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Demonstration of LoA Method For Hydrologic Model Evaluation
These are hydrometeorological datasets obtained from US national data repositories as inputs to a hydrologic model and model simulations over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. The datasets purpose is to demonstrate the applicability of the machine learning-based Limits of Acceptability (LoA) method and hydrologic signatures to evaluate the Sacramento Soil Moisture Accounting (SAC-SMA).
This dataset is associated with the following publication:
Gupta, A., M.M. Hantush, and R.S. Govindaraju. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 641: 131774, (2024).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Mohamed Hantush",
"hasEmail": "mailto:hantush.mohamed@epa.gov"
}
|
| description | These are hydrometeorological datasets obtained from US national data repositories as inputs to a hydrologic model and model simulations over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. The datasets purpose is to demonstrate the applicability of the machine learning-based Limits of Acceptability (LoA) method and hydrologic signatures to evaluate the Sacramento Soil Moisture Accounting (SAC-SMA). This dataset is associated with the following publication: Gupta, A., M.M. Hantush, and R.S. Govindaraju. Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. JOURNAL OF HYDROLOGY. Elsevier Science Ltd, New York, NY, USA, 641: 131774, (2024). |
| distribution |
[
{
"title": "https://zenodo.org/records/10483643",
"accessURL": "https://zenodo.org/records/10483643"
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{
"title": "https://zenodo.org/records/10483702",
"accessURL": "https://zenodo.org/records/10483702"
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{
"title": "https://zenodo.org/records/10483938",
"accessURL": "https://zenodo.org/records/10483938"
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{
"title": "https://zenodo.org/records/10483964",
"accessURL": "https://zenodo.org/records/10483964"
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{
"title": "https://zenodo.org/records/10515763",
"accessURL": "https://zenodo.org/records/10515763"
},
{
"title": "https://zenodo.org/records/10515777",
"accessURL": "https://zenodo.org/records/10515777"
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{
"title": "https://zenodo.org/records/10530454",
"accessURL": "https://zenodo.org/records/10530454"
},
{
"title": "ScienceHub_data_Model Uncertainty_LoA.xlsx",
"mediaType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1531956/ScienceHub_data_Model%20Uncertainty_LoA.xlsx"
}
]
|
| identifier | https://doi.org/10.23719/1531956 |
| keyword |
[
"Limits of Acceptability",
"Sacramento Soil Moisture Accounting",
"Saint Joseph River Watershed",
"Uncertainty Estimation",
"hydrologic model",
"machine learning"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html |
| modified | 2024-06-03 |
| programCode |
[
"020:000"
]
|
| publisher |
{
"name": "U.S. EPA Office of Research and Development (ORD)",
"subOrganizationOf": {
"name": "U.S. Environmental Protection Agency",
"subOrganizationOf": {
"name": "U.S. Government"
}
}
}
|
| references |
[
"https://doi.org/10.1016/j.jhydrol.2024.131774"
]
|
| rights |
null
|
| title | Demonstration of LoA Method For Hydrologic Model Evaluation |
