Environment and Interdisciplinary Development

Abstract Introduction: Drought is one of the most pressing environmental and economic challenges facing agricultural regions in Iran. It poses a serious threat to food security, rural livelihoods, and sustainable development. In areas such as Bavi County, where agriculture is a primary source of income, drought can have devastating consequences for farmers and their communities. Understanding how farmers perceive drought and the strategies they adopt to cope with and adapt to its impacts is essential for designing effective and context-sensitive policies. While meteorological definitions of drought focus on precipitation deficits and hydrological imbalances, farmers often interpret drought through the lens of their lived experiences, which may include social, economic, and cultural dimensions. This study aims to explore the subjective definitions of drought among farmers in Bavi County and to identify the coping and adaptive behaviors they employ in response to this phenomenon.
Materials and Methods: This research employed a qualitative phenomenological approach to capture the lived experiences of farmers dealing with drought. The study population consisted of farmers residing in Bavi County, selected through snowball sampling to ensure the inclusion of diverse perspectives. Data collection methods included 25 semi-structured in-depth interviews, direct field observations, photography, and document analysis. Interviews were conducted in the local language and transcribed verbatim. Colaizzi’s method was used for data analysis, allowing for the extraction of significant statements, formulation of meanings, and identification of key themes. This approach facilitated a deep understanding of how farmers define drought and the behavioral responses they adopt.
Results: The findings revealed that farmers primarily define drought in terms of reduced water availability, decreased rainfall, and irregular precipitation patterns. Their definitions often reflect immediate and tangible impacts on crop production, irrigation practices, and livestock management. Beyond physical indicators, some farmers also associated drought with economic hardship, social stress, and psychological burden. A total of 18 distinct behavioral responses to drought were identified and categorized into two main types: Coping (Reactive) Behaviors and Adaptive (Proactive) Behaviors. Coping behaviors included short-term actions such as reducing irrigation frequency, selling livestock, borrowing money, and shifting to drought-resistant crops. Adaptive behaviors, on the other hand, involved long-term strategies such as investing in water-saving technologies, diversifying income sources, participating in cooperative farming, and engaging in community-based water management initiatives.
Discussion: The study highlights that farmers in Bavi County possess a multidimensional understanding of drought that goes beyond technical definitions. Their perceptions are shaped by environmental realities, socio-economic conditions, cultural norms, and personal experiences. The diversity of coping and adaptive behaviors reflects the complexity of drought as a lived phenomenon and underscores the importance of localized responses. While coping strategies may provide immediate relief, they often lack sustainability and can exacerbate vulnerability in the long run. Adaptive strategies, though more resource-intensive, offer pathways toward resilience and long-term sustainability. The findings suggest that policy interventions should prioritize capacity-building, access to financial and technical resources, and participatory planning to support farmers in transitioning from reactive to proactive drought management.

Abstract Introduction: Climate change is one of the most pressing environmental challenges of the modern era, impacting natural resources and human livelihoods through rising temperatures, altered precipitation patterns, and increased frequency of extreme events such as droughts and floods. Due to its predominantly arid and semi-arid climate, Iran is particularly vulnerable to these changes. Evidence suggests that regions such as the northwest and the Zagros Mountains have experienced concurrent temperature increases and precipitation declines, a trend that is projected to intensify in the near future. This study aims to assess and visualize Iran’s climatic conditions over the next 20 years, quantifying the magnitude and extent of projected changes to inform national and regional planning efforts.
