Improved hydrologic forecasting through synthesis of critical storage components and timescales across watersheds worldwide

Models that predict the flow of rivers and streams are critically important for planning flood control, hydropower, and reservoir operations, as well as for management of fish and wildlife populations. As temperatures and precipitation regimes change globally, the need to improve and develop these models for a wider spatial coverage and higher spatial fidelity becomes more imperative. Currently, one of the biggest impediments to developing robust streamflow knowledge is incomplete understanding of the range of timescales over which water is stored (e.g., in snowpack, soils, and groundwater) in watersheds, as well as the processes and factors that control those storage timescales. This working group will address that knowledge gap by developing a globally extensive database (GRiN-QC) of high quality streamflow and ancillary measurements that are likely to be predictive of streamflow (e.g., precipitation, temperature, snowpack, soil moisture, shallow aquifers) for watersheds ranging in size from headwaters to large river basins. Second, the working group will develop a complementary database (GRiN-T2) of storage timescales for each of the watersheds, derived from flow models, tracer studies, and emerging statistical techniques for analyzing time-series data. These activities will support the development of flow forecasting models and yield fundamental understanding of how geological, geographical, and climatic factors control storage within watersheds.







Publications:



Moges, E., Ruddell, B. L., Zhang, L., Driscoll, J. M., & Larsen, L. G. (2022). Strength and memory of precipitation's control over streamflow across the conterminous United 

States. Water Resources Research, 58, e2021WR030186. https://doi.org/10.1029/2021WR030186.



Principal Investigators:

Laurel Larsen (UC Berkeley)

Jessica M Driscoll (New Mexico Water Science Center)

David Gochis (University Corporation for Atmospheric Research)

Judson W. Harvey (USGS)



Participants:

Martha Scholl (USGS)

Noah Schmadel (USGS)

Ben Ruddell (Northern Arizona University)

Edom Moges (University of California-Berkeley)

Jim Kirchner (ETH-Zurich)

Reed Maxwell (Colorado School of Mines)

Zexuan Xu (Lawrence Berkeley National Lab)

Huancui Hu (Pacific Northwest National Lab)

Saalem Adera (University of California, Berkeley)

Liang Zhang (University of California-Berkeley)

Mike Waddington (McMaster University)

Genevieve Ali (University of Guelph)