Abstract
AbstractIn hydrological modeling, the identification of model mechanisms best suited for representing individual hydrological (physical) processes is of major scientific and operational interest. We present a statistical hypothesis‐testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a “dominant” mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms. The uncertainty in the test statistic is approximated using bootstrap sampling from the model ensemble. Synthetic experiments (with varying error magnitude and multiple replicates) and real data experiments are conducted using the hydrological modeling system FUSE (7 processes and 2–4 mechanisms per process yielding 624 feasible model structures) and data from the Leizarán catchment in northern Spain. The mechanism identification method is reliable: it identifies the correct mechanism as dominant in all synthetic trials where an identification is made. As data/model errors increase, statistical power (identifiability) decreases, manifesting as trials where no mechanism is identified as dominant. The real data case study results are broadly consistent with the synthetic analysis, with dominant mechanisms identified for 4 of 7 processes. Insights on which processes are most/least identifiable are also reported. The mechanism identification method is expected to contribute to broader community efforts on improving model identification and process representation in hydrology.
Highlights
Predictions of streamflow and available water resources are important scientifically and operationally
We present a statistical hypothesis-testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a “dominant” mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms
The development of hydrological models that provide an accurate representation of catchment dynamics and produce accurate streamflow predictions represents a formidable model identification challenge
Summary
Predictions of streamflow and available water resources are important scientifically and operationally. Such predictions are typically obtained using hydrological models of varying degrees of complexity. Models help to understand and communicate catchments functioning and internal process dynamics (e.g., Wheather et al, 1993; Beven, 2010; Gupta et al, 2012). Hydrological processes are approximated using “hydrological mechanisms,” i.e., sets of equations intended to describe that process. A single hydrological model represents a combination of mechanisms (one per hydrological process). Models represent working hypotheses of the catchment they are applied to, and mechanisms represent working hypotheses of the hydrological processes they are intended to represent
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
