  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.
