Spatiotemporal Analysis of Soil Moisture Variability and Precipitation Response Across Soil Texture Classes in East Kazakhstan

Dmitry Chernykh, Roman Biryukov, Andrey Bondarovich, Lilia Lubenets, Anatoly Pavlenko, Kamilla Rakhymbek, Denis Revenko () and Zheniskul Zhantassova
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Dmitry Chernykh: Institute for Water and Environmental Problems, Siberian Branch of the Russian Academy of Sciences, Barnaul 656038, Russia
Roman Biryukov: Institute for Water and Environmental Problems, Siberian Branch of the Russian Academy of Sciences, Barnaul 656038, Russia
Andrey Bondarovich: Altai State University, Barnaul 656049, Russia
Lilia Lubenets: Institute for Water and Environmental Problems, Siberian Branch of the Russian Academy of Sciences, Barnaul 656038, Russia
Anatoly Pavlenko: Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan
Kamilla Rakhymbek: Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan
Denis Revenko: Astana IT University, Astana 010000, Kazakhstan
Zheniskul Zhantassova: Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk 070004, Kazakhstan

Land, 2025, vol. 14, issue 6, 1-20

Abstract: 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.

Keywords: soil moisture content; soil texture classes; ERA-5-land reanalysis; precipitation; East Kazakhstan region (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:6:p:1136-:d:1662342

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