Risk regulation of water allocation in irrigation areas under changing water supply and demand conditions

The uncertainty of the hydrological environment and unbalanced water resource allocation result in a high risk of irrigation water shortages in regional agriculture, which seriously affects the sustainable development of agricultural systems. In this paper, we propose a risk regulation based modeling approach for the optimal allocation of agricultural water resources in a complex stochastic environment. The approach includes a conditional value-at-risk (CVaR) model, two-stage stochastic programming (TSP) model, two-dimensional joint distribution probability (JP) model, fractal criteria, and a multiple forms of chance-constrained programming (CCP) model. The model can weigh the contradiction between the intended target and associated penalties attributed to unknown hydrological events, measure the risk between system benefits and expected losses in agricultural water allocation at different confidence levels, and address the randomness in the objective function and constraints (including the left end term, right end term, and left and right end terms). To verify the applicability of the method, it is applied to the Jinxi Irrigation District in China to optimize the allocation and risk regulation of limited water resources under the variable runoff conditions of the Songhua River and crop water demands in the irrigation area. By adjusting parameters such as risk preference and probability of violation, the risk of water shortages in the irrigation area can be regulated, and the multidimensional impacts of different water allocation schemes on agricultural economic benefits, social benefits, ecology and environment can be determined. The case study reveals that the CTSP-CCJP method is sensitive, applicable to complex and uncertain environments and important for the efficient use of agricultural water resources and risk reduction.

Automated summary

This paper proposes a risk regulation based modeling approach for the optimal allocation of agricultural water resources. By considering uncertainty and randomness, the approach helps decision-makers evaluate and manage the risk of irrigation water shortages and determine the optimal water allocation scheme.