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

Summary

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.

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