Potential Impacts of Climate Change on Water ...

4/3/2012

Potential Impacts of Climate Change on Water Quantity and Quality in North Carolina Rabin Bhattarai1, Ge Sun2, Steve McNulty2, Peter Caldwell2, Erika Cohen2, Jennifer Moore Myers2,Yang Zhang1 1 North Carolina State University, y Raleigh, g NC 2 USDA Forest Service, Eastern Forest Environmental Threat Assessment Center Raleigh, NC

NC WRRI Annual Conference March 28, 2012

Projected Impacts of Climate Change

(Nellemann et al., 2009)

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Climate Change in the Southeastern U.S. (Karl et al., 2009)  Rise in average annual temperature by about 1.2° C since

1970, with the greatest increase occurring in the winter.

 Current precipitation trends indicate an increase in

autumn precipitation.

 Projected rise in temperature by about 2.5°C and 5°C

under lower and higher emissions scenarios respectively by 2080. 2080

 The future frequency, duration, and intensity of

droughts in the region are likely to increase.

Objective and Methodology  Objective  Quantify potential impacts of climate change on water quantity (runoff and

WaSSI) and qqualityy (rainfall erosivity) y in North Carolina

 Methodology  Water Supply and Stress Index (WaSSI) model  Inputs for WaSSI  Parameter-elevation Regressions on Independent Slopes Model (PRISM) data for 19962005: digital grid estimates of point measurements of precipitation, temperature  3 future GCM scenarios for 2046-2055: Coupled Global Climate model (CGCM3), Geophysical Fluid Dynamics Laboratory Coupled Climate model (GFDL-CM2) (GFDL CM2) and Hadley Centre Coupled Model (HadCM3)  Model Evaluation and Future Trend Analysis  Comparison of WaSSI simulated discharge with gaging station data from USGS (19962005)  Analysis of changes in runoff, flow duration, water stress and rainfall runoff erosivity due to climate change

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Water Supply and Stress Index (WaSSI)

WaSSI =

Demand Supply

Water Stress threshold: Average annual WaSSI ≥ 0.4 (Vörösmarty et al., 2000)

Factors Affecting Water Supply and Demand

Sun et al. (2008)

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WaSSI Model

P = Precipitation, Q = Runoff, ET = Evapotranspiration, ∆S = Change in soil moisture storage, NEE = Net Ecosystem Exchange, GEP = Gross Ecosystem Production, Re = Ecosystem Respiration, LAI = Leaf Area Index, Ta = Temperature, WRR = Water Resources Region

(Sun et al., 2011)

Rainfall-Runoff Erosivity Factor (R)  The rainfall-runoff erosivity factor (R) - an index of the intensity and amount of rainfall occurring at a given location over a long period of time.  R-values can be estimated using monthly or annual precipitation data (Renard and Freimund, 1994).  A useful surrogate for assessing potential changes in future surface erosion related to climate change as the R-factor is directly affected by changes in climate, specifically in rainfall.  An important parameter in the Revised Universal Soil Loss Equation (RUSLE)  A = R × K × LS × C × P A = Average annual soil loss, K = Soil erodibility factor, LS = Slope length-gradient factor, C = Crop/vegetation and management factor, P = Support practice factor

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20000 18000 16000 14000 12000 10000 8000 6000 4000 2000 0

Neuse River near Goldsboro, HUC 3020201 (Coastal) Observed

9000

Simulated

7000

NSE = 0.76

6000 5000 4000 3000

0 12

24

36

48 60 72 Time (months)

84

96

Rocky River near Norwood, HUC 3040105 (Piedmont)

108

120

0

Simulated

6000

NSE = 0.78

5000 4000 3000 2000 1000 0 12

24

36

48 60 72 Time (months)

84

96

108

120

12

24

36

48 60 Time (months)

72

84

96

French Broad River at Rosman, HUC 6010105 (Mountains)

Observed

Discharge (cfs)

Discharge (cfs)

Simulated

NSE = 0.73

1000

7000

0

Observed

2000

0

8000

Deep River at Moncure, HUC 3030003 (Piedmont)

8000 Disscharge (cfs)

Disch harge (cfs)

WaSSI Model Validation (1996 - 2005)

1000 900 800 700 600 500 400 300 200 100 0

108

120

Observed Simulated

NSE = 0.71

0

12

24

36

48 60 Time (months)

