Optimizing spatial distribution of watershed-scale hydrologic models using Gaussian Mixture Models

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.