Assessment of the impacts of climate change on land surface temperature and drought risk in agricultural land use in Khuzestan province

Introduction
Climate change is intensifying global droughts, posing severe threats to the environment, agriculture, and human livelihoods. The Earth's temperature is rising at twice the global average, driving significant changes in planetary conditions. Escalating land surface temperatures have degraded ecosystems, depleted soil quality, and reduced water availability, leading to the expansion of arid regions. These shifts stress vegetation and diminish agricultural yields, exacerbating food insecurity, particularly in developing nations. Drought and water scarcity are now prominent trends in arid and semi-arid regions. Altered precipitation patterns, influenced by climate change, disrupt rainfall quantity and distribution, further straining water resources. In countries like Iran, with predominantly dry climates, these climatic shifts are especially acute. Iran ranks among the top six nations for natural disasters, with over 83 percent of its crises tied to earthquakes, floods, and droughts. Drought, a recurring issue, inflicts significant damage on Iran's society, water, and soil resources. Khuzestan Province, a key agricultural and economic hub in southwestern Iran with rich water resources, is significantly affected by climate change. Rivers like Karun, Karkheh, and Jarahi, vital for drinking water, agriculture, and industry, are impacted by these changes. This study evaluates the impact of climate change on land surface temperature and drought risk in the agricultural regions of Khuzestan Province, aiming to address these critical challenges.
Materials and Methods
This study utilized two primary data sources to assess climate change impacts in Khuzestan Province. Climate parameters, including precipitation, radiation, and maximum/minimum temperatures, were obtained from the Abadan synoptic station (1985–2015) from the Meteorological Organization. Additionally, satellite data for 19 stations, covering temperature, radiation, and precipitation, were sourced from the ERA5 database for 1950–2025. Due to incomplete data from 21 meteorological stations, satellite data were used for the first time in this region to model and forecast temperature and precipitation. The Mann-Kendall Z-statistic test, applied at 95 and 99 percent significance levels, analyzed trends in temperature and precipitation changes. To address the low resolution of general circulation models for regional studies, the LARS-WG8 statistical model was employed for downscaling. This model used daily time series data, including precipitation (mm), maximum/minimum temperatures (C°), and radiation (MJ/m²/day), with a base period of 1985–2015, aligned with CMIP6 models. Projections were based on the CanESM5 model under the high-emission SSP5-8.5 scenario, downscaled for short-term (2021–2040) and medium-term (2031–2050) periods. Trends in modeled data were also evaluated using the Mann-Kendall test. Model performance was assessed in Excel using percentage error, correlation coefficient (R), and root mean square error (RMSE). Additionally, satellite data from Google Earth Engine were analyzed to calculate land surface temperature (LST), normalized difference vegetation index (NDVI), standardized precipitation index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI).
Results and Discussion
An analysis of monthly and annual temperature trends from 1950 to 2025 across 19 stations in Khuzestan shows a significant upward trend in nearly all months. Projections using the LARS-WG8 model under the SSP585 scenario  CanESM5 indicate an approximate 3 C^° increase in both the near future (2021–2040) and medium-term future (2031–2050) compared to the 1985–2015 baseline. Data from the Abadan synoptic station and satellite observations confirm this warming, with satellite data predicting even hotter summers. In contrast, precipitation is projected to decline across all months in both future periods, with December and January remaining the wettest months. The 24- year NDVI record shows substantial vegetation cover fluctuations, with the lowest values in 2000, 2008, and 2009, likely linked to low rainfall and drought. A recovery has been observed since 2010, possibly reflecting improved climatic or hydrological conditions. Land surface temperature has shown a steady rise since 2010. The Standardized Precipitation Index (SPI) indicates severe droughts in 2000–2004, 2010,  2021, and 2022. The Standardized Precipitation Evapotranspiration Index (SPEI) shows that from 1980 to 1999, conditions were generally normal with alternating mild to moderate droughts. From 2000 to 2024, mild to moderate droughts predominated, while 2008–2022 marked a shift toward severe drought conditions. According to the SPEI index, the most extreme droughts occurred in December 2010 (−2.53) and December 2021 (−2.48).
Conclusion
This study comprehensively analyzes the drought trend in Khuzestan Province from 1950 to 2025 using meteorological and remote sensing data. Initial findings show a significant increase in temperature in all months except November, along with a decrease in precipitation in the long term, which is consistent with the CanESM5 model predictions of a 3 C^° increase in temperature and a decrease in precipitation. These results confirm that global warming and human activities, especially in industrial cities such as Ahvaz and Abadan, have exacerbated drought conditions. The SPI and SPEI indices show severe drought periods between 2000 and 2022, accompanied by a decrease in NDVI and adverse effects on agriculture and vegetation. These findings highlight the impact of non-precipitation factors, such as artificial irrigation, in mitigating the effects of drought. However, limitations include uncertainties in the SSP5-8.5 scenarios, reliance on data from specific weather stations, and weak correlations between SPI and NDVI. To address these issues, future research should use hybrid climate models, extend data collection to rural areas, and integrate artificial intelligence for advanced analysis. Practical recommendations include promoting drought-resistant crops, optimizing irrigation systems, and educating local communities. This study provides a basis for water resources management and climate policies, and emphasizes the urgent need for adaptive measures to cope with intensifying droughts.