The study of the hydrological regimes of rivers in different regions of the globe has revealed the need to include the soil moisture content in flood prediction models. This paper investigates the nature of the dependence of soil moisture content on soil texture in the East Kazakhstan region. Data from ERA-5-land reanalysis, soil maps, hydrogeological maps, and the meteorological data of Kazhydromet were used. The years for analysis were selected due to their different moisture conditions. This study analyzed soil moisture within the root zone (0–28 cm depth). A JavaScript-based algorithm was developed in Google Earth Engine to analyze soil moisture and total precipitation across five Soil Texture Index categories during the growing seasons (April–September) of 2013, 2022, and 2023. Final cartographic processing and spatial distribution analysis were conducted using ESRI ArcGIS Pro 3.3. The study of soil moisture’s relationship with different soil textures in the East Kazakhstan region has revealed several key trends. The maximum values of soil moisture for each texture class change very slightly from year to year. The minimum soil moisture values fluctuate more strongly from year to year. The regression analysis demonstrates a statistically significant relationship between precipitation and soil moisture. The best performance is achieved when using a 1-day lag for 2013 and varying optimal lags for 2022 and 2023 (ranging from 1 to 3 days) during the high-precipitation period (months 6–9), with filtering applied to remove days with negligible rainfall.

The assessment of the hydrological functions of landscapes and the landscape–hydrological background is an important instrument for minimizing damage from rivers and preventing water conflicts under conditions of data scarcity for hydrological modeling. To assess the climate–hydrological background of the East Kazakhstan region, the Selyaninov Hydro-thermal Coefficient and the Vysotsky–Ivanov Moisture Coefficient were used. The East Kazakhstan region is a typical continental arid and semi-arid region. The presence of mountain ranges, such as the Altai, makes the climate and environment in the region highly varied. A dataset from 30 weather stations for the period 1961–2023 was used for calculations. Three interpolation methods and landscape extrapolation were used to construct maps of the coefficients. Over the observation period, the values of the moisture indices at the weather stations in the region fluctuated within a wide range. Both coefficients are in the range from extra arid to extra humid climates.

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.

Existing hydrologic landscape classifications, such as Hydrologic Landscape Regions (HLRs) and Hydrologic Response Units (HRUs), group catchments by similarity of climate, geology, and topography, but do not directly link this classification to runoff-generation function, limiting their use for ranking hydrological activity in data-sparse mountain regions. The concept of a landscape–hydrological background, integrating climatic–hydrological, soil–hydrological, and topo–hydrological components, was used as the basis for a classification framework applied to 15 model catchments in the East Kazakhstan Region. The proposed approach provides a functional representation of landscape organization by linking environmental characteristics directly to runoff-generation processes and hydrological activity. The analysis revealed two distinct hydrological domains: a runoff-generating mountain domain (Bukhtarma, Uba, and Ulba), where highly active hydrological landscapes occupy 55.2–63.9% of the catchment area, and a transitional mountain-foothill domain (Narym, Kurchum, and Kalzhyr), where their share decreases to 18.0–25.4%. The highest landscape diversity, with Shannon indices of 1.31–1.53, occurs in the transitional catchments, reflecting the most balanced combination of functional groups. This study demonstrates that hydrological activity is controlled by the combined influences of catchment size, relief differentiation, and the spatial extent of high-mountain environments rather than by elevation alone. The proposed framework has practical applications for watershed management, water resources planning, environmental monitoring, and climate change adaptation, and is particularly valuable for data-sparse mountain regions.