https://jdcr.birjand.ac.ir/ Journal of Drought and Climate change Research en daily 1 Thu, 17 Apr 2025 00:00:00 +0330 Thu, 17 Apr 2025 00:00:00 +0330 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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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. https://jdcr.birjand.ac.ir/article_4130.html AbstractLong-term monitoring of vegetation cover plays an important role in understanding ecosystem dynamics and supporting sustainable natural resource management. In this study, spatial and temporal variations of vegetation cover in the Maroon watershed during 2001–2023 were investigated using a time series of the Normalized Difference Vegetation Index (NDVI). First, the spatiotemporal distribution of annual NDVI was analyzed. Then, the non-parametric Mann–Kendall test and Sen’s slope estimator were applied to determine the direction and significance of trends. In addition, the Sequential Mann–Kendall (SQMK) test was used to detect change points in vegetation cover trends.The results showed that despite temporal fluctuations, vegetation cover in the watershed maintained a relatively stable spatial pattern, with the highest vegetation density concentrated in the southern and southwestern parts of the basin. Among the study years, 2008 represented the minimum vegetation cover, while 2023 showed the highest greenness across the entire period.Trend analysis indicated that the lower NDVI classes (≤0.1 and 0.1–0.2) experienced a significant decreasing trend at the 99% confidence level, whereas the 0.2–0.4 class showed a significant increasing trend. Higher NDVI classes showed no significant trends and remained relatively stable. The SQMK test also identified major change points around 2012–2014.Overall, the reduction of low-NDVI areas and the expansion of moderate NDVI classes indicate a gradual shift from very sparse to denser vegetation, suggesting a relative improvement in vegetation conditions in parts of the Maroon watershed over the past two decades. https://jdcr.birjand.ac.ir/article_4139.html Drought is a major natural hazard with widespread impacts on agriculture, water resources, and food security. This study aimed to assess the spatiotemporal characteristics of seasonal agricultural drought across the counties of Hamadan Province and to model it using teleconnection patterns. The Temperature-Vegetation Dryness Index (TVDI) was extracted seasonally from Google Earth Engine using MODIS Terra satellite imagery (2003–2022), and its spatiotemporal dynamics were analyzed. TVDI trends were assessed using the Mann-Kendall and Sen's slope tests. Additionally, the correlation between 13 teleconnection patterns and TVDI was evaluated; indices with the strongest correlations were selected for multiple linear regression (MLR) modeling. The highest seasonal mean TVDI occurred in summer (0.90 in Razan and Famen), while the lowest occurred in winter (0.38 in Asadabad). On an annual scale, Famenin recorded the highest mean TVDI (0.87) and Malayer the lowest (0.49). TVDI showed a significant increasing trend on both seasonal (excluding autumn) and annual scales, with slopes ranging from 0.005 to 0.001 and 0.003 to 0.002, respectively. Drought zoning indicated that most of the province falls under moderate to severe drought classes. TVDI correlated significantly with GLOBALST, NAO, and PDO at certain time lags, and with AMO across all lags from 0 to 12 seasons. The AMO-based MLR model demonstrated strong performance in estimating TVDI, with an RMSE of 0.093, R² of 0.78, and Nash-Sutcliffe efficiency of 0.76. https://jdcr.birjand.ac.ir/article_4149.html Drought, salinity and soil-borne fungi can constrain wheat establishment at the seedling stage. This greenhouse study examined whether Trichoderma viride improves wheat performance under separately imposed drought and salinity and limits damage associated with Fusarium infection. A four-factor factorial experiment was conducted in a completely randomized design with three replicate pots per treatment combination. The factors were three wheat genetic backgrounds, three pathogen treatments (a non-inoculated control, Fusarium oxysporum and F. graminearum), T. viride application (absence or presence) and three abiotic environments (a non-stressed control, salinity induced by 100 mM NaCl and drought maintained at 50% field capacity). Shoot dry weight, superoxide dismutase (SOD) activity and disease severity were measured. Drought imposes a greater limitation on shoot dry weight than salinity, with reductions of 36.6% and 25.3%, respectively. Across the tested environments, T. viride increases shoot dry weight by approximately 40% and raises SOD activity