Freshwater Use by U.S. Power Plants: 

Electricity’s Thirst for a Precious Resource

Appendix A: Key EW3 Methods and Assumptions 

The UCS EW3 energy-water database is a compilation of information from various sources, and 

analyses based on those data. This appendix outlines different aspects of the compilation and 

analysis.

EIA Data 

The core of the UCS EW3 database is information collected by the Energy Information Agency 

(EIA) in 2008 on forms 860 and 923. In 2008, the EIA required reports only from operators of 

organic-fueled (coal, natural gas, oil, and biomass) steam electric plants with capacities greater 

than 100 MW. 

Form 860 is the “Annual Electric Generator Report,” a 60-page questionnaire on the 

characteristics of 19,558 generators (at approximately 5,928 plants) for that year. Form 860 

collects information separately about generators (capacity, ownership, age, major fuel use, and 

cogeneration), boilers (design firing rate, compliance with various air regulations, and what 

generators and emissions controls they were connected with), and cooling systems (cooling type, 

cooling tower type if applicable, cooling pond size, and design parameters for pumps). Of 19,558 

generators covered by reports in 2008, owners reported that 15,694 were in operation that year. 

Operational information at the plant, generator, and cooling system scale is reported on EIA form 

923, the “Power Plant Operations Report.” The 2008 EIA dataset includes reported and 

estimated monthly power generation at 5,242 electrical plants (nearly the entire set of operational 

plants in 2008). 

Form 923 also supports detailed schedules, including Schedule 8, Part D, a database of 1,531 

cooling systems. This information includes water withdrawal and consumption rates (in cubic 

feet per second) that are either estimated or measured. Of the more than 19,000 generators for 

which EIA received data in 2008, 4,545 were of a type (“prime mover”) that we determined 

required cooling. Of these, only 3,583 generators at 1,748 plants were operational in 2008. 

We tagged generators as requiring cooling if they included steam-driven turbines, heat-recovery 

steam generators on combined-cycle units, single-shaft combined-cycle units, or binary-cycle 

geothermal turbines. Our analysis assumes that combustion turbines, combustion turbine 

components of combined-cycle generators, internal combustion engines, and wind, photovoltaic, 

and hydroelectric generators do not require cooling. 
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Appendix A: Methodology

2 

Net Breakdown to Generating Unit Level, and Assumptions 

Of 3,583 water-cooled operational units identified, only 2,757 reported electrical output at the 

generator level on Form 923 Schedule 5A. We had to estimate net output at the generator scale at 

the remaining units, as only plant information was available. If a generator was the only unit at 

the plant, then we attributed the plant’s electrical output from EIA Form 923 to that generator; 

this applied to a single generator in this analysis. 

For the remaining 1,872 generators, we applied a simple algorithm. If a plant had any units for 

which operators submitted Schedule 5A, we deducted the electrical output from those units from 

overall plant output as reported on the primary Form 923. We then distributed the remaining 

electricity output from the plant among all remaining generators in proportion to nameplate 

capacity. This approach assumes that all generators at a plant have the same capacity factor 

(utilization). We found no additional information by which to improve the parsing of generation. 

We did not include plants where generators were retired or on standby, or for which operators 

reported no electrical output, in our analyses. 

In some instances, total electrical output reported for the plant on Form 923 or the generatorlevel production on Schedule 5A was negative, or the algorithm above returned a negative value 

for the residual output. These values may be legitimate for units that run at spinning reserve only, 

or that provide peaking capacity but have a higher parasitic load than electrical output in most 

operational hours. Only 649 of the 19,558 generators in the database reported negative output, 

usually at a very low capacity factor. Of the 3,583 units in this water-use analysis, we estimated 

only 29 to have negative electricity output (at very small values), and of those, all but 3 reported 

the negative output directly on Form 923 Schedule 5A. We did not include those facilities in the 

EW3 analysis. 

Reported Water Consumption and Withdrawals 

To associate cooling water data with specific generators, we scaled the reported water 

withdrawals and consumption of each cooling system back to the generator that used that system. 

Cooling systems are associated with boilers, and boilers are associated with generators. 

However, at any connection, there may be multiple stages that merge or divide (that is, a single 

cooling system used by multiple boilers, which feed multiple generators). Although information 

that allows for characterization of those linkages is available, information that accurately 

diagrams the complex connections is not readily available. 

