Transcript Document

Modeling regional consequences of
climate variability and change
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
University of Washington Program on Climate Change
Summer Institute
Issues in regional climate modeling and evaluating impacts
June 19, 2003
Leavenworth, WA
Questions
• Can we distinguish between human-caused and natural variability in
streamflow records?
• Can we distinguish climate influences from human (land use change)
influences on the land surface branch of the hydrologic cycle?
• How can the sensitivities and vulnerabilities of water resource systems
to climate change best be assessed?
• What information is needed from climate models (and at what spatial
and temporal resolution) to project the impacts of climate change on
hydrology and water resources?
• What information do water resource managers need to incorporate the
effects of climate change into planning and design?
• On what timeline and under what circumstances do socioeconomic
changes matter for climate impacts projections?
• What are the implications of climate prediction uncertainty for water
resources planning?
• What are the distinguishing features of water resources issues in the
Pacific Northwest?
1) Can we distinguish between humancaused and natural variability in
streamflow records?
a) The future as indicated by
climate models
 Increasing T -> increased atmospheric
moisture -> increased P
 Hence increased risk of hydrologic
extremes
source: Ziegler et al, J. Clim, 2003
A widely advanced hypothesis regarding
acceleration of the global water cycle:
• “According to model predictions, the most significant
manifestation of climate change would be an acceleration
of the global water cycle, leading to … a general
exacerbation of extreme hydrologic regimes, floods and
droughts” (NASA Global Water and Energy Cycle
solicitation, 2000).
• “There is evidence that suggests that the global hydrologic
cycle may be intensifying, leading to an increase in the
frequency of extremes” (Hornberger et al, USGCRP water
cycle science plan)
• Climate models generally project an acceleration in the
rate of global water cycling and an increase in global
precipitation … (Morel, GEWEX News, 2001)
b) The situation as indicated by
observations over the last ~ century
• Increased in mean and “extreme” P over
much of continental U.S. except winter
• But no apparent changes in floods (although
many upward trends in low flows over
much of the country)
(from Lins and Slack, 1999)
“Since 1910, precipitation has increased by
about 10% across the contiguous United
States. The increase in precipitation is
reflected primarily in the heavy and extreme
daily precipitation events. For example,
over half (53%) of the total increase of
precipitation is due to positive trends in the
upper 10 percentiles of the precipitation
distribution.”
(Karl and Knight, BAMS, 1998)
Percent contribution of upper 10th percentile daily
precipitation to annual total, averaged over U.S.
from Karl and Knight, 1998
Groisman et al (2001)
• In three of five regions of the eastern two-thirds of
the contiguous U.S., a significant increase in the
frequency of “very heavy” precipitation events (>
101.6 mm/day) occurred during the 20th century.
• The return period of “very heavy” precipitation
events changed during the past century in the
Midwest from 10 to 7 years, in the South from 4
to 2.7 years, and in the Northeast from 26 to 11
years.
c) Is there any relationship between
trends in heavy precipitation and
(lack of) trends in floods?
Source: Groisman et al, BAMS, 2001
“In the Eastern half of the United States we
found a significant relationship between the
frequency of heavy precipitation and high
streamflow events both annually and during
the months of maximum streamflow. An
increase of spring heavy precipitation events
over the eastern United States indicates with
high probability that during the 20th century
an increase of high streamflow conditions has
also occurred.”
Groisman et al, BAMS, 2001
Conclusions
• Results of U.S. studies seemingly inconsistent, but
based on statistical analysis, number of trends in
annual maximum flood is barely larger than would be
expected by chance (and probably not field significant)
• Natural variability is large enough to obscure fairly
large changes, suggests aggregation and/or compositing
approaches (but these tend to complicate
interpretation)
• Some of the apparent inconsistencies may well have to
do with attempts to perform “simple” time series type
approaches to a complicated nonlinear process (issues
e.g. with spatial scale of precipitation-runoff
interactions and their variability with season,
antecedent conditions, temporal signature of extreme
precipitation, and surface conditions
• Is it really possible to make more progress on the
problem without a dynamic modeling approach?
2) Can we distinguish climate influences
from human (land use change) influences
on the land surface branch of the
hydrologic cycle?
Estimated 1850 and
1990 global land cover
Source:
National Institute of Public Health and the
Environment (RIVM, Netherlands)
and the Center for Sustainability and
the Global Environment (SAGE,
University of Wisconsin).
Columbia River basin estimated 1900 and
1990 vegetation cover (from ICBEMP)
Early Conifer
Middle Conifer
Late Conifer
Early Deciduous
Middle
Deciduous
Late Deciduous
Brush
Agriculture
Water
Historical (1900)
Current (1990)
3) How sensitive is the climate
system to land surface feedbacks?
