Location: Range Management Research
Project Number: 3050-11210-009-096-A
Project Type: Cooperative Agreement
Start Date: Jun 30, 2021
End Date: Jun 30, 2022
Operational models used to generate authoritative weather forecasts are implementations of the Unified Model (UM), a state-of-the-art numerical weather prediction capability developed and managed by the United Kingdom Met Office. The current UM configuration struggles to simulate individual dust event hazards. A novel surface erodibility parameterization, called ERDC-Geo, uses geomorphic landform maps to generate a spatially varying dust emission flux-scaling factor. Preliminary results suggest ERDC-Geo enhances the dust emission scheme. To further improve UM simulations, spatial coverage of the landform dataset needs to be expanded to global coverage for all desert regions through machine learning and artificial intelligence (ML/AI) approaches. An independent aeolian landform dataset is therefore required to evaluate the representativeness of the resultant dataset in desert regions outside the ML/AI training data region. We will accomplish this work through four objectives: 1. Route a course using GIS data layers to guide field team efforts. 2. Design a field methodology based on qualitative assessments of landform types. 3. Collect landform data for regions in the Chihuahuan Desert. 4. Aggregate landform information from existing landform datasets.
The cooperator will: 1. Review selected field locations to identify landforms and provide feedback to the United States Department of Agriculture (USDA) and Cold Regions Research and Engineering Laboratory (CRREL) team. 2. Review field methodologies for collecting landform observations and provide feedback to the USDA and CRREL team. 3. Conduct fieldwork to collect landform observations at locations specified by the USDA and CRREL team. 4. Provide both raw and summarized field landform observation data to the USDA and CRREL team. 5. Aggregate available rangeland monitoring datasets containing landform descriptions into a single database to support landform dataset validation. Public data from the Bureau of Land Management’s (BLM) Assessment, Inventory and Monitoring (AIM) program, Natural Resources Conservation Service’s (NRCS) Landscape Monitoring Framework (LMF), and USDA Jornada Multiscale Rangeland Variance (MURV) study will be harmonized and aggregated in an online Landscape Data Commons. 6. Crosswalk observed landforms in monitoring datasets to ERDC landform classes for validation. 7. Review landform dataset and provide feedback on landform dataset accuracy in northern Chihuahuan Desert. 8. Assist in final white paper documentation.