Identification of nonlinearity in rainfall-flow response using data-based mechanistic modeling

Abstract

Data-based mechanistic (DBM) modeling is an established approach to time series model identification and estimation, which seeks model structures and parameters that are both statistically optimal and consistent with plausible mechanistic interpretations of the system. This paper describes the application of the DBM method to 10 min, relatively high precision, rainfall-flow data, including observations of both surface flow and subsurface flow. For a generally wet winter period, the preferred surface flow model is nonlinear in flow generation and linear in routing, while the preferred subsurface flow model is linear in flow generation and nonlinear in routing. These models have mechanistic interpretations in terms of mass balance, hydrodynamics, and conceptual flow pathways. The four-parameter surface and subsurface flow models explain 91% and 96% of the variance of the corresponding observations. Other plausible models were identified but were less parsimonious or were more reliant on prior perceptions. For a wet summer validation period, the models performed as well as in the calibration period; however, when a long dry spell was included, the performance deteriorated. It is speculated that this is because of complex wetting-drying dynamics and potential nonstationarity of the soil properties that are not sufficiently revealed in the available data. Conceptual models informed by the DBM results matched the DBM model performance for subsurface flow but gave poorer performance for the more complex surface flow responses. It is concluded that the DBM method can identify nonlinearity in both flow generation and routing and provide conceptual insights that can go beyond prior expectations