.ir/article_3986.html Groundwater, the planet’s largest freshwater reservoir, faces mounting stress from overuse and climate change, demanding interpretable, uncertainty-aware modeling tools. Fuzzy-based modeling offer unique solutions, yet their global research landscape, scientific evolution, intellectual structure, and global diffusion remains unmapped. To address this gap, we present the first focused bibliometric synthesis of fuzzy-based approaches for groundwater level estimation, analyzing 189 Web of Science–indexed articles published between 2000 and 2025 using Bibliometrix and VOSviewer. Our analysis quantifies publication growth, identifies leading countries, institutions, and authors, and maps thematic clusters. Results reveal a 19.8% annual growth rate since 2016, with Iran dominating in publications and the United States achieving the highest citations. Key institutions include Islamic Azad University, University of Tabriz, and University of Tehran, while leading authors such as Kisi, El-Shafie, and Nourani anchor the intellectual structure of the field. Science-mapping demonstrates the dominance of hybrid frameworks, particularly those integrating wavelet transforms, metaheuristics, or adaptive neuro-fuzzy inference systems (ANFIS). Keyword co-occurrence networks (minimum occurrence = 5) and temporal overlay visualizations demonstrate a paradigm shift from standalone fuzzy systems toward climate-aware, physics-informed hybrids. This study consolidates a scattered body of knowledge and establishes fuzzy-based groundwater modeling as a maturing sub-discipline. We identify critical frontiers including explainable AI integration, improved spatial scalability, and applications in data-scarce transboundary aquifers. Future research should prioritize cross-paradigmatic collaboration to bridge data-driven and process-based modeling, enhancing both scientific rigor and policy relevance, particularly in the context of escalating water insecurity driven by climate change. https://jdcr.birjand.ac.ir/article_3991.html Climate change threatens the fragile ecosystems of arid regions such as Hormozgan province. This study aims to predict future vegetation dynamics (NDVI) up to the year 2100 by comparing advanced machine learning models under the pessimistic climate change scenario (SSP3-7.0). To this end, MODIS NDVI time series (2000-2018) and ERA5 climate data were extracted for two representative points within the province. The performance of four machine learning algorithms (GPR, GAM, RF, and XGBoost) was evaluated using rigorous statistical metrics, and the optimal model was employed to project future NDVI using data from the GFDL-ESM4 model under the SSP3-7.0 scenario. Evaluation results indicated that the Gaussian Process Regression (GPR) for the first point and eXtreme Gradient Boosting (XGBoost) for the second point achieved the highest performance in the testing phase, with Kling-Gupta Efficiency (KGE) values exceeding 0.88. Projections towards the 2100 horizon revealed two divergent ecological responses: the first point is projected to experience a significant 42% increase in NDVI (a “greening” phenomenon), whereas the second point is expected to show modest growth before stabilizing (a 10.9% increase). This spatial heterogeneity indicates that the region’s ecosystems exhibit varying responses. We conclude that drought management strategies must be location-specific and tailored to the unique growth potential and resilience of each area. https://jdcr.birjand.ac.ir/article_3994.html Qanats, as one of the most important indigenous water supply systems in arid and semi-arid regions, have been severely affected by climate change and the ongoing recent droughts. In this regard, employing advanced machine learning models can play a key role in developing reliable forecasting systems to support climate change adaptation planning. objective of this research is to simulate the monthly discharge of the Balade Qanat complex in Ferdows County using a set of machine learning models, including single algorithms such as XG Boost, SVR, Random Forest, and Gradient Boosting, as well as an advanced ensemble approach, Stacking. This simulation uses climatic, hydrological data, and drought indices over a 10-year period. dominant approach in modeling is comparing the performance of individual models against the final ensemble model. The obtained results showed that under the region’s variable climatic conditions, the Stacking ensemble approach exhibited a significantly stronger performance than single models like XG Boost. Stacking model was selected as the optimal model, achieving the highest coefficient of determination (R²) and the highest (KGE = 0.93, R² = 