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Abstract: 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 (AGU25)

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

Phillip Goodling1, John Christopher Hammond2, Jeremy Diaz3, Hayley Corson-Dosch4, Aaron Heldmyer5, Scott D Hamshaw6, Ryan McShane7, Jesse Ross8, Roy R Sando9, Caelan Simeone10, Leah Staub2, William D Watkins11, Michael Wieczorek2, Kendall Wnuk2 and Jacob A Zwart12, (1)U.S. Geological Survey, Earth Surface Processes Division, Baltimore, MD, United States, (2)U.S. Geological Survey, Maryland-Delaware-DC Water Science Center, Catonsville, United States, (3)U.S. Geological Survey, Water Mission Area, Reston, United States, (4)U.S. Geological Survey, Water Resources Mission Area, Madison, United States, (5)USGS Geological Survey, WY-MT WSC, Cheyenne, United States, (6)U.S. Geological Survey, Water Mission Area-Integrated Modeling and Prediction Division, Bristol, United States, (7)USGS Wyoming-Montana Water Science Center, Cheyenne, WY, United States, (8)US Geological Survey, Water Mission Area, Los Angeles, United States, (9)USGS Wyoming-Montana Water Science Center, Helena, United States, (10)U.S. Geological Survey, Oregon Water Science Center, Portland, United States, (11)USGS Integrated Information Dissemination Division, Data Science Branch, Middleton, United States, (12)USGS Integrated Information Dissemination Division, Data Science Branch, San Francisco, United States
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