Modeling Water Trading under Uncertainty for Supporting Water Resources Management in an Arid Region

Abstract

In this study, a joint-probabilistic interval multistage programming (JIMP) method is developed for planning water resources management under uncertainty. The JIMP method can tackle uncertainties presented in terms of interval parameters in objective function and constraints, in addition to random variables in the left and right-hand sides of constraints. It can also reflect the dynamics in terms of decisions for water resources allocation through transactions at discrete points of a complete scenario set over a multistage context. Moreover, the JIMP method can be used for analyzing various policy scenarios that are associated with different levels of economic consequences when the promised water-allocation targets are violated. The JIMP method is applied to a real case of planning water trading for supporting the regional sustainable development of the Kaidu-Qongque River basin, which is one of the most arid regions of China. Monte Carlo simulation is introduced into the JIMP framework for evaluating the probability distributions of the water-trading ratio. Results of water-trading amount, water-allocation pattern, and system benefit under different probabilities have been obtained, which reveals that the water-trading scheme is an effective manner to allocate limited water resources with a maximized system benefit in such an arid region. However, the results disclose that a variety of factors such as trading ratio, recycling ratio, trading cost, and water availability have significant effects on the water-allocation pattern and system benefit. Results also show that the market approach can help mitigate water shortage in such an arid region; however, enormous deficits would still occur (particularly for agriculture) as a result of excessive exploration of human activity and overexpansion of cultivated land, which has had adverse effects on the socio-economic development of such an arid region. These findings can help decision makers to adjust the water-resources allocation policy and trading-market behavior pattern to support sustainability in arid regions.