Drought mitigation under urbanization through an intelligent water allocation system

Abstract

Due to the uneven spatio-temporal distribution of water resources and increasing water demands driven by urbanization, making effective water allocation is a critical and challenging issue nowadays. This study proposes an intelligent water allocation system (IWAS) to reduce the water pressure of multiple sectors and enhance the resilience of the water allocation system under urbanization during droughts. We first make forecasts on future water demands in response to urbanization for Taoyuan City in Taiwan by using system dynamics (SD) based on historical agricultural and industrial data as well as population statistics. We next design six water supply scenarios formed by a combination of ten-day inflow data collected from the Shihmen Reservoir in two drought years and three initial reservoir storages. The non-dominated sorting genetic algorithm-II (NSGA-II) is then used to search the minimal modified shortage index (MSI) and the maximal ratio of water storage to reservoir capacity (RWS) subject to future water demand forecasts under six designed drought scenarios. M-5 rule curves take care of water shortage simulation and serve as a benchmark. The results of the NSGA-II indicate that the improvement rates of MSI and RWS can reach up to 24% and 9.6%, respectively, as compared to those of M-5 rule curves. Furthermore, when we incorporate a great number of irrigation ponds spreading over the study area into the NSGA-II model, the improvement rates of MSI and RWS can increase by at most 35.5% and 1.5%, respectively, as compared to those of the NSGA-II without incorporating irrigation ponds. The results demonstrate that the proposed IWAS can greatly improve the effectiveness and advantages of water allocation, especially when irrigation ponds are considered as a water supply source, in response to future urban water demands under drought scenarios, and therefore provide decision makers with apt reference guidelines for sustainable water resources management.