This study focuses on flood risk assessment in a vulnerable reach of the Buktyrma River basin, located in the East Kazakhstan region. The main goal was to develop and validate a modelling workflow that uses open-access data and software to simulate flood dynamics and visualize the results through an interactive GIS-based interface. The approach involved statistical analysis of 24 years of annual maximum discharge and water level data from gauging station to define a representative flood event. Terrain data were derived from the 30 m Copernicus Digital Elevation Model, which was used to construct the hydraulic geometry of the study area. Two-dimensional flood modeling was carried out in HEC-RAS 6.6, incorporating spatially differentiated Manning’s roughness values based on cadastral land-use classification maps. The modeling results were verified using satellite imagery from Landsat 7 by calculating the Normalized Difference Water Index for the 2018 flood event, which had a 4 % exceedance probability. The comparison showed a high degree of agreement, indicating that the simulated flood zone overlapped 87 % with the NDWI-derived water mask, and the total inundation area differed by less than 2 %. Model outputs such as flood depth, flow velocity, and cross-sectional profiles were visualized, and the resulting flood map was uploaded within a web-GIS platform. The study demonstrates a transparent and cost-effective methodology that can be applied to other river basins in Kazakhstan, offering a practical tool for spatial planning, risk mitigation, and early warning systems based entirely on publicly available data and software.

The Selyaninov hydrothermal coefficient (HTC) is widely used to characterize moisture conditions in agrometeorology. In arid regions of Central Asia, sparse observational networks increase reliance on reanalysis datasets; however, their applicability requires regional validation. This study presents the first comparative assessment of ERA5 and ERA5-Land for calculating the HTC in Eastern Kazakhstan over 1960–2024, using data from 28 Kazhydromet stations distributed from lowland steppes (200–660 m) to mountainous terrain (above 1000 m). Both products systematically overestimate HTC, primarily due to precipitation bias. Performance at lowland stations remains acceptable (median KGE ≈ 0.55, MAE ≈ 0.09–0.17), whereas in mountainous areas, KGE becomes negative. Nevertheless, interannual HTC variability is reasonably captured (Spearman’s correlation = 0.50–0.80). ERA5-Land provides only a moderate improvement over ERA5 in lowlands; in mountainous areas, its higher resolution offers no clear advantage, and both datasets require bias correction. Trend analysis reveals no consistent direction in moisture changes from Kazhydromet observations, while both reanalyses indicate aridification, with sign agreement at only 13 out of 28 stations. The results show that using ERA5/ERA5-Land as proxies in climate studies requires accounting for bias propagation, while their application in CMIP6-based regional projections remains a subject for future research.

This study presents a dataset of meteorological and soil-hydrological instrumental observations collected at three agrometeorological stations in the East Kazakhstan Region during the growing seasons of 2022–2025. The dataset includes time series from automatic weather stations: WS “OCES-1” (Solnechnoe village) provides hourly data over four years (2022–2025; 14,614 records; 65 variables), while WS “OCES-2” (Lugovoe village; 203,279 records) and WS “Altyn Kazan” (Sulusary village; 207,115 records) provide minute-resolution data for 2025 (49 variables each). Measured parameters at 200 cm height include air temperature and humidity, atmospheric pressure, precipitation, wind speed and direction; soil measurements down to 100 cm depth include temperature and moisture. Also, field-based express measurements of volumetric soil moisture within a 1 m profile (every 10 cm) were collected during three campaigns (May–August 2025), resulting in a total of 253 measurements. The stations are located across steppe and forest-steppe landscapes of the transboundary Altai–Sayan mountain region on active agricultural lands under diverse soil–climatic conditions. Climate types correspond to Dfb and Dfa per the Köppen–Geiger classification. Soils are classified under WRB as Chernozems and Calcic Chernozems. The dataset is published in CSV format on Zenodo under a CC-BY 4.0 license.

In this work, we present the Snow Depth and Snow Water Equivalent Dataset for specific areas located in the East Kazakhstan Region that can be exploited to monitor and understand water resource dynamics in mountain regions. The present dataset represents a georeferenced collection of snow depth, snow density, and derived snow water equivalent (SWE) measurements obtained through manual snow surveys. Snow survey observations were conducted during field campaigns in the East Kazakhstan Region during the period of maximum snow accumulation from 27 February to 6 March 2025. Snow survey sites were selected to maximize coverage of diverse landscape settings and snow accumulation conditions. In total, 111 snow survey sites were established across the East Kazakhstan Region, and 2331 snow depth measurements and 555 snow density measurements were collected. In post-field (laboratory) processing, snow water equivalent (SWE) was calculated for all snow survey sites based on measured snow depth and snow density values.

Based on data regarding the snow water equivalent, daily average air temperatures, and daily precipitation values obtained from meteorological stations in the East Kazakhstan Region of the Republic of Kazakhstan, calculations of the temperature coefficients of snowmelt have been performed. The study was based on long-term observations, which increases the reliability of the identified patterns. The analysis allowed for the identification of the features of both spatial and temporal differentiation of this coefficient, as well as the establishment of possible causes for its variations. Probable errors arising during the calculations were identified, and the prospects for using the obtained results to determine the maximum snow water reserves in the study basin were considered. It was established that the nature of snowmelt is influenced not only by daily average temperatures and precipitation amounts. This underlines the need to further expand the range of parameters considered and to improve the methodological approaches to estimating snow water reserves. The conclusions drawn may prove useful for water resource planning and flood forecasting under changing climatic conditions, as well as for optimizing the region’s hydrological models.

Floods are among the most frequent and devastating natural disasters, causing significant economic damage and loss of life worldwide. Effective flood risk management relies on accurate modeling techniques that can predict vulnerable areas and assess potential impacts. In this study, flood dynamics are simulated in the Irtysh River Basin near Ust-Kamenogorsk, a city in East Kazakhstan prone to seasonal flooding using high-resolution satellite imagery and digital elevation data. The primary objective is to visually model flood risks based on terrain characteristics. The study utilizes imagery sourced from the Mapbox platform, which combines data from MODIS, Landsat 7, Maxar, and the Google Earth Engine, providing access to Sentinel-2 surface reflectance imagery at 10-meter resolution. Elevation data from the Copernicus global digital elevation model, with a 30-meter resolution, is used to simulate flood progression. The flood simulation involves calculating flood depth relative to the terrain’s elevation, allowing for a pixel-by-pixel determination of submerged areas. Each simulation incrementally increases water levels to generate a sequence of images, showcasing the progression of flooding over time. The study describes hydraulic soil characteristics usage, and focuses on visualizing flood risk based on terrain data and water level changes. The simulation results indicate that flooding initially impacts riverbanks as water flow starts from the northwest of the city with critical infrastructure becoming vulnerable once water levels exceed 2 meters from the lowest elevation point. These findings highlight the potential of high-resolution satellite imagery and terrain data for flood risk assessment and improving urban flood preparedness. The results provide valuable insights into flood progression enabling more informed decision-making for disaster mitigation.