Statistical Analysis of Radar Rainfall Error Propagation

Abstract

The prediction uncertainty of a hydrologic model is closely related to model formulation and the uncertainties in model parameters and inputs. Currently, the foremost challenges concern not only whether hydrologic model outputs match observations, but also whether or not model predictions are meaningful and useful in the contexts of land use and climate change. The latter is difficult to determine given that model inputs, such as rainfall, have errors and uncertainties that cannot be entirely eliminated. In this paper the physically based simulation methodology developed by Sharif et al. is used to expand this investigation of the propagation of radar rainfall estimation errors in hydrologic simulations. The methodology includes a physics-based mesoscale atmospheric model, a three-dimensional radar simulator, and a two-dimensional infiltration-excess hydrologic model. A time series of simulated three-dimensional precipitation fields over a large domain and a small study watershed are used, which allows development of a large set of rainfall events with different rainfall volumes and vertical reflectivity profiles. Simulation results reveal dominant range-dependent error sources, and frequent amplification of radar rainfall estimation errors in terms of predicted hydrograph characteristics. It is found that in the case of Hortonian runoff predictions, the variance of hydrograph prediction error due to radar rainfall errors decreases for all radar ranges as the event magnitude increases. However, errors in Hortonian runoff predictions increase significantly with range, particularly beyond about 80 km, where the reflectivity signal is increasingly dominated by three-dimensional rainfall heterogeneity with increasing range under otherwise ideal observing conditions.