یک مدل برنامه ریزی محدودیت شانس احتمالی مشترک چندگانه با تصادفی سمت چپ برای برنامه ریزی محصول تحت عدم قطعیت

The characteristics of the agricultural water management system is its great complexity and uncertainty as well as dynamic variations in the system components, which results in dynamic characteristics in optimizing the agricultural water allocation and crop area planning. In this study, an interval multistage joint-probabilistic left-hand-side chance-constrained programming (IMJLCP) model is developed for crop area planning in response to these issues. This method is derived from incorporating the techniques of multistage stochastic programming and joint-probabilistic left-hand-side chance-constrained programming within a general interval optimization framework. It can address uncertainties presented as both discrete intervals and probability distributions, and also reflect dynamic characteristics of the system conditions. Moreover, it can reflect randomness in the left-hand-side of the constraints and examine the reliability level of satisfying constraints at both joint and individual probabilities. The developed method is applied to a case study of dynamic agricultural water management and irrigated crop area planning in different growth stages in the middle reaches of Heihe River Basin, taking groundwater and surface water use into account. Six scenarios with different joint (i.e. p = 0.01, 0.05 and 0.1) and individual probabilities (i.e. same and increasing) of the irrigation quota are examined, and a multilayered scenario tree will be provided for a dynamic analysis in a planning horizon. The results indicate that different levels of constraints violation reflect the attitudes of managers to economic benefit and risk. Furthermore, it can help managers to identify desired decision alternatives in intra- and inter-seasonal water allocation among different crops in different subareas. This application makes it highly feasible to enhance the efficiency of irrigation water and ensure sustainable use of water resources, especially for the arid regions dominated by agriculture.