1. Climate impact assessment in the western U. S
Climate impact assessment in the western U.S. using coupled macroscale hydrology and water management models
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
National Climate Center
China Meteorological Administration
April 14, 2005
Beijing, China

2. Outline of this talk Climate variability and change context
Prediction and assessment approach
Accelerated Climate Prediction Initiative (ACPI)
Hydrology and water management implications for Columbia, Sacramento-San Joaquin, and Colorado River basins
Conclusions and comparative analysis

3. 1) Climate variability and change context

4. Humans are altering atmospheric composition
Methane has increased 151%, nitrous oxide 17%, also greenhouse gases

5. The earth is warming -- abruptly
What it is: Average surface temperature year by year
3 main features:
warming to 1940
cooling
warming since 1970
causes of these are different
were generally warmer than the top dots shown (continuing warming trend)

6. Natural Climate Influence Human Climate Influence
Red: observations of global average temperature
Grey: simulations with a climate model (huge computer program, like weather prediction model only run for hundreds of years)
Natural influence: volcanoes, solar variations – guesses before ~1970, better since then
Human influence: greenhouse gases, sulfate aerosols
it is possible to simulate the climate of the last 100 years, and the conclusion is that humans didn’t matter much before 1960 – the early warming and the cooling were largely natural, and the late-century warming was largely human-caused
All Climate Influences

7. The IPCC used a wide range of assumptions about future economic development, from rapid global economic growth relying mostly on fossil fuels to slow growth in which the developed world goes heavily toward a service economy and the developing world is left behind. For this wide range, CO2 doubles between 2050 and Best guess: 1C by mid-century, 3C by 2100.
Note that constant global temperature is not among the projections

8. Temperature trends in the PNW over the instrumental record
Almost every station shows warming (filled circles)
Urbanization not a major source of warming
BC: 0.5C (0.9F) in coastal region and 1.1C (2F) in CRB

9. Trends in timing of spring snowmelt (1948-2000)
+20d later
–20d earlier
Courtesy of Mike Dettinger, Iris Stewart, Dan Cayan

10. Trends in snowpack

11. This shows 10-year averages of temperature for the whole PNW; 1990’s were the warmest decade of the 20th century in the Northwest (as well as globally), by almost 1 degree. The rate of warming from 1970 to 2000 has been roughly what the CGCM1 simulates.
We looked at 8 scenarios produced by several different general circulation models from climate modeling centers around the world. All used the same simple scenario for CO2: 1%/year. The spread here does not include the spread from other greenhouse gas scenarios.
Looking ahead to the 2020s and 2040s, the warmest, average, and coolest of these 8 models are shown as red, yellow, and green. The average rate of warming is a bit less than 1 degree per decade. Even the coolest scenario still shows about 2F more warming by the 2040s.

12. 2) Prediction and assessment approach

13. Global climate simulations, next ~100 yrs
Scenarios
Performance
Measures
Downscaling
Global climate simulations, next ~100 yrs
Delta
Precip,
Temp
Reliability
of System
Objectives
Reservoir
Model
Hydrologic
Model (VIC)
DamReleases,
Regulated
Streamflow
Natural
Streamflow

15. Accelerated Climate Prediction Initiative (ACPI) – NCAR/DOE Parallel Climate Model (PCM) grid over western U.S.

16. Bias Correction and Downscaling Approach
climate model scenario
meteorological outputs
 hydrologic model inputs
snowpack
runoff
streamflow
2.8 (T42)/0.5 degree resolution
monthly total P, avg. T
1/8-1/4 degree resolution
daily P, Tmin, Tmax
important point(s):
the approach attempts to make use of forecast skill from 2 sources:
better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models;
the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling).
Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

17. Bias Correction
from NCDC observations
from PCM historical run
raw climate scenario
bias-corrected climate scenario
month m
Note: future scenario temperature trend (relative to control run) removed before, and replaced after, bias-correction step.

