12 citations as recorded by crossref.

1. Camargo, Köppen and Thornthwaite climate classification systems in defining climatical regions of the state of São Paulo, Brazil G. de Souza Rolim & L. de O. Aparecido 10.1002/joc.4372
2. A climate classification for corrosion control in electronic system design M. Spooner et al. 10.1016/j.mlwa.2022.100397
3. On Using a Clustering Approach for Global Climate Classification P. Netzel & T. Stepinski 10.1175/JCLI-D-15-0640.1
4. Understanding global teleconnections to surface air temperatures in Japan based on a new climate classification P. Oettli et al. 10.1002/joc.6754
5. Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis H. Do et al. 10.1029/2019WR024945
6. Changes in Köppen-Geiger climate types under a future climate for Australia: hydrological implications R. Crosbie et al. 10.5194/hess-16-3341-2012
7. 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 A. Paez-Trujilo et al. 10.5194/nhess-23-3863-2023
8. Evaluation of CMIP5 present climate simulations using the Köppen-Trewartha climate classification M. Belda et al. 10.3354/cr01316
9. Identifying untapped potential: a geospatial analysis of Florida and California's 2009 recycled water production J. Archer et al. 10.2166/wrd.2018.012
10. Using multivariate regression trees and multiobjective tradeoff sets to reveal fundamental insights about water resources systems R. Smith et al. 10.1016/j.envsoft.2019.104498
11. Climate Classifications: the Value of Unsupervised Clustering J. Zscheischler et al. 10.1016/j.procs.2012.04.096
12. Technical note: Towards a continuous classification of climate using bivariate colour mapping A. Teuling 10.5194/hess-15-3071-2011