Materials and Methods: This research utilizes climate projections from the sixth generation of Coupled Model Intercomparison Project (CMIP6) under four greenhouse gas emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the period 2021–2040, comparing them with the historical baseline (1995–2014). In the first step, raw global climate model outputs were downscaled using the Change Factor (CF) method. Minimum and maximum temperature and precipitation data were extracted for both the baseline and future periods, and delta values were calculated for each variable. To correct biases and better capture local variability, observational data from the TerraClimate database (with a spatial resolution of approximately 5 km) were employed. Subsequently, predictive maps for minimum temperature, maximum temperature, and precipitation over the next 20 years were generated. To integrate these variables into a single metric, Weighted Linear Combination (WLC) was applied, where precipitation was assigned a higher weight through linear fuzzy membership functions. This approach yielded a composite index for assessing climate change intensity across the country. Finally, to compare historical and future trends, the climate change intensity map for the past 64 years was integrated with the projected two-decade map using an equal-weighted WLC framework.
Results: Findings indicate that in the next two decades, a significant increase in both minimum and maximum temperatures is inevitable across most parts of Iran. The projected rise may exceed 2°C for minimum temperature and 1.5°C for maximum temperature in some areas. Additionally, model outputs suggest a considerable decline in precipitation over parts of the Zagros region, the northwest, and critical watersheds such as Lake Urmia. This reduction may exacerbate soil dryness, decrease snowpack reserves, and deplete groundwater resources. The multi-criteria combined model identified the western and northwestern regions as the most severely affected by climate change, as they experience both significant warming and precipitation decline. This pattern is consistent with historical trends observed over the past 64 years, reinforcing the notion that warming and precipitation loss in these areas are part of a persistent, worsening trajectory. These results align with previous national studies and IPCC reports, emphasizing the urgent need for adaptation strategies and effective water resource management.
Discussion: This study highlights that in the coming decades, Iran will face heightened challenges related to increasing temperatures and declining precipitation. The warming trend and decreasing rainfall, particularly in the Zagros and northwestern regions, could have irreversible consequences for mountainous ecosystems, water resources, agriculture, and local livelihoods. Future research should incorporate dynamic downscaling models to provide a more detailed analysis of extreme climatic events and expand the station-based monitoring network to enhance data accuracy. Additionally, national and regional water and land management policies, public awareness campaigns, and greenhouse gas emission reduction strategies must be prioritized to mitigate the negative impacts of climate change in the years ahead.

Abstract Introduction: Global warming is one of the most critical challenges of the 21st century, with extensive implications for ecosystems, the environment, and human societies. Human activities such as fossil fuel consumption, deforestation, and industrial processes have led to a substantial increase in greenhouse gas concentrations in the atmosphere, resulting in global warming and a cascade of environmental effects. Understanding the impacts of global warming on the environment is crucial for developing effective mitigation and adaptation strategies. Therefore, research on topics such as global warming is of paramount importance.
Materials and Methods: This study utilized daily climatic data, including minimum and maximum temperatures, precipitation, and sunshine hours from 120 synoptic stations across Iran over 30 years (1993-2022). The Mann-Kendall test and Sen's slope estimator were employed to assess the trends in annual average temperature changes. To forecast future temperatures influenced by global warming, outputs from the CanESM2 model (from CMIP5 models) were used under two scenarios: RCP2.6 and RCP8.5. Given the large-scale outputs of these models, downscaling was necessary for station-level studies, and the LARS-WG model, one of the most widely used downscaling models, was applied.
Results: The results indicated a significant increasing trend in the annual average temperature of Iran, with a slope of 0.053 (p<0.05) during the base period. The most increasing trend was observed at Bostan and Qorveh, while the least was observed at Shiraz. Out of the 120 stations, 11 showed no significant trend, with only the Quchan station exhibiting a decreasing trend (not significant at the 5% level). Under the RCP2.6 and RCP8.5 scenarios, the annual average temperature in Iran is projected to reach 18.84 and 19.6°C during 2021-2040, 19.40 and 20.19°C during 2041-2060, 19.42 and 21.7°C during 2061-2080, and 18.88 and 22.99°C during 2081-2100, respectively. In the base period, the temperature range across all stations was between 8-28°C, while by the end of the century, the distribution of stations will shift towards higher temperatures, with no station experiencing an annual average temperature below 14°C under the RCP8.5 scenario. Spatially, southern regions will experience the most significant temperature increases, with average annual temperatures projected to rise to between 32 and 34°C by the century's end under the pessimistic scenario.
