Datasets

Subject
Creator
Type
Date

subject: Hydrology type: dataset

Total is 55 Results
gSSURGO-based Floodplain Maps of North Carolina

10.4231/R7G15XT5

Liuying Du , Nikhil Sangwan , Venkatesh Mohan Merwade ORCID logo

07/21/2016

This dataset provides a shapefile showing the natural floodplain for North 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 North Carolina shapefile

gSSURGO-based Floodplain Maps of North Dakota

10.4231/R7B85637

Liuying Du , Nikhil Sangwan , Venkatesh Mohan Merwade ORCID logo

07/21/2016

This dataset provides a shapefile showing the natural floodplain for North 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 North Dakota shapefile

Data for Analyzing the Effect of Data Splitting and Covariate Shift on Machine Leaning Based Streamflow Prediction in Ungauged Basins

10.4231/0PG5-KC30

Pin-ching Li , Sayan Dey ORCID logo , Venkatesh Mohan Merwade ORCID logo

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

Monitored soil moisture and temperature in Indiana crop rotated field

10.4231/7NSM-JJ67

Keith A Cherkauer ORCID logo , Laura C Bowling ORCID logo , 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

Quantifying Dissolved Organic Carbon Dynamics Using a Three-Dimensional Terrestrial Ecosystem Model at High Spatial-Temporal Resolutions

10.4231/7YY6-HQ02

Chang Liao ORCID logo , Laodong Guo , Qianlai Zhuang ORCID logo , 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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