0.92 with the lowest RMSE = 12.21. This superior performance emphasizes the capability of Stacking models in reducing variance and correcting systematic biases of individual models when dealing with the complex and nonlinear behavior of qanat discharges. It is concluded that the Stacking model, due to its ability to extract complex nonlinear patterns and improve generalization, is a superior management tool for decision-making in the sustainable exploitation of qanat water resources in water-stressed climates. https://jdcr.birjand.ac.ir/article_3995.html Climate change, accelerated by human activities, has led to the shrinkage and disappearance of salt lakes worldwide. In northwestern Iran, the gradual drying of Lake Urmia has exposed lakebed sediments to wind erosion, turning sandy-saline areas in its southeastern region into a primary source of dust generation. This study examines the effects of various concentrations and application methods of sodium alginate on increasing the resistance of soil samples from these sandy-saline areas against wind erosion. Sodium alginate was applied at four concentrations (0%, 0.5%, 1%, and 2%) using three methods: dry spraying, wet spraying, and mixing with soil followed by compaction. Key properties such as crust thickness, compressive strength, and changes in compressive strength with soil depth were evaluated. Wind tunnel experiments were conducted to measure soil loss, while electron microscopy imaging and elemental analysis were used to investigate the structural bonds formed between soil particles. Results revealed that a 0.5% sodium alginate concentration produced thicker crusts across all application methods. The highest compressive strength (up to 13,053 kPa) was achieved using the 2% sodium alginate concentration with the mixing and compaction method. Wind tunnel tests demonstrated a significant reduction in soil loss, decreasing from 47.29% in the distilled water treatment (control) to 10.75% and 6.37% with 0.5% and 1% sodium alginate treatments, respectively. Microscopic analyses of the crusts,, showed that sodium alginate remained effectively integrated into the soil samples. By forming a durable surface coating on soil particles, sodium alginate enhanced the soil’s resistance to wind erosion. https://jdcr.birjand.ac.ir/article_3997.html Climate change, as one of the emerging global challenges, has profound effects on the environmental, economic, social, and political structures of various regions. In sensitive and vulnerable areas such as Gonbad-e Kavous County, these consequences manifest in a multidimensional and complex manner. The significance and necessity of this research lie in its comprehensive analysis of the reflection of the consequences of climate change and drought on the vulnerability of local communities from the perspective of geopolitical approaches (including economic, social, political-security, and environmental dimensions). A precise understanding of these consequences paves the way for designing sustainable policies and managing climate crises at both regional and national levels. The main objective of this study is to investigate and rank the consequences of climate change and drought and to analyze the relationship among its various dimensions in Gonbad-e Kavous County. The research method is applied, and its approach is descriptive-analytical. Data were collected through library research, online sources, and a survey (researcher-made questionnaire). The statistical population comprised 97,147 households in the county, with a sample size of 117 individuals selected through random sampling. By providing detailed analyses, this research lays the groundwork for adopting scientific strategies and targeted policymaking to reduce the vulnerability of local communities to climate change. https://jdcr.birjand.ac.ir/article_4011.html Dry and wet spells are frequent extreme weather events that have a major global impact on ecosystems, water resources, and agriculture. This study examine the frequency and trend of maximum consecutive dry and wet spells in Northwestern Nigeria using long-term daily rainfall data from 1980 to 2021. The temporal trend of dry and wet spells were analysed using the Mann-Kendall Test Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were deployed for severity, intensity, and duration of drought. Pearson’s correlation was used for the relationship between dry spell, wet spell, SPI and SPEI. Both dry and wet spells exhibited positive and negative trends. The analysis reveals a significant trend in dry and wet spell patterns, indicating increasing dryness in some areas and wetness specifically Kano, Sokoto and Kebbi. Standardised Precipitation Index (SPI) and