by 76.2% under salinity and 77.8% under drought. In Fusarium-inoculated plants, T. viride reduces disease severity by 46.2% for F. oxysporum and 69.2% for F. graminearum. Shoot dry weight, SOD activity and disease severity also differ significantly among the three wheat materials. Because each ploidy level was represented by a single material, these differences are interpreted as responses of the tested genetic backgrounds rather than independent ploidy effects. Under the conditions of this experiment, T. viride improved shoot growth and antioxidant activity while reducing Fusarium disease across the pathogen and abiotic environments examined. https://jdcr.birjand.ac.ir/article_4161.html Accuracy increase in forecasting process of meteorological phenomena, including dew point temperature due to freshwater shortage and the consequences of climate change, are significant topics. In this regard, the improved clustering algorithm was presented by implementing it in five meteorological stations of West Azerbaijan Province. At the algorithm implementation beginning, dew point temperature was determined by sixteen experimental models with different mathematical structures, and then the dew point temperature data were entered into the clustering process. Then, the new data obtained by averaging the data of each cluster were entered into the artificial intelligence model. Based on the evaluation statistics, two models M1 and M8 with temperature and relative humidity dependence and different mathematical structure had high accuracy. Some models, including the models of group seven with different mathematical structures, were highly sensitive to changes in coefficients. The performance of the extended clustering method increased significantly compared to the two superior models (the root mean square error reduction at all stations from M1 and M8 to the latter method was 59.71% and 66.31%, relative root mean square error reduction was 59.7% and 66.36%, mean absolute percentage square error reduction was 62.74% and 70.8%). The minimum error rate from least to greatest, includes Maku, Urmia, Khoy, Mahabad, and Salmas. The improved clustering method had an overestimation except Salmas station. The research algorithm was able to greatly increase the dew point temperature estimation accuracy by operating different models with different efficiencies and classifying their data and extracting information correctly. https://jdcr.birjand.ac.ir/article_4164.html Drought stress is one of the most important limiting factors in forage maize production in semi-arid regions. This study was conducted to investigate the effect of foliar application of salicylic acid on improving growth and yield of forage maize under drought stress conditions. The experiment was carried out during the 2019–2020 growing season in Faruj County as a split-plot design based on a randomized complete block design with three replications. The main factor included three irrigation levels (100, 75, and 60% of crop water requirement), and the sub-factor consisted of three levels of salicylic acid foliar application (no spraying, 0.50, and 0.75 mM).The results showed that the interaction effect of irrigation and salicylic acid foliar application was significant at the 5% probability level for plant height, stem dry weight, plant dry weight, plant fresh weight, and ear fresh weight. In addition, stem diameter and leaf area index were significantly affected by the interaction of foliar application and different irrigation levels at the 1% probability level. The highest leaf area index (7.14) was obtained under full irrigation, and under severe drought stress, foliar application of 0.75 mM salicylic acid increased this trait by 44.28% and 22.14% compared with the control and 0.50 mM concentration, respectively. Under severe drought stress, stem diameter reached 18.48 mm with the application of 0.75 mM salicylic acid, showing a 27.27% increase compared with no spraying. https://jdcr.birjand.ac.ir/article_4167.html This study aimed to assess the performance of the Hooshab smart irrigation management system in reducing irrigation water use, increasing Mihan wheat cultivar grain yield, and improving physical water productivity in Aligoodarz County, Iran. The experiment was conducted over two growing seasons (2023–2024 and 2024–2025) as a comparative field study under a conventional sprinkler irrigation system, involving two irrigation management treatments: smart irrigation management using the Hooshab system and the conventional farmer-managed irrigation practice (control). Four independent field replicates, each with an approximate area of 10 ha, were selected for each treatment. Each field was