Improving low-flow estimates in the NextGen Framework through improved subsurface conceptualization and parameterization

Objective:

This project seeks to improve the prediction of low flows—periods when river levels are low and primarily sustained by groundwater discharge—by enhancing the representation of subsurface flow processes. The research will pursue two primary objectives. First, it will develop a new, CONUS-scale dataset characterizing the capacity and efficiency of water movement through soil and rock, tailored for application within the catchments defined by the NextGen hydrofabric. Second, it will integrate a newly developed, parsimonious groundwater model—Groundwater for Ungauged Basins (GrUB)—into the NextGen modeling framework. This integration aims to enable robust streamflow simulations in data-sparse regions, ultimately supporting more accurate and scalable water predictions.

Approach:

To achieve the research objectives, we will undertake the following steps: (1) estimate hydraulic conductivity (K) and specific yield (S) for gauged catchments within the GAGES-II dataset by fitting empirical streamflow recession equations during wet and dry periods, respectively; (2) using catchment-averaged physioclimatic attributes at the hydrofabric scale, develop machine learning models to predict K and S across all hydrofabric catchments; (3) apply the derived hydraulic property estimates to implement the newly developed Groundwater for Ungauged Basins (GrUB) module within selected land surface models (e.g., Noah-OWP-Modular) and rainfall-runoff models (e.g., CFE); and (4) evaluate the models’ ability to simulate low-flow conditions during dry seasons (e.g., summer months) in 15 to 20 GAGES-II watersheds representing a range of hydroclimatic settings.

Impact:

Abstract:

This project aims to improve how we predict low flows—periods when river levels are low, often during dry season—by better capturing the role of groundwater. Low flows are primarily sustained by groundwater discharge and are essential for maintaining stream health, water supply, and ecosystem function during droughts. However, existing models within the NextGen hydrologic framework use oversimplified representations of groundwater processes, leading to unreliable predictions, especially in dry periods. This is partly because key subsurface properties like how easily water moves through soil and how much water can be stored underground are poorly known.

To address this, the project will (1) create a national-scale dataset of subsurface hydraulic properties tailored for the NextGen hydrofabric, and (2) incorporate a new, simplified groundwater model—GrUB (Groundwater for Ungauged Basins)—into NextGen. By improving how groundwater is represented, the project will enhance streamflow simulations in both gaged and ungaged watersheds, supporting more accurate forecasting and better water management under drought conditions.