Appropriate spatiotemporal scale selection for water use simulation in China

Abstract

Water use simulation plays a pivotal role in water resource management globally. Simulating water use at regional raster scale enables better alignment with available water resources, facilitating efficient allocation. However, there remains a deficiency in methods of spatiotemporal scale selection for ensuring the simulation accuracy while also guaranteeing the information density at each raster scale. A novel framework has been proposed to select the appropriate spatiotemporal scales for water use simulation. The framework utilizes an iterative input variables selection (IIS) algorithm to identify optimal input variables for water use simulation and an end-to-end deep learning-based spatiotemporal scale adaptive selection (SSAS) model to determine the appropriate spatiotemporal scales. Due to China's substantial population, water demand, and the growing challenges of global warming, the country is particularly susceptible to water scarcity. The proposed framework was applied to select the appropriate spatiotemporal scales for simulating irrigation, domestic, and industrial water use across 341 prefectures in China. The results indicate that the appropriate spatial scales for irrigation water use simulation range from 1 km to 5 km in most places, while they vary from 1 km to 4 km for domestic and industrial water use simulation. Furthermore, the appropriate temporal scale generally spans from 10 days to 45 days for all three types of water use simulation. It is interesting to find that the simulation accuracy is significantly impacted by the selection of appropriate temporal scales through the parameter sensitivity analysis. Our proposed framework supports water resource management and facilitates efficient water resource allocation to mitigate water scarcity.