managed, irrigated, and evaluated as an independent experimental unit. Irrigation water applied, grain yield, and physical water productivity were measured. Data were analyzed using Independent Samples t-tes at the 5% probability level. The results showed that irrigation management treatment had a significant effect on irrigation water use, grain yield, and physical water productivity (P < 0.05). Averaged over the two growing seasons, water use under the hooshab treatment was 17.15% lower than in the conventional practice. Meanwhile, grain yield, increased slightly by 8.15%. onsequently, water productivity rose by an average of 30.3%. By optimizing irrigation timing and application depth, the Hooshab smart irrigation management system reduced water use while improving wheat yield and physical water productivity. These findings suggest that the Hooshab system can serve as an effective decision-support tool for sustainable irrigation management in semi-arid regions. https://jdcr.birjand.ac.ir/article_4192.html Although numerous studies have employed the AquaCrop model to simulate wheat growth, the evaluation of this model over multiple years and on farmers' fields has received limited attention from researchers. Given Iran’s agricultural policies aimed at increasing wheat yields, it is crucial to assess the model's performance under real-field conditions over several years. To this end, data were collected from wheat farms spanning 4,000 hectares in the cities of Shush, Ahvaz, Hoveyzeh, and Khorramshahr during 2020 to 2023, in line with the national policy objectives. The model’s performance was evaluated using metrics such as Mean Bias Error (MBE), Root Mean Square Error (RMSE), Normalized RMSE (NRMSE), Agreement Index (d), Model Efficiency (EF), and Coefficient of Determination (R²). Results indicated that AquaCrop performed with excellent accuracy across all locations, with NRMSE values below 0.1. Over the study period, model accuracy improved, with NRMSE decreasing from 0.09 in 2020 to 0.05 in 2023. In all studied cities, the model tended to underestimate yields, as reflected by negative MBE values, a trend consistent throughout the years. Overall, the findings suggest that geographical location had little impact on the model’s accuracy. Instead, improvements over time, likely due to enhanced wheat yields on farms, contributed to increased model precision. https://jdcr.birjand.ac.ir/article_4193.html Climate change is an environmental challenge with effects on natural systems. The Hiv and Shalamzar watershed in the southern Alborz slopes, possessing species Astragalus, Thymus, Achillea, Glycyrrhiza, Cichorium, Hypericum, and Alhagi persarum, are rich in medicinal plants. Despite studies, no comprehensive systematic review has been conducted. This research aimed to collect, evaluate, and synthesize findings on climate change impacts on ecological capacity of medicinal plants in these watersheds. This systematic review followed PRISMA guidelines. A search was performed in domestic and international databases. The time frame was 2011–2024 for Persian and 2015–2026 for English articles. After screening, 60 studies were selected for final analysis. Data were analyzed using thematic analysis and descriptive statistics. Annual temperature increased by 1.2°C and growing season precipitation decreased by 15% over three decades. Density of Astragalus, Thymus, and Achillea declined by up to 25%. Flowering of Cichorium occurred 10-15 days earlier, and Glycyrrhiza shifted to higher altitudes (2500-3000 m). Active compounds including alkaloids, flavonoids, and essential oils decreased by up to 30%. Alhagi persarum faces severe habitat reduction in eastern Alborz. Highland areas as climate refugia prevented up to 40% of population decline. It is predicted that by 2031, 30% of medicinal plant distribution area will be lost. Climate change has reduced ecological capacity. Species vulnerability, altitudinal shifts, reduced active compounds, and threats to Alhagi persarum are consequences. Overharvesting by local communities has accelerated extinction rates. Management of climate refugia, sustainable cultivation development, awareness, and adaptation programs are essential for preserving these resources. https://jdcr.birjand.ac.ir/article_4217.html Drought and dust are major environmental hazards in Iran, yet their long-term spatial concurrence remains poorly characterized. This study investigates the spatiotemporal patterns and concurrent trends of drought and dust occurrence across Iran during 2000–2024 using daily MODIS MAIAC aerosol optical depth (AOD) and monthly TerraClimate Palmer Drought Severity Index (PDSI) data. Long-term trends in dust frequency