Improving the efficiency of water distribution using digital technologies in the context of variable water resources in the Almaty region

Abstract

Abstract
Background and Aim. Climate change, water scarcity, increasing agricultural demand for irrigation water, and the deterioration of hydraulic infrastructure necessitate improvements in water distribution management. This issue is particularly relevant to the Almaty region due to its dependence on the transboundary flow of the Ili River and interannual variability in water availability. Traditional water
distribution schemes lack the flexibility required under conditions of hydrological uncertainty. This study aimed to assess water distribution efficiency and develop an approach to adaptive irrigation system management based on digital technologies and economic-mathematical modelling.
Materials and Methods. The study was based on statistical data on water use and agricultural production in the Almaty region for 2010–2024, as well as materials from the Ministry of Water Resources and Irrigation of the Republic of Kazakhstan. Descriptive statistics, hydrological time-series
analysis, and correlation analysis were applied. The coefficient of variation was used to calculate the interannual variability of river runoff, while the relationship between water losses and water distribution efficiency was evaluated through the incorporation of Pearson’s correlation coefficient and the coefficient of determination. An economic-mathematical model for the stochastic optimization of water distribution was developed, accounting for available water resources, water users’ demands, delivery efficiency, and economic losses caused by water deficits.
Results. Specific water use for regular irrigation decreased by 22.0%, water conveyance losses declined from 38% to 25%, and the coefficient of water distribution efficiency increased from 0.62 to 0.77. A strong inverse relationship was identified between water losses and water distribution efficiency (r = –0.89; R² = 0.79). The coefficient of variation of river runoff was 11.8%, confirming the need to account for hydrological uncertainty. Within the developed model, low-water, average-water, and highwater availability scenarios were formulated based on river runoff variability. The model enables the recalculation of water supply volume Qᵢ considering the available resource W(ω), water demand Dᵢ,
delivery efficiency ηᵢ, and economic losses under changing hydrological conditions.
Conclusion. The findings substantiate the transition from fixed normative water allocation to scenario-based adaptive water distribution management. Integrating the economic-mathematical model with GIS, IoT, SCADA, and a digital twin enables the updating of input parameters and recalculation of water supply regimes according to actual and hydrological conditions.