To best estimate water use at the generator scale, we assumed that each generator passed 

electrical output down to each boiler associated with it in equal parts, and that those boilers 

passed that output down to their associated cooling systems in equal parts. If a boiler served 

more than one generator, we summed the output (or partial output) from all served generators to 

the boiler. 

We applied the same approach at the cooling system level relative to the boiler, passing water 

use (withdrawals and consumption) to all boilers served by the cooling system, prorated by the 

amount of generation served by each boiler. We then passed this water use to the generator 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

3 

served by those boilers, again prorated by the electrical output of the generator served by those 

boilers. 

Of the 3,583 units requiring water for cooling, 21 percent (769) did not have a boiler association 

(and therefore no recorded water use available), 50 percent (1,788) had a single boiler 

association, and the remaining 28 percent (1,026) recorded more than one boiler. The units 

associated with more than one boiler represented only 8 percent of the electrical output in this 

analysis. However, not all boilers recorded a cooling system association, and of those that did, 

not all reported cooling water use (either withdrawals or consumption). Operators reported only 

1,526 cooling structures at 729 plants on Form 923 Schedule 8D. Of these, operators reported 

both withdrawals and discharges for only 74 percent (1,133). 

After passing this information through the boiler level and back to the generator level, we had 

estimated withdrawals for 39 percent of the generators (1,410), and estimated consumption for 

27 percent (991). 

Cooling System and Tower Type 

Each power plant operator self-reports the type of cooling structure to the EIA through Form 

860. The cooling systems that operators could select in 2008 included once-through systems 

using freshwater, saline water, or cooling pond(s) or canal(s); recirculating systems with forced, 

induced, or natural-draft towers; or recirculating systems that relied on a cooling pond or canal. 

Options for towers included mechanical draft or natural draft, wet- or dry-cooled, or a 

combination wet-dry process. 

To associate specific generators with particular cooling structure “types” and towers, we did a 

trace-through similar to that for water use described above. However, because the types are 

discrete values, we associated the first boiler’s first cooling structure type with each generator. 

After tracing cooling structures through to generators, we found that operators of only 1,741 of 

the 3,583 generators (representing 68 percent of electrical output) reported a cooling structure to 

the EIA. 

We checked reported cooling system data using Google Earth. For each plant identified as 

requiring cooling and having a capacity greater than 60 megawatts (MW), we checked the 

cooling type using visual observation via Google Earth, reassigning cooling system types to 

plants as warranted. We reassigned cooling types by first determining if a plant had a naturaldraft or cellular cooling tower structure on site, or if the plant had a dry-cooling system rack. If 

neither cooling system could be located, we found the closest water body, and located a 

discharge plume, intake structure, or canal for once-through cooling systems. 

For most plants using once-through cooling, both the intakes and discharge structures were clear, 

and canals or special cooling diversions to local water bodies easily identified. We carefully 

vetted sites before determining that a plant used a form of once-through cooling. We also used 

Google Earth to check all plant sites reported by the EIA as having a cooling pond or canal for 

once-through or recirculating systems. We recategorized most of those as having cooling ponds. 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

4 

Of the 3,576 generators of interest, we manually corrected 848 units (representing 32 percent of 

electrical output), or added new information to the database. Only 970 units smaller than 60 MW 

did not report to the EIA, and those units represented only 1.8 percent of total electrical output. 

We did not manually correct those plants. 

Environmental Compliance Systems 

Our visual analyses also uncovered an additional category of cooling strategies, which we 

captured in the database. Google Earth imagery showed six plants with cooling towers 

downstream of their boiler condensation loop, with those towers instead attached to discharge 

canals or streams. The towers, distant enough from plants with explicitly once-through cooling 

systems, represent what we termed environmental-compliance cooling: cooling towers in place 

solely to reduce the temperature of effluent. 

We designated these facilities as once-through in the database, but noted the environmentalcompliance systems. Plants with this form of environmental-compliance towers have very 

different cooling and water-use characteristics from either once-through or tower-cooled plants. 

We were not in a position to characterize water use by such downstream towers, however, and 

did not include their water use in the analysis. 

