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The partitioning of precipitation (P) water input on land between green (evapotranspiration, ET) and blue (runoff, R) water fluxes distributes the annually renewable freshwater resource among sectors and ecosystems. We decipher the worldwide pattern and key determinants of this water flux partitioning (WFP) and investigate its predictability based on a machine learning (ML) model trained and tested on data for 3,614 hydrological catchments around the world. The results show considerably higher WFP to the green (ET/P) than the blue (R/P) flux in most of the world. Land-use changes toward expanded agriculture and forestry will increase this WFP asymmetry, jeopardizing blue-water availability and making it more vulnerable to future P changes for other sectors and ecosystems. The predictive ML-model of WFP developed in this study can be used with climate model projections of P to assess future blue and green water security for various regions, sectors, and ecosystems around the world.

Local SDG action is imperative to reach the 2030 Agenda, but different strategies for progressing on one SDG locally may cause different 'spillovers' on the same and other SDGs beyond local and national borders. We call for research efforts to empower local authorities to 'account globally' when acting locally., Comment: Comment article, 10 pages. Original submission, a later (post peer-review) version is accepted for publication in NPJ Urban Sustainability