Abstract

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Edwin P. Maurer(1) and Philip B.
Duffy(2) (1)Department of Civil Engineering,
Santa Clara University, Santa Clara, CA
95053 (2)Atmospheric Science Division, Lawrence
Livermore National Laboratory, Livermore CA 94551
Uncertainty in Projections of Impacts of Climate
Change on Sierra Nevada mountain hydrology in
California
Poster U53A-0705
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Implementation of Hydrologic Model
Results
Abstract Understanding the uncertainty in the
projected impacts of climate change on
Californias Sierra Nevada hydrology will clarify
where hydrologic impacts can be expected with
higher confidence, and will help address
scientific questions related to possible
improvements in climate modeling. In this study,
we focus on California, a region that is
vulnerable to hydrologic impacts of climate
change. We statistically bias correct and
downscale the monthly temperature and
precipitation projections from 10 global climate
models (GCMs) from the Coupled Model
Intercomparison Project. These GCM simulations
include both a control period (with unchanging
CO2 and other atmospheric forcing) and a
perturbed period with a 1 percent per year
increase in CO2 concentration. We force a
distributed hydrologic model with bias-corrected
and statistically downscaled GCM data, and
generate streamflow at strategic points in the
Sacramento-San Joaquin River basin. Among our
findings are that inter-model variability does
not prevent significant detection of decreases in
summer low flows, increases in winter flows or
the shifting of flow to earlier in the year.
Uncertainty due to sampling of a 20-year period
in an extended GCM simulation accounts for the
majority of inter-model variability for summer
and fall months, while varying GCM responses to
future (perturbed) temperature and precipitation
forcing add to the variability in the winter.
Inter-model variation in projected precipitation
accounts for most of the uncertainty in winter
and spring flow increases in both the North and
South regions, with a greater influence in the
North. The influence of inter-model precipitation
variability on late summer streamflow decreases
in later years, as higher temperatures dominate
the hydrologic response, and melting snowpack has
less influence.
Simulation Set 1 Streamflow Simulations with 10
GCMs
Bias corrected precipitation and temperature are
spatially downscaled to a 1/8 resolution by
interpolation of scale and shift factors of each
month to the 1961-1990 months base period
average. Downscaling over the study domain is
illustrated below.

• 3 northern gauges lumped together inflows to
  major reservoirs in Northern Sierra.
• 4 southern gauges lumped inflows from major
  reservoirs in higher elevation, southern Sierra
  Nevada.
• Together they account for most of the
  Sacramento-San Joaquin streamflow originating
  from the Sierra Nevada mountains.

Bias-corrected HadCM3 Precipitation, mm/d
Bias-corrected, downscaled HadCM3 Precipitation
Northern Gauges
Perturbed Years 51-70
Control Period
Perturbed Years 21-40

• Control period minor variability due to
  differences in flow sequencing and spatial
  correlation in GCMs.
• Inter-model variation appears within first few
  decades, reflecting differences in GCM
  parameterization, resolution, CO2 sensitivity.
• Between 30 and 60 years, uncertainty does not
  appear to increase, except perhaps in early
  Spring in South.

Southern Gauges
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Selection and Use of GCMs In This Study

• VIC Model is driven with GCM-simulated
  (bias-corrected, downscaled) P, T
• Reproduces Q for historic period
• Produces runoff, streamflow, snow, soil moisture,

Simulation Set 1 Streamflow statistics for the
composite hydrograph of the northern three
gauges. Mean and standard deviation (SD) are in
ft3/s, tprob is the probability (according to a
2-tailed t-test for differences in mean from two
distributions with unequal variances) of claiming
the mean is different from the control period
mean when they are actually the same. 1-tprob is
the confidence level that the mean of the
perturbed is different from the mean of the
control. CV is the coefficient of variation.
Statistics are calculated across different
climate models and thus measure the degree of
consistency between results of different models.

• Output from all 10 GCMs participating in most
  recent phase of Coupled Model Intercomparison
  Project. Model simulations included
• Specified control (constant CO2)
• Perturbed (1/year CO2 increase) simulations

• VIC Model Features
• Developed over 10 years
• Energy and water budget closure at each time step
• Multiple vegetation classes in each cell
• Sub-grid elevation band definition (for snow)
• Subgrid infiltration/runoff variability

Northern Gauges Streamflow
Southern Gauges Streamflow
Statistical comparison of the day of year to the
centroid of the annual (water year) runoff
hydrograph.
Future climate for California Simulation Set 1
Precipitation and Temperature Projections 70
years at 1/year CO2 increase
Precipitation
Temperature
Regional P, T for California P displays no
apparent trend T shows increasing trend in all
seasons and for all GCMs

• Inter-model variability due to sampling a 20-year
  time slice (unsynchronized low frequency
  variability in GCMs ) accounts for much almost
  all summer and fall intermodel variability.
  Differing GCM responses to CO2 future forcing
  plays larger role in winter/spring
• Greater uncertainty of changes during seasonal
  transitions (November and May), especially late
  in perturbed period (shown by lower
  significance).
• Increase in March-April flows more significant in
  South than North
• Shift in timing of annual hydrograph (occurrence
  of center of mass of Oct-Sep flow volume) 11 days
  earlier in North, 18 days earlier in South very
  robust across models.

• GCMs have biases on order of anticipated changes
• GCM spatial scale is incompatible with hydrologic
  processes

Cannot use GCM output directly
Precipitation
Temperature
Example of Bias in GCMs 40-year control period
GCM simulations One grid cell Latitude 39N
Longitude 123W Biases in both median and
variability
Simulation Set 2 PCM Precipitation for all GCMs

• Table shows the percent of inter-model
  variability in monthly streamflow for the
  composite North and South hydrographs
  attributable to inter-model variability in
  precipitation. The remainder is attributable to
  inter-model temperature variability.
• Inter-model variation in projected precipitation
  accounts for 72-90 of total inter-model
  variation for Oct-Feb flow changes.
• Inter-model precipitation variability more
  dominant than temperature variability for
  streamflow uncertainty except during May-July in
  the North and June-August in the South.
• Precipitation variability in September is less
  important in later period, showing lessened
  effect on late-summer low flow.

Future climate for California Simulation Set 2
Second set of simulations used same P, T forcing
as Set 1, but with PCM simulated P for all GCMs.
This helped isolate the contribution of
inter-model P variability, generally considered
more variable between models. PCM was selected
since its showed the greatest correspondence each
season between climatological P and also was
least sensitive to CO2 changes. The fraction of
streamflow variability attributed to
precipitation is calculated as
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Parting Thoughts

• Intermodel variability between GCMs does not
  prevent significant detection of decreases in
  summer streamflow, even by years 21-40.
• Both increases in winter streamflow and decreases
  in summer low flows exceed intermodel variability
  by years 51-70, as is the retreat of the midpoint
  of the annual hydrograph.
• As temperatures continue to rise, lagging effects
  of snow and soil moisture are less able to
  persist through summer (due to more winter
  precipitation falling as rain and higher
  evapotransipiration), and winter precipitation
  variability becomes less important for late
  summer low flow changes.

To correct for the bias in the GCMs, the
technique of Wood et al. (2004 2002) was
applied. This uses a quantile mapping technique
that constrains the GCM to reproduce all
statistical moments of the observed precipitation
and temperature for a climatological (control)
period, while allowing both the mean, variance,
and other moments to evolve in the future as
simulated by each GCM.
TP indicates both T and P vary between all GCMs
(Set 1) T indicates only T varies between GCMs
(Set 2)