Authors: Norm Jones, Gus Williams – Brigham Young University; T. Prabhakar Clement – University of Alabama, Donna Rizzo –University of Vermont
Presentation Type: Lightning Talk
Title: Advancing Science to Better Characterize Drought and Groundwater-Driven Low-Flow Conditions in NOAA and USGS National-Scale Models
Abstract: Our team of researchers from Brigham Young University (BYU), The University of Alabama (UA), and the University of Vermont (UVM) is developing state-of-the-art methods to improve continental United States (CONUS)-scale operational streamflow forecasts for low-flow conditions. We hypothesize that in several regions within the CONUS, the low flows are dominated by baseflow from groundwater rather than runoff from precipitation. Our goal is to train machine learning algorithms using multiple datasets including landcover/land-use, gravity anomalies, rainfall, soil moisture, and groundwater level datasets to identify regions where low flows are influenced by groundwater. Once these regions have been identified, we will develop methods to predict baseflows more accurately in regional streams. We will refine and augment groundwater data using Earth observations to compensate for sparse and missing groundwater data. We will use the resulting prediction methods to generate forcings and find linkages between model conditions and the NextGen National Water Model (NWM) using the HydroFabric framework. This work will improve the NWM predictions for low flow during extreme drought conditions, which to date have received less attention due to the emphasis on predicting extreme flooding events. These predictions will support operations where low-flow drought conditions are critical for managing water supply, flow rates for critical infrastructure, ecological sustainability, and river navigation. Early research on this project has resulted in a new Python package for digital baseflow separation methods and a machine learning model to identify periods in stream gage hydrographs corresponding to baseflow dominant periods. We will soon be applying these methods to all stream gages in the US using Google Big Query.