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Published January 11, 2024 | Version 1
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Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 3

  • 1. ROR icon University of Cincinnati
  • 2. ROR icon Environmental Protection Agency
  • 3. ROR icon Purdue University West Lafayette

Description

This resource contains data corresponding to the study titled "Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures"

A detailed description of the dataset is contained in a readme file contained in the resource. 

This is only a part of this resource, other parts are shared on zenodo with appropriate titles. This resource is for the case where 99.5th percentile was used as upper LoA and 5% outliers were allowed.

For further information on this resource, please contact abhigupta.1611@gmail.com

Files

readme.txt

Files (33.0 GB)

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