Rainfall-runoff model parameter estimation and uncertainty evaluation on small plots

Four seasonal rainfall simulations in 2009 and 2010 were applied to a field containing 36 plots (0.75x2m each), resulting in 144 runoff events. In all simulations, a constant rate of rainfall was applied then halted 60min after initiation of runoff, with plot-scale monitoring of runoff every 5min during that period. Runoff was simulated with the Kinematic Runoff and Erosion/Simulator of Transport with Infiltration and Runoff (KINEROS2/STWIR) field-scale model, whose hydrodynamics are based on the kinematic wave equation. Because of the non-linear nature of the model and a highly parameterized model with respect to the available data, several approaches were investigated to upscale nine runoff-related parameters from a series of small monitored plots to the field scale. Inverse modeling was performed using the model-independent Parameter ESTimation (PEST) algorithm to individually calibrate the nine KINEROS2/STWIR parameters on 36 plots. The parameters were averaged, and bootstrapping was used to assess uncertainty of the parameters via estimation of confidence intervals (CI). A Monte Carlo simulation using the bootstrap results showed reasonable field-scale representation of flow rates. Median values of calibrated parameters were within the 95% CI obtained with bootstrapping. The simulated results for the median values associated with the 90% CI flow rates produced similar trends as those exhibited with the observed data, suggesting that median values of the calibrated parameters from the PEST inverse modeling could be used to represent the field scale. Copyright (c) 2013 John Wiley & Sons, Ltd.