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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Research Project #441743

Research Project: Development of an All-lands Wind Erosion Model

Location: Range Management Research

Project Number: 3050-11210-009-005-A
Project Type: Cooperative Agreement

Start Date: Apr 1, 2022
End Date: Jun 30, 2023

Objective:
The broad program objective is to produce a wind erosion decision-support model and analyses that contribute to management objectives of partner agencies, including the Natural Resources Conservation Service (NRCS) and Bureau of Land Management (BLM). The research will develop a wind erosion model from existing and new theory, provide calibration and validation to establish model uncertainty across land cover types, and conduct model applications at different scales integrating data from national monitoring networks.

Approach:
1) Maintain AERO model code and develop AERO model to support CEAP-GL goals. 2) Guide CEAP-GL team on use of national monitoring datasets and remote sensing products to support multi-scale assessments including wind erosion, dust emission and other indicators with respect to conservation practices. 3) Build our understanding of where, when, and how much wind erosion and dust emission occur across the US with respect to conservation practices and that contribute to objectives of CEAP-GL conservation assessments as guided by CEAP-GL. 4) Develop the underpinning science and knowledge required to incorporate wind erosion information into decision support for conservation practices, including using Ecological Site Descriptions (ESDs) and state-and-transition models (STMs) to support assessments of the impacts of land use and land cover change and land management on wind erosion and dust emissions in the US. 5) Link harmonized data to ESDs and Ecological Site Groups (ESGs) to connect quantitative information including status of indicators and current condition to conservation practices. 6) Produce multi-indicator analyses from harmonized data and other data to support CEAP-GL objectives at different scales as guided by CEAP-GL objectives.