Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6173-2021
https://doi.org/10.5194/hess-25-6173-2021
Research article
 | 
06 Dec 2021
Research article |  | 06 Dec 2021

Coherence of global hydroclimate classification systems

Kathryn L. McCurley Pisarello and James W. Jawitz

Related authors

Data for wetlandscapes and their changes around the world
Navid Ghajarnia, Georgia Destouni, Josefin Thorslund, Zahra Kalantari, Imenne Åhlén, Jesús A. Anaya-Acevedo, Juan F. Blanco-Libreros, Sonia Borja, Sergey Chalov, Aleksandra Chalova, Kwok P. Chun, Nicola Clerici, Amanda Desormeaux, Bethany B. Garfield, Pierre Girard, Olga Gorelits, Amy Hansen, Fernando Jaramillo, Jerker Jarsjö, Adnane Labbaci, John Livsey, Giorgos Maneas, Kathryn McCurley Pisarello, Sebastián Palomino-Ángel, Jan Pietroń, René M. Price, Victor H. Rivera-Monroy, Jorge Salgado, A. Britta K. Sannel, Samaneh Seifollahi-Aghmiuni, Ylva Sjöberg, Pavel Terskii, Guillaume Vigouroux, Lucia Licero-Villanueva, and David Zamora
Earth Syst. Sci. Data, 12, 1083–1100, https://doi.org/10.5194/essd-12-1083-2020,https://doi.org/10.5194/essd-12-1083-2020, 2020
Short summary

Related subject area

Subject: Global hydrology | Techniques and Approaches: Mathematical applications
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Fadji Z. Maina, Alan Rhoades, Erica R. Siirila-Woodburn, and Peter-James Dennedy-Frank
Hydrol. Earth Syst. Sci., 26, 3589–3609, https://doi.org/10.5194/hess-26-3589-2022,https://doi.org/10.5194/hess-26-3589-2022, 2022
Short summary
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar
Hydrol. Earth Syst. Sci., 26, 3263–3297, https://doi.org/10.5194/hess-26-3263-2022,https://doi.org/10.5194/hess-26-3263-2022, 2022
Short summary
Design flood estimation for global river networks based on machine learning models
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021,https://doi.org/10.5194/hess-25-5981-2021, 2021
Short summary
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021,https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021,https://doi.org/10.5194/hess-25-2513-2021, 2021
Short summary

Cited articles

Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high resolution global dataset of monthly climate and climatic water balance from 1958–2015, Scientific Data, 5, 170191, https://doi.org/10.1038/sdata.2017.191, 2018. 
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and Wood, E. F.: Present and future Köppen Geiger climate classification maps at 1 km resolution, Scientific Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214, 2018. 
Berghuijs, W. R. and Woods, R. A.: A simple framework to quantitatively describe monthly precipitation and temperature climatology, Int. J. Climatol., 36, 3161–3174, 2016. 
Bivand, R., Pebesma, E., and Gomez-Rubio, V.: Applied Spatial Data Analysis with R, 2nd edn., Springer, NY, 2013. 
Boland, M. R., Parhi, P., Gentine, P., and Tatonetti, N. P.: Climate classification is an important factor in assessing quality-of-care across hospitals, Sci. Rep.-UK, 7, 1–6, 2017. 
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

Short summary
Climate classification systems divide the Earth into zones of similar climates. We compared the within-zone hydroclimate similarity and zone shape complexity of a suite of climate classification systems, including new ones formed in this study. The most frequently used system had high similarity but high complexity. We propose the Water-Energy Clustering framework, which also had high similarity but lower complexity. This new system is therefore proposed for future hydroclimate assessments.