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.

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks.

The assessment of the maximum snow-water equivalent in mountains is important for understanding the mechanism of their formation, as well as for hydrological calculations. The low density of the observation network and the high complexity of ground-based snow-measuring operations have led to the widespread use of remote methods to obtain such data. In this study, the maximum water reserve of the Uba River basin was calculated for the period of 2020–2023, based on data from the Sentinel-2 satellite regarding the position of the seasonal snow line, obtained using the temperature-based melt-index method. This study determined the snowmelt coefficients for the meteorological stations at Zmeinogorsk, Shemonaikha, and Ridder. Maps were constructed to show the distribution of the maximum snow-water equivalent in the Uba River basin. The spatial differentiation features of the snow cover were revealed, depending on the elevation, slope exposure, and distance from the watersheds. It was established that the altitudinal distribution of snow cover on the northern and southern macro-slopes of the ridges is asymmetric: in the western part of the basin, within the elevation range of 500–1200 m, the maximum water reserves of snow cover are greater on the southern slopes, but they become higher on the northern slopes above 1200 m. In the eastern part of the basin, they are always larger on the northern slopes. The greatest differences in the distribution of snow cover between the slopes occur near the watersheds.

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.

This paper presents a study on designing and implementing a series of GIS assignments for an educational course on flood mapping, structured using Bloom’s Taxonomy and the scaffolding teaching method. Geographic Information Systems (GIS) education often involves the acquisition of complex technical skills, requiring a structured learning approach to ensure a progressive mastery of concepts. In this study, a sequence of practical assignments was developed at increasing levels of complexity corresponding to Bloom’s cognitive levels, from basic knowledge acquisition to higher-order evaluation tasks. The scaffolding approach was utilized to facilitate student learning, wherein extensive guidance was provided in early tasks and gradually removed in later ones as students gained competence. The research was conducted in an upper-level undergraduate course, “Methodology for Mapping Flood Emergency Areas”, at the Sarsen Amanzholov East Kazakhstan University, with 21 enrolled students. The assignments integrated real-world flood mapping scenarios using GIS tools such as ArcGIS Pro and QGIS, enabling students to apply theoretical knowledge in practical settings. Results from the study indicated that a structured, scaffolded approach significantly improved student performance and confidence in GIS skills. Quantitative analysis of assignment grades showed steady improvement as students progressed to more complex tasks, while qualitative feedback revealed high engagement and perceived learning value. The findings underscore the effectiveness of combining Bloom’s Taxonomy with scaffolded instruction in GIS education, providing a practical framework for curriculum design. This approach has the potential to enhance learning outcomes in technical subjects, particularly in geospatial analysis, and offers recommendations for educators on implementing scaffolded assignments effectively. Further research could explore long-term skill retention and the application of this methodology in other technical disciplines.

Статья посвящена сбору доступной информации о водных объектах Восточно-Казахстанской области, полученной из открытых источников Национальной гидрометеорологической службы Республики Казахстан и Министерства по чрезвычайным ситуациям Республики Казахстан с целью формирования базы атрибутивных данных с пространственной привязкой в ГИС и формированию основы последующих исследований, направленных на разработку и внедрение моделей прогнозирования наводнений. Проведен обзор исследований, посвященных прогнозу наводнений в разных регионах Казахстана и в других странах, в результате чего сформированы критерии оценки данных и системы гидрологического мониторинга. Приводится характеристика гидрологического режима основных репрезентативных водных объектов Восточного Казахстана. На основе пространственного анализа сети гидрологических постов и известных зон затопления дана оценка пространственному охвату государственной системы наблюдений и определены рекомендации по ее расширению. Сделаны выводы о возможности применения собранных данных при создании прогнозных моделей.