5-11 Forecast Smarter, Not Harder: Improving Regression-Based Streamflow Prediction in Snow-Dominated Basins with Random Forest and Water Year Clustering

Session: Advances in Mountain Hydrology: Connecting Cryosphere, Surface, and Subsurface Processes

Presenting Author:

Authors:

Abstract:

Streamflow forecasts in snow-dominated basins are often limited by spatial and temporal variability in snow accumulation and melt processes, especially as climate change alters precipitation phase and snowpack dynamics. This study presents a data-driven forecasting framework that integrates machine learning-based predictor selection with cluster-based regression modeling to improve seasonal streamflow forecasts in snow-dominated basins.

A suite of snow and climate metrics were selected to capture temperature variability, precipitation characteristics, snow water equivalent, and elevation-dependent processes across 130 western US mountain basins from the CAMELS dataset. Random forest models were then used to evaluate the relative importance of each predictor in explaining April-July streamflow, providing a basin-specific, ranked set of candidate variables for regression forecasting.

To account for interannual climate variability, k-means clustering was applied to group water years based on winter and spring snow and temperature conditions. Separate regression models were trained within each cluster using the top ranked predictors identified in the random forest analysis. This cluster-informed approach improved model performance relative to traditional regression techniques, yielding lower RMSE and higher R² in basins where snowpack evolution is complex or transitional.

By combining machine learning with unsupervised classification, this framework provides a flexible and transferable method to enhance streamflow forecasting skill in snow-impacted regions. Results underscore the value of adapting predictor selection and model structure to hydroclimatic variability in a changing climate.

Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025

doi: 10.1130/abs/2025AM-7600

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