Kyrgyzbay et al. (2026) Spatial Assessment of Flood Susceptibility in the Abai Region, Kazakhstan

Identification

• Journal: Water
• Year: 2026
• Date: 2026-03-30
• Authors: K. Kyrgyzbay, Talgat Usmanov, Janay SAGIN, Baktybek Duisebek, Ranida Arystanova, S. A. Kulbekova, Arman Utepov, R. Amanzholova
• DOI: 10.3390/w18070817

Research Groups

• School of Information Technology and Engineering, Kazakh British Technical University, Almaty, Kazakhstan
• Faculty of Geography and Environmental Sciences, Al-Farabi Kazakh National University, Almaty, Kazakhstan
• Department of Geological and Environmental Sciences, Western Michigan University, Kalamazoo, MI, USA
• Department of Water Resources and Melioration, Kazakh National Agrarian Research University, Almaty, Kazakhstan
• Ahmedsafin Institute of Hydrogeology and Environmental Geoscience, Satbayev University, Almaty, Kazakhstan

Short Summary

This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region, Kazakhstan, using a multi-criteria Geographic Information System (GIS) approach that integrates twelve flood-conditioning factors with the Analytical Hierarchy Process (AHP). The analysis identified distance to rivers (19.66%) and precipitation (16.42%) as the most influential drivers, classifying 25.0% of the region as high susceptibility, with the model demonstrating strong predictive performance (ROC-AUC = 0.893).

Objective

• To develop a region-specific flood-susceptibility assessment for the newly established Abai Region to support spatial decision-making in a data-scarce environment.
• Characterize regional flood drivers by evaluating the interaction of cryospheric (snowmelt), topographic (e.g., HAND and slope), and anthropogenic factors across Eastern Kazakhstan.
• Apply a multi-criteria GIS–AHP framework adapted to continental hydroclimatic conditions through regionally derived factor weights.
• Delineate susceptibility zones for spatial planning, including the identification of priority areas for infrastructure protection and land-use regulation.

Study Configuration

• Spatial Scale: Abai Region, northeastern Kazakhstan, covering an area of approximately 181,818.2 km². All spatial datasets were standardized to a 30 meter by 30 meter spatial resolution.
• Temporal Scale: Precipitation data from 2000–2024. Soil texture data from 2017. Landsat 9 imagery for NDVI. Historical flood occurrence records for validation (no specific date range provided).

Methodology and Data

• Models used:
  • Geographic Information System (GIS)
  • Analytical Hierarchy Process (AHP)
  • Weighted Linear Combination (WLC)
  • Inverse Distance Weighting (IDW) for precipitation interpolation
  • Receiver Operating Characteristic (ROC) analysis for model validation
  • Confusion matrix for classification metrics
• Data sources:
  • Digital Elevation Model (DEM): Shuttle Radar Topographic Mission (SRTM) DEM (30 m resolution).
  • Slope (S): Derived from SRTM DEM.
  • Height Above Nearest Drainage (HAND): Derived from SRTM DEM.
  • Drainage Density (DD): Derived from hydrologically corrected flow-accumulation raster.
  • Distance from Rivers (DRI): OpenStreetMap vector data.
  • Precipitation (P): Meteorological stations of RSE “Kazhydromet” and KazNIIMOS (2000–2024).
  • Topographic Wetness Index (TWI): Derived from flow-accumulation and percent-slope rasters.
  • Soil Texture (ST): SoilGrids 250 m dataset (2017).
  • Land-use/Land Cover (LULC): Not explicitly stated source, but generally from satellite imagery.
  • Normalized Difference Vegetation Index (NDVI): Landsat 9 satellite (USGS) surface reflectance data (Bands 4 and 5).
  • Distance from Roads (DRO): OpenStreetMap road network.
  • Population Density (PD): WorldPop 3-arc-second (~100 m) resolution GeoTIFF dataset.
  • Flood inventory: Independent flood-occurrence data from historical records and documented inundation areas at 44 settlement locations.

Main Results

• The most influential flood-conditioning factors were identified as distance from rivers (19.66%) and precipitation (16.42%).
• Topo-hydrological factors, including Height Above Nearest Drainage (HAND) (10.62%), drainage density (8.89%), elevation (8.24%), slope (8.24%), and Topographic Wetness Index (TWI) (8.24%), received moderate weights.
• Soil texture (5.82%), land-use/land cover (4.34%), and Normalized Difference Vegetation Index (NDVI) (2.37%) were secondary modifiers.
• Anthropogenic factors, population density (2.55%) and distance from roads (4.61%), received the lowest weights.
• The flood susceptibility map classified the Abai Region into five categories:
  • Very High susceptibility: 0.2% of the area.
  • High susceptibility: 25.0% of the area.
  • Moderate susceptibility: 56.6% of the area.
  • Low susceptibility: 18.1% of the area.
  • Very Low susceptibility: 0.1% of the area.
• High susceptibility zones are primarily concentrated in low-lying floodplains and foothill areas adjacent to major river channels.
• Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) value of 0.893, indicating strong agreement with observed flood occurrences.
• Additional validation metrics showed an overall accuracy of 83.3%, precision of 0.75, recall of 1.0, and a Kappa coefficient of 0.67.
• The Analytical Hierarchy Process (AHP) pairwise comparison matrix demonstrated high internal consistency with a Consistency Ratio (CR) of 0.0206.
• Sensitivity analysis, perturbing dominant factor weights by ±10%, confirmed the model's robustness, with the High susceptibility class varying by -4.7% to +5.1% and the Moderate class by ±2.2%.

Contributions

• Provides the first comprehensive flood-susceptibility assessment for the newly established Abai Region, addressing a significant local data gap.
• Considers flood generation influenced by the interaction of spring snowmelt and rapid temperature variability, which is characteristic of the Central Asian steppe-to-mountain transition.
• Integrates the Height Above Nearest Drainage (HAND) metric along with selected anthropogenic indicators to enhance terrain-based representation of flood processes, improving the practical value for regional planning.

Funding

• Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant IRN BR27197639), for the project "Flood-drought mitigation innovations with managed aquifer recharge hydrogeological strategies for the Zhambyl, Almaty, Zhetysu, Abay, and East Kazakhstan regions."

Citation

@article{Kyrgyzbay2026Spatial,
  author = {Kyrgyzbay, K. and Usmanov, Talgat and SAGIN, Janay and Duisebek, Baktybek and Arystanova, Ranida and Kulbekova, S. A. and Utepov, Arman and Amanzholova, R.},
  title = {Spatial Assessment of Flood Susceptibility in the Abai Region, Kazakhstan},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18070817},
  url = {https://doi.org/10.3390/w18070817}
}