On the Propagation of Radar-rainfall Estimation Uncertainties Into the Simulation of Different Rainfall-runoff Processes

Recent advances in remote sensing of rainfall provide unprecedented opportunities for acquiring rainfall measurements with high spatial and temporal resolutions. In particular, the Next Generation Weather Radar (NEXRAD) system is a promising resource for improving the accuracy and reliability of hydrologic predictions and operational forecasting. However, rainfall estimates from radar measurements are subject to uncertainties caused by both instrumental effects and lack of unique relation between radar-rainfall estimations and the surface rainfall quantities. The effect of such uncertainties on the predictive ability of hydrologic models is an active area of research. This study will investigate the propagation of radar-rainfall estimation uncertainties into the simulation of different rainfall-runoff processes. The analysis is performed over a mid-size watershed that is heavily monitored by a dense network of rainfall and streamflow gauges. The study uses a physically-based distributed hydrological model (Gridded Surface Subsurface Hydrologic Analysis, GSSHA). The model will be calibrated and validated using several historical rainfall-runoff storms. Radar estimation errors will be first assessed in terms of their marginal error distribution and spatial and temporal auto-correlations. Then, radar- based runoff simulations will be assessed in comparison to simulations using data from the dense rain gauge network in the watershed. The study will examine the effect of radar-rainfall uncertainties on simulations of various processes such as infiltration, surface runoff, soil moisture, and streamflow. Then, a simulation-based stochastic model for the radar error will be developed based on its identified characteristics (i.e., marginal distribution and spatio-temporal correlations). This model will provide a tool for generating multiple realizations of the radar error field and enable examination of the probabilistic nature of the error propagation into runoff predictions.