dry spells are significantly correlated. The findings of this study can inform decision-making for water resource planning, agriculture, and disaster risk reduction related to flood and drought disasters https://jdcr.birjand.ac.ir/article_4017.html Meteorological drought forecasting is essential for sustainable water resources management and climate risk mitigation in arid and semi-arid regions. This study evaluates the performance of hybrid machine learning and time-series approaches for meteorological drought forecasting in Khuzestan Province, Iran. Monthly precipitation data collected from eight synoptic stations during the 1989–2020 period were used to calculate the Standardized Precipitation Index (SPI) at 1-, 3-, 6-, and 12-month time scales. The proposed hybrid framework integrates ARMA-based temporal dependency analysis, GPR nonlinear learning capability, and AF-based adaptive optimization to improve drought forecasting accuracy. Several standalone and hybrid forecasting models were evaluated using the correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe efficiency (NS), and mean absolute error (MAE) indices. The results demonstrated that longer temporal scales, particularly SPI-12 and SPI-6, provided more reliable forecasting performance compared with shorter-term indices. Among the investigated models, the proposed triple hybrid framework achieved the highest predictive accuracy across most stations and SPI time scales. For example, under SPI-12 conditions at Bostan station, the proposed framework achieved R, RMSE, NS, and MAE values of 0.922, 0.177, 0.949, and 0.145, respectively, compared with 0.896, 0.215, 0.909, and 0.185 obtained by the ARMA-AF model. The findings highlight the potential of structured hybrid forecasting frameworks for improving meteorological drought prediction reliability in climatically complex regions. https://jdcr.birjand.ac.ir/article_4025.html This study evaluated the effectiveness of beneficial microorganisms in mitigating salinity, ionic toxicity, and heavy metal stress after irrigation with reclaimed water, in a ten-years-old olive trees during the 2022 and 2023 seasons. Trees were inoculated with Bacillus subtilis, Bacillus cereus, or the arbuscular mycorrhizal fungus Glomus mosseae, and compared with control. The inoculation treatments significantly increased soil Ca (calcium) and K (potassium) availability, and decreased Na (sodium), Cl (chloride), and Cd (cadmium) availability. The treated trees exhibited lower leaf Na, Cl, and Cd, with enhanced relative water content and fruit oil level, higher carbohydrate and proline levels, and increased peroxidase activity. The photosynthetic rate, stomatal conductance, transpiration, and intercellular CO₂ concentration, shoot fresh and dry biomass and growth significantly boosted by microorganisms. B. subtilis showed the best performance. Data demonstrated that beneficial microorganisms may serve as an effective and sustainable strategy to enhance olive resilience and productivity under saline soils and reclaimed water irrigation. https://jdcr.birjand.ac.ir/article_4038.html Introduction: Urban air pollution is a major global health threat, exacerbated by climate change through altered temperature inversions, boundary layer dynamics, and pollutant dispersion. Ground based monitoring networks are costly and spatially limited. This study assesses the feasibility of estimating the Air Quality Index (AQI) from Google Earth satellite imagery of Tehran using three digital image processing methods in MATLAB. Materials and Methods: Seven cloud free satellite images acquired between July 2022 and April 2024 (AQI range: 64-172) were analyzed. Three image derived features were correlated with AQI: (1) pixel intensity standard deviation from normalized grayscale histograms, (2) Pearson cross correlation coefficients relative to a clean day reference, and (3) mean edge pixel density from Canny edge detection. Linear regression models were trained on five images and validated on two withheld test images. Results and Discussion: Pixel standard deviation showed a strong inverse relationship with AQI (R² = 0.82; regression slope: 1160.98 ± 312.4, intercept: 361.71 ± 75.3), with test errors of 1.25% and 29.66%. Canny edge detection mean density yielded R² = 0.80 (slope: 925.41 ± 268.7, intercept: 319.35 ± 64.9) with balanced errors of 10.11% and 12.81%, indicating greater seasonal robustness. Cross correlation confirmed that pollution distorts structural image similarity, but showed limited predictive consistency across seasons due to illumination variability. Conclusion: The results suggest that freely available optical satellite imagery may encode air quality relevant