Calculated Water Withdrawals and Consumption 

To calculate withdrawals and consumption by generator and power plant, we incorporated into 

the database a series of water coefficients based on a meta-analysis of published estimates of 

water use by various fuels and cooling technologies from the National Renewable Energy 

Laboratory (NREL) (Macknick et al. 2011). The one exception was geothermal plants. To more 

accurately reflect freshwater use by such plants, given that they use geothermal fluids in place of 

freshwater, we used geothermal water-use coefficients from Argonne National Laboratory (Clark 

et al. 2011) in place of data from NREL. 

We applied the median, minimum, and maximum water coefficients (gallons per megawatt-hour, 

or MWh) to the best estimate of electrical output at the generator scale to calculate the range of 

possible water withdrawals and consumption. To harmonize plant characteristics with the results 

of the NREL meta-analysis, we made selected assumptions in applying the coefficients. We 

assumed, for example, that all coal units were generic rather than super and subcritical, as we 

had insufficient information on plant types to distinguish them. We also assumed that oil- and 

natural gas–fired steam plants using cooling ponds—plants without a designation in the NREL 

coefficients—had the same water-use habits as coal-fired steam plants with cooling ponds. 

Water Source Categories 

To adequately characterize water use for electricity generation, we needed to identify sources of 

water for cooling. We collected names of such sources from EIA Form 860, Schedule 2, for 

2008, which provided plant-reported data. Because this dataset did not provide information on 

water sources for nuclear plants, we obtained additional names of water sources from EIA Form 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

5 

767 from 1996 to 2000. We coded the reported names according to these water source 

categories: 

• Air cooled: All water source data that indicated air cooling. 

• Ocean: All data with “channel,” “ocean,” “bay,” “harbor,” or “sea” in the name of the 

water source. 

• Surface water: All water source data with “river” or “lake,” “pond,” “creek,” 

“reservoir,” or “stream,” or similar signifiers. 

• Groundwater: All water source data with “aquifer,” “well,” or “groundwater.” 

• Municipal water: All water source data with “agency,” “city,” “municipal,” or 

“authority,” where a surface, groundwater, or municipal wastewater resource was not 

explicitly designated. 

• Wastewater: All water source data with “reuse,” “waste,” “WWTP” (wastewater 

treatment plant), or “treatment.” We included municipal sources that explicitly noted 

wastewater here. 

• Groundwater/surface water /municipal/wastewater hybrids: As indicated by the 

source names, which we coded accordingly. 

• Unknown ocean: All plants with no name for the water source located within one mile 

of shoreline, according to geolocational data. 

• Unknown freshwater: All plants with no name for the water source located more than 

one mile inland, according to geolocational data. 

Temperature 

We compiled datasets for maximum temperatures of intake and outflow water for power plant 

cooling units from EIA Form 923, "Power Plant Operations Report," in 2008. 

Carbon Emissions 

To assess carbon emissions and intensity, we incorporated CO2 data reported to the U.S. 

Environmental Protection Agency by operators of power plants with continuous emissions 

monitoring systems (CEMS), and compiled by SNL Energy. The CO2 data covered emissions 

from approximately 1,200 plants, which together were responsible for 92 percent of electrical 

output from carbon-emitting plants. The plants without associated carbon data were non-emitting 

plants, such as hydroelectric, wind, solar, and geothermal, or non-reporting CO2-producing 

plants. 

Reporting Gaps 

Comparisons with Other Datasets 

We compared our calculations of water withdrawals based on the database outlined above with 

withdrawals reported by the EIA for 2008, along with withdrawals from thermoelectric power 

plants reported by the U.S. Geological Survey (USGS) in 2005 (USGS 2009), the most recent 

five-year water census from USGS. Because USGS water use data are reported by county, we 

used a dataset resampled at the eight-digit hydrologic unit code (HUC-8) level, provided by the 

Raleigh Eastern Forest Environmental Threat Assessment Center of the U.S. Forest Service for 

use in its water stress assessment model (Sun et al. 2008). 
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Appendix A: Methodology

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Identification of Nonreporting Plants 

To characterize nonreporting plants, we identified all plants reporting no water use, and 

differentiated between those that did not have to report to the EIA in 2008 and those that did. 

We used EIA requirements for 2008, which mandated reporting only by operators of organicfueled (coal, natural gas, oil, and biomass) steam electric plants with capacities greater than 100 

MW, to filter out nonreporters. For plants using both reported and nonreported fuels (e.g., 

coal/nuclear or biomass/solar), we assumed that operators divided reported water use correctly 

between generators. We filtered out the electrical output and water use for nonreported fuels 

(such as nuclear and solar), including it only as part of nameplate capacity. 

