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

The global freshwater system: Patterns and predictability of green-blue water flux partitioning