18. interpolated to VIC scale
Downscaling
observed
mean fields
(1/8-1/4 degree)
monthly PCM
anomaly (T42)
VIC-scale
monthly simulation
interpolated to VIC scale
Bias correction

19. Regional Bias: spatial example GSM: NCEP Global Spectral Model
obs prcp GSM prcp
obs temp GSM temp
JULY
Regional Bias: spatial example GSM: NCEP Global Spectral Model
important point(s):
here we see the biases from a spatial view -- note the large temperature bias in raw GSM forcing (for July), and in precip over the eastern edge of the Ohio Basin (as an example). In the third column of images, the biases have been removed by our method, so they should look like the first column (observed) – and they do.

20. For sample cell located over Ohio River basin, biases in monthly Ptot & Tavg are large!
important point(s):
the first hurdle in making forecasts is to overcome the regional biases. this and the next slide show the bias -- first for one GSM cell, where you can see plotted the observed forcings vs. the GSM raw climatology forcings (for the climatology period ‘79-’99). biases are so large in temperature that a hydrologic model would be blown out of the water.
this is from one set (May) of climatology & forecast ensembles back on the East Coast

21. Experimental Forecasting Approach One Way Linkage of GSM and VIC models
a b c.
TGSM
TOBS
a) bias correction:
climate model climatology  observed climatology
b) statistical downscaling:
GSM ( deg.)  VIC (1/4-1/8 deg)
c) temporal disaggregation (via resampling of observed patterns):
monthly  daily
important point(s):
this is our approach to turning GSM forecasts into VIC input: first, bias correction:
1. using the climatologies of the observed precip & temperature to define parallel distributions (for each month in the forecast), we translate each met. value in the GSM ensembles to a quantile value, then retrieve the met. variable value for that quantile from the observed distribution. (at the ends of the empirical distributions, we use fitted theoretical ones if needed).
then downscaling:
2. that was all done at the GSM scale. then we interpolate the anomalies to the to the VIC resolution (nothing fancy here).
then we impose the daily pattern by resampling the historic VIC forcings (P&T for each month taken from the same year to preserve correlations), and then scaling monthly avg. temp and shifting month. tot. P to reproduce the forecast anomalies. after all, when you sample at random, the daily pattern you get won’t have the monthly anomaly you need for the forecast signal.
the bias correction step is critical, as the next 2 plots will show.

22. Experimental Forecasting Approach Developing a Bias Correction
1979 SSTs
etc.
from 1999
SSTs
10 member climatology ens.
July Tavg, for 1 GSM cell
GSM
Observed *
* for each month, each GSM grid cell and variable

23. Experimental Forecasting Approach Applying a Bias Correction

24. Experimental Forecasting Approach Statistical Downscaling: step 1 is interpolation
(bias corrected) anomaly
anomaly at VIC scale
shown
1 month,
1 variable (T)

25. Experimental Forecasting Approach Statistical Downscaling: step 2 combines spatial VIC-scale variability and smooth anomaly field
anomaly
VIC-scale monthly forecast
and
mean fields

26. Experimental Forecasting Approach Lastly, temporal disaggregation…
VIC-scale monthly forecast

27. Experimental Forecasting Approach Downscaling-Disaggregation Test
Process into the daily VIC-scale input time series
Force hydrology model to produce streamflow
Ohio R. Metropolis, IL
Start with GSM-scale monthly observed
T & P (“unbiased”) time series
Is simulated streamflow unbiased against observed streamflow?

