Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado

Yan, Qina; Wainwright, Haruko; Uhlemann, Sebastian; Dafflon, Baptiste; Falco, Nicola; Steefel, Carl

doi:10.15485/1876449

Title: Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado

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Abstract

The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.

Authors:

;

; Uhlemann, Sebastian;

;

Lawrence Berkeley National Laboratory

Publication Date:: Sat Jan 01 00:00:00 EST 2022

Other Number(s):: paf_809_820

Research Org.:: Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) (United States)

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Subject:: 54 ENVIRONMENTAL SCIENCES

Keywords:: soil thickness; pumphouse; east river; 2D Geospatial Maps; Soil Thickness; Land Surface Elevation

Geolocation:: 39.034,-106.88|38.88,-106.88|38.88,-107.05|39.034,-107.05|39.034,-106.88

OSTI Identifier:: 1876449

DOI:: https://doi.org/10.15485/1876449

Project Location:

Citation Formats


                    Yan, Qina, Wainwright, Haruko, Uhlemann, Sebastian, Dafflon, Baptiste, Falco, Nicola, and Steefel, Carl. Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado.  United States: N. p., 2022. 
        Web.  doi:10.15485/1876449.

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                    Yan, Qina, Wainwright, Haruko, Uhlemann, Sebastian, Dafflon, Baptiste, Falco, Nicola, & Steefel, Carl. Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado.  United States.  doi:https://doi.org/10.15485/1876449

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                    Yan, Qina, Wainwright, Haruko, Uhlemann, Sebastian, Dafflon, Baptiste, Falco, Nicola, and Steefel, Carl. 2022.  
"Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado".  United States.  doi:https://doi.org/10.15485/1876449.  https://www.osti.gov/servlets/purl/1876449. Pub date:Sat Jan 01 00:00:00 EST 2022

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@article{osti_1876449,

  title        = {Soil thickness map at two hillslopes near the pumphouse in the east river watershed, Colorado},

  author       = {Yan, Qina and Wainwright, Haruko and Uhlemann, Sebastian and Dafflon, Baptiste and Falco, Nicola and Steefel, Carl},

  abstractNote = {The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.},

  doi          = {10.15485/1876449},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Jan 01 00:00:00 EST 2022},

  month        = {Sat Jan 01 00:00:00 EST 2022}

}

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DOI: https://doi.org/10.15485/1876449

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