Global water models allow us to explore the terrestrial water cycle in earth-sized digital laboratories to support science and guide policy. However, these models are still subject to considerable but also reducible uncertainties that can be attributed to mainly three sources: (1) imbalances in data quality and availability across geographical regions and between hydrologic variables, (2) poorly quantified human influence on the water cycle, and (3) difficulties in tailoring process representations to regionally diverse hydrologic systems. New, more accurate, and larger datasets, as well as better accumulated and even enhanced process knowledge, will help to reduce these uncertainties and thus improve model consistency with our perceptions and accuracy given existing observations. This review examines the sources of uncertainty crucial for global water models and proposes actions to mitigate them, thereby providing a roadmap for model advancement. Following this path will yield more consistent and accurate models that are urgently needed to tackle key scientific and societal challenges.
Reinecke R., Stein L., Gnann S., Andersson J.C.M., Arheimer B., Bierkens M., Bonetti S., Güntner A., Kollet S., Mishra S., Moosdorf N., Nazari S., Pokhrel Y., Prudhomme C., Schewe J., Shen C. and Wagener T. (2025) Uncertainties as a Guide for Global Water Model Advancement. Wiley Interdisciplinary Reviews Water 12(3): e7002. 10.1002/wat2.70025
@article{Reinecke2025,
Title = {Uncertainties as a Guide for Global Water Model Advancement},
Author = {Reinecke, Robert and Stein, Lina and Gnann, Sebastian and Andersson, Jafet C. M. and Arheimer, Berit and Bierkens, Marc and Bonetti, Sara and Güntner, Andreas and Kollet, Stefan and Mishra, Sulagna and Moosdorf, Nils and Nazari, Sara and Pokhrel, Yadu and Prudhomme, Christel and Schewe, Jacob and Shen, Chaopeng and Wagener, Thorsten},
Editor = {},
Journal = {Wiley Interdisciplinary Reviews Water},
Year = {2025},
Pages = {e7002},
Volume = {12},
Doi = {10.1002/wat2.70025},
Abstract = {Global water models allow us to explore the terrestrial water cycle in earth-sized digital laboratories to support science and guide policy. However, these models are still subject to considerable but also reducible uncertainties that can be attributed to mainly three sources: (1) imbalances in data quality and availability across geographical regions and between hydrologic variables, (2) poorly quantified human influence on the water cycle, and (3) difficulties in tailoring process representations to regionally diverse hydrologic systems. New, more accurate, and larger datasets, as well as better accumulated and even enhanced process knowledge, will help to reduce these uncertainties and thus improve model consistency with our perceptions and accuracy given existing observations. This review examines the sources of uncertainty crucial for global water models and proposes actions to mitigate them, thereby providing a roadmap for model advancement. Following this path will yield more consistent and accurate models that are urgently needed to tackle key scientific and societal challenges.},
}
TY - JOUR
AU - Reinecke, Robert
AU - Stein, Lina
AU - Gnann, Sebastian
AU - Andersson, Jafet C. M.
AU - Arheimer, Berit
AU - Bierkens, Marc
AU - Bonetti, Sara
AU - Güntner, Andreas
AU - Kollet, Stefan
AU - Mishra, Sulagna
AU - Moosdorf, Nils
AU - Nazari, Sara
AU - Pokhrel, Yadu
AU - Prudhomme, Christel
AU - Schewe, Jacob
AU - Shen, Chaopeng
AU - Wagener, Thorsten
TI - Uncertainties as a Guide for Global Water Model Advancement
T2 - Wiley Interdisciplinary Reviews Water
PY - 2025
SP - e7002
VL - 12
DO - 10.1002/wat2.70025
AB - Global water models allow us to explore the terrestrial water cycle in earth-sized digital laboratories to support science and guide policy. However, these models are still subject to considerable but also reducible uncertainties that can be attributed to mainly three sources: (1) imbalances in data quality and availability across geographical regions and between hydrologic variables, (2) poorly quantified human influence on the water cycle, and (3) difficulties in tailoring process representations to regionally diverse hydrologic systems. New, more accurate, and larger datasets, as well as better accumulated and even enhanced process knowledge, will help to reduce these uncertainties and thus improve model consistency with our perceptions and accuracy given existing observations. This review examines the sources of uncertainty crucial for global water models and proposes actions to mitigate them, thereby providing a roadmap for model advancement. Following this path will yield more consistent and accurate models that are urgently needed to tackle key scientific and societal challenges.
ER -
