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<p>The primary objective of this study was to investigate, for the first time on a global scale, the effects of spatial and temporal variability in climate and land use on the occurrence of floods in the twenty-first century using different shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (i.e., SSP1-RCP2.6 and SSP5-RCP8.5). Uncertainties in climate and land-use change projections were considered using an average of 13 global climate models (GCMs) and the Land-Use Harmonization 2 (LUH2) dataset. Eight dynamic precipitation variables, seven land-use fractions, and seven topographic factors were used to generate flood susceptibility maps based on different current and future scenarios (2041–2060 and 2061–2080). Predictive models were developed using three machine learning algorithms (regularized logistic regression, boosted classification tree, and random forest [RF]). The predictive ability of these models was assessed in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The results indicated that RF outperformed the other models and had the highest predictive ability during the testing phase (sensitivity = 0.91, specificity = 0.86, PPV = 0.88, NPV = 0.90, and AUC = 0.96). In all current and future scenarios, the results revealed the impacts of land use and climate change in expanding the area characterized by high/very high flood susceptibility, particularly in Oceania (New Zealand, Fiji, Guam, and the Solomon Islands), Europe, and several Asian and African countries. Relative importance analysis based on the results of the best-performing model (RF) revealed the significant role of managed pasture, urban land, and precipitation-related variables in mapping flood-prone areas, while topographic variables played a comparatively minor role. These results provide a more accurate basis for assessing and mitigating the impact of floods, enhancing resilience, and ensuring public safety by considering future changes in land use and climate conditions.</p>

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Evaluating the Impact of Climate and Land-Use Change on Flood Susceptibility on a Global Scale

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Fatemeh Rezaie,
  • Saeid Janizadeh,
  • Sayed M. Bateni,
  • Changhyun Jun,
  • Dongkyun Kim,
  • Zahra Kalantari,
  • Essam Heggy

摘要

The primary objective of this study was to investigate, for the first time on a global scale, the effects of spatial and temporal variability in climate and land use on the occurrence of floods in the twenty-first century using different shared socioeconomic pathway-representative concentration pathway (SSP-RCP) scenarios (i.e., SSP1-RCP2.6 and SSP5-RCP8.5). Uncertainties in climate and land-use change projections were considered using an average of 13 global climate models (GCMs) and the Land-Use Harmonization 2 (LUH2) dataset. Eight dynamic precipitation variables, seven land-use fractions, and seven topographic factors were used to generate flood susceptibility maps based on different current and future scenarios (2041–2060 and 2061–2080). Predictive models were developed using three machine learning algorithms (regularized logistic regression, boosted classification tree, and random forest [RF]). The predictive ability of these models was assessed in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The results indicated that RF outperformed the other models and had the highest predictive ability during the testing phase (sensitivity = 0.91, specificity = 0.86, PPV = 0.88, NPV = 0.90, and AUC = 0.96). In all current and future scenarios, the results revealed the impacts of land use and climate change in expanding the area characterized by high/very high flood susceptibility, particularly in Oceania (New Zealand, Fiji, Guam, and the Solomon Islands), Europe, and several Asian and African countries. Relative importance analysis based on the results of the best-performing model (RF) revealed the significant role of managed pasture, urban land, and precipitation-related variables in mapping flood-prone areas, while topographic variables played a comparatively minor role. These results provide a more accurate basis for assessing and mitigating the impact of floods, enhancing resilience, and ensuring public safety by considering future changes in land use and climate conditions.