Abstract
Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. In this study, a two-stage stochastic fractional programming (TSFP) method is developed for planning an agricultural water resources management system under uncertainty. TSFP can provide an effective linkage between conflicting economic benefits and the associated penalties; it can also balance conflicting objectives and maximize the system marginal benefit with per unit of input under uncertainty. The developed TSFP method is applied to a real case of agricultural water resources management of the Zhangweinan River Basin China, which is one of the main food and cotton producing regions in north China and faces serious water shortage. The results demonstrate that the TSFP model is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors. Results also indicate that, under the optimized irrigation target, the optimized water allocation rate of Minyou Channel and Zhangnan Channel are 57.3% and 42.7%, respectively, which adapts the changes in the actual agricultural water resources management problem. Compared with the inexact two-stage water management (ITSP) method, TSFP could more effectively address the sustainable water management problem, provide more information regarding tradeoffs between multiple input factors and system benefits, and help the water managers maintain sustainable water resources development of the Zhangweinan River Basin.
Highlights
With speedy population growth and shift economic development, the constantly increasing demand for water in terms of sufficient quantities and satisfied qualities has forced researchers to draw optimal water resources management policies [1,2,3]
A two-stage stochastic fractional programming (TSFP) method has been developed for planning agricultural water resources management under uncertainty
The TSFP method can solve ratio optimization problems associated with stochastic information and reflect the marginal benefit
Summary
With speedy population growth and shift economic development, the constantly increasing demand for water in terms of sufficient quantities and satisfied qualities has forced researchers to draw optimal water resources management policies [1,2,3]. As the biggest consumer of the limited water resources, irrigated agriculture uses about 70% of the world’s freshwater withdrawals, and causes salinity, drainage and environmental quality problems currently affecting many parts of the world [4]. It is a challenge for agricultural water resources managers to maintain sustainable development under situations of increasing water demand [5]. The effective planning of agriculture water resources under such complexities and uncertainties is important for regional sustainable development in watershed systems
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