Assessing Parameter, Precipitation, and Predictive Uncertainty in a Distributed Hydrological Model Using Sequential Data Assimilation with the Particle Filter

摘要

Sequential data assimilation techniques offer the possibility to handle different sources of uncertainty explicitly in hydrological models and hence improve their predictive capabilities. Amongst the different techniques, sequential Monte Carlo or particle filter methods offer the capability to handle nonlinear/non-Gaussian state-space models while preserving the spatial variability of updated state variables, both desirable features when assimilating data in distributed hydrological models. In this work we apply the residual resampling particle filter to assess parameter, precipitation, and predictive uncertainty in the distributed rainfall-runoff model LISFLOOD. We compare estimated posterior parameter distributions with results of the Shuffled Complex Evolution Metropolis global optimization algorithm obtained using identical input data for the Meuse catchment. Both approaches result in well identifiable posterior parameter distributions and provide a good fit to the observed hydrograph. The resulting posterior distributions, however, vary considerably in shape, location and scale. This illustrates that the concept of equifinality not only applies to simple conceptual models but is also valid for complex, physically-based, distributed models. We show that considering additionally precipitation uncertainty not only increases the spread of the posterior parameter distributions but may also result in a completely different location parameter and/or shape of the distributions. The analysis of precipitation uncertainty reveals that there is no systematic bias in the precipitation grids. In comparison to other studies where a uniform precipitation is applied, the posterior precipitation error variance is significantly reduced when accounting for spatial variability. Furthermore, considering precipitation and parameter uncertainty leads to an improvement in predictive capabilities. However, results also indicate that model structural uncertainty may be equally important, in spite of using a physically-based distributed hydrological model that should theoretically provide an improved description of the hydrological system dynamics.