Discussion: The findings of this study indicate that the increasing trend in Iran's average temperature observed in recent years will continue in the future, albeit with variations across different climate scenarios. Therefore, appropriate planning can help mitigate the extent of the increase and potential damage. Given the impact of temperature on the environment, economy, and all aspects of human life, it is essential to address this issue to ensure risk management and informed decision-making are conducted with precision planning. The results of this research will assist managers and planners in implementing sustainable strategies for adapting to future conditions and enhancing resilience. The results of this study highlight a concerning trend of increasing temperatures in Iran due to global warming, emphasizing the urgent need for effective planning and adaptation strategies to mitigate its impacts on the environment and society.

Abstract Introduction: Currently, food insecurity, particularly in less-developed countries, has been exacerbated by climatic fluctuations, threatening the livelihoods of many households. An examination of climate change impacts in the Lake Urmia Basin reveals that long-term precipitation patterns and annual mean temperature have been significantly affected by climate change. Since this basin is a key agricultural region, intensified climatic fluctuations combined with human interventions have disrupted the lake’s ecosystem, damaged agricultural lands, and undermined local livelihoods. The main objective of this study is to analyze the impacts of climate change and drought on the livelihoods and food security of local communities in the Lake Urmia Basin. Specifically, the study seeks to answer how water resource depletion and declining agricultural production affect household livelihoods and food security, and what socio-economic consequences arise for local communities.
Materials and Methods: This applied research was conducted using a descriptive–survey method in the field. The statistical population included 300 residents from five rural settlements in the southern part of the Lake Urmia Basin. The primary data collection tool was a structured questionnaire encompassing diverse indicators to assess household livelihood status, food security, and adaptation strategies. Field data were collected through a non-random convenience sampling method. Finally, the data were processed using SPSS software, and the results were presented in the form of statistical tables and data analysis.
Results: The results indicate that drying of farmlands and orchards and shortages of water resources are the most direct impacts of drought, leading to reduced agricultural and livestock production and decreased household income. Loss of natural resource–based jobs, livestock mortality, and increased rural outmigration were also reported as major consequences. The findings further show that household food security has been severely compromised: a substantial share of household income is spent on purchasing food, while financial capacity to afford sufficient and diverse diets has markedly decreased. This highlights the fragility of food security and the weakening of livelihood resilience.
Discussion: The study concludes that drought, through water scarcity and reduced agricultural production, threatens the food security and livelihoods of rural households. The high dependency on governmental support and adoption of high-risk livelihood strategies underscores the fragility of household resilience. Strengthening targeted support, promoting drought-resistant cropping patterns, and enhancing food security policies are among the key recommended strategies. Rather than focusing solely on the climatic dimensions of drought in Lake Urmia, this study emphasizes the direct consequences of the phenomenon on household livelihoods and local food security. The research’s main innovation lies in its simultaneous analysis of the environmental and social impacts of drought and its explanation of the interconnections between reduced water access, livelihood instability, and food insecurity—a topic that has received limited attention in previous studies in this region. The findings can inform the development of targeted policies for sustainable development, livelihood planning, and food security in climate-vulnerable areas.

Abstract Introduction: The accelerated pace of urbanization across the globe has resulted in a marked increase in impervious surface coverage, population density, intensified construction activities, and elevated energy consumption. These factors collectively contribute to the amplification of the Urban Heat Island (UHI) phenomenon. Specifically, the substitution of natural vegetation with artificial, impermeable surfaces, the spatial concentration of anthropogenic activities, rising energy demands, and the disruption of natural cooling processesare among the primary drivers intensifying this effect. Given the necessity for comprehensive spatial and temporal datasets in estimating Land Surface Temperature (LST), recent advancements in remote sensing technologies have significantly enhanced our understanding of the spatiotemporal dynamics of urban heat islands. Numerous studies to date have investigated the relationships between land use/land cover changes, construction density, urban biophysical characteristics, and vegetation cover with surface temperature patterns and the intensity of heat islands. Additionally, the broader socio-environmental implications of elevated surface temperatures—such as increased energy consumption, heightened heat stress, and adverse effects on human health—have been extensively explored. Despite the valuable insights provided by previous research, the majority of studies have primarily concentrated on the urban scale. 
