The relative importance of model type and input features for water supply forecasting in snow-dominated basins of the southwestern US - Madeline R. Pernat; Joseph Kasprzyk; Edith Zagona; Sydney D. Walker; Ben Livneh (Journal of Hydrology: Regional Studies)
Study region: This study focuses on five watersheds in the southwestern United States, where April–July (AMJJ) water supply forecasts (WSFs) inform water management. Climate change has altered long-relied-upon relationships between April 1st snow water equivalent (SWE) and AMJJ water supply, threatening the skill of traditional forecasting approaches. Study focus: This work evaluates how the interaction between model type (e.g., multiple linear regression, random forest) and feature selection influences AMJJ WSF skill. Five machine learning model types are applied in each basin. A new wrapper-based feature selection method identifies the Best Feature Set—selected from a broad pool of station-based, meteorological, and climatological features—for each basin–model type combination. Results show that the most important features vary by both basin and model type, and that model types perform similarly when each is trained on its respective Best Feature Set. New hydrologic insights: April 1st SWE is most important in highly snow-dominated basins, while April 1st precipitation accumulation becomes more important in less snow-dominated systems. Station-based features from multiple lag times are consistently selected, suggesting that earlier observations provide additional predictive value. Among meteorological and climatological features, specific humidity and the Atlantic Multidecadal Oscillation are frequently selected across basins and model types, indicating broad predictive utility. Overall, results suggest that feature selection has a greater influence on forecast skill than model type choice.