Spatial Analyses 

Plant Locations 

We acquired the latitudes and longitudes reported by operators of 6,600 plants (unpublished) to 

the EIA and compiled by the Civil Society Institute. The accuracy of these locational data varied, 

but they allowed manual identification using Google Earth of the plant of interest (based on 

location, type, and size) within several miles of the geocoded location. In the West (the 

electricity regions of the Western Electricity Coordinating Council and Texas), we corrected 

locational data manually. If locational data were determined to be inaccurate, we derived power 

plant information using traditional Google Internet searches. We then used this information to 

assign accurate latitudes and longitudes by finding the physical location of the power plants 

through Google Earth. 

HUC Delineation and Assumptions 

For components of the EW3 analysis that assessed water use and reporting at the scale of water 

resource region, we plotted plant latitudes and longitudes in ArcView using 

GCS_North_American_1983 as the datum. We then assigned each plant to the relevant two-digit 

hydrologic unit code (HUC-2). To assess the accuracy of the categorization at the HUC-2 level, 

we checked that the state indicated by each plant’s latitude and longitude matched the state that 

the plant operator reported as its physical address. 

We similarly assigned plants to the HUC-8 by latitude and longitude, but noted some problems 

with this assignment. First, 187 generators mapped outside of HUC-8 areas, in either the ocean 

or other large water bodies. We assigned these plants to the nearest HUC-8 by distance, and also 

checked them visually through Google Earth. 

Second, as noted, the latitudes and longitudes reported by power plant operators seem to vary in 

quality, as inspection with Google Earth indicated that some plants might actually be sited 

several miles from their reported locations. While we corrected locations for every plant in arid 

electricity regions of the Western Electricity Coordinating Council and Texas, our analysis did 

not include similarly rigorous corrections of locational data east of these regions. However, a 

random sample of 120 geolocations in Google Earth indicated that 90 percent of power plants 

were located within the HUC-8 indicated by their latitudes and longitudes. 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

7 

Water Stress 

WaSSI 

Researchers have formulated a number of ways to quantify different aspects of water stress and 

water vulnerability in both peer-reviewed and gray literature. In considering how to assess water 

stress as part of the EW3 analysis, we focused primarily on peer-reviewed approaches, 

including:1

• Alcamo, J.; Henrichs, T.; Rösch, T. 2000. World Water in 2025: Global Modeling and 

Scenario Analysis. In: Rijsberman, F. (ed.) World Water Scenarios. Earthscan 

Publications, 243-281 

• Elcock, D., and J.A. Kuiper. Water vulnerabilities for existing coal-fired power plants. 

2010. DOE/NETL-2010/1429. Golden, CO: U.S. Department of Energy, National Energy 

Technology Laboratory. 

• Gleick, P.H. 1990. Vulnerability of water systems. In Climate change and U.S. water 

resources, edited by P.E. Waggoner. New York, NY: Wiley, pp. 223–240. 

• Hurd, B., N. Leary, R. Jones, and J. Smith. 1999. Relative regional vulnerability of water 

resources to climate change. Journal of the American Water Resources Association 35(6). 

• Jenicek, E.M., and N.R.D. Myers, D.F. Fournier, K. Miller, M. Hessel, R. Carroll, and R. 

Holmes. 2009. Army installations water sustainability assessment: An evaluation of 

vulnerability to water supply. ERDC/CERL TR-09-38. Champaign, IL: U.S. Army Corps 

of Engineers, U.S. Army Research and Development Center, Construction Engineering 

Research Laboratory. 

• Strzepek, K., G. Yohe, J. Neumann, and B. Boehlert. 2010. Characterizing change in 

drought risk for the United States from climate change. Environmental Research Letters

(5):44012. 

• Sun, G., S.G. McNulty, J.A. Moore Myers, and E.C. Cohen. 2008. Impacts of multiple 

stresses on water demand and supply across the southeastern United States. Journal of 

American Water Resources Association 44(6):1441–1457. 

• Vorsmarty, C.J., P. Green, J. Salisbury, and R. Lammers. 2000. Global water resources: 

Vulnerability from climate change and population growth. Science (289):284–288. 