Materials and Methods: In the present study, seasonal variations in heat island intensity across Malayer County were analyzed using Land Surface Temperature (LST) data derived from the MODIS sensor aboard the Terra satellite and the TIRS sensor on Landsat 8, covering the period from 2019 to 2024. Selected surface biophysical parameters were used to guide the analysis. To facilitate meaningful comparison across different images, LST values were normalized between their respective minimum and maximum values. For the purpose of comparing LST across various land use types, land cover was categorized into five classes—orchards and croplands, wastelands, rangelands, water bodies, and urban areas—through the Maximum Likelihood Classification (MLC) method implemented in Google Earth Engine. This classification was based on a composite of all available Landsat OLI images from the year 2023, representing the broader study period. Furthermore, to assess the relationships between surface characteristics and LST, key biophysical indicators including the Normalized Difference Vegetation Index (NDVI), Fractional Vegetation Cover (FVC), and surface albedo were calculated within the Google Earth Engine platform. 
Results: The results indicated that the highest land surface temperatures, both during the day and at night, occurred in August, while the lowest values for both temporal intervals were recorded in January. A statistically significant negative correlation (r = -0.603) was observed between mean surface albedo and monthly NDVI, suggesting an inverse relationship between vegetation greenness and surface reflectivity. Moreover, the Fractional Vegetation Cover (FVC) demonstrated a negative correlation with nighttime land surface temperature (NLST), particularly during months characterized by greater vegetation abundance. Notably, in April, May, and January—especially within rangelands—the correlation coefficient between FVC and NLST reached as low as -0.65, underscoring the cooling effect of vegetative cover on surface temperatures. 
Discussion: The intensity of surface heat island formation is influenced by land use type, such as agricultural practices or irrigation, and exhibits distinct spatial and temporal variations between daytime and nighttime periods. During the daytime, areas characterized by orchards, relatively dense vegetation, and higher elevations exhibit lower surface temperatures, functioning as local cooling zones in contrast to their surrounding environments. Conversely, nighttime observations revealed that the urban core represents the warmest region, whereas rural settlements, adjacent agricultural lands, and orchards maintain comparatively lower temperatures. Additionally, elevated terrains—particularly those located in the northwestern part of the region—consistently emerged as the coldest zones across most seasons. These findings have practical implications for regional planning and management, offering valuable insights for sectors such as agriculture, environmental conservation, climatology, urban and rural development, public health, and social welfare.

Abstract Introduction: Given the constraints of resources and population growth, alongside the need to maximize productivity while preserving water, soil, and the environment for future generations, the importance of focusing on agricultural sector productivity is increasing. This study aims to investigate the relationship between climatic variables and the growth of total factor productivity of rainfed wheat in Isfahan province.
Materials and Methods: Data on precipitation, minimum temperature, maximum temperature, mean temperature, precipitation intensity, annual interest rate, total fertilizer consumption, total pesticide consumption (including herbicides, insecticides, fungicides, etc.), total labor, production cost, and yield were collected from the Central Bank of Iran, the National Meteorological Organization, the Agricultural Jihad Organization, and the Agricultural Jihad Organization of Isfahan province. These data were categorized by district for the period 2002–2024. The total factor productivity (TFP) of rainfed wheat, selected based on the largest cultivated area and highest production levels over the past 10 agricultural years, was estimated using the Solow Residual Method, panel data analysis, and a fixed effects model. Furthermore, the correlation coefficient was calculated to examine the relationship between climatic variables—precipitation intensity, mean temperature, maximum temperature, minimum temperature, and cumulative precipitation—and the growth of overall productivity of rainfed wheat. This analysis was conducted according to the climatic zones of Isfahan province: (1) warm and rainfed desert climate, (2) semi-arid climate, (3) cold mountainous climate, and (4) semi-cold and semi-arid climate.
