Eunsang Cho

Texas State University | Assistant Professor

Subject Areas: Hydrology, Remote Sensing, Seasonal Snow, Machine Learning, Land Surface Modeling

 Recent Activity

ABSTRACT:

This resource includes the 25-year return level extreme maps of 4-km gridded snow water equivalent (SWE) and 7-day snowmelt and runoff potential (RP) and over North America. The maps are developed by using 25-km gridded historical and future simulation data sets from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) under the Representative Concentration Pathway [RCP] 8.5. For full details, please see Cho et al. (2021) in Geophysical Research Letters.

Map Metadata (+proj=longlat +ellps=WGS84 +towgs84=0,0,0,-0,-0,-0,0 +no_defs)
SWE maps (unit: mm)
7-day snowmelt maps (unit: mm/7-day)
7-day runoff potential maps (unit: mm/7-day)

Preferred citation:
Cho, E., McCrary R. R., and Jacobs, J. M. (2021). Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes over North America. Geophysical Research Letters

Corresponding author: Eunsang Cho (eunsang.cho@nasa.gov; escho@umd.edu)

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ABSTRACT:

This resource is a repository of the Unpiloted Aerial System (UAS) lidar-based maps of snow depth, local gradient of snow depth, and static variables (1-m spatial resolution) over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States (N 43.10892°, W 70.94853°, 35 m above sea level). Snow surface elevations were collected on January 23rd, 2019 and December 4th, 2019. The respective bare earth baseline elevations were collected following snowmelt on April 11th, 2019 and March 18th, 2020. The total area surveyed was approximately 0.11 sqkm, of which 0.7 sqkm was open field and 0.4 sqkm was mixed deciduous and coniferous forest. The static variables include plant functional type (0 = fields, 0.1 = deciduous needleleaf, and 0.2 = evergreen broadleaf) roughness (cm), slope (%), shadow hours (hours), aspect (degree), inter-pixel variability of lidar returns (STD; m), topographic compound index (TCI; unitless), and total local gradient of snow-off condition (LG; cm). Please see Cho et al. (2020) in Journal of Hydrology for full details.

Map Metadata (+proj=utm +zone=19 +datum=WGS84 +units=m +no_defs)

Preferred citation:
Cho, E., Hunsaker, A. G., Jacobs, J. M., Palace, M., Sullivan, F. B., & Burakowski, E. A. (2021). Maximum Entropy Modeling to Identify Physical Drivers of Shallow Snowpack Heterogeneity using Unpiloted Aerial System (UAS) Lidar. Journal of Hydrology, 126722. https://doi.org/10.1016/j.jhydrol.2021.126722

Corresponding author: Eunsang Cho (escho@umd.edu)

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ABSTRACT:

This resource is a repository of the 25- and 100-year return level design maps of 4-km gridded snow water equivalent (SWE) and 1- and 7-day snowmelt (plus precipitation) over the Contiguous United States (CONUS). The maps are developed using long-term observation-based 4-km gridded SWE developed by University of Arizona (UA SWE) incorporating the 1-km gridded national snow model product (SNOw Data Assimilation System; SNODAS). Please see Cho et al. (2020) in Water Resources Research (WRR) for full details.

Map Metadata (+proj=longlat +ellps=WGS84 +towgs84=0,0,0,-0,-0,-0,0 +no_defs)
SWE maps (unit: mm)
1-day snowmelt maps (unit: mm/1-day)
7-day snowmelt maps (unit: mm/7-day)

Preferred citation:
Cho, E. and Jacobs, J. M. (2020). Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design over the Contiguous United States. Water Resources Research

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu; eunsang.cho@nasa.gov)

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ABSTRACT:

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84)
Raster value key:
0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset
1 = Undrained (UD)
2 = Subsurface Drained (SD)

Preferred citation:
Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)

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 Contact

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Resources
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Resource Resource
Annual Subsurface Drainage Map (Red River of the North Basin; Cho et al., 2019)
Created: Aug. 9, 2019, 11:48 p.m.
Authors: Cho, Eunsang · Jennifer M. Jacobs · Xinhua Jia · Simon Kraatz

ABSTRACT:

This resource is a repository of the annual subsurface drainage (so-called "Tile Drainage") maps for the Bois de Sioux Watershed (BdSW), Minnesota and the Red River of the North Basin (RRB), separately. The RRB maps cover a 101,500 km2 area in the United States, which overlies portions of North Dakota, South Daokta, and Minnesota. The maps provide annual subsurface drainage system maps for recent four years, 2009, 2011, 2014, and 2017 (In 2017, the subsurface drainage maps including the Sentinel-1 Synthetic Aperture Radar as an additional input are also provided). Please see Cho et al. (2019) in Water Resources Research (WRR) for full details.

