@proceedings {JRN54862,
	title = {Multi-scale wind erosion monitoring and assessment for US rangelands},
	volume = {January 28-February 2, 2018},
	year = {2018},
	month = {01/28/2018},
	publisher = {Society for Range Management Meeting Proceedings},
	address = {Reno, NV},
	abstract = {<p>Wind erosion is a major resource concern for rangeland managers. Although wind erosion is a naturally occurring process in many drylands, land use activities, and land management in particular, can accelerate wind-driven soil loss {\textendash} impacting ecosystem dynamics and agricultural production, air quality, human health, biogeochemical cycles, and climate. Wind erosion has large variability in space and time, and as a consequence is difficult to monitor effectively in ways that can support its management. Existing US air quality monitoring programs provide indicators of wind erosion intensity (e.g., particulate matter [PM] concentrations) and associated benchmarks (e.g., Clean Air Act) are regulated federally but provide no information about which landscapes are eroding, but how much, or when. Standardized rangeland monitoring programs like the BLM{\textquoteright}s Assessment, Inventory and Monitoring (AIM) Strategy and the NRCS{\textquoteright}s National Resources Inventory (NRI) collect information about soil properties (texture) and vegetation (foliar cover, canopy gap size, height) that, coupled with remote sensing products and numerical models, can be used to estimate wind erosion and dust emission rates across spatial scales to better inform management. Here I present an overview of the National Wind Erosion Research Network and ongoing efforts to develop decision-support tools that connect existing soil, vegetation and air quality monitoring programs to provide information to federal agencies and the public about wind erosion rates. I demonstrate how core indicators used in the AIM and NRI monitoring programs can be integrated to assess dust emission rates; enabling air quality, human health and ecosystem impacts of dust to be linked to changes in range condition and benchmarks for rangeland management. I then describe how AIM and NRI data can be used to interpret remote sensing-driven model estimates of wind erosion and understand interactions with land use and management activities across ecological sites.</p>
},
	author = {Nicholas Webb}
}
