Data for:"FRESF model: An ArcGIS toolbox for rapid assessment of the supply, demand, and flow of flood regulation ecosystem servicess"|生态系统服务评估数据集|洪水管理数据集

River floods are globally the most damaging and costliest natural disasters. The functions of nonstructural flood mitigation measures (e.g., water retention by vegetation and soil) provide critical regulation ecosystem services to human beings. For example, the Sponge Cities are national watershed management projects in China to increase the nonstructural flood regulation services, aiming to enable the urban ecosystem to absorb, infiltrate, and retain storm water to mitigate pluvial flooding. Decision-makers need to acquire critical areas for the supply, demand, and flow of flood regulation ecosystem services, which would affect the delivery of services and the distribution of any benefits among stakeholders. However, relatively few empirical studies present this information. One reason is the lack of appropriate methods and tools for a rapid assessment of flood regulation ecosystem services that do not require substantial resources or rely heavily on hydrological process data. This article presents the Flood Regulation Ecosystem Service Flow (FRESF) model to address this gap. FRESF is an open-access and open-source ArcGIS script toolbox for an integrated evaluation of flood regulation ecosystem services' supply, demand, and flow. Based on a land surface process model and a generic framework quantifying ecosystem services flow, FRESF provides a rapid and serviceable assessment framework for the relevant ecosystem services at a relatively low cost and effort. The validation results suggest that the outputs are moderately consistent with the actual flood conditions, compared with the flood disaster of the Yangtze River in 2020. Its open codes and flexible inputs imply that ecologists, planners, decision-makers, and developers involved in ecosystem services would have the opportunity to test and apply the FRESF model in several contexts and even contribute further improvements.