Statistical Hypothesis Testing to Identify Dominant Mechanisms in Ungauged Catchments

Código

I-EBHE0106

Autores

Cristina Prieto

Tema

WG 1.10: Hydrologic Design - Solutions & Communication

Resumo

Hydrological modelling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrology. A major challenge is to identify a model-structure that reflects the hydrological processes relevant to the catchment of interest. Paraphrasing a well-known adage, ?all models are wrong, but some model-mechanisms (process representations) might be useful.? This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest.? We extend a method previously introduced for mechanism identification in gauged basins, by formulating the Bayesian inference equations in the space of (regionalized) flow indices principal components and by accounting for posterior parameter uncertainty. We use a flexible hydrological model to generate candidate mechanisms and model-structures. Then, we use statistical hypothesis testing to identify the "dominant" (more a posteriori probable). We assume that the error in the regionalization of flow indices principal components dominates the error of the hydrological model structure. The method is illustrated in 92 catchments from northern Spain. We treat 16 out of the 92 catchments as ungauged. We use 624 model-structures from FUSE (flexible hydrological model framework). The case study includes real data and synthetic experiments. The probability o making an identification (correct or wrong), remains stable at ~25%, both in the real and in the synthetic experiments. In synthetic experiments, where the ?true? mechanism is known, we can evaluate the reliability, i.e., the probability of identifying the true mechanism when the method makes an identification. Reliability varies between 60%-95% depending on the magnitude of the combined regionalization and hydrological error. Routing is among the most identifiable processes, whereas percolation and unsaturated zone processes are the least identifiable. The study contributes perspectives on hydrological mechanism identification under data-scarce conditions. Prieto et al. (2022) An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments,?WRR,?58(3), doi:10.1029/2021WR030705.