and drought conditions were assessed using Sen’s slope and the Mann–Kendall test. Dust occurrence showed strong spatial heterogeneity but predominantly increasing trends, particularly across central, eastern, and northeastern Iran. Drought conditions were similarly heterogeneous, with increasing trends concentrated mainly in eastern and southeastern regions. Integrated analysis revealed that concurrent increases in drought conditions and dust events covered 17% of Iran, while increasing drought with non-significant dust trends formed the largest class (24%). Concurrent decreases were negligible (<1%). These findings highlight substantial regional differences in drought–dust dynamics and demonstrate the value of an integrated trend-based framework for identifying areas where drought and dust trends occur concurrently under climate change. https://jdcr.birjand.ac.ir/article_4223.html Climatic changes affect not only the average precipitation but also the statistical structure of precipitation distribution, such as the frequency of extreme events, the skewness and kurtosis of the series. Therefore, in this research, for a deeper understanding of the impact of climate change on precipitation, the change in the probability functions of the precipitation data series was investigated. The length of the statistical period studied was t from 1951 to 2025, which includes 87 stations with 40 to 75 years of data. First, the change points were detected using the Pettitt method, then the best-fitting probability distributions for the series before and after each change point were determined and analyzed. The results indicated that, with few exceptions, the precipitation probability distributions before and after the change points differed at nearly all stations and in all months with significant change points. This suggests that, in addition to changes in the mean, other statistical characteristics of the precipitation series also changed, reflecting substantial shifts in precipitation patterns. After the change points, most stations exhibited a decrease in the probability of heavy precipitation and an increase in the probability of low precipitation, indicating a rise in the frequency of weak or short-term precipitation events. In other words, the probability density in low-precipitation intervals increased significantly. This pattern suggests that the precipitation regime in most stations with significant change points has shifted toward drier conditions, with the dominant precipitation systems losing capacity to produce heavy rainfall. https://jdcr.birjand.ac.ir/article_4234.html Climate change has become one of the most important challenges facing agriculture and global food security in recent decades. Rising air temperatures, changes in precipitation patterns, frequent droughts, and heat waves have seriously affected the growth and yield of many crop plants. Among environmental stresses, heat stress is one of the major limiting factors of crop production; by disrupting physiological and biochemical processes in plants, it leads to yield reduction, especially during sensitive developmental stages such as flowering and grain filling.This review article, based on reliable national and international scientific sources, examines the effects of heat stress on the growth and yield of crop plants. The results indicate that temperatures above the optimal growth range reduce grain formation and filling through enzyme denaturation, decreased photosynthesis, increased respiration, damage to cell membranes, reduced chlorophyll stability, and disruption of pollination. The simultaneous occurrence of heat and drought stress causes more severe effects, leading to reductions in growth, leaf area, and yield.Plants respond to these stresses through mechanisms such as activation of antioxidant systems, accumulation of osmolytes, and regulation of hormones such as abscisic acid and salicylic acid. The application of compounds such as salicylic acid, humic acid, brassinolide, brassinosteroids, and others, along with management strategies including breeding and use of heat-tolerant cultivars, adjustment of sowing dates, and optimal irrigation and nutrient management, can be effective in enhancing heat tolerance and maintaining production stability under climate change conditions. https://jdcr.birjand.ac.ir/article_4237.html Industrialization of societies has led to an increase in fossil fuel consumption, which has led to greenhouse gas emissions, an increase in global average temperature, and changes in climate metrics. Climate change shifts the distribution pattern and species richness of many plant groups. In the present study, an attempt has been made to first