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https://doi.org/10.5194/egusphere-2023-1171
https://doi.org/10.5194/egusphere-2023-1171
19 Sep 2023
 | 19 Sep 2023

Seasonal variation in landcover estimates reveals sensitivities and opportunities for environmental models

Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang

Abstract. Most readily available landuse/landcover (LULC) data are developed using growing season remote sensing images often at annual time steps. We used the Dynamic World near real-time global LULC dataset to compare how geospatial environmental models of water quality and hydrology respond to growing vs. non-growing season LULC for temperate watersheds of the eastern United States. Non-growing season LULC had more built area and less tree cover than growing season data due to seasonal impacts on classifications rather than actual LULC changes (e.g., quick construction or succession). In mixed-LULC watersheds, seasonal LULC classification inconsistencies could lead to differences in model outputs depending on the LULC season used, such as an increase in watershed nitrogen yields simulated by the Soil and Water Assessment Tool. Within reason, using separate calibration for each season may compensate for these inconsistencies, but lead to different model parameter optimizations. Our findings provide guidelines on the use of near real-time and high temporal resolution LULC in geospatial models.

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Journal article(s) based on this preprint

06 Dec 2024
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024,https://doi.org/10.5194/hess-28-5295-2024, 2024
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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We studied how streamflow and water quality models respond to landcover data collected by...
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