10.4231/R7JS9ND6
Liuying Du , Nikhil Sangwan , Venkatesh Mohan Merwade
07/22/2016
This dataset provides a shapefile showing the natural floodplain for South Carolina. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
biota Environment floodplain maps Forestry and Natural Resources Geographic Information Systems (GIS) geoscientific gSSURGO soil Data Hydrology inland water shapefile South Carolina
10.4231/R7F18WPS
Liuying Du , Nikhil Sangwan , Venkatesh Mohan Merwade
07/22/2016
This dataset provides a shapefile showing the natural floodplain for South Dakota. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
biota Environment floodplain maps Forestry and Natural Resources Geographic Information Systems (GIS) geoscientific gSSURGO soil Data Hydrology inland water shapefile South Dakota
10.4231/R7988509
Liuying Du , Nikhil Sangwan , Venkatesh Mohan Merwade
07/22/2016
This dataset provides a shapefile showing the natural floodplain for Tennessee. These floodplain polygons for the entire state are extracted from the gSSURGO soil data from the Natural Resources Conservation Service (NRCS).
biota Environment floodplain maps Forestry and Natural Resources Geographic Information Systems (GIS) geoscientific gSSURGO soil Data Hydrology inland water shapefile Tennessee
10.4231/0PG5-KC30
Pin-ching Li , Sayan Dey , Venkatesh Mohan Merwade
01/23/2023
This resource contains the data used in the study "Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins" published in Water Resources Research (doi: 10.1029/2023WR034464)
Artificial Neural Network (ANN) covariate shift Hydrology Machine Learning prediction in ungauged basin Random Forest streamflow prediction
10.4231/7NSM-JJ67
Keith A Cherkauer , Laura C Bowling , Stuart D Smith
12/18/2019
Soil moisture and temperature data were collected at five different depths from October 2017 to September 2019 in a crop rotated field.
Agricultural and Biological Engineering Agriculture Environment Hydrology Indiana Soil Moisture Soil Temperature Time Series Climate Data
10.4231/7YY6-HQ02
Chang Liao , Laodong Guo , Qianlai Zhuang , Ruby Leung
12/02/2019
Arctic ecosystems are very sensitive to the global climate change. This study provides a modeling framework to adequately quantify the Arctic land ecosystem carbon budget by considering the lateral transport of carbon affected by permafrost...
Alaska Arctic Region Biogeochemistry C Carbon Cycle Climate Change Earth and Atmospheric Sciences ECO3D Ecosystem Hydrology LSM Permafrost TEM
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