Of these, we selected the only indicator that captures both demand and available supply at a fineenough scale to adequately capture water stress: the Water Supply Stress Index, or WaSSI (Sun 

et al. 2008; 2011). The WaSSI is calculated as the ratio of water demand to water supply. The 

other measures looked only at supply, or at relatively coarse scales. The exception is the 

approach taken by the U.S. Army Corps of Engineers (Jenicek et al. 2009). However, that 

approach is not yet peer-reviewed, so we used the more rigorous peer-reviewed approach of Sun 

et al. 2008. 

For each of the 2,106 eight-digit hydrologic units (HUC-8) nationwide, WaSSI calculates water 

supply as the sum of a) annual surface water supply, averaged from 2003 to 2007 (Caldwell 

 

1

 The one non-peer-reviewed approach we considered was developed by the U.S. Army Corps of Engineers (Jenicek 

et al. 2009). 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

8 

2011a); b) groundwater supply; and c) return flows from major water users, including cities, 

agriculture, and power plants. Groundwater supply and return flows are resampled from county 

to the HUC-8 level from USGS water use data (USGS 2009), except for thermoelectric 

groundwater withdrawals and return flows, which are from our analysis. Interbasin transfers are 

not explicitly taken into account in surface water supply, although they are reported implicitly in 

return flows from water users. This is an important caveat in interpreting the WaSSI analysis, 

particularly in regions with significant conveyance systems. 

WaSSI calculates water demand as withdrawals by seven major users (commercial, domestic, 

industrial, irrigation, livestock, mining, and thermoelectric). Again, these data are derived from 

USGS (USGS 2009), except for thermoelectric withdrawals, which are from our analysis. 

The higher a basin's WaSSI, the greater its water stress. For our analysis, we considered basins 

with a WaSSI above 0.4 to be highly stressed (Caldwell 2011b). In basins with a WaSSI 

exceeding 1.0, more water is consumed than is supplied within the HUC-8 basin, meaning that 

water is supplied by other regions, surface waters and aquifers are being depleted, or both. 

Power Plant Contributions to Water Stress 

To identify basins where power plants contribute to the water burden, we calculated the WaSSI 

for each HUC-8 basin nationwide, both with and without power plant water withdrawals. 

Mapping the difference between the two highlighted the contribution of electricity generation to 

local water stress across the country. Those watersheds listed in Appendix B (Table B-8) are the 

25 with the highest difference between those two WaSSI scores (“WaSSI anomaly”) based on 

median NREL values 

Because WaSSI is a broad indicator of water stress based on supply, we removed as hotspots or 

power-stressed watersheds any that are located directly on one of the Great Lakes and for which 

power plants in the basin listed one of the lakes as their cooling sources. Those and other basins 

on the Great Lakes would merit closer attention to determine power plant-induced water stress. 

Ecological Effects 

We outlined the potential impact of electricity-related activities on aquatic species by comparing 

power plant peak water discharge temperatures and locations to total counts of aquaticassociated2

 and aquatic-obligate3 species (NatureServe 2011). 

 

2 Aquatic associated species are those ranked G1-G2 by NatureServe, or that have federal status under the U.S. 

Endangered Species Act. Aquatic associated is defined as any species known to be associated with—but not 

necessarily dependent on—aquatic or wetland habitats at some point in its lifecycle, based on NatureServe’s habitat 

data, or on taxonomic group in the absence of habitat data. Such species include aquatic insects, amphibians, and 

shorebirds, for example. 

3 Aquatic obligate species are those ranked G1-G2 by NatureServe, or that have federal status under the U.S. 

Endangered Species Act. Aquatic obligate is defined as any species known to spend all or part of its lifecycle living 

in the water, based on NatureServe’s habitat data, or on taxonomic group in the absence of habitat data. These 

species include fish, crayfish, mussels, and dragonflies, for example. 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

9 

Power Plant Water Use Projections 

To examine potential water use by power plants over the coming decades, we modeled a basecase electricity development scenario and applied water coefficients to the results. We started 

with the base case for growth in electricity demand from the EIA (EIA 2011).4

 We used 

Regional Energy Deployment System (ReEDS), a computer model developed by the NREL that 

optimizes the build-out of electricity capacity and transmission across the United States through 

2050 (NREL 2009). 

As inputs and assumptions, we used the EIA’s base case for fuel costs, and middle-of-the-road 

estimates based on our survey of a range of sources of technology costs and performance. Our 

projections of resource availability were based on the NREL’s assessment of renewable energy 

resources (2009) and EIA 2011 for fuels. 

