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.

常见的空间分布方法（例如水文响应单位）是主观的，费时的，并且无法捕获流域属性的全部范围。统计学习技术的最新进展为解决这个问题提供了新的方法。我们建议使用高斯混合模型（GMM）进行水文模型的空间分布。GMM客观地选择最能代表与水文循环有关的流域特征分布的一组建模位置。我们在内华达山脉的两个水文上截然不同的源头流域中证明了该方法，并表明该方法可以满足或超越传统分布式模型在整个水量平衡上对多个指标的性能，而所花费的时间却很少。最后，我们使用单变量GMM来识别盆地中水文过程的最重要驱动因素。GMM方法可提供更健壮，客观和可重复的模型，这对于推进水文研究和运营决策至关重要。