Alan F. HamletDennis P. Lettenmaier

JISAO Center for Science in the Earth System Climate Impacts Group

and Department of Civil and Environmental EngineeringUniversity of Washington

October, 2004

Weekly and Daily Climate Change Streamflow Scenarios and Estimates

of Changing Crop Water Demand

http://www.hydro.washington.edu/Lettenmaier/Presentations/2004/hamlet_daily_flow_crop_et_2004.ppt

Problem:

1) Monthly naturalized streamflow observations are frequently available for a large number of sites over long periods of time, but availability of weekly and daily observations is typically very limited.

2) Monthly climate change scenarios are useful, but for many studies weekly or daily flows are required (e.g. flood control).

Methods are needed to:

•Produce weekly and daily “observed” records that are consistent from monthly naturalized data.

•Produce climate change scenarios at weekly and daily time step that are consistent with observed data sets.

Observed Monthly AverageStreamflow Data

SimulatedDaily Streamflow

Data

Simulated Weekly Streamflow

Data

Reconstructed “Observed”Daily

Record

Reconstructed “Observed”Daily

Record

VIC Hydrologic ModelDriven by Observed

Temperature and Precipitation

Data

Monthly average value comes from observed data. The daily and weekly time history come from the simulations.

Short Time Step Streamflow Reconstruction Process

0

10000

20000

30000

40000

50000

60000

1 62 123 184 245 306 367 428 489 550 611 672 733 794 855 916 977 1038 1099 1160 1221 1282 1343 1404 1465 1526 1587 1648 1709 1770

obs week

adj vic week

0

5000

10000

15000

20000

25000

30000

35000

40000

1 62 123 184 245 306 367 428 489 550 611 672 733 794 855 916 977 1038 1099 1160 1221 1282 1343 1404 1465 1526 1587 1648 1709 1770

obs daily

adj vic daily

Reconstructed Naturalized Weekly and Daily Flows at Palisades Dam for 1958-1992

Str

eam

flow

(cf

s)

Weekly Flow 1958-1992

Daily Flow 1958-1962

Bias Corrected Monthly or WeeklyClimate Change

Streamflow

SimulatedDaily Streamflow

Data

Daily Time StepStreamflow ScenarioVIC Hydrologic Model

Driven by Climate Change

Temperature and Precipitation

Scenario

Monthly or weekly data comes from bias corrected simulation. The daily time history used to construct the daily data comes from the simulations.

Short Time Step Climate Change Streamflow Scenario

0

100000

200000

300000

400000

500000

600000

19

28

19

29

19

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19

31

19

32

19

33

Str

eam

flo

w (

acre

-ft/

wee

k)

"observed"

mpi2040

Weekly Climate Change Scenario for Palisades(MPI 2040 “warm and dry”)

Long-Term Estimates of Potential Evapotranspiration from a Reference

Crop

Problem:

Quantitative, spatially-explicit estimates of evaporation from irrigated crops are needed for:

•Estimates of future surface water diversions and return flows as a function of climate, irrigation technology, crop type, etc.

•Estimating losses from aquifers due to groundwater pumping for irrigation and aquifer recharge due to surface water application.

Methods:

A well-tested and frequently used method is to estimate the “potential evaporation” (PotET) from a well-watered reference crop (e.g. mature alfalfa), and then relate this to the PotET for other crops using linear factors that vary with crop type and season:

Actual Crop PotET = Kc * (PotETref)

(where Kc varies with date and actual crop)

PotETref is often estimated by the Penman Monteith equation. See e.g. :

http://www.cprl.ars.usda.gov/wmru/pdfs/982123.pdf

Conceptual Diagram of the Penman Monteith Approach

“Aerodynamic Resistance”Wind SpeedCrop Height

“Canopy Resistance”Stomotal Resistance

Leaf Area Index

“Surface Energy”Incoming Solar Radiation

Outgoing Longwave

“Vapor Pressure Deficit”Temperature

Relative Humidity

Potential Evapotranspiration

Gridded DailyPrecipitation and Temperature

Records1915-2002

VICHydrology Model

Daily Time Series of

Estimated Reference

Crop PotET1915-2002

Schematic Diagram of Simulation Tool for Producing Long Records of PotET

Seasonal Cycle of PotET for a Single Grid Cellin the Snake River Plain

0

1

2

3

4

5

6

7

8

9

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Water Year Week

Avg

Po

tET

(m

m)

1950

1994

Average July PotET for Alfalfa Reference Crop

Potential ET (mm)

Precipitation Fraction, 2020s

0.5

0.75

1

1.25

1.5

1.75

J F M A M J J A S O N D

Frac

tion

hadCM2

hadCM3

PCM3

ECHAM4

mean

Delta T, 2020s

-1

0

1

2

3

4

5

J F M A M J J A S O N D

De

gre

es

C

hadCM2

hadCM3

PCM3

ECHAM4

mean

Delta T, 2040s

-1

0

1

2

3

4

5

J F M A M J J A S O N D

De

gre

es

C

hadCM2

hadCM3

PCM3

ECHAM4

mean

Precipitation Fraction, 2040s

0.5

0.75

1

1.25

1.5

1.75

J F M A M J J A S O N D

Fra

ctio

n

hadCM2

hadCM3

PCM3

ECHAM4

mean

Four Delta Method Climate Change Scenarios for the PNW

~ + 1.7 C ~ + 2.5 C

Somewhat wetter winters and perhaps somewhat dryer summers

Average July PotET over the Southern Plain Region Current Climate vs. MPI2040 scenario

Current Climate MPI2040

PotET (mm/day)

Trends in July Avg PotET over the Southern Plain Region from 1915-2002

y = -0.0061x + 7.3589

5

5.5

6

6.5

7

7.5

8

8.5

1915

1920

1925

1930

1935

1940

1945

1950

1955

1960

1965

1970

1975

1980

1985

1990

1995

2000

Ref

eren

ce C

rop

Po

tET

(m

m/d

ay)

jul

Linear (jul)

Conclusions

Long-term gridded temperature and precipitation records can be used to drive hydrologic models to simulate potential ET for a reference crop.

Simple experiments in which the temperature is perturbed while other explanatory variables remain about the same suggest that crop water demand ought to be going up over time as the region warms.

The long term historic simulations, however, show that the trends are downward over time. One possible explanation for these trends is associated with increasing night time temperatures, which indicate that atmospheric moisture content is systematically increasing. This reduces the vapor pressure deficit and the incoming solar radiation.

These results suggest that changes in relative humidity, cloudiness, and wind may play a more dominant role than temperature alone in controlling ET. If so, more sophisticated methods for evaluating the effects of changing climate on these variables will be needed to better assess the potential changes.