Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations
Links
- More information: Publisher Index Page (via DOI)
- Superseded Publications:
- Open Access Version: Publisher Index Page
- Download citation as: RIS | Dublin Core
Abstract
Forecasts of streamflow drought, when streamflow declines below typical levels, are notably less available than for floods or meteorological drought, despite widespread impacts. We apply machine learning (ML) models to forecast streamflow drought 1–13 weeks ahead at 3,219 streamgages across the conterminous United States. We applied two ML methods (Long short-term memory neural networks; Light Gradient-Boosting Machine) and two benchmark models (persistence; Autoregressive Integrated Moving Average) to predict weekly streamflow percentiles with independent models for each forecast horizon. ML models outperformed benchmarks in predicting continuous streamflow percentiles below 30%. ML models generally performed worse than persistence models for discrete classification (moderate, severe, extreme) but exceeded the benchmark models for drought onset/termination. Performance was better for less intense droughts and shorter horizons, with predictive power for 1–4 weeks for severe droughts (10% threshold). This work highlights challenges and opportunities to advance hydrological drought forecasting and supports a new experimental forecasting tool.
Suggested Citation
Hammond, J., Goodling, P.J., Diaz, J.A., Corson-Dosch, H.R., Heldmyer, A.J., Hamshaw, S.D., McShane, R., Ross, J.C., Sando, R., Simeone, C., Smith, E.A., Staub, L.E., Watkins, D., Wieczorek, M., Wnuk, K., Zwart, J.A., 2026, Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations: Frontiers in Water, v. 7, 1709138, 32 p., https://doi.org/10.3389/frwa.2025.1709138.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations |
| Series title | Frontiers in Water |
| DOI | 10.3389/frwa.2025.1709138 |
| Volume | 7 |
| Publication Date | January 07, 2026 |
| Year Published | 2026 |
| Language | English |
| Publisher | Frontiers Media |
| Contributing office(s) | Maryland-Delaware-District of Columbia Water Science Center |
| Description | 1709138, 32 p. |
| Country | United States |
| Other Geospatial | conterminous United States |