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Abstract In this study, a data-driven approach employed by utilizing the product called JRC-Global surface water mapping layers V1.4 on the Google Earth Engine (GEE) to map and monitor the effects of climate change on surface water resources. Key climatic variables affecting water bodies, including air temperature (AT), actual evapotranspiration (ETa), and total precipitation, were analyzed from 2000 to 2021 using the temperature-vegetation index (TVX) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The findings demonstrate a clear association between global warming and the shrinking of surface water resources in the LUB. According to the results, an increase in AT corresponded to a decrease in water surface area, highlighting the significant influence of AT and ETa on controlling the water surface in the LUB (partial rho of − 0.65 and − 0.68, respectively). Conversely, no significant relationship was found with precipitation and water surface area (partial rho of + 0.25). Notably, the results of the study indicate that over the past four decades, approximately 40% of the water bodies in the LUB remained permanent. This suggests a loss of around 30% of the permanent water resources, which have transitioned into seasonal water bodies, accounting for nearly 13% of the total. This research provides a comprehensive framework for monitoring surface water resource variations and assessing the impact of climate change on water resources. It aids in the development of sustainable water management strategies and plans, supporting the preservation and effective use of water resources.

Abstract The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development.

Abstract The aim of the current study was to suggest a multi-hazard probability assessment in Fars Province, Shiraz City, and its four strategic watersheds. At first, we construct maps depicting the most effective factors on floods (12 factors), forest fires (10 factors), and landslides (10 factors), and used the Boruta algorithm to prioritize the impact of each respective factor on the occurrence of each hazard. Subsequently, flood, landslides, and forest fire susceptibility maps prepared using a Random Forest (RF) model in the R statistical software. Results indicate that 42.83% of the study area are not susceptible to any hazards, while 2.67% of the area is at risk of all three hazards. The results of the multi-hazard map in Shiraz City indicate that 25% of Shiraz city is very susceptible to flooding, while 16% is very susceptible to landslide occurrences. For four strategic watersheds, it is notable that in the Dorodzan Watershed, landslides and floods are the most important hazards; whereas, flood occurrences cover the largest area of the Maharlou Watershed. In contrast, the Tashk-Bakhtegan Watershed is so sensible to floods and landslides, respectively. Finally, in the Ghareaghaj Watershed, forest fire ranks as the strongest hazard, followed by floods. The validation results indicate an AUC of 0.834, 0.939, and 0.943 for the flood, landslide, and forest fire susceptibility maps, respectively. Also, other accuracy measures including, specificity, sensitivity, TSS, CCI, and Gini coefficient confirmed results of the AUC values. These results allow us to forecast the spatial behavior of such multi-hazard events, and researchers and stakeholders alike can apply them to evaluate hazards under various mitigation scenarios.