Optimizing spatial distribution of watershed-scale hydrologic models using Gaussian Mixture Models. (August 2021)

• Record Type: Journal Article
• Authors: Maurer, Tessa; Avanzi, Francesco; Oroza, Carlos A.; Glaser, Steven D.; Conklin, Martha; Bales, Roger C.
• Is Part Of: Environmental modelling & software. Volume 142(2021)
• Publication Date: 2021-08
• Subjects: Physically based hydrologic models -- Spatial distribution -- Gaussian mixture models -- Statistical learning; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105076
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Common methods for spatial distribution, such as hydrologic response units, are subjective, time-consuming, and fail to capture the full range of basin attributes. Recent advances in statistical-learning techniques allow for new approaches to this problem. We propose the use of Gaussian Mixture Models (GMMs) for spatial distribution of hydrologic models. GMMs objectively select the set of modeling locations that best represent the distribution of watershed features relevant to the hydrologic cycle. We demonstrate this method in two hydrologically distinct headwater catchments of the Sierra Nevada and show that it meets or exceeds the performance of traditionally distributed models for multiple metrics across the water balance at a fraction of the time cost. Finally, we use univariate GMMs to identify the most-important drivers of hydrologic processes in a basin. The GMM method allows for more robust, objective, and repeatable models, which are critical for advancing hydrologic research and operational decision making. Highlights: Gaussian Mixture Models can be used for spatially distributing hydrologic models. GMM is objective, efficient, and rooted in physical basin characteristics. GMM meets the performance of traditional models at a fraction of the cost. Univariate GMMs can be used to identify drivers of hydrologic processes in a basin.