Demonstration of LoA Method For Hydrologic Model Evaluation

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).

Resources

Name Format Description Link
0 https://zenodo.org/records/10483643
0 https://zenodo.org/records/10483702
0 https://zenodo.org/records/10483938
0 https://zenodo.org/records/10483964
0 https://zenodo.org/records/10530454
0 https://zenodo.org/records/10515777
0 https://zenodo.org/records/10515763
53 ScienceHub_data_Model%20Uncertainty_LoA.xlsx https://pasteur.epa.gov/uploads/10.23719/1531956/ScienceHub_data_Model%20Uncertainty_LoA.xlsx

Tags

  • uncertainty-estimation
  • limits-of-acceptability
  • saint-joseph-river-watershed
  • sacramento-soil-moisture-accounting
  • machine-learning
  • hydrologic-model

Topics

Categories