GIS-based flood risk assessment in the Zhabay River Basin, Kazakhstan

Ainur Mussina, Lyazzat K. Makhmudova, Akhmetkal Rakhmetullayevich Medeu, Marzhan Ospanova, Assel Abdullayeva, Marzhan Tursyngali, Saule Zhaksylyk, Javier Rodrigo-Ilarri, María-Elena Rodrigo-Clavero

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.1016/j.pdisas.2026.100694
Data confidence 70 medium
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Abstract

Flooding is one of the most damaging natural hazards in the steppe regions of northern Kazakhstan, where rapid spring snowmelt frequently causes severe inundation. In the Zhabay River basin, recent flood events have led to significant socio-economic losses in Atbasar and nearby settlements. Despite their recurrence, spatially explicit flood risk assessments for this basin remain limited. To address this gap, the present study develops an integrated GIS-based framework that combines flood hazard assessment, vulnerability analysis, inundation scenario modeling, and satellite-based validation for comprehensive flood risk assessment. Flood hazard was assessed using indicators representing both basin susceptibility and hydrometeorological conditions controlling flood generation. Indicator weights were derived using the Analytical Hierarchy Process (AHP). Flood vulnerability was assessed using socio-economic and environmental vulnerability indicators. Integration of hazard and vulnerability layers enabled the development of a spatial flood risk model. Model performance was evaluated against observed flood extents derived from Sentinel-2 imagery using the Modified Normalized Difference Water Index (MNDWI) for flood events. Model validation using ROC analysis demonstrated good to excellent predictive performance, with AUC values of 0.920, 0.820, and 0.906 for the 2017, 2019, and 2024 flood events, respectively. The proposed framework provides a basis for flood risk management and disaster risk reduction in snow-fed river basins. The main novelty of the study lies in the integration of hydrometeorological flood-triggering factors, spatial vulnerability indicators, inundation scenario modeling, and satellite-based validation within a unified GIS framework, providing a transferable approach for flood risk assessment in data-scarce snow-fed river basins.