Hydrologic Performance Simulation of Green Infrastructures: Why Data-Driven Modelling Can Be Useful?

Abstract

Green infrastructures are decentralized semi-natural solutions for managing stormwater runoff. To assess their effectiveness in urban drainage, many process-based hydrological models have been developed. However, these models sometimes fail to deliver satisfactory results due to inadequate representations of the involved hydrological processes or due to limited field measurements for model setup. Data-driven modelling simulates directly the connections between the state variables of the system (such as the input variable and the output variable), and thus reduces the need for hydrological process characterization. Yet the usefulness of this modelling approach in green infrastructure related studies has not been fully explored. To demonstrate its effectiveness, two bioretention systems in the U.S. are studied. The designs of these two systems are unique, making them difficult to be modelled directly using the standard process-based models. In one site, the observations are also censored, i.e., only the overflow/no overflow binary outcomes are being monitored, with which the model calibration is inhibited. We showed that using the state-of-the-art data-driven modelling framework (e.g., CARET and MXNET), the occurrence or the outflow discharge rate of the bioretention systems at a given time can be accurately simulated only using the rainfall, evapotranspiration and the inflow time series. We conclude that data-driven modelling can be useful for simulating the hydrologic performance of green infrastructures, especially when the existing process-based models are inadequate in representing the design variabilities or the data for model setup is limited.