Geoinformation modeling of flood zones in mountainous areas of the North Caucasus based on satellite and meteorological data

  • O.S. Ermolaeva Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, 49 Timiryazevskaya Str., Moscow 127434, Russian Federation
  • S.O. Kurashkin Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, 49 Timiryazevskaya Str., Moscow 127434, Russian Federation; Bauman Moscow State Technical University 5, 2nd Baumanskaya Str., Moscow 105005, Russian Federation
  • O.S. Puchkova Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, 49 Timiryazevskaya Str., Moscow 127434, Russian Federation
  • A.V. Babkina Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, 49 Timiryazevskaya Str., Moscow 127434, Russian Federation
  • O.V. Ivanov Admiral F.F. Ushakov State Maritime University, 93 Lenin Ave., Novorossiysk 353918, Russian Federation
DOI: https://doi.org/10.46698/VNC.2026.61.21.001
Keywords: geoinformation modeling, flooding, North Caucasus, remote sensing, Sentinel-1, Sentinel-2, digital elevation model, hydrological analysis, GIS, meteorological data, natural hazards, floods

Abstract

Relevance. Extreme precipitation and flood events are becoming increasingly frequent in the mountainous regions of the North Caucasus. This underscores the need for the accurate identification and forecasting of inundation zones. Addressing this challenge requires the development of advanced methods based on Earth remote sensing data and geoinformation technologies. Aim. This study aims to develop a geoinformation model for identifying flood-prone areas in the mountainous territories of the North Caucasus based on satellite, meteorological, and terrain data. Methods. Sentinel-1 radar imagery, Sentinel-2 and Landsat-8 multispectral data, SRTM DEM and ALOS PALSAR DEM elevation models, as well as ERA5 and GPM IMERG meteorological datasets were used. Data processing and spatial analysis were performed using QGIS, ArcGIS Pro, Google Earth Engine, and Python. Flood-zone classification was carried out using the Random Forest algorithm. Results. Flood-prone areas were primarily associated with floodplains, intermountain depressions, and low-slope terrain. Critical precipitation thresholds contributing to hazardous hydrological conditions were identified. The integration of radar and optical satellite data demonstrated high effectiveness for flood monitoring under complex terrain conditions. The developed model achieved an overall classification accuracy of 91–93 % and can be applied for flood monitoring, hydrological risk assessment, and early warning system support.

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Published
2026-06-30
Issue
Vol 16 No 2 (2026): Geology and Geophysics of the South of Russia, 2026 №2
Section
Geoinformatics