High-resolution spatiotemporal analysis and driver attribution of floods in Kazakhstan using SHAP and remote sensing integration

作者：Yixuan Liu, Alim Samat, Peijun Du, Jin Chen, Jilili Abuduwaili, Kaiyue Luo, Enzhao Zhu, Dana Shokparova · 发表于：Climate Risk Management · 年份：2025 · DOI：10.1016/j.crm.2025.100783 · 被引用次数：5 · 研究领域：Flood Risk Assessment and Management、Hydrology and Drought Analysis、Groundwater and Watershed Analysis

The escalating impacts of global climate change and extreme weather have intensified flood risks worldwide, including in arid and semi-arid regions traditionally considered low-risk. This study examines the spatiotemporal dynamics of flood events across Kazakhstan from 2000 to 2024 by integrating remote sensing (RS) with machine learning (ML). Using Google Earth Engine (GEE), we address data gaps and cloud interference through spatiotemporal fusion (STARFM), denoising, smoothing, and sample transferring techniques. In addition, this study incorporates the Time-Disaggregated Water Frequency (TWF) method, which enables the identification of water bodies with temporal variability, eliminates permanent water bodies, and distinguishes flood from non-flood conditions in seasonal water bodies, thereby enhancing the accuracy of flood reconstruction and enabling precise delineation of flood inundation areas. Landsat and MODIS imagery are combined to produce high-resolution flood distribution maps, while spectral similarity indicators guide the transfer of samples from the Global Flood Database. A range of spectral, texture, environmental, and socioeconomic features is extracted, with flood classification performed using random forest (RF) and attribution analysis conducted via XGBoost and SHAP. Results highlight a high flood risk in northern, southwestern, and western Kazakhstan, primarily driven by changes in precipitation (PRE), temperature (TEM), soil moisture (SM), and land use. F...