Results: The Cobb-Douglas production function was identified as the most suitable model for rainfed wheat production in Isfahan province, based on significant coefficients and a strong R-squared value. The highest and lowest variations in rainfed wheat productivity growth were observed in Faridan district (cold mountainous climate) with 0.009 units and Khansar district (cold mountainous climate) with -0.067 units, respectively. Correlation analysis showed that mean temperature and precipitation intensity have a significant negative relationship with total factor productivity growth, while minimum temperature has a significant positive relationship with the total factor productivity of rainfed wheat.
Discussion: The results of this study demonstrate a significant relationship between climatic factors and total factor productivity growth of rainfed wheat across different districts of Isfahan province. Although geographical and climatic factors are beyond the direct control of policymakers, understanding their impacts allows for better management of their adverse effects on agricultural productivity growth.

Abstract Introduction: The output of general circulation models (GCMs) lacks the spatial and temporal accuracy required for regional and local studies due to the low spatial resolution of the grid. This has led to the development of regional models and statistical and dynamical downscaling. Among the statistical methods, LARS-WG and SDSM models `are considered to be the most convenient and reliable downscaling tools.
Methods and Materials: In this study, the performance of these two models were evaluated using downscaling the output of CMIP6 models and simulating temperature and precipitation variables in the Urmia Lake basin. The meteorological stations studied included 7 synoptic stations with a statistical period of 30 years corresponding to the base period of the models (1985-2014). The error coefficients including MSE, RMSE, MAE and R2 were also used to evaluate the performance of the models.
Results: Both LARS-WG and SDSM models have high ability in simulating temperature and precipitation variables in the studied area. However, their accuracy was not the same at different stations and also for different climatic variables. Based on the results, both models have lower accuracy in simulating precipitation than temperature, which could be due to the complexity of the precipitation process and its nature. The SDSM model has the lowest error in simulating temperature and precipitation data in the study basin. Although the LARS-WG model also has a good ability to simulate temperature and precipitation data for the small scale and has performed better in some stations, but its capability is not as good as the SDSM model. In SDSM model, the downscaling operation is performed by creating a regression relationship between predictors and predictors at a station, but in the LARS-WG model, independent and large-scale atmospheric variables do not play a direct role in simulating the data. Rather, the model initially analyzes the observational data to determine their parameters and statistical properties. Furthermore, in line with the type of future changes in large-scale climate variables, it changes the statistical parameters of the observational data and attempts to reproduce the data in future periods. Considering the error metrics and comparing the two models under study with each other, one cannot definitely prefer one over the other. 
Discussions: Due to the type of simulation process and the combined structure of the SDSM model in data downscaling and the direct use of general atmospheric circulation models and large-scale NCEP and ERA5 data in the sixth report, this model has greater accuracy in simulating data in the studied basin. On the other hand, the LARS-WG model is superior due to the simplicity of the model structure, the input data to the model, the need for less skill, and the speed of operation, and it gives the user more flexibility. However, the SDSM model has a more complex process and requires more accuracy and time, as well as relatively high user expertise. In addition to climatic variables, this model can also be used for hydrological and environmental variables, while the LARS-WG model is only applicable to temperature, precipitation, radiation, and evaporation variables. But on the other hand, for the LARS-WG model, considering the climatic characteristics of the studied region, new climate change scenarios can be defined, which can be useful in the application of these models in climate change areas. Overall, it can be concluded that these models, despite their differences, can produce the statistical behavior of climate data from a weather station in terms of mean, standard deviation, etc., which are identical to the statistical behavior of observational data, and none of the models has absolute superiority over the other. Given that in any region, before implementing climate change models, it is necessary to downscale and evaluate the performance of the models, the results of this research can be used to verify the output of CMIP6 models in predicting climate variables in future periods in different climates.

