Fig 1.
Illustration of hypothetical virtual water flows (VWF) between two cities: Atlanta and Pittsburgh.
The meaning of the variables is as follows: water footprint of consumption, WFC; water footprint of production, WFP; domestic water, DW; and commercial water, CW.
Fig 2.
(a) Urban scaling of commodity consumption (expressed in monetary value) with population size. For food commodities, β = 1.10 (95% CI [0.98, 1.22]) and R2 = 0.84. For industrial commodities, β = 0.86 (95% CI [0.79, 0.94]) and R2 = 0.89. (b) Urban scaling of commodity production (expressed in monetary value) with population size. For food commodities, β = 1.05 (95% CI [0.85, 1.25]) and R2 = 0.63. For industrial commodities, β = 0.95 (95% CI [0.79, 1.11]) and R2 = 0.70. For all cases, p-value<0.001 and the line indicates the best-fitted line by ordinary least squares in logarithmic scale.
Table 1.
Summary of scaling exponents used to explain the scaling of WFC and WFP.
Fig 3.
(a) Scaling of the water footprint of consumption (WFC) with population size where the scaling exponent is 0.92 (95% CI [0.75, 1.09]) and R2 = 0.65. (b) Scaling of the water footprint of production (WFP) with population size where scaling exponent is 0.91 (95% CI [0.66, 1.17]) and R2 = 0.44. For all cases, p-value<0.001 and the line indicates the best-fitted line by ordinary least squares in logarithmic scale.
Fig 4.
Scaling of the urban water footprint (WF) with population size and GDP.
For WF vs. population, the scaling exponent is 0.88 (95% CI [0.70, 1.06]) and R2 = 0.61. For WF vs. GDP, the scaling exponent is 0.74 (95% CI [0.58, 0.91]) and R2 = 0.56. For all cases, p-value<0.001 and the line indicates the best-fitted line by ordinary least squares in logarithmic scale.
Fig 5.
Spatial distribution of the (a) blue water footprint (m3/year) and (b) blue and green water footprint (m3/year) of consumption and production for the analyzed US cities. The water footprint of consumption is separated into direct and indirect contributions.