72

84

96

108

120

NSE = Nash-Sutcliffe Efficiency index

Comparison of Average Annual Rainfall (Future – Current)

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Comparison of Average Annual Temperature (Future – Current)

Impact on Average Annual Runoff (Future – Current)

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Impact on Flow Duration 20000 16000

7000

PRISM

Neuse River near Golsboro HUC 3020201

18000

Flow (cfs)

Flow w (cfs)

PRISM GCMs

5000

14000 12000 10000 8000 6000

4000 3000 2000 1000

4000

0

2000

0.00

0 0.00

0.20

0.40 0.60 Probability of exceedance (Pe)

0.80

Rocky River near Norwood HUC 3040105

8000 7000

0.20

0.40 0.60 0.80 Probability of exceedance (Pe)

1.00

1.00

PRISM

1000

GCMs

900

French Broad River at Rosman HUC 6010105

800

6000

PRISM GCMs

700

5000

Flow (cfs)

Flow (cfs)

Deep River at Moncure HUC 3030003

6000

GCMs

4000 3000

600 500 400 300

2000

200

1000

100

0

0 0.00

0.20

0.40 0.60 Probability of exceedance (Pe)

0.80

1.00

0.00

0.20

0.40 0.60 0.80 Probability of exceedance (Pe)

1.00

Average Annual WaSSI (Current and Future)

An average annualWaSSI ≥ 0.4 indicates water stress

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Impact on Monthly WaSSI Avg monthly WaSSI in NC Piedmont region

3.50

PRISM

3.00 Avg_GCMs

2.50

0.60

WaSSI W

2.00

Avg monthly WaSSI in NC Coastal region

0.50 0.40

1 50 1.50

PRISM

1.00

Avg_GCMs

0.50

0

2

4

6 8 Time (months)

10

12

14

0.20 0.10

Avg monthly WaSSI in NC Mountain region

2.00

0.00 0

2

4

6 8 Time (months)

10

12

1.80

14

PRISM

1.60 1.40

Avg_GCMs

1.20 WaSSI

WaSSI

0.00 0.30

1.00 0.80 0.60 0.40 0.20 0.00 0

2

4

6 8 Time (months)

10

12

14

Impact on Average Annual Erosivity (Future – Current)

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Conclusions  In response to projected climate changes (2.3% increase in average annual

rainfall, 2.3 degree C increase in average annual temperature) in North Carolina  Runoff: 4.6% decrease in average annual runoff mainly due to increase in ET  Coastal: 11.2% decrease  Piedmont: Pi d 2 6% decrease 2.6% d  Mountains: 0.4% decrease  Water Stress: 4.7% increase in average annual WaSSI due to change in demand

and supply

 Coastal: 6.8% increase  Piedmont: 6.4% increase  Mountains: 1.4% increase

 Rainfall-runoff erosivity: y 1.5% decrease in average g annual R due to rainfall

variability

 Coastal: 3.3% decrease  Piedmont: 3.5% increase  Mountains: 5.5% increase

 Policy implications due to climate change: water supply for drinking water,

power plant water availability, irrigation water use, soil erosion potential in the hilly areas

Acknowledgements NSF Award #1049200 PRISM Climate Group (Oregon State University) Coupled Global Climate model (CGCM3) group Geophysical Fluid Dynamics Laboratory Coupled Climate model (GFDL-CM2) group  Hadley Centre Coupled Model (HadCM3) group    

Thank you!

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References 

Karl, T.R., J.M. Melillo, and T.C. Peterson (eds.). 2009. Global Climate Change Impacts in the United States. Cambridge University Press, New York.



Nellemann, C., Nellemann C MacDevette MacDevette, M M., Manders Manders, T T., Eickhout Eickhout, B B., Svihus, Svihus B., B Prins, Prins A. A G G., Kaltenborn, Kaltenborn B. B P. (Eds). February 2009. The environmental food crisis – The environment’s role inmaverting future food crises. A UNEP rapid response assessment. United Nations Environment Program.



Renard, K. G., and J. R. Freimund. 1994. Using monthly precipitation data to estimate the R-factor in the revised USLE. Journal of Hydrology 157:287–306.



Vörösmarty C J, Green P, Salisbury J and Lammers R B 2000 Global water resources: vulnerability from climate change and population growth Science 289 284–8.

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