Transcript hamlet_daily_flow_crop_et_oct_2004

Weekly and Daily Climate Change
Streamflow Scenarios and Estimates
of Changing Crop Water Demand
JISAO Center for Science in the Earth System
Climate Impacts Group
and Department of Civil and Environmental Engineering
University of Washington
October, 2004
http://www.hydro.washington.edu/Lettenmaier/Presentations/2004/hamlet_daily_flow_crop_et_2004.ppt
Alan F. Hamlet
Dennis P. Lettenmaier
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.
Short Time Step Streamflow Reconstruction Process
Observed
Monthly Average
Streamflow Data
Monthly average value
comes from observed data.
The daily and weekly time
history come from the
simulations.
Reconstructed “Observed”
Daily
Record
VIC Hydrologic Model
Driven by Observed
Temperature
and Precipitation
Data
Simulated
Daily Streamflow
Data
Reconstructed “Observed”
Daily
Record
Simulated
Weekly Streamflow
Data
Reconstructed Naturalized Weekly and Daily Flows
at Palisades Dam for 1958-1992
60000
Weekly Flow 1958-1992
50000
40000
obs week
30000
adj vic week
Streamflow (cfs)
20000
10000
0
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
40000
Daily Flow 1958-1962
35000
30000
25000
obs daily
20000
adj vic daily
15000
10000
5000
0
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
Short Time Step Climate Change Streamflow Scenario
Bias Corrected
Monthly or Weekly
Climate Change
Streamflow
VIC 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.
Daily Time Step
Streamflow Scenario
Simulated
Daily Streamflow
Data
Weekly Climate Change Scenario for Palisades
(MPI 2040 “warm and dry”)
500000
400000
"observed"
300000
mpi2040
200000
100000
1933
1932
1931
1930
1929
0
1928
Streamflow (acre-ft/week)
600000
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 Speed
Crop Height
“Canopy Resistance”
Stomotal Resistance
Leaf Area Index
“Surface Energy”
Incoming Solar Radiation
Outgoing Longwave
“Vapor Pressure Deficit”
Temperature
Relative Humidity
Potential
Evapotranspiration
Schematic Diagram of Simulation Tool for Producing Long Records of PotET
Gridded Daily
Precipitation and Temperature
Records
1915-2002
VIC
Hydrology Model
Daily Time
Series of
Estimated
Reference
Crop PotET
1915-2002
Seasonal Cycle of PotET for a Single Grid Cell
in the Snake River Plain
9
8
7
Avg PotET (mm)
6
5
1950
1994
4
3
2
1
0
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
Average July PotET for Alfalfa Reference Crop
Potential ET (mm)
Four Delta Method Climate Change Scenarios for the PNW
Delta T, 2020s
Delta T, 2040s
5
5
~ + 1.7 C
~ + 2.5 C
4
hadCM2
3
hadCM3
2
PCM3
ECHAM4
1
Degrees C
Degrees C
4
mean
0
hadCM2
3
hadCM3
2
PCM3
ECHAM4
1
mean
0
J
F
M
A
M
J
J
A
S
O
N
D
J
-1
F
M
A
Precipitation Fraction, 2020s
J
J
A
S
O
N
D
Precipitation Fraction, 2040s
1.75
1.75
1.5
1.5
hadCM2
hadCM3
1.25
PCM3
1
ECHAM4
Fraction
Fraction
M
-1
hadCM2
hadCM3
1.25
PCM3
1
ECHAM4
mean
0.75
mean
0.75
0.5
0.5
J
F
M
A
M
J
J
A
S
O
N
D
J
F
M
A
M
J
J
A
S
O
N
D
Somewhat wetter winters and perhaps somewhat dryer summers
Average July PotET over the Southern Plain Region
Current Climate vs. MPI2040 scenario
Current Climate
PotET (mm/day)
MPI2040
Trends in July Avg PotET over the Southern Plain Region from 1915-2002
8.5
7.5
7
jul
Linear (jul)
6.5
6
y = -0.0061x + 7.3589
5.5
2000
1995
1990
1985
1980
1975
1970
1965
1960
1955
1950
1945
1940
1935
1930
1925
1920
5
1915
Reference Crop PotET (mm/day)
8
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.