Unraveling the effect of inter-basin water transfer on reducing water scarcity and its inequality in China.

Securing water supply in the face of increasing water scarcity is one important challenge faced by humanity in sustainable development. Inter-basin water transfer is widely applied to provide water supply security in regions where water demand exceeds water availability. However, the effect of inter-basin water transfer on alleviating water scarcity and its inequality is poorly understood especially at the national scale. Based on a newly compiled database of inter-basin water transfer projects in China, here we report a first national assessment of their effect on securing water supply in different basins. We developed a number of indices to facilitate quantifying the effect of water transfer on water scarcity and its inequality. The capacity of inter-basin transfer projects has been steadily increased, which achieved ~48.5 billion m3 yr-1 by 2016 (equivalent to ~8% of the national water use). The results indicate that water transfer has impacted water supply of 43 sub-basins out of a total of 76 sub-basins, but it hardly changes a basin's water scarcity level (e.g., from water scarcity to low water scarcity). Approximately three quarters of people in China are affected by water transfer. More than a half of the national population (705 million) benefit from alleviated water scarcity, leading to the inequality coefficient reduced from 0.64 under natural water availability condition to 0.59 considering water transfer in 2016. However, 357 million people in water transfer source basins are subject to increased water scarcity, in which ~21% are from water stressed sub-basins. This study reveals for the first time water transfer induced water scarcity and inequality change across sub-basins in China, and highlights the challenges to secure water supply across basins.

A fuzzy inference method based on association rule analysis with application to river flood forecasting.

In this paper, a computationally efficient version of the widely used Takagi-Sugeno (T-S) fuzzy reasoning method is proposed, and applied to river flood forecasting. It is well known that the number of fuzzy rules of traditional fuzzy reasoning methods exponentially increases as the number of input parameters increases, often causing prohibitive computational burden. The proposed method greatly reduces the number of fuzzy rules by making use of the association rule analysis on historical data, and therefore achieves computational efficiency for the cases of a large number of input parameters. In the end, we apply this new method to a case study of river flood forecasting, which demonstrates that the proposed fuzzy reasoning engine can achieve better prediction accuracy than the widely used Muskingum-Cunge scheme.