Quantifying Dynamic Linkages Between Precipitation, Groundwater Recharge, and Runoff using Ensemble Rainfall-Runoff Analysis

Understanding streamflow generation at the catchment scale requires quantifying how different components of the system are linked, and how they respond to meteorological forcing. Here we use a data-driven nonlinear deconvolution and demixing approach, Ensemble Rainfall-Runoff Analysis (ERRA), to characterize and quantify dynamic linkages between precipitation, groundwater recharge, and streamflow in a mesoscale intensively farmed catchment. Streamflow in this catchment is flashy, but occurs at time lags that are too long to be plausibly attributed to overland flow runoff. Instead, the impulse responses of groundwater recharge to precipitation, and of streamflow to groundwater recharge, imply that this intermittent runoff is primarily driven by precipitation infiltrating to recharge groundwater, followed by linear-reservoir discharge of groundwater to streamflow. Streamflow increases nonlinearly with increasing precipitation intensity or groundwater recharge, and exhibits almost no runoff response to precipitation or recharge rates of less than 10 mm d−1. Groundwater recharge is both nonlinear, increasing more-than-proportionally with precipitation intensity, and nonstationary, increasing with antecedent wetness. Simulations with the infiltration model Hydrus-1D reproduce the observed water table time series reasonably well (NSE=0.70). However, the model’s impulse response is inconsistent with the observed impulse response estimated from measured precipitation and groundwater recharge, illustrating that goodness-of-fit statistics can be weak tests of model realism. Our analysis demonstrates how impulse responses estimated by ERRA can help quantify nonlinearity and nonstationarity in hydrologic processes, and can help clarify the mechanistic linkages between precipitation and streamflow at the catchment scale.