28. Downscaling Method Comparisons
Objective 1: examine the value of components of the forecast approach
Objective 2: compare to dynamical downscaling
Objective 3: verify downscaling methods for retrospective
simulation against observed climate/hydrology

29. Downscaling Method Comparisons: framework
PCM: DOE Parallel Climate Model
(2.8 degree resolution)
RCM: PNNL Regional Climate Model
(1/2 degree resolution)
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.
used HISTORICAL climate scenario from PCM,
for 20 year period
Forecasting approach after dynamical downscaling
Forecasting approach

30. Downscaling Method Comparisons Domain and Model resolutions
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

31. pcm rcm
Downscaling Method Comparisons Precipitation downscaled vs. observed ( averages)
OBS
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

32. pcm rcm
Downscaling Method Comparisons Temperature downscaled vs. observed ( averages)
OBS
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

33. Downscaling Method Comparisons SWE based on: downscaled vs
Downscaling Method Comparisons SWE based on: downscaled vs. observed P & T ( averages)
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

34. Downscaling Method Comparisons Basin Averages based on: downscaled vs
Downscaling Method Comparisons Basin Averages based on: downscaled vs. observed P & T ( averages)
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

35. Downscaling Method Comparisons Streamflow based on: downscaled vs
Downscaling Method Comparisons Streamflow based on: downscaled vs. observed P & T ( averages)
important point(s):
The streamflow results are shown for The Columbia River at the Dalles as two ensembles (climatology and forecast) plotted alongside each other, for each forecast month.
The clear message from this plot is that the forecast (red plusses) distributions are clustered significantly below the climatology distributions, to a diminishing extent at the end of the forecast period (when runoff is dominated more by precipitation than snowmelt).
Note, however, that the climatology period doesn’t include the recent end point, 1977, so if it did, the distributions might extend further down with respect to the forecast ensembles – not enough to change the significance of the results, however.

36. Downscaling Method Comparisons Conclusions
bias-correction and statistical downscaling (e.g., forecast approach) gives comparable results using raw climate model output or dynamically downscaled output
simple interpolation and statistical downscaling alone, without bias-correction, does not produce fields suitable for use in hydrologic simulation
bias-correction and statistical downscaling of climate model output removes substantial bias from hydrologic results, but not all. The climate models must also have some accuracy.

37. Overview of ColSim Reservoir Model
Reservoir Operating Policies
Reservoir Storage
Regulated Streamflow
Flood Control
Energy Production
Irrigation Consumption
Streamflow Augmentation
Physical System
of Dams
and Reservoirs
Streamflow Time Series

38. Dam Operations in ColSim
Storage Dams
Virgin
Regulated
Run-of-River Dams
Flow In=Flow out + Energy H

39. ColSim Storage Reservoirs + Run of River Reservoirs
Releases Depend on:
Storage and Inflow
Rule Curves (streamflow forecasts)
Flood Control Requirements
Energy Requirements
Minimum Flow Requirements
System Flow Requirements
Inflow
ColSim
Consumptive use
Inflow
Inflow
Consumptive use
Inflow
Inflow
Inflow +
Inflow
Run of River Reservoirs
(inflow=outflow + energy)
System Checkpoint

40. 3) Accelerated Climate Prediction Initiative (ACPI)

41. GCM grid mesh over western U. S
GCM grid mesh over western U.S. (NCAR/DOE Parallel Climate Model at ~ 2.8 degrees lat-long)

42. Climate Change Scenarios
PCM Simulations (~ 3 degrees lat-long)
Historical B06.22 (greenhouse CO2+aerosols forcing)
Climate Control B06.45 (CO2+aerosols at 1995 levels)
Climate Change B06.44 (BAU6, future scenario forcing)
Climate Change B06.46 (BAU6, future scenario forcing)
Climate Change B06.47 (BAU6, future scenario forcing)
PNNL Regional Climate Model (RCM) Simulations (~ ¾ degree lat-long)
important point(s):
GSM forecasts take the form of monthly ensembles of length 6 months
we get them early in each month for a start date of the following month.
the climatology ensemble enables us to define the climate model bias and correct it
climatology ensembles run out 6 months just like the forecasts, but use observed rather than predicted tropical Pacific SSTs
also:
210 ensembles for GSM climatology are derived from observed SSTs in each year of the 21 year climatology period ( ) combined with 10 initial atmospheric conditions for each year
GSM is at T42 spatial resolution, but moving to T62 soon (resolution improvement of about 1/3)
Climate Control B06.45 derived-subset
Climate Change B06.44 derived-subset