Map Metadata (Proj=longlat +datum=WGS84)
Raster value key:
0 = NoData, masked by non-agricultural areas (e.g. urban, water, forest, or wetland land) and high gradient cultivated crop areas (slope > 2%) based on the USGS National Land Cover Dataset (NLCD) and the USGS National Elevation Dataset
1 = Undrained (UD)
2 = Subsurface Drained (SD)

Preferred citation:
Cho, E., Jacobs, J. M., Jia, X., & Kraatz, S. (2019). Identifying Subsurface Drainage using Satellite Big Data and Machine Learning via Google Earth Engine. Water Resources Research, 55. https://doi.org/10.1029/2019WR024892

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu)

Show More
Resource Resource

ABSTRACT:

This resource is a repository of the 25- and 100-year return level design maps of 4-km gridded snow water equivalent (SWE) and 1- and 7-day snowmelt (plus precipitation) over the Contiguous United States (CONUS). The maps are developed using long-term observation-based 4-km gridded SWE developed by University of Arizona (UA SWE) incorporating the 1-km gridded national snow model product (SNOw Data Assimilation System; SNODAS). Please see Cho et al. (2020) in Water Resources Research (WRR) for full details.

Map Metadata (+proj=longlat +ellps=WGS84 +towgs84=0,0,0,-0,-0,-0,0 +no_defs)
SWE maps (unit: mm)
1-day snowmelt maps (unit: mm/1-day)
7-day snowmelt maps (unit: mm/7-day)

Preferred citation:
Cho, E. and Jacobs, J. M. (2020). Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design over the Contiguous United States. Water Resources Research

Corresponding author: Eunsang Cho (ec1072@wildcats.unh.edu; eunsang.cho@nasa.gov)

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Resource Resource
Unpiloted aerial system (UAS) LiDAR snow depth and static variable maps (New Hampshire; Cho et al., 2021)
Created: July 30, 2021, 9:08 p.m.
Authors: Cho, Eunsang · Adam G. Hunsaker · Jennifer M. Jacobs · Michael Palace · Franklin B. Sullivan · Elizabeth A. Burakowski

ABSTRACT:

This resource is a repository of the Unpiloted Aerial System (UAS) lidar-based maps of snow depth, local gradient of snow depth, and static variables (1-m spatial resolution) over open terrain and forests at the University of New Hampshire Thompson Farm Research Observatory, New Hampshire, United States (N 43.10892°, W 70.94853°, 35 m above sea level). Snow surface elevations were collected on January 23rd, 2019 and December 4th, 2019. The respective bare earth baseline elevations were collected following snowmelt on April 11th, 2019 and March 18th, 2020. The total area surveyed was approximately 0.11 sqkm, of which 0.7 sqkm was open field and 0.4 sqkm was mixed deciduous and coniferous forest. The static variables include plant functional type (0 = fields, 0.1 = deciduous needleleaf, and 0.2 = evergreen broadleaf) roughness (cm), slope (%), shadow hours (hours), aspect (degree), inter-pixel variability of lidar returns (STD; m), topographic compound index (TCI; unitless), and total local gradient of snow-off condition (LG; cm). Please see Cho et al. (2020) in Journal of Hydrology for full details.

Map Metadata (+proj=utm +zone=19 +datum=WGS84 +units=m +no_defs)

Preferred citation:
Cho, E., Hunsaker, A. G., Jacobs, J. M., Palace, M., Sullivan, F. B., & Burakowski, E. A. (2021). Maximum Entropy Modeling to Identify Physical Drivers of Shallow Snowpack Heterogeneity using Unpiloted Aerial System (UAS) Lidar. Journal of Hydrology, 126722. https://doi.org/10.1016/j.jhydrol.2021.126722

Corresponding author: Eunsang Cho (escho@umd.edu)

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Resource Resource
Historical and Future Extreme SWE, Snowmelt, and Runoff Potential maps of the NA-CORDEX Climate Models
Created: Oct. 19, 2021, 3:42 p.m.
Authors: Cho, Eunsang · Rachel R. McCrary · Jennifer M. Jacobs

ABSTRACT:

This resource includes the 25-year return level extreme maps of 4-km gridded snow water equivalent (SWE) and 7-day snowmelt and runoff potential (RP) and over North America. The maps are developed by using 25-km gridded historical and future simulation data sets from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) under the Representative Concentration Pathway [RCP] 8.5. For full details, please see Cho et al. (2021) in Geophysical Research Letters.

Map Metadata (+proj=longlat +ellps=WGS84 +towgs84=0,0,0,-0,-0,-0,0 +no_defs)
SWE maps (unit: mm)
7-day snowmelt maps (unit: mm/7-day)
7-day runoff potential maps (unit: mm/7-day)

Preferred citation:
Cho, E., McCrary R. R., and Jacobs, J. M. (2021). Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes over North America. Geophysical Research Letters

Corresponding author: Eunsang Cho (eunsang.cho@nasa.gov; escho@umd.edu)

Show More