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<p>Accurate forecasting of spring flood volume and low-flow discharge is essential for water resources management and flood risk mitigation, particularly in mountain basins with snow-dominated hydrology. This study proposes an enhanced statistical approach to predict these hydrological components by integrating both conventional hydrometeorological predictors and additional indicators derived from soil–water storage dynamics. The methodology is applied to the Buktyrma River basin (Kazakhstan), characterized by a spring–summer flood regime with prolonged runoff lasting over two months. Key predictors include cold-season precipitation, mean air temperature during the melt period, flood duration, and a newly introduced soil–water indicator defined as the difference between the discharge at the end of the previous flood and the minimum winter discharge. The proposed multiple regression models demonstrate improved predictive performance for both flood volume and low-flow discharge compared to conventional approaches. These results contribute to advancing hydrological forecasting capabilities under data-scarce conditions typical of Central Asia and provide a basis for operational implementation in other similar catchments. To analyze the relationships between hydrological and meteorological characteristics, correlation analysis, autocorrelation function (ACF), partial autocorrelation function (PACF), and principal component analysis (PCA) were performed. Correlation analysis revealed a moderate positive relationship between flood volume and cold period precipitation (r = 0.61). Autocorrelation analysis revealed the absence of a pronounced interannual dependence in the studied series. PCA results showed that the first principal component explains more than 80% of the data variance, confirming the significant role of precipitation in flood formation.The introduced predictor makes it possible to indirectly account for the depletion of soil moisture capacity in the catchment from the total water capacity of the catchment from the end of the previous year's flood to the beginning of the next flood.</p>

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Improved forecasting of spring flood volume and low-flow discharge in the Buktyrma river (Kazakhstan) using hydrometeorological and soil–water indicators

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  • 出版模式：

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  • 发表日期：

    2026年8月3日

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  • Serik B. Sairov,
  • Nurgalym T. Serikbay,
  • Javier Rodrigo-Ilarri,
  • Sayat K. Alimkulov,
  • Tursyn A. Tillakarim,
  • María-Elena Rodrigo-Clavero

摘要

Accurate forecasting of spring flood volume and low-flow discharge is essential for water resources management and flood risk mitigation, particularly in mountain basins with snow-dominated hydrology. This study proposes an enhanced statistical approach to predict these hydrological components by integrating both conventional hydrometeorological predictors and additional indicators derived from soil–water storage dynamics. The methodology is applied to the Buktyrma River basin (Kazakhstan), characterized by a spring–summer flood regime with prolonged runoff lasting over two months. Key predictors include cold-season precipitation, mean air temperature during the melt period, flood duration, and a newly introduced soil–water indicator defined as the difference between the discharge at the end of the previous flood and the minimum winter discharge. The proposed multiple regression models demonstrate improved predictive performance for both flood volume and low-flow discharge compared to conventional approaches. These results contribute to advancing hydrological forecasting capabilities under data-scarce conditions typical of Central Asia and provide a basis for operational implementation in other similar catchments. To analyze the relationships between hydrological and meteorological characteristics, correlation analysis, autocorrelation function (ACF), partial autocorrelation function (PACF), and principal component analysis (PCA) were performed. Correlation analysis revealed a moderate positive relationship between flood volume and cold period precipitation (r = 0.61). Autocorrelation analysis revealed the absence of a pronounced interannual dependence in the studied series. PCA results showed that the first principal component explains more than 80% of the data variance, confirming the significant role of precipitation in flood formation.The introduced predictor makes it possible to indirectly account for the depletion of soil moisture capacity in the catchment from the total water capacity of the catchment from the end of the previous year's flood to the beginning of the next flood.