This document discusses water stress on U.S. power production at decadal timescales due to climate change. It finds that over 200 counties may face water scarcity by the 2030s which could put over 1 quad of power production at risk. It also finds increased stream temperatures exceeding EPA limits in some counties. The analysis considers uncertainties from climate models, emissions scenarios and internal climate variability in projecting these impacts through 2040. It demonstrates a framework for assessing regional water availability and associated risks to thermoelectric power plants.

1. Introduction and Background
Water Stress on U.S. Power Production at Decadal Time Horizons
Poulomi Ganguli*1, Devashish Kumar1, Janet Yun1, Geoffrey Short2, James Klausner2, Auroop R. Ganguly1
Solution Framework
Uncertainty in Estimation of Water Availability
Acknowledgements
1. Inter-model Differences in Changes in Fresh Water Availability Due to Internal Variability
 91% of total electricity in US was produced by thermoelectric power plants, which accounted for
41% of surface water withdrawal (Cooperman et al., 2012)
 In U.S. 98% of thermoelectric power plants fueled by coal, nuclear, natural gas and other
sources use water for cooling (EIA, 2014).
 Electricity demand in U.S. will grow by 29% at the rate of 0.9% per year by 2040 (EIA, 2014).
 Wet Cooled thermoelectric plants accounts for larger generation capacity as compared to other
plant categories'.
 Climate uncertainty and population growth are the major driving factors that may alter water-
energy nexus at decadal time scale.
• Total power production at risk is assessed by aggregating annual
production capacity of all power plants in the counties, where the WAACI
index is negative and stream temperature (Tstream) is above the EPA
prescribed threshold limit (TEPA = 32°C).
• Stream temperature is projected using nonlinear support vector regression
technique.
• The median values of the bias corrected air temperature from climate
models are used as predictor to develop regression relationships.
• In near term, more than 200 counties in Contiguous U.S. are likely to be
exposed to water scarcity for coming decades.
• Stream gauges in more than five counties in 2030s’ and ten counties in
2040s’ showed significant increase in water temperature, which exceeded
the EPA limit.
Conclusions
*Contact Information:
p.ganguli@neu.edu
 Primary funding source: U.S. DOE’s ARPA-E under DOE Purchase
Order #DE-AR0000482.
 Partial funding source: U.S. NSF Expeditions in Computing Award
#1029711 and the Office of the Provost of Northeastern University in
Boston, MA, USA.
, http://www.northeastern.edu/sds/1 Sustainability & Data Sciences Lab, Civil and Environmental Engineering, Northeastern University
2 Advanced Research Projects Agency – Energy (ARPA-E), United States Department of Energy
Water Stress on U.S. Power Production: Demonstration of Proof of Concept
 Climate models used:
CCSM4, GISS-E2H & MIROC5
 Initial Condition Runs: r1i1p1 & r2i1p1
 Current Water stress (i.e., decrease in
availability & increase in stream
temperature) is quantified over 5 year
segments (2010s: 2008 – 2012) for
RCP8.5 emission scenario.
 Water availability is quantified by
Water Availability Absolute Change
Index (WAACI)
Figure 2. Water Availability Absolute Change Index (WAACI) and Stream
Temperature Trend during 2010s’
 
1
1 n
t
t
WAACI P E
n
per capita water demand population

 
   
 
 

Per capita water demand = 1700 m3/year
(Falkenmark, 1986)
Water Stress at Decadal Time Scale
Figure 3. Sources of Uncertainty in Projected Global
Mean Temperature
Source: Stocker et al. 2013, IPCC AR5 WG I
Figure 4. Uncertainty in Projected Climate Variables
(a) Global decadal mean annual temperature (b) East Asia decadal mean JJA precipitation
(a) (b)
Source: Hawkins and Sutton (2009)
Figure 5. Climate Change Projections at Different Time Scales
 Internal Variability: Sensitivity to initial
conditions
 Model Spread: Inadequate physics or lack
of understanding of model parameters
 RCP Scenario Spread: Uncertainties in
Greenhouse Gas (GHG) emission scenarios
Uncertainty in Climate Projections arises
due to ..
 Shorter lead time
 Extremes
 Low frequency signals
Internal Variability dominates in presence of ..
Figure 1. Spatial Distribution of Thermoelectric Plants and their Capacity by Cooling System Type and Fuel
1 Quad
* = 293071.083 GWh
Source: EPRI, 2011
Source: http://www.eia.gov/todayinenergy/
 Changes in fresh water
availability (P – E) at each grid
point is calculated by taking
differences in 5-year average
of (P – E) from 2030s’ and
2010s’.
 rcp8.5 GHG emission scenario
& Ensemble minimum (2nd
minima) of climate models are
considered for changes in
runoff computation.
Δ (P – E)2030 = (P – E)[2028-2032]
- (P – E)[2008-2012]
Figure 6. Changes in Fresh Water Availability in 2030s’ Relative to 2010s’
3. Uncertainty due to GHG Emission Scenario in Fresh Water Availability
Figure 8. Changes in Fresh Water Availability in different GHG
Emission Scenarios
• Changes in fresh water
availability for 2030s’ relative to
2010s’ is shown for ensemble
minimum of climate models.
• Intensification of drying pattern is
observed over the Midwest, Gulf
coast and Southwest regions and
wet patterns over Northeast and
Pacific Northwest regions.
Power Production at Risk for Wet Cooled Plants
Figure 9. Schematic of
Solution Framework
Figure 10. Power Production at Risk at Projected Time Windows
Note: Ensemble minimum of climate model is considered for computation
Limitations of the Proof of Concept
• Fresh water availability at projected time scale is estimated considering only
three climate models and two GHG emission scenarios (rcp2.6 & rcp8.5).
• Uncertainty due to internal climate variability is considered by taking output from
only two initial conditions from climate models.
• Bilinear interpolation technique is employed to estimate regional water
availability; more robust estimates may be obtained by downscaling climate data.
• Future water demand is considered only from municipal and domestic public
supply; demands from other sectors are assumed as constant.
• A visual risk analysis is performed combining water scarcity and projected
stream temperature trends in spatial proximity of the power plants’ locations.
Source: Stocker et al. 2013, IPCC AR5 WG I
References
• A. Cooperman et al., Part 2, ASHRAE J. 54 (2012).
• EIA, Technical Report No. DOE/EIA-0383 (2014).
• M. Falkenmark, Ambio, 192-200 (1986).
• E. Hawkins, R. Sutton, Bull. Amer. Meteor. Soc. 90,
1095-1107 (2009).
• T.F. Stocker et al. IPCC AR5 Working Group I: Physical
Sci. Basis, 1535 (2013).
• EPRI Technical Report (2011). Accessed in March 2014.
• Resource Rev: Meeting the world’s energy, materials,
food & energy needs. McKinsey white paper (2011).
Accessed in June, 2014 .
• Estimate of WAACI index in
2030s’ for ensemble minimum &
ensemble median of climate
models are shown.
• Intensification of water scarcity
can be observed in many
regions for ensemble minimum
case.
2. Uncertainty among Models in Fresh Water Availability
Figure 7. Estimate of WAACI index in 2030s’ for Multimodel Ensemble
of Climate Models
*A single quad would provide all energy demand for New York City for ~ 3 months (Source:
McKinsey white paper, 2011).
Perspective
Note: GWh = Gigawatt-hour