information accessible through simple image operations, without specialized sensors or atmospheric correction. https://jdcr.birjand.ac.ir/article_4039.html Climate change is one of the most significant challenges of the present century and has profound consequences for local communities,especially in sensitive regions such as Iran.These conditions make the necessity of entrepreneurship education within society even more crucial. Entrepreneurship,by emphasizing education,social participation,the utilization of local capacities and adaptation to environmental changes,can play an effective role in enhancing community resilience.The aim of this study is to design a community based resilient entrepreneurship model in the face of climate change in Fars Province. This research was conducted using a mixed methods approach.In the qualitative section,grounded theory was employed.Data were collected through semi structured interviews with16experts, including entrepreneurs,environmental department specialists,and university faculty members,and were analyzed through coding. In the quantitative section,the statistical population consisted of active companies in the industrial towns of Fars Province.Among280companies,126 were selected as the sample and analyzed using structural equation modeling. Findings from the qualitative part led to the identification of32main indicators,organized into5causal factors,6intervening factors,7contextual factors,9 strategies,and3outcomes within the model.Quantitative results indicated that all structural paths were significant(β=1.96to2.58,p < 0.05),and fit indices confirmed the adequacy of the model.These results reflect the presence of strong relationships among contextual conditions,strategies, and resulting outcomes. Overall,the study indicates that resilient community based entrepreneurship can serve as an effective strategy for adapting to climate change and enhancing community resilience.The proposed model,emphasizing entrepreneurial competencies,local knowledge,education and social participation,and supportive policymaking,contributes to the entrepreneurship literature and can practically guide policymakers and local institutions in designing effective interventions to increase resilience against climate change. https://jdcr.birjand.ac.ir/article_4040.html The wind chill phenomenon plays a significant role in thermal comfort and cold stress in cold climates. This study evaluates the long-term evolution of cold stress in Tabriz by analyzing daily mean temperature and wind speed data from the Tabriz synoptic station over a 63-year period (1961–2023). The Wind Chill Equivalent Temperature (WCET) and Heat Loss indices were calculated using standard meteorological models. Trend and mutation analyses were conducted utilizing the Mann–Kendall test, Sen's slope estimator, and Sequential Mann–Kendall (SMK) analysis, following autocorrelation pre-processing. The results indicate a gradual warming tendency in annual mean WCET, alongside a general, though statistically non-significant, decreasing tendency in the persistence of prolonged cold spells. SMK analysis suggests a structural shift around 1975, after which the duration of cold spells exhibited a downward trajectory characterized by strong interannual and temporal fluctuations. While the frequency and intensity of extreme cold events have diminished compared to the 1960s and 1970s, the potential for severe cold waves remains. The findings provide empirical data that can inform regional climate change adaptation strategies and urban infrastructure planning. https://jdcr.birjand.ac.ir/article_4041.html The environmental collapse of terminal lake basins necessitates a rigorous distinction between climatic variability and anthropogenic pressures to inform restoration policies. This study aims to diagnose the primary drivers of desiccation in the Lake Urmia Basin through a continuous-time modeling approach. A Probabilistic Neural Ordinary Differential Equation (Neural ODE) framework was developed as a "Digital Twin" of the basin, calibrated on the natural period (1981–1996) using multi-source satellite imagery and ERA5-Land reanalysis data. By projecting these learned natural dynamics through 2025, counterfactual simulations were employed to quantify the relative contributions of climate warming and water withdrawal. Results indicate that while climate warming (representing temperature and evaporation shifts) accounts for approximately 37% of the surface area decline, direct anthropogenic intervention is responsible for 63% (±5%) of the total deficit. Furthermore, seasonal decomposition reveals a critical water deficit during winter months, consistent with the interception of runoff by