Stormwater management modeling in "sponge city" construction: current state and future directions

In response to urban pluvial flooding and pollution, the Chinese government proposed a “sponge city” policy in 2013 that aims to improve urban stormwater management and promote sustainable urban development. However, at present, sponge city construction is still in its exploratory stage. It is still not clear which models are capable of simulating the six key processes (i.e., “retention,” “infiltration,” “storage,” “purification,” “discharge,” and “utilization”) of sponge city practices. Its various benefits (e.g., social, economic and environmental benefits) have not yet been systematically investigated in the context of the sponge city. In this study, we reviewed and compared 19 urban stormwater management models (including 13 hydrological models and 10 decision-support tools, as there are 4 overlap ones) and investigated their application in China. Firstly, we examined the mechanisms behind the hydrological models and compared the abilities of the models to simulate various processes. Secondly, we analyzed what kinds of benefits can be addressed by these decision support tools (DSTs). Finally, we discussed the applications and limitations of the models in various climate zones in China. The findings suggest that none of the models consider the impact of climate change on the sponge city practices (SCP) and none of DSTs can simulate the negative performance of SCP. Furthermore, the lack of sufficient databases in China limited the applications of many of the models. Additionally, we found that the hydrological processes corresponding to “storage” were given more attention in southern China, and “infiltration” of stormwater was of greater concern in northern China. In the context of sponge city construction, this paper provides suggestions for future model development of urban stormwater management in China, such as the development of a stormwater database and the incorporation of long-term climate change impacts into the model.