43. Future streamflows 3 ensembles averaged summarized into 3 periods;

44. Regional Climate Model (RCM) grid and hydrologic model domains
important point(s):
the overall forecasting approach involves using forecast model (the global spectral model) T & P output at a coarse timestep & scale as hydrologic model input at a finer timestep and scale.
to make a hydrologic forecast, you need a transformation of the forecasts that first overcomes climate model bias and the scale differences, then simulates the water balance.
also, GSM is really run at very fine timestep (~5-15 minutes) but only the monthly anomalies are archived for our use. most of the signal is at the monthly scale, however, so this is acceptable.

45. ACPI: PCM-climate change scenarios, historic simulation v air temperature observations

46. ACPI: PCM-climate change scenarios, historic simulation v precipitation observations

47. 4a) Hydrology and water management implications: Columbia River Basin

48. PCM Business-as-Usual scenarios Columbia River Basin (Basin Averages)
BAU 3-run average
historical ( )
control ( )
PCM
Business-as-Usual
scenarios
Columbia River Basin
(Basin Averages)
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

49. RCM Business-as-Usual scenarios Columbia River Basin (Basin Averages)
PCM BAU B06.44
RCM BAU B06.44
control ( )
historical ( )
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

51. PCM Business-As-Usual Mean Monthly Hydrographs Columbia River Basin
@ The Dalles, OR
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.
month
month

52. CRB Operation Alternative 1 (early refill)

53. CRB Operation Alternative 2 (reduce flood storage by 20%)
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
45,000,000
50,000,000
55,000,000 O N D J F M A S
End of Month Total System Storage (acre-feet)
Max Storage
Control
Base Climate Change
Change (Alt. 2)
Dead Pool

59. 4b) Hydrology and water management implications: Sacramento-San Joaquin River Basin

60. PCM Business-as-Usual scenarios California (Basin Average)
BAU 3-run average
historical ( )
control ( )
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

61. PCM Snowpack Changes Business-as-Usual Scenarios California
April 1 SWE
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

62. PCM Business-As-Usual Mean Monthly Hydrographs
Shasta Reservoir Inflows
important point(s):
we’re modeling most of the US at 1/8 degree now with the VIC model, but we are performing this forecasting exercise in the Columbia River basin. The plusses show the grid of the numerical weather prediction (forecasting) model that we used (GSM), and the ¼ degree hydrology model resolution can just be discerned in the figure. 24 climate model grid points were used, and 1,668 VIC model cells. We’ve aggregate the VIC model to ¼ degree from 1/8 degree in the Columbia River basin to speed the forecast runs.

63. Sacramento River Basin
Trinity
Trinity Lake Storage: 2448 taf
Shasta
Lake Shasta
Storage: 4552 taf
Whiskeytown
Storage: 241 taf
Lake Oroville
Storage: 3538 taf
Folsom Lake
Storage: 977 taf
Whiskeytown
Oroville (SWP)
Trinity River
Clear Creek
Oroville is operated by the SWP
Feather River
Dam
Power Plant
River
Transfer
Sacramento River
American River
Folsom
Delta

64. Delta & San Joaquin R Basin
Sacramento-San Joaquin Delta
Area: 1200 mi2
Delta
Pardee/Camanche
Reservoir
Storage: 615 taf
San Luis Reservoir
CVP: 971 taf
SWP: 1070 taf
Mokelumne River
Millerton Lake
Storage: 761 taf
New Melones Res
Storage: 2420 taf
Don Pedro/McClure
Storage: 3055 taf
Pardee & Camanche
Delta Outflow
Delta
Calaveras River
New Hogan
Stanislaus River
San Luis
San Joaquin River
New Hogan is only about 300 taf and is not pictured
San Luis Reservoir jointly operated by Federal and State agencies
- State water flows to demand targets through 400 mi California Aqueduct
- Federal water flows to demand targets through Delta-Mendota Canal – some water is exported back to Upper San Joaquin River
The Sacramento-San Joaquin Delta is the most important thing to point out. Many environmental rules (fish and water quality) in the Delta govern the operation of reservoirs on both the Sacramento and San Joaquin R, as well as the San Luis Reservoir (which is downstream via man-made channels).
New Melones
Dam
Power Plant
River/Canal
Transfer
Tuolumne & Merced Rivers
Eastman, Hensley, & Millerton
New Don Pedro & McClure

