Hydrologic process inference in large-scale models under human impacts

Computational models describing the relationship between climate and streamflow are a tool that scientists and engineers use to improve our understanding of the natural world and in for the water cycle, it helps in important disaster forecasting such as floods and storms, and water management to ensure reliable water supply and assessment. However, the influence of human actions on the water cycle, also known as the hydrologic cycle, remains a major source of uncertainty in streamflow estimates.

This project will use modeling to address this uncertainty by focusing on dams, which control about 75% of annual runoff in the continental United States. By analyzing how the representation of human actions affects the reliability of hydrologic models, the project will help scientists and engineers keep pace with human-induced changes in the water cycle.

Moreover, the outcomes of this project will enhance infrastructure for research by creating an observational dataset describing dam operations across the United State. This dataset will expand the tools available to the hydrologic modeling community. To broadly disseminate findings, datasets, and models, the project will complement traditional dissemination avenues with two scientific workshops.

Finally, the project will provide an opportunity for training Postdoctoral researchers and students.

The representation of dam operations is a major source of structural uncertainty that curbs our ability to study hydrologic processes and fluxes in regulated basins. The project will build on the convergence of four research domains, namely remote sensing, large-scale hydrology, catchment hydrology, and water resources systems analysis, by bringing together new models and data such as observed and remotely sensed reservoir storage at the CONUS scale.

By combining elements from these domains, the project will develop a computational framework demonstrated for 18 large river basins with one in each HUC2, with a total of about 200 dams. Through this framework, the project will pursue four outcomes by (1) characterizing the structural uncertainty associated with the choice of dam operation models, (2) explaining how this uncertainty propagates to the parameterization of large-scale hydrologic models, (3) quantifying the impact of this uncertainty on the simulation of hydrologic processes, and (4) generalizing practical modelling guidelines.

To test the generality and scale of the approach, the investigators will apply their approach to two major river basins with the US - the Colorado and Columbia River basins.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.