Production of a hydrologic data
set for the continental U.S.
Using VIC land surface model, simulation
run for 50 years at 3-hour time step
Input
Time series of spatial data
One terabyte of output archived
Predictability due to Soil
Moisture
•Widespread
predictability at 0 lead
(1½ month)
•Little predictability in
zones where winter
runoff is high
•For summer runoff,
significant predictability
up to 3 seasons
Predictability due to Soil
Moisture
•Widespread
predictability at 0 lead
(1½ month)
•Little predictability in
zones where winter
runoff is high
•For summer runoff,
significant predictability
up to 3 seasons
Exploratory Work on Teleconnection
between SST and Soil Moisture
Study Domain and Datasets
Sea surface temperature:
Extended Reconstruction of Global Sea
Surface Temperature data set based on
COADS data. (1847-1997) developed by
T.M. Smith and R.W. Reynolds, NCDC.
The original data resolution is
2ºlongitude, 2 º latitude. It was
interpolated into 0.5 º resolution (The
ocean domain is chosen according to the
Bin Yu and J.M. Wallace’s paper, 2000,
J. Climate, 13, 2794-2800)
Soil Moisture: VIC retrospective
land surface dataset (1950-1997). The
original data with 1/8 degree resolution
is aggregated into 0.5 º resolution.
Soil Moisture Predictability by Persistence and SST
PCs
The highest variance
explained is more
than 90%. For June,
over 40% of the
variance is
explained over
most of the study
domain, including
Mexico.
SST and Persistence
Persistence
Introducing SST PCs
benefits long-time lead
predictability (of June
soil moisture), but no
significant benefits for
less than 6-month lead
time predictability.
4) How can the sensitivities and
vulnerabilities of water resource
systems to climate change best be
assessed?
Climate
Scenarios
Global climate
simulations, next
~100 yrs
Hydrologic
Model (VIC)
Natural
Streamflow
Downscaling
Delta
Precip,
Temp
Performance
Measures
Reliability
of System
Objectives
Reservoir
Model
DamReleases,
Regulated
Streamflow
Weak links
• Chain of models (accumulation of errors)
• Extrapolation of (hydrology model)
parameterizations beyond tested range
• Climate (and hydrology) model biases
• Downscaling issues
• Improper characterization of management
environment and objectives
5) What information is needed from
climate models (and at what spatial and
temporal resolution) to project the
impacts of climate change on hydrology
and water resources?
• Precipitation, precipitation, and
precipitation (and then temperature,
humidity, wind, surface solar and longwave
radiation, and other forcing variables)
• Quantifiable accuracy at the native
resolution of the climate model (not clear
that the push for higher and higher
resolution is helping us)
• Better understanding of the interaction of
topography and future change
6) What information do water resource
managers need to incorporate the effects
of climate change into planning and
design?
• The joint (in space and time) probability
distribution of future streamflows
• In practice, we only get at this via simulation
given surface climate forcings and a hydrology
model, hence see previous question
• But, perhaps some method of quantifying the
uncertainty across models (in a method other than
multiple scenarios, which leads to throwing up
hands and saying that it’s all too uncertain to be
useful
• Note that the default (used by essentially all water
resources planners for large systems) is to base
planning on an historic set of observed
streamflows – typically of length around 50 years
7) On what timeline and under what
circumstances do socioeconomic
changes matter for climate impacts
projections?
• Lesser of economic planning horizon
(typically around 40 years depending on
discount rate) and institutional planning
horizon (typically 10-20 years)
• Longer in cases where decisions require or
would result in irreversible commitments
8) What are the implications of climate
prediction uncertainty for water
resources planning?
• In an ideal world, none – planners deal with
uncertainty all the time, it just needs to be couched
in terms they are used to dealing with (e.g., natural
variability is not very well characterized in an
observation record of length ~50 years)
• In practice, lots – climate prediction uncertainty is
used as an excuse to do nothing (in fairness,
absence of planning methods that aren’t focused
on using longest length of historic record
available, and standards of professional practice,
play a role as well)
9) What are the distinguishing features of
water resources issues in the Pacific
Northwest?
• Relatively low natural interannual variability
• Mediterranean climate leads to extended low flow
period in late summer and early fall
• Strong effects of snow on seasonal hydrographs,
especially in Columbia basin interior (mixed and
some rainfall dominant basins on the west side)
• Relatively small (relative to mean annual flow)
reservoir storage, meaning it is operated mostly
for seasonal, rather than interannual carryover
• Large role of hydropower (greater than anywhere
else in the U.S., dominates reservoir operation in
the Columbia basin
• ESA listing of salmonids (major effects on
reservoir operation), and other environmental
considerations especially affect low flow
management