Abstract Introduction: Wetlands, as critical ecosystems, play a vital role in ecological balance and human community support by providing services such as water purification, flood control, and biodiversity conservation. The Shadegan International Wetland in Khuzestan, registered under the Ramsar Convention (1971), holds global significance due to its biodiversity and ecosystem services. This wetland faces challenges including drought, reduced water allocation, industrial and agricultural pollution, and dust storms driven by climate change, all threatening its survival. Climate change, by altering hydrological patterns, underscores the need for a comprehensive roadmap for wetland management. The Ecosystem-Based Management (EBM) approach, integrating ecological, social, economic, governance, and cultural factors, offers an effective framework for the conservation and restoration of the Shadegan Wetland. Developing a wetland-climate change roadmap based on EBM is a crucial step toward mitigating the adverse impacts of climate change and enhancing the sustainability of the Shadegan ecosystem.
Materials and Methods: The research was conducted in three phases: 1) Qualitative analysis of domestic and international scientific documents related to EBM and climate change to develop a roadmap, using qualitative content analysis of EBM components through a meta-synthesis method with NVivo14 software. Coding was performed based on five main systems (economic, ecological, social, governance, and cultural) and their subsystems. Inter-coder reliability (ICR) was assessed by two independent coders, with the Kappa coefficient calculated. 2) Qualitative analysis of the Shadegan Wetland Management Plan. 3) Quantitative assessment using a data-input-output matrix to evaluate the role of climate change and EBM in wetland management, with policy recommendations derived using SPSS software.
Results: The wetland-climate change roadmap, based on the EBM approach, comprises 54 components across five key systems (economic, ecological, social, governance, and cultural) and 19 sectors affected by climate change, serving as a benchmark for evaluating the Shadegan Wetland Management Plan. The data-input-output matrix analysis revealed an overall plan coverage of 31.48%, with significant gaps (87% of components and 94% of sectors). The economic system, with 23% coverage, performed the weakest, with only one fully addressed component (green infrastructure) and gaps in ecosystem service valuation and circular economy. Proposed solutions include sustainable aquaculture with native species, bird-watching ecotourism, and solar-powered boats. The ecological system (30.77% coverage) benefits from reedbed restoration and biofilters for biodiversity conservation, with 10 gaps identified. The social system (44.44% coverage) is enhanced by a stakeholder coordination mobile application. The governance system (45% coverage) requires a digital platform and adaptive management. The cultural system (20.83% coverage) is improved through a digital museum and local festivals. This plan ensures Shadegan’s sustainability by addressing emission reduction, adaptation, and risk mitigation.
Discussion: The innovations of this study, such as solar-powered boats and biofilters, align with FAO (2012) and Ramsar (2018) but offer a more localized approach tailored to Khuzestan’s climate. Ecosystem service valuation, akin to Costanza (2017), has local applicability and differs from TEEB (2010). The stakeholder coordination mobile application modernizes participatory management compared to traditional methods by Brooks (2008). The digital governance platform enhances transparency in Khuzestan’s complex conditions, surpassing Stram (2009). The digital museum and cultural tourism improve local livelihoods and, unlike UNEP (2016), globalize wetland values. Across 19 climate change sectors, solutions such as Integrated Water Resources Management (IWRM) in water, GIS in environmental management, and biogas in energy align with IPCC (2014) and FAO (2017), yet integrating wetlands into management provides a more holistic approach. Limitations, such as long-term data gaps and stakeholder coordination, can be addressed through education and digital technology. This plan serves as a model for other Iranian wetlands, such as Hamoun and Anzali.