We modeled the mix of power plant generation at biannual time steps to 2036 and beyond for the 

134 electricity subregions in the continental United States. The base case assumes growing 

populations, and existing policies and economics, to project the mix and locations of power plant 

types to meet demand. 

Using electricity generation and capacity figures for each subregion provided in the ReEDS 

output, we then applied water coefficients derived from Macknick et al. 2011 to compare 

calculated water use in 2008 with that in 2036. To accommodate the different types of 

technologies in ReEDS (which has 23, versus more than 60 NREL coefficients), we used 

weighted water coefficients for each fuel/technology based on the mix of cooling types in 2008. 

For 2036: 

• In each subregion where capacity for that fuel/technology type was lower than in 2008, 

we applied the 2008 water coefficient, assuming that capacity of that type was retired 

proportionally with cooling types. (That is, the mix of once-through and recirculating 

plants stayed constant, for example.) We also assumed that no new plants were built that 

would change the water factor. 

• In each subregion with a capacity in 2036 greater than in 2008, we applied the 2008 

water coefficient to the 2008 capacity level, and a recirculating-cooling water coefficient 

to all new capacity. Those estimates assumed that 2008 capacity (or the water-factor 

equivalent) would still be online in 2036, supplemented by more water-efficient (lowerwithdrawal) plants. 

We then multiplied the water factors for each year by the generation for that year, and assessed 

changes at the national, Southwest, and Southeast levels. 

We also analyzed projected changes in CO2 emissions in each of those geographic jurisdictions 

using the CO2 figures included in the EW3 database. 

 
Freshwater Use by U.S. Power Plants: Electricity’s Thirst for a Precious Resource 

Appendix A: Methodology

10 

Water and Carbon Intensities of Electricity Providers 

To calculate the water and carbon intensities of the top electricity providers in the United States, 

we aggregated data by plant owner at the parent company level, based on data available through 

EIA reporting. We calculated 2008 electricity generation figures and selected the 15 topproducing companies. We then calculated freshwater withdrawal and consumption intensities 

(total minimum and maximum freshwater values for each company, divided by the company’s 

total generation). We also calculated carbon intensity (reported emissions for each company, 

divided by the company’s total generation). 

Methodology References 

Caldwell, P. 2011. Raleigh, NC: U.S. Forest Service, Eastern Forest Environmental Threat 

Assessment Center. Personal communication, June 9. 

Clark, C.E, C.B. Harto, J.L. Sullivan, and M.Q. Wang. 2011. Water use in the development and 

operation of geothermal power plants. Argonnne, IL: Argonne 

Energy Information Administration (EIA). 2011. Annual energy outlook 2011 with projections 

to 2035. Washington, DC: U.S. Department of Energy. Online at 

http://www.eia.gov/forecasts/aeo/.

Macknick, J., R. Newmark, G. Heath, and K.C. Hallett. 2011. A review of operational water 

consumption and withdrawal factors for electricity generating technologies. NREL/TP-6A20-

50900. Golden, CO: National Renewable Energy Laboratory. 

Master, L.L., S.R. Flack, and B.A. Stein. 1998. Rivers of life: critical watersheds for protecting 

freshwater biodiversity. Arlington, VA: Nature Conservancy. Online at 

http://www.tnc.org/science/. 

National Renewable Energy Laboratory (NREL). 2009. ReEDS model documentation: Base case 

data and model description. Golden, CO. Online at 

http://www.nrel.gov/analysis/reeds/pdfs/reeds_full_report.pdf. 

NatureServe. 2011. NatureServe’s central databases. Arlington, VA. 

http://www.natureserve.org/ 

Sun, G., P. Caldwell, A. Noormets, E. Cohen, S.G. McNulty, E. Treasure, J.-C. Domec, Q. Mu, 

J. Xiao, R. John, and J. Chen. 2011. Upscaling key ecosystem functions across the conterminous 

United States by a water-centric ecosystem model. Journal of Geophysical Research 116:1–16. 

Sun, G., S.G. McNulty, J.A. Moore Myers, and E.C. Cohen. 2008. Impacts of multiple stresses 

on water demand and supply across the southeastern United States. Journal of American Water 

Resources Association 44(6):1441–1457. 

U.S. Geological Survey (USGS). 2011. National water information system. Reston, VA. Online 

at http://nwis.waterdata.usgs.gov.