Virtual water transfers in U.S. cities from domestic commodity flowsIkechukwu Chris Ahams 1 , Willa Paterson 1, and Alfonso Mejia 1

OBJECTIVE/APPROACH

STUDY AREA

For this study, we considered 65 major US cities based on the available commodity flow datafrom the US Federal Highway Administration3. The city boundaries were delineated according tothe Metropolitan Statistical Areas and Combined Statistical Areas definitions of the US Office ofManagement and Budget. Fig. 1 shows the spatial distribution of the 65 selected cities, togetherwith their boundaries and approximate population.

Urban water sustainability requires that cities improve their water-use efficiency to reduce orshift water requirements. To attain this goal, cities need to track their water use and the impacttheir consumption patterns have on freshwater resources. We implement here the waterfootprint (WF) concept1 to quantify the domestic or internal water use of consumption andproduction for 65 major US cities. We use available commodity flow, water-use, and virtualwater content data to estimate the WF2,3.

The commodities analyzed make up ~51% of the total domestic freight flow (Fig.4). For cities, the industrial commodities make up ~90% by weight, leaving theagricultural commodities with ~10% (Fig. 4).

On average, the analyzed cities produce ~30% of their own domestic WF of consumption whilethe remaining 70% is imported from other locations, mostly non-city sources. This dependency isillustrated in Fig. 2 for the 65 selected cities. Fig. 2 shows that there are large variations in thelevel of virtual water dependency of cities to domestic locations outside the city.

In terms of the net per capita WF, Fig. 3 shows that some citiesare net consumers while others are net producers. To computethe net per capita WF, we used the WF of production minus theWF of consumption. However, the majority of the citiesconsidered here are net consumers, as may be expected giventhe strong dominance of the agricultural sector in WF estimatesand the limited availability of productive land areas within cities.

1. Hoekstra, A. Y. and M. M. Mekonnen. 2012. The water footprint of humanity. Proceedings of the National Academy of Sciences.2. Dang, Q., X. Lin, and M. Konar. 2015. Agricultural virtual water flows within the United States. Water Resources Research.3. Paterson, W., R. Rushforth, B. Ruddell, M. Konar, I. Ahams, J. Gironás, A. Mijic, and A. Mejia. 2015. Water Footprint of Cities: A Review and Suggestions for Future Research. Sustainability.

Fig. 1

Animal feed, 12.60%Cereal grains, 58.09%Live animals, 2.16%Meat, 12.14%Milled grain prods., 15.01%

Other industrial commodities, 30.35%Base metals, 5.03%Basic chemicals, 5.72%Coal-n.e.c., 17.80%Nonmetal min. prods., 21.15Waste/scrap, 19.95%

Proportion of individual industrial commodities consumed

Proportion of individual agricultural commodities consumed

Other industrial commodities, 29.58Base metals, 5.83%Basic chemicals, 6.41%Coal-n.e.c., 15.94%Nonmetal min. prods., 21.66%Waste/scrap, 20.58

Animal feed, 15.82%

Cereal grains, 48.43%

Live animals, 2.98%

Meat, 12.36%

Milled grain prods., 20.41%

Proportion of individual industrial commodities produced

Proportion of individual agricultural commodities produced

Fig. 2

Fig. 3

Fig. 6Fig. 5

Fig. 4

REFERENCES

CONCLUSIONS

Water footprint of consumption Water footprint of production

RESULTS

1The Pennsylvania State University, Department of Civil and Environmental Engineering, 212 Sackett Building, University Park, PA 16802-1408

Corresponding author: Ikechukwu C. Ahams, 1ca102@psu.edu

Fig. 7 Fig. 8

- The urban per capita WF varies greatly from city to city, thus indicatingthat cities are heterogeneous in terms of their agricultural andindustrial production and consumption.

- City resiliency is likely to exhibit large variations given that some citiesare mostly self-reliant while other depend strongly on locations outsidetheir boundaries.

Both Fig. 5 and Fig. 6 highlight the spatial heterogeneity of WF estimatesacross cities. The WF of consumption is dominated by agriculturalcommodities, making up ~ 99% of the total WF of consumption (Fig. 5). The10 largest cities account for ~34% of the total WF of consumption (Fig. 5).

The WF of production is also dominated by agricultural commodities,making up ~ 99% of the total WF of production (Fig. 6). The 10 largest citiesaccount for ~32% of the total WF of production (Fig. 6).

The WF of consumption (Fig. 7) shows more reliable (significantly higher R2

values) scaling relationships, both in terms of population and GDP, than theWF of production (Fig. 8), except for the industrial sector.

Overall, the scaling tends to be sublinear (i.e., the WF decreases faster withincreasing size), suggesting that large cities place less stress on domesticwater resources than small cities.

- The scaling of the urban WF of consumption with population and GDP,together with trade information, could be used to explore how cities mayshift stress from domestic to external freshwater sources as they grow.

- A few areas of improvement that we will investigate in the future are:linking key industrial commodities to indirect sources and determiningmore localized industrial water use coefficients.

H13D-1569