1
Return

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

delete2026-08-03
delete0
delete
OA
AI
S
Serik Sairov
N
N.T. Serikbay
J
Javier Rodrigo-Ilarri *
S
Sayat Alimkulov
T
Tursyn A. Tillakarim
M
María-Elena Rodrigo-Clavero
DOI:10.1007/s13201-026-02930-xdelete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
Snowmelt-driven runoff
Hydrometeorological predictors
Mountain river basins
Buktyrma river
Kazakhstan

Journal

A
Applied Water Science
IF:
5.7
Papers:
2.2K
Citations:
1.2W

Organization

I
Institute of Geography and Water Security
Scholars:
48
Papers: 13
Citations: 0
R
research center
Scholars:
362
Papers: 176
Citations: 0
Cited Papers

Cited Papers

Citing Papers

Citing Papers