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 University2 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

tt

WAACI P En

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 GWhSource: 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