Augmenting geophysical interpretation of data-driven operational water supply forecast modeling for a western US river using a hybrid machine learning approach

In the largely dry and increasingly heavily populated western US, operational modeling systems for seasonal river runoff volume forecasting are key elements of the practical water and hydropower management infrastructure. Explainability of model results in terms of known hydroclimatic processes and conditions is a core requirement for these systems. To improve geophysical interpretability of a standard statistical modeling approach to operational water supply forecasting (WSF), we introduce a hybrid statistical-artificial intelligence method. The procedure involves using a recently developed unsupervised machine learning algorithm designed for improved explainability (non-negative matrix factorization with k-means clustering, NMFk) to extract a compact basin-scale hydroclimatic index from available precipitation and snowpack data; that index is then used as the predictor variate in a largely conventional probabilistic regression on seasonal water supply. The resulting method, dominant-signal NMFk regression, is applied to a challenging forecast site, the Owyhee River, drawn from the US Department of Agriculture Natural Resources Conservation Service WSF system. Outcomes demonstrate that improved interpretability and plausibility relative to conventional statistical methods are achieved through physical consistency of NMFk results with nonnegativity of the environmental data being analyzed. In particular, the nonnegativity property facilitates identifying potential geophysical relationships to input variable type (snow water equivalent vs. accumulated precipitation), location, and underlying hydrologic processes; and it encourages nonnegative river runoff predictions, improving physical realism of WSFs over conventional statistical approaches in certain cases. The method also offers straightforward interpretation of relationships to known forms of climate variability. However, testing suggests that with these capabilities come limitations. Its primary anticipated role, at present, is to augment geophysical interpretation when needed, by serving as a complement alongside other methods in a next-generation US West-wide operational forecasting system.

Automated summary

In a study focused on operational water supply forecasting in the western US, a hybrid statistical-artificial intelligence method was introduced to improve geophysical interpretability. The method involved extracting a compact basin-scale hydroclimatic index using machine learning algorithms and applying it in a probabilistic regression model. Results showed improved interpretability and physical realism compared to traditional statistical methods, with potential for enhancing geophysical relationships and climate variability interpretations.