Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty

TitleHierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty
Publication TypeJournal Article
Year of Publication2016
AuthorsMoges, Edom, Demissie Yonas, and Li Hongyi
JournalWater Resources Research
Date Published04/2016
Abstract / Summary

In most water resources applications, any particular model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses were used to identify the presence of multiple dominant processes, and the adequacy of a single model, as well as to develop the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response. This article is protected by copyright. All rights reserved.

URLhttp://onlinelibrary.wiley.com/wol1/doi/10.1002/2015WR018266/abstract
DOI10.1002/2015WR018266
Funding Program: 
Journal: Water Resources Research
Year of Publication: 2016
Date Published: 04/2016

In most water resources applications, any particular model structure might be inadequate to capture the dynamic multi-scale interactions among different hydrological processes. Calibrating single models for dynamic catchments, where multiple dominant processes exist, can result in displacement of errors from structure to parameters, which in turn leads to over-correction and biased predictions. An alternative to a single model structure is to develop local expert structures that are effective in representing the dominant components of the hydrologic process and adaptively integrate them based on an indicator variable. In this study, the Hierarchical Mixture of Experts (HME) framework is applied to integrate expert model structures representing the different components of the hydrologic process. Various signature diagnostic analyses were used to identify the presence of multiple dominant processes, and the adequacy of a single model, as well as to develop the structures of the expert models. The approaches are applied for two distinct catchments, the Guadalupe River (Texas) and the French Broad River (North Carolina) from the Model Parameter Estimation Experiment (MOPEX), using different structures of the HBV model. The results show that the HME approach has a better performance over the single model for the Guadalupe catchment, where multiple dominant processes are witnessed through diagnostic measures. Whereas the diagnostics and aggregated performance measures prove that French Broad has a homogeneous catchment response, making the single model adequate to capture the response. This article is protected by copyright. All rights reserved.

DOI: 10.1002/2015WR018266
Citation:
Moges, E, Y Demissie, and H Li.  2016.  "Hierarchical mixture of experts and diagnostic modeling approach to reduce hydrologic model structural uncertainty."  Water Resources Research.  https://doi.org/10.1002/2015WR018266.