EAR-PF: Resolving the multi-scale hydraulics of shallow overland flows on patchily-vegetated hillslopes: towards a simple predictive framework

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Dr. Octavia V. Crompton has been awarded an NSF Earth Sciences Postdoctoral Fellowship to develop tools that predict the redistribution of surface flow from bare soil to vegetated areas during intense rainfall events. This redistribution maintains ecosystems in regions where annual rainfall alone is insufficient to support plant growth. At present, this water redistribution can only be predicted using complex and computationally demanding numerical models. Dr. Crompton’s research will simplify these predictions by linking hydrological processes on small scales to hillslope-outcomes using the spatial pattern of bare and vegetated sites as a basis for upscaling. In addition to providing insights into the physics of overland flow in dryland landscapes, the study will develop basic scientific knowledge needed by land managers and ecologists to assess landscape vulnerability and design viable restoration strategies. Dr. Crompton will be mentored during her postdoctoral research by Professor Gabriel Katul at Duke University, and Professors Davide Poggi and Constantino Manes at Turin Polytechnical University, Italy. While at Duke University, Dr. Crompton will partner with the Duke University Environmental Science Summer Program to teach environmental science to students from underrepresented groups in STEM. Aiming to broaden participation in quantitative environmental sciences, Dr. Crompton will develop and teach a curriculum on computational modeling of environmental processes, complementing the program's existing field-based learning approach. The research has several objectives: First, a series of flume experiments will resolve uncertainties regarding the physics of very shallow ( 3 cm deep), infiltrating flows through vegetation patches. This will generate some of the first experimental treatments of these flows, and enable new understanding of the physics. Second, a modeling framework based on the Saint-Venant equations will be used to upscale physics from vegetation patch to hillslope scales, accounting for spatial heterogeneity. Finally, reduced complexity models, derived from spatial averaging and analytical approximations to the flow, will be developed to predict hillslope-scale hydrological outcomes, and their predictions tested using flume experiments. The resulting tools will improve predictions relevant to dryland restoration, remediation and conservation, by enabling explicit description of the effects of within-storm hydrologic processes. Broader impacts of this project will include a better understanding of dryland degradation, which has large social and environmental consequences, affecting 10 to 20% of global drylands and impacting approximately 1.5 billion people. This project was funded by the Hydrological Science program in the Earth Science division.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.