subject: Hydrology type: dataset
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/GYN8-W684
Fariborz Daneshvar , Jane R Frankenberger , Johann Alexander Vera Mercado , Keith A Cherkauer , Laura C Bowling
12/03/2020
This dataset provides spatially-distributed land cover properties required for SWAT hydrologic modeling in the Arequipa Department of Peru. Developed methodology for estimation of land cover properties is also provided.
Agricultural and Biological Engineering ArcGIS Arequipa Arequipa Nexus Institute Crops Geographic Information Systems (GIS) grassland Hydrologic Modeling Hydrology Peru SWAT model UNSA Watershed
10.4231/N5MX-9W30
Fariborz Daneshvar , Jane R Frankenberger , Keith A Cherkauer , Laura C Bowling , MartÃn Juan C. Villalta Soto
10/30/2020
This dataset provides spatially-distributed soil physical properties required for SWAT hydrologic modeling in the Arequipa Department of Peru, based on regional soil maps and available soil profiles. Developed methodology is also provided.
Agricultural and Biological Engineering ArcGIS Arequipa Arequipa Nexus Institute Digital Soil Mapping Geographic Information Systems (GIS) Hydrologic Modeling Hydrology Peru soil SWAT model UNSA Watershed
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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