examine the trend of changes in climate parameters at Mazandaran province. Then, using the time series of the NDVI and long-term temperature and precipitation data in the past and future, the status of vegetation cover in the province's rangelands was evaluated using the random forest algorithm, and after determining the pattern of changes, the status of rangelands as the main habitat of medicinal plants in the future was predicted. According to the results, the average rate of change in minimum, maximum temperature, and precipitation in the optimistic scenario was 1.84, 0.29, and 1.39 mm, and in the pessimistic scenario was 2.29, 0.28, and 0.8 mm. A study of the status of the habitat in the near future shows that some of the habitats with high and good desirability were transferred to the category with middle desirability. The overall result of with increasing temperature, soil moisture decreases, and these conditions can limit the distribution, growth and development of plant species and reduce the desirability of habitats. https://jdcr.birjand.ac.ir/article_4283.html Precipitation is a key climatic variable with high spatiotemporal variability and its changes affect water availability and agriculture, particularly in arid and semi-arid regions. This study aims to analyze precipitation trends and meteorological drought in Zanjan Province, Iran, over a 22-year period (2001-2023). Precipitation trends were examined at monthly and annual scales using non-parametric Mann-Kendall and Spearman tests, along with the Theil-Sen slope estimator, across eight counties. Drought was evaluated using the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The results indicated that annual trends were not significant at any station, while significant monthly trends were observed in June (Zanjan), September (Abhar) and March (Abbar). Precipitation followed a spring-concentrated pattern, with 37% of annual rainfall occurring in spring. The average annual precipitation was 283 mm, with Qeydar recording the highest (393 mm) and Abbar the lowest (254 mm). Light rainfall events (≤1 mm) were most frequent, with Qeydar having the highest number of rainy days (102) and Garmab the lowest (67). SPI analysis revealed normal conditions during approximately 60% of the study period; however, recent years (2021-2023) exhibited mild to extreme drought across most stations. SPEI analysis indicated that moderate drought dominated the one-year scale (36%) and extreme drought prevailed on the ten-year scale (46%). Severe to extreme conditions concentrated in northern, eastern and southeastern areas. The findings indicate repeated moisture deficits in Zanjan Province during the study period. This study provides insights for agricultural planners to develop adaptive strategies for mitigating drought impacts. https://jdcr.birjand.ac.ir/article_4293.html This research examines the spatio-temporal evolution of drought dynamics across a climatic gradient in the Zagros forests throughout a three-decade period (1993–2022). To ensure analytical precision, the region was stratified into five distinct climatic classes (arid, semi-arid, Mediterranean, sub-humid, and very humid), utilizing the SPEI index across four temporal scales. Additionally, the Integrated Drought Severity-Persistence Index (IDSPI) was developed to concurrently assess the magnitude, duration, and persistence of extreme drought events. Our findings demonstrate a statistically significant decline in precipitation across all climatic zones. The semi-arid climate emerged as the most vulnerable, characterized by the highest cumulative intensity. Notably, IDSPI analyses indicate that during the terminal phase of the study period (2021–2022), extreme droughts (SPEI ≤ −2.0) migrated from arid regions toward Mediterranean and humid zones. This shift signifies a diminishing resilience of humid ecosystems to prolonged hydroclimatic shocks. Ultimately, this study confirms that drought in the Zagros forests has transitioned from a localized occurrence in arid lands to a systemic crisis pervading humid regions. These insights highlight the imperative for a critical re-evaluation of water resource management frameworks within the humid zones of the Zagros. https://jdcr.birjand.ac.ir/article_4294.html Drought monitoring is a key factor in water resources management and reducing vulnerability in susceptible areas. However, accurate monitoring across various geographical regions, especially those with high climatic diversity and severe limitations in access to long-term station data (such as southeastern Iran), has always faced serious and fundamental challenges. In this regard, global gridded databases, which are produced through