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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1171', Anonymous Referee #1, 19 Oct 2023
    • AC1: 'Reply on RC1', Dan Myers, 20 Oct 2023
    • AC2: 'Reply on RC1', Dan Myers, 29 Jan 2024
  • RC2: 'Comment on egusphere-2023-1171', Anonymous Referee #2, 04 Jan 2024
    • AC3: 'Reply on RC2', Dan Myers, 29 Jan 2024
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang

Data sets

Seasonal landcover variation and environmental modeling data Daniel Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren Ficklin, and Xuesong Zhang https://doi.org/10.17632/bbb9xbpv22.3

Model code and software

Seasonal landcover variation and environmental modeling scripts Daniel Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren Ficklin, and Xuesong Zhang https://github.com/Danmyers901/Calibration/tree/master/Landcover

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

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Latest update: 12 Jun 2024
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Short summary
We studied how streamflow and water quality models respond to landcover data collected by satellites during the growing season versus the non-growing season. The landcover data showed more trees during the growing season, and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resources management.