66. Current Climate vs. Projected Climate
Storage Decreases
Sacramento
Range: %
Mean: 8 %
San Joaquin
Range: %
Mean: 11 %

67. Current Climate vs. Projected Climate
Hydropower Losses
Central Valley
Range: %
Mean: 9 %
Sacramento System
Range: 3 – 19 %
Mean: 9%
San Joaquin System
Range: 16 – 63 %
Mean: 28%

68. 4c) Hydrology and water management implications: Colorado River basin

69. Timeseries Annual Average
PCM Projected Colorado R. Temperature
Timeseries Annual Average
ctrl. avg.
hist. avg.
Period Period Period

70. Timeseries Annual Average
PCM Projected Colorado R. Precipitation
Timeseries Annual Average
hist. avg.
ctrl. avg.
Period Period Period

71. Annual Average Hydrograph
Simulated Historic ( ) Period 1 ( ) Control (static 1995 climate) Period 2 ( ) Period 3 ( )

72. Projected Spatial Change in Runoff
90 % 86 % 82 % 83 %

73. April 1 Snow Water Equivalent

74. Natural Flow at Lee Ferry, AZ
allocated 20.3 BCM
Currently used BCM

76. Storage Reservoirs Run of River Reservoirs
CRRM
Historic Streamflows to Validate
Projected Inflows to assess future performance of system
Monthly timestep
Basin storage aggregated into 4 storage reservoirs
Lake Powell and Lake Mead have 85% of basin storage
Reservoir evaporation = f(reservoir surface area, mean monthly temperature)
Hydropower = f(release, reservoir elevation)
Storage Reservoirs Run of River Reservoirs

77. Water Management Model (CRRM)
Multi Species Conservation Program year 2000 demands
upper basin BCM
lower basin BCM
Mexico BCM
Minimum Annual Release from Glen Canyon Dam of 10.8 BCM
Minimum Annual Release from Imperial Dam of 1.8 BCM

78. Total Basin Storage

79. Annual Releases to the Lower Basin
target release

80. Annual Releases to Mexico
target release

81. Annual Hydropower Production

82. Uncontrolled Spills

83. Deliveries to CAP & MWD

84. Annual Average Change (mm/yr) in:
Runoff
Precipitation
Evapo-transpiration
mm / yr.

85. Colorado River Basin Annual Average Precipitation (mm/yr)
NE cell
NW cell
Colorado River Basin Annual Average Precipitation (mm/yr) X X X
(mm/yr)
SW cell

89. 5) Conclusions and Comparative analysis
1) Columbia River reservoir system primarily provides within-year storage (total storage/mean flow ~ 0.3). California is intermediate (~ 0.3), Colorado is an over-year system (~4)
2) Climate sensitivities in Columbia basin are dominated by seasonality shifts in streamflow, and may even be beneficial for hydropower. However, fish flow targets would be difficult to meet under altered climate, and mitigation by altered operation is essentially impossible.
3) California system operation is dominated by water supply (mostly ag), reliability of which would be reduced significantly by a combination of seaonality shifts and reduced (annual) volumes. Partial mitigation by altered operations is possible, but complicated by flood issues.
4) Colorado system is sensitive primarily to annual streamflow volumes. Low runoff ratio makes the system highly sensitive to modest changes in precipitation (in winter, esp, in headwaters). Sensitivity to altered operations is modest, and mitigation possibilities by increased storage are nil (even if otherwise feasible).