Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3863-2023
© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
18 Dec 2023
Research article |
| 18 Dec 2023
| 18 Dec 2023
Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin
Related authors
Hydrol. Earth Syst. Sci., 28, 3739–3753, 2024
Hydrol. Earth Syst. Sci. Discuss., 2023
Revised manuscript not accepted
Hydrol. Earth Syst. Sci. Discuss., 2022
Manuscript not accepted for further review
Hydrol. Earth Syst. Sci. Discuss., 2022
Preprint withdrawn
Hydrol. Earth Syst. Sci. Discuss., 2021
Preprint withdrawn
Cited articles
Abbaspour, K. C., Vaghefi, S. A., and Srinivasan, R.: A Guideline for Successful Calibration and Uncertainty Analysis for Soil and Water Assessment: A Review of Papers from the 2016 International SWAT Conference, Water (Basel), 10, 6, https://doi.org/10.3390/w10010006, 2018.
Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., Van Griensven, A., Liew, M. W. Van, Kannan, N., Jha, M. K., Harmel, D., Member, A., Van Liew, M. W., and Arnold, J.-F. G.: SWAT: Model Use, Calibration, and validation, Trans ASABE, 55, 1491–1508, 2012.
ASABE: Guidelines for Calibrating, Validating, and Evaluating Hydrologic and Water Quality (H/WQ) Models, American Society of Agricultural and Biological Engineers, https://doi.org/10.13031/trans.12806, 2017.
Bertels, D. and Willems, P.: Physics-informed machine learning method for modelling transport of a conservative pollutant in surface water systems, J. Hydrol., 619, 129354, https://doi.org/10.1016/j.jhydrol.2023.129354, 2023.
Borcard, D., Gillet, F., and Legendre, P.: Cluster analysis, in: Numerical Ecology with R. Use R!, Springer, Cham, https://doi.org/10.1007/978-3-319-71404-2_4, 2018.
