Evaluating a conceptual hydrological model at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures - 5

This resource contains data corresponding to the study titled "Evaluation of hydrological models 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. Version-2 is up-to-date and contains the results discussed in the final vrsion of the manuscript:

Gupta, A., Hantush, M. M., Govindaraju, R. S., & Beven, K. (2024). Evaluation of hydrological models at gauged and ungauged basins using machine learning-based limits-of-acceptability and hydrological signatures. Journal of Hydrology, 131774.

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