Long-term trends in spring season land surface roughness and its relationship to vegetation cover chance across the southwestern united states

Authors: DHITAL, SAROJ - New Mexico State University; Webb, Nicholas - Nick; CHAPPELL, ADRIAN - Cardiff University; McCord, Sarah; KAPLAN, MICHAEL - Desert Research Institute; TYREE, GAYLE - Us Geological Survey; NAUMAN, TRAVIS - Natural Resources Conservation Service (NRCS, USDA); DUNIWAY, MICHAEL - Us Geological Survey; LEGRAND, SANDRA - Us Army Engineer Research And Dvelopment Center; LETCHER, THEODORE - Us Army Engineer Research And Dvelopment Center

Submitted to: Watershed Management Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 10/11/2023
Publication Date: 10/24/2023
Citation: Dhital, S., Webb, N.P., Chappell, A., McCord, S.E., Kaplan, M.L., Tyree, G.L., Nauman, T.W., Duniway, M.C., LeGrand, S.L., Letcher, T.W. 2023. Long-term trends in spring season land surface roughness and its relationship to vegetation cover chance across the southwestern united states. Watershed Management Conference Proceedings. Abstract.

Interpretive Summary: Increasing springtime (March-May) dust activity across the southwest US is a growing problem as it impacts human health through the degradation of air quality, natural ecosystems and agricultural production through loss of soil nutrients, and water resources through dust deposition on snow. Predicting future dust activity and possible dust source regions across the Southwest requires a complete understanding of the current state of the land surface roughness – a first order control on aeolian processes – and how vegetation community changes influence land surface roughness. Moreover, an accurate prediction of dust-on-snow events that impact snowmelt rates is essential to support water resource management in the Southwest. In this study, we applied a recently developed remote sensing technique and investigated the long-term and decadal change in land surface roughness and vegetation cover across the southwest using 20 years of datasets from the Moderate Resolution Imaging Spectroradiometer and Rangeland Analysis Platform. Additionally, we test the capability of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with the same MODIS albedo-based drag partition model in simulating dust storms that result in dust-on-snow episodes. Our results enable the identification of regions with changes in springtime land surface roughness and its links to vegetation change during the past two decades. Additionally, our simulation results suggest that the WRF-Chem model with the MODIS albedo-based drag partition in the Air Force Weather Agency dust emission module may be applicable in simulating dust-on-snow episodes in the Southwest US.

Technical Abstract: Increasing springtime (March-May) dust activity across the southwest United States is a growing problem as it impacts the following environmentally sensitive disciplines: 1) human health through the degradation of air quality, 2) natural ecosystems and agricultural production through loss of soil nutrients, and 3) water resources through dust deposition on snow. In the Southwest, dust transported from regionally active source areas affects mountain snowpack. Deposited dust changes snow properties, accelerates snowmelt, and alters the hydrologic cycle in the Colorado River Basin (CRB) and Rio Grande Basin (RGB), which are the main sources of the Southwest’s water resources sustaining more than 40 million people. Predicting future dust activity and possible dust source regions across the Southwest requires a complete understanding of the current state of the land surface roughness – a first order control on aeolian processes – and how vegetation community changes influence land surface roughness. Moreover, an accurate prediction of dust-on-snow events that impact snowmelt rates is essential to support water resource management in the Southwest where communities and agriculture face critical water shortages, presently compounded by a regional multi-year megadrought.   In this study, we analyzed spatiotemporal patterns of land surface roughness and vegetation cover and their relationships within the Southwest dust source areas. We used an albedo-based roughness model to calculate the normalized surface wind shear velocity, us*/Uh, applying 20 years (2001-2020) of MODIS daily albedo and vegetation functional group cover datasets from the Rangeland Analysis Platform (both rescaled to 4 km). These data were subsequently employed to calculate decadal and long-term roughness trends using the Mann-Kendall trend test. We then examined correlations between land surface roughness and vegetation cover to elucidate the contributions of annual and perennial herbaceous and woody vegetation change to roughness trends. Additionally, we test the capability of the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with the same MODIS albedo-based drag partition model in simulating dust storms that result in dust-on-snow episodes. For this purpose, we simulated two springtime Southwest dust episodes. Our results show where springtime surface roughness is changing and where those trends are linked to vegetation change. The trends in surface roughness and vegetation cover provide insights needed to predict future vegetation dynamics and active dust sources. This trend analysis highlights how the albedo-based roughness model can be used to monitor land surface roughness change and identify how ecosystem changes contribute to changes in surface roughness, which provides a direct link to dust mitigation and land management practices. Additionally, our simulation results suggest that the WRF-Chem model with the albedo-based drag partition in the Air Force Weather Agency dust emission module may be a viable tool for resolving dynamic aeolian processes and simulating dust-on-snow episodes in the Southwest United States.