Is Precipitation Responsible for the Most Hydrological Model Uncertainty?

作者：András Bàrdossy, Chris Kilsby, Stephen Birkinshaw, Ning Wang, Faizan Anwar · 发表于：Frontiers in Water · 年份：2022 · DOI：10.3389/frwa.2022.836554 · 被引用次数：60 · 研究领域：Hydrology and Watershed Management Studies、Flood Risk Assessment and Management、Hydrology and Drought Analysis

Rainfall-runoff modeling is highly uncertain for a number of different reasons. Hydrological processes are quite complex, and their simplifications in the models lead to inaccuracies. Model parameters themselves are uncertain—physical parameters because of their observations and conceptual parameters due to their limited identifiability. Furthermore, the main model input—precipitation is uncertain due to the limited number of available observations and the high spatio-temporal variability. The quantification of model output uncertainty is essential for their use. Most approaches used for the quantification of uncertainty in rainfall-runoff modeling assign the uncertainty to the model parameters. In this contribution, the role of precipitation uncertainty is investigated. Instead of a standard sensitivity analysis of the model output with respect to the input variations, it is investigated to what extent realistic precipitation fields could improve model performance. Realistic precipitation fields are defined as gridded realizations of precipitation which reproduce the observed values at the observation locations, with values which reproduce the distribution of the observed values and with spatial variability the same as the spatial variability of the observations. The above conditions apply to each observation time step. Through an inverse modeling approach based on Random Mixing precipitation fields fulfilling the above conditions and reproducing the discharge output better ...