the integration of remote sensing, numerical models, and measured data, are considered effective solutions for areas lacking sufficient data. Nevertheless, their accuracy varies across different climates; therefore, evaluating the performance of gridded indices against station-based ones is essential to ensure their correlation and agreement. The present study investigates the correlation and agreement between the station-based Standardized Precipitation Index (SPI) and the gridded SPIbase at timescales of 1, 3, 6, 9, and 12 months to assess the substitutability of gridded data. The station-based SPI was calculated using precipitation data from 8 meteorological stations in southeastern Iran (1986–2024). SPIbase was extracted from the Global Precipitation Climatology Centre data for the same period. The evaluation of consistency and agreement between the two indices was carried out using Pearson correlation, weighted Cohen's Kappa, and Bland–Altman plots. The results showed strong correlation and good agreement between the two indices at most stations. The correlation increased from short-term (1 and 3 months) to long-term (6, 9, and 12 months) scales. As a main finding, the gridded SPI in southeastern Iran has the capability to appropriately substitute station-based data for near-real-time drought monitoring. https://jdcr.birjand.ac.ir/article_4295.html How water is used in farming plays a key role in understanding the difficulties of managing groundwater resources. Therefore, it is important to recognize and model how farmers behave as decision-makers—since their choices affect the environment, as well as officials and other stakeholders. The Ardabil Plain aquifer, which is the main source of water for farming in the area, has seen a major drop in its groundwater levels in recent years due to over-extraction. This study presents a combined framework that uses agent-based modeling (ABM) and artificial intelligence (AI) to simulate how farmers act when using groundwater and to assess how their actions impact the aquifer. Machine learning and neural networks are used to model how farmers make decisions. When modeling their choice of which crops to grow, the dryness conditions in the coming water year are taken into account. For this, a climate-based drought index is forecasted using rainfall data from weather prediction models—after these data have been downscaled and corrected for errors. By building this ABM-AI combined model, we analyze how farmers interact with each other and how willing they are to protect groundwater. We also evaluate how different management plans would affect farmers' behavior and the water conditions in the region. The findings show that good management plans can cut groundwater pumping from wells by 8.8% on average. In addition, these plans lead to more land being used to grow crops that need less water and are of strategic importance to the Ardabil Plain. https://jdcr.birjand.ac.ir/article_4306.html This study projects absolute temperature extremes using the Combined Thermal Hazard Index (CTHI). An evaluation of the CMIP6 multi-model ensemble (CMIP6-MME), comprising 14 bias-corrected models, reveals pronounced spatial heterogeneity in its ability to reproduce absolute temperature extremes during the historical period (1990–2014). Based on the refined Index of Agreement, the model performance for absolute maximum temperature (TXx) exceeds that for absolute minimum temperature (TNn). Although model performance degrades across complex topographic zones such as the Zagros Mountains, the model shows strong agreement with observational data in the interior regions. The projections indicate widespread warming across the region, with no negative temperature anomalies. In the near term, climate system inertia yields a convergent thermodynamic response; however, significant divergence among emission scenarios emerges toward the end of the century. Specifically, the spatially averaged anomalies of TNn and TXx stabilize between 1.8 °C and 2.0 °C under the SSP1-2.6 scenario, but reach 4.4 °C to 5.7 °C under SSP3-7.0 and exceed 8.0 °C under SSP5-8.5. The locus of these extreme changes—where anomalies exceed 8.0 °C—is concentrated over the Zagros, Alborz, and northwestern highlands, driven directly by the elevation-dependent warming (EDW) mechanism and the snow-albedo feedback. By the end of the century, CTHI values stabilize at low levels (maximum 0.25) under SSP1-2.6, but exceed 0.8 across large parts of the country under SSP5-8.5. This shift in the thermal regime, coupled with reduced snow-cover persistence and intensified actual evapotranspiration, will pose irreversible challenges to the water security of these basins.