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