Articles | Volume 18, issue 6
https://doi.org/10.5194/bg-18-2027-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-2027-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Understanding the effect of fire on vegetation composition and gross primary production in a semi-arid shrubland ecosystem using the Ecosystem Demography (EDv2.2) model
Karun Pandit
CORRESPONDING AUTHOR
School of Forest, Fisheries, and Geomatics Sciences, University of Florida, 1745 McCarty Drive, Gainesville, FL 32611, USA
Hamid Dashti
School of Natural Resources and the Environment, University of Arizona, 1064 East Lowell Street, Tucson, AZ 8572, USA
Andrew T. Hudak
Rocky Mountain Research Station, US Forest Service, 1221 South Main Street,
Moscow, ID 83843, USA
Nancy F. Glenn
Department of Geosciences, Boise State University, 1910 University Dr, Boise, ID 83725, USA
Alejandro N. Flores
Department of Geosciences, Boise State University, 1910 University Dr, Boise, ID 83725, USA
Douglas J. Shinneman
Forest and Rangeland Ecosystem Science Center, US Geological Survey, 970 Lusk St., Boise, ID 83706, USA
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Tracking seasonal snow on glaciers is critical for understanding glacier health. Yet previous work has not directly compared machine learning algorithms for snow classification across satellite image products. To address this, we developed a new automated workflow for tracking seasonal snow on glaciers using several image products and machine learning models. Applying this method can help provide insights into glacier health, water resources, and the effects of climate change on snow cover.
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Lena Wang, Sharon Billings, Li Li, Daniel Hirmas, Keira Johnson, Devon Kerins, Julio Pachon, Curtis Beutler, Karla Jarecke, Vaishnavi Varikuti, Micah Unruh, Hoori Ajami, Holly Barnard, Alejandro Flores, Kenneth Williams, and Pamela Sullivan
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Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
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Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
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It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-451, https://doi.org/10.5194/hess-2020-451, 2020
Manuscript not accepted for further review
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Short summary
A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand...
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