upstream infrastructure. Identifying this seasonal timing of water scarcity is highly critical for drought risk management and restoration policy, as it distinguishes baseline evaporative losses from direct infrastructure-driven runoff interception, thereby guiding targeted reservoir release strategies. These findings demonstrate that the basin retains significant hydro-climatic potential for recovery. The study concludes that restoration efforts should prioritize the reform of winter reservoir release policies and the enforcement of agricultural consumption caps rather than focusing solely on climate adaptation. https://jdcr.birjand.ac.ir/article_4048.html This study aimed to understand and explain the lived experience of drought among farmers and herders of the Bahluli tribe in Zirkuh County, South Khorasan Province. Accordingly, the present study employed grounded theory methodology to investigate the drought experience among the herders and farmers of the Bahluli tribe. Participants were selected through purposive sampling from among tribal members with a background in farming and animal husbandry. Theoretical saturation was achieved after conducting 29 interviews. Data from in-depth interviews were analyzed using MAXQDA software, and based on grounded theory procedures, 29 open codes, 11 axial codes, and 5 core codes were extracted. The findings indicated that 11 axial categories namely "quantitative and qualitative decline of water resources," "reduced biological yield of agricultural and livestock products," "economic and infrastructural challenges," "psychological and social consequences of the crisis," "drought experience," "trust deficit and reduced self-sufficiency," "resource consumption optimization strategies," "economic and social strategies for crisis management," "social consequences of the crisis," "water resource conflicts," and "psychological effects of the crisis"—play a role in shaping the lived experience of farmers and herders regarding the phenomenon of drought. Based on the qualitative analysis of data from interviews with 29 herders and farmers of the Bahluli tribe, drought experience was identified as a complex and multidimensional phenomenon. The underlying causes of drought included the quantitative and qualitative decline of water resources, reduced biological yield of agricultural and livestock products, economic and infrastructural challenges, and the psychological and social consequences of the crisis. https://jdcr.birjand.ac.ir/article_4049.html Khuzestan Province is regarded as one of the critical dust hotspots in the Middle East. This study aims to conduct a 25-year (2001–2025) spatio-temporal analysis of dust days and their linkage with vegetation health across the province. Aerosol Optical Depth (AOD) and Normalized Difference Vegetation Index (NDVI) data from MODIS products were processed in Google Earth Engine and analyzed using two complementary approaches: linear regression and the Mann–Kendall test accompanied by Sen’s slope estimator. The quantitative relationship between the two variables was further examined through a Generalized Least Squares (GLS) model with a first-order autoregressive (AR(1)) structure. Findings indicated that the peak of dust days (69 days in 2008) coincided with the minimum NDVI (0.123). Spatially, a southern and western belt stretching from Susangerd to Behbahan exhibited a significant increasing trend in dust alongside concurrent vegetation degradation. The critical hotspot, where increased dust and vegetation decline are co-located, covers approximately 4,058 km², whereas the northern parts of the province showed no significant trend. This spatial overlap confirms the role of internal dust sources driven by desiccated wetlands and abandoned agricultural lands. Furthermore, a non-linear, saturation-type relationship between NDVI and dust days was detected, indicating that the greatest dust reduction per unit increase in vegetation occurs at NDVI values below 0.14. Consequently, management actions should be prioritized and concentrated on the southern areas, particularly the Hoor-al-Azim and Shadegan wetlands and the lands south of Ahvaz. https://jdcr.birjand.ac.ir/article_4067.html The study of trends in climatic components reflects the occurrence of climate change impacts and is considered the most crucial part of climate change studies. Accordingly, this study aims to evaluate the annual trends of climatic variables, including mean temperature and precipitation, at four synoptic stations, in Semnan Province during the 2000–2022. The daily data were collected from the meteorological organization, and preprocessing procedures, such as outlier and missing values handling, were