Groundwater modules are critically important to the simulation of low flows in land surface models (LSMs) and rainfall-runoff models. Here, we develop a Groundwater for Ungauged Basins (GrUB) module that uses only physically-based properties for which data are widely available, thus allowing its application without the need for calibration. GrUB is designed to be computationally simple and readily adaptable to a wide variety of LSMs and rainfall-runoff models. We assess the performance of GrUB in 84 US watersheds by incorporating it into HBV, a popular rainfall-runoff model. We compare predictions of low flows by the native (calibrated) HBV groundwater module with those by the (uncalibrated) GrUB module and find that GrUB generates error metrics that are equivalent to (or superior to) those generated by the native HBV groundwater module. To assess whether predictions by GrUB are robust to changes in the structure and parameterization of the overlying hydrologic model, we run tests for two artificial scenarios: Slow Recharge with rates of percolation below 0.1 mm/day, and Fast Recharge with rates of percolation of up to 1000 mm/day. GrUB proves to be robust to these extreme changes, with mean absolute error (MAE) of predictions of low flows only increasing by an average of up to 19%, while average MAE increases by up to 157% when the same tests are performed on HBV without the GrUB module. We suggest GrUB as a potential tool for improving predictions of low flows in LSMs as well as rainfall-runoff models where calibration data are unavailable.

River channels store large volumes of water globally, critically impacting ecological and biogeochemical processes. Despite the importance of river channel storage, there is not yet an observational constraint on this quantity. We introduce a 26-year record of entirely remotely sensed volumetric channel water storage (CWS) change on 26 major world rivers. We find mainstem volumetric CWS climatology amplitude (CA) represents an appreciable amount of basin-wide terrestrial water storage variability (median 2.78%, range 0.04-12.54% across world rivers), despite mainstem rivers themselves represent an average of just 0.2% of basin area. We find that two global river routing schemes coupled with land surface models reasonably approximate CA (within ±50%) in only 11.5 % (CaMa-Flood) and 30.7 % (HyMap) of rivers considered. These findings demonstrate volumetric CWS is a useful quantity for assessing global hydrological model performance, and for advancing understanding of spatial patterns in global hydrology.

Sediment trapping behind dams is currently a major source of bias in large-scale hydro-geomorphic models, hindering robust analyses of anthropogenic influences on sediment fluxes in freshwater and coastal systems. This study focuses on developing a new reservoir trapping efficiency (Te) parameter to account for the impacts of dams in hydrological models. This goal was achieved by harnessing a novel remote sensing data product which offers high-resolution and spatially continuous maps of suspended sediment concentration across the Contiguous United States (CONUS). Validation of remote sensing-derived surface sediment fluxes against USGS depth-averaged sediment fluxes showed that this remote sensing dataset can be used to calculate Te with high accuracy (R2 = 0.98). Te calculated for 116 dams across the CONUS, using upstream and downstream sediment fluxes from their reservoirs, range from 0.3% to 98% with a mean of 43%. Contrary to the previous understanding that large reservoirs have larger Te and vice versa, these data reveal that large reservoirs can have a wide range of Te values. A suite of 21 explanatory variables were used to develop an empirical Te model using multiple regression. The strongest model predicts Te using five variables: dam height, incoming sediment flux, outgoing water discharge, reservoir length, and Aridity Index. A global model was also developed using explanatory variables obtained from a global dam database to conduct a global-scale analysis of Te. These CONUS- and global-scale Te models can be integrated into hydro-geomorphic models to more accurately predict river sediment transport by representing sediment trapping in reservoirs.