Uncertainties as a Guide for Global Water Model Advancement

Global water models increasingly 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 uncertainties that mainly originate from 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 improved knowledge, will help to reduce these uncertainties and eventually lead to model advancement. In this review, we explore sources of uncertainty critical to global water models and define actions to reduce them where possible, therefore providing a guide for global model advancement. Following this path will increase the robustness of model outputs, which is urgently needed to tackle key scientific and societal challenges.