performed, and daily data were aggregated to annual scales. An auto correlation test was then performed prior to trend analysis. To conduct the annual trend analysis, two nonparametric tests, the Mann–Kendall and the Spearman tests, were incorporated. The results of the autocorrelation tests indicated that major climate variables are time-independent, and the minimum and mean temperatures in Garmsar, and rainfall in Damghan and Mayamey, exhibit significant autocorrelation. Although, the trend in many of climate variables in not significant statistically, the maximum and minimum increasing of temperature was recorded in Garmsar and Mayamey. While, the maximum and minimum decreasing of rainfall was observed in Semnan and Damghan. The applicability of both MKT and SPT tests suggests that the MKT is more reliable and applicable due to its greater interpretability, sensitivity to autocorrelation, and ability to estimate trend slope. Furthermore, the spatial-temporal examination of trends revealed that the increasing trend of temperature in more possible (stronger) than that of rainfall. Also, the variability of rainfall and temperature in the western regions of Semnan provinces is higher than in other regions. https://jdcr.birjand.ac.ir/article_4071.html Potential evapotranspiration (ET₀) is a fundamental component of the water cycle and is highly sensitive to climate change. In this study, the Hargreaves-Samani (HS) method was calibrated regionally for Iran for the first time, using observational data from 152 synoptic stations during the baseline period 1995–2014 against the FAO56 Penman-Monteith reference method. Four calibration approaches (daily and monthly scales, including linear and second-order fits) were evaluated on the aggregated data of six climatic-altitudinal zones. The monthly linear method showed the highest performance, with a mean R² of 0.89 and a mean RMSE of 0.77 mm day⁻¹, and was selected as the most suitable option. Consequently, correction coefficients were derived for the six zones and applied to estimate future ET₀ (2030–2090) using outputs from three CMIP6 models (ACCESS-CM2, IPSL-CM6A-LR, and CNRM-CM6-1) under three emission scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). Results indicate an increasing trend of ET₀ across all regions of Iran, with average increases during the far future period (2071–2090) ranging from 58% to 129% relative to the baseline. The highest sensitivity is observed in high-latitude low-altitude zones and dry central areas, while mountainous regions show the smallest increases. The largest absolute increases occur in summer months (up to 15.5 mm month⁻¹), whereas the largest relative increases occur in winter (up to 182%). These findings provide a serious warning for future water stress in the agricultural sector and underscore the urgent need to revise cropping patterns—particularly in central arid regions during summer—and to adopt climate-adaptive water management strategies. https://jdcr.birjand.ac.ir/article_4109.html :Climatic fluctuations—especially drought—affect agricultural activities. The consecutive occurrence of droughts in recent years has led to reduced farm yields and production instability. This study aims to analyze the impact of drought on wheat yield in Boroujen. Method and Data:This research was conducted using a descriptive–analytical approach. The required data for calculating drought indices and wheat yield were obtained from existing databases. Statistical software was used to analyze the relationship between drought severity and wheat yield. Findings:Comparison of the data indicated a positive and significant relationship between drought severity and reduction in wheat yield, with a correlation coefficient of r = 0.397. The coefficient of determination showed that about 16% of the variations in wheat yield can be explained by drought, highlighting its considerable role in yield instability. Conclusion:Based on the results of the PNPI and SIAP indices, the drought condition in Boroujen during the study period demonstrated a gradual decrease in index values and a shift of the regional climate toward drier conditions. Both indices indicated severe droughts in 2008 and moderate droughts in 2010, 2016, and 2017. Statistical analyses—including Pearson correlation and multiple linear regression—confirmed a significant relationship between drought indices and wheat crop quality. https://jdcr.birjand.ac.ir/article_4113.html Drought is considered one of the most significant natural hazards in protected areas; therefore, monitoring its impacts on these regions can contribute substantially to their effective management. Accordingly, this study investigated drought trends in the