Intelligent Water Management System Under the Influence of Urbanization

Facing the uneven spatio-temporal distribution of water resources and the increasing water demands caused by population growth and industrial development, the water regulation of the Shimen Reservoir for Taoyuan has become a critical issue. This study aims to build an intelligent water allocation system in order to suitably make water regulation with flexible water transfer from irrigation sectors to industrial and municipal sectors for reducing water pressure in public sectors during drought periods. In addition, the farm ponds in Taoyuan are also considered as a back-up water resource in this study for enhancing the resilience of the intelligent water allocation system. This study first simulates the future water demands of Taoyuan for the period of 2015 and 2030 by using the system dynamics theory based on agricultural and industrial data as well as population statistics collected during 2005 and 2014. We next design nine water supply scenarios in response to the possible drought conditions in the future based on the ten-day inflow data collected from the Shimen Reservoir in three drought years (1977, 1984, 2002) and three initial reservoir storage capacities (50%, 40%, 30%) of these drought years. According to the simulation results of future water demand and supply, the M-5 rule curves are used to simulate the water shortage conditions during 2015 and 2030 while the non-dominated sorting genetic algorithm-II (NSGA-II) is used to search the minimal modified shortage index (MSI) and the maximal ratio of effective reservoir storage capacity. The results of the NSGA-II for these nine designed scenarios indicate that the improvement rates of the MSI (as compared to the MSI obtained from M5 rule curves) ranges between 6.9% and 24% while the averaged effective reservoir storage capacity ratio for ten-day periods reaches as high as 9.6%. When the back-up water resource of farm ponds in the study area is incorporated into the proposed intelligent water allocation system, the new results of the NSGA-II indicate that the improvement rates of the MSI and the averaged effective reservoir storage capacity ratio for ten-day periods will further reach as high as 35.5% and 1.9%, respectively. The results of this study demonstrate that the multi-objective reservoir operation strategy obtained from the NSGA-II with the back-up water resource of farm ponds can make effective water allocation in response to urban water demands and thus provide decision makers with reference guidelines in sustainable water resources management. We hope that the proposed intelligent water allocation system will pave the way to future research for integrated water resources management.