Darmian and Sarbisheh Protected Area during the period 2000–2022 using satellite data within the Google Earth Engine platform. To this end, meteorological drought conditions were first assessed using climatic-meteorological variables, including the Standardized Precipitation Index (SPI) derived from CHIRPS and soil moisture from SMAP satellite. Subsequently, changes in vegetation cover were evaluated through time-series analysis of vegetation indices extracted from MODIS satellite imagery, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Standardized Vegetation Index (SVI). The Mann–Kendall test applied to the vegetation cover area time series indicated a statistically significant increasing trend(Z = 8.96, p < 0.001, τ = 0.27), while Sen’s slope estimated an average increase of 27.26 area units per observation over the study period. This increasing trend was also reflected in the vegetation index time series. The results indicated that, while the monthly SPI exhibited a decreasing trend and soil moisture showed a relatively stable trend, both suggesting an intensification of meteorological drought in the region, the vegetation indices displayed an relatively increasing trend. The significant differences observed between areas inside and outside the protected boundary confirm the effectiveness of conservation measures, such as restrictions on livestock grazing and control of human exploitation, in mitigating drought severity. https://jdcr.birjand.ac.ir/article_4126.html storms are a particularly important natural hazard of atmospheric origin. Tropical cyclones are responsible for half of the damage caused by these storms. This study identifies the synoptic patterns governing the tracks of tropical cyclones in the northwestern Indian Ocean basin using a descriptive–analytical approach. First, the basin’s tropical cyclone characteristics, including dates, cyclone intensities, and cyclone positions, were extracted from the Indian Meteorological Department (IMD) website for the latitude–longitude bounds 0–27°N and 53–75°E. Next, 700 hPa geopotential height data with a horizontal resolution of 0.25° from ECMWF ERA5 were obtained for a 41-year period (1982–2022). A total of 169 cyclone-days were selected. A factor analysis with an S-mode configuration was applied. Each cyclone day was represented as an 89×109 matrix, i.e., 9,701 spatial grid cells. This number of cells applies to 169 days. All data-processing steps up to pattern identification were performed in MATLAB. Ten 700 hPa geopotential-height patterns were obtained. These patterns reflect the dominant synoptic configurations shaping the tracks of tropical cyclones in the study region. Finally, maps of these ten patterns were produced using the R programming language. The results indicate that the primary control on cyclone tracks in the study area is an Mid-level high over the Arabian Peninsula. Other influential factors include incursions of mid-latitude lows and Cut-offs into the region thermal lows over Pakistan and northern/northwestern India, the Mid-level high over the over western India, the northward shift of the monsoon trough, and the cyclone’s beta effect. https://jdcr.birjand.ac.ir/article_4127.html Cold waves are critical extreme phenomena that annually inflict substantial damage on human societies across various regions. This study investigates the changing trends and atmospheric mechanisms of cold waves in Isfahan city. Daily minimum temperature data from the Isfahan synoptic station were utilized over a 51‑year period (1970–2021). Temperature thresholds were derived using the 1st, 5th, and 10th percentiles, and intense, long‑lasting cold wave episodes were identified. Geopotential height data at 500 hPa and temperature advection at 1000 hPa were obtained from the NCEP/NCAR reanalysis database. Factor analysis and cluster analysis were employed to extract the contributing atmospheric factors and prevailing circulation patterns. Results indicate a mild increasing trend in minimum temperature, accompanied by a significant decrease in cold wave frequency across all selected thresholds over the past half‑century. Synoptic analysis reveals that 17 atmospheric factors account for approximately 89% of the total variance, which are categorised into four dominant circulation patterns: cut‑off lows, southward extension of the polar vortex, omega blocking, and dipole blocking. Furthermore, lower‑tropospheric temperature advection analysis demonstrates that intense cold‑air advection from high latitudes—particularly in the second and fourth patterns—combined with the persistence of blocking systems, plays the foremost role in the abrupt and sustained temperature drops over the study area.