Transcript Slide 1

125 Years of Hydrologic Change in the
Puget Sound Basin: The Relative
Signatures of Climate and Land Cover
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
Water Initiative Seminar Series
Utah State University
November 18, 2008
Outline of this talk
• The role of hydrology in Earth system science
• What are the grand challenges in hydrology?
• Understanding hydrologic change: The Puget
Sound basin as a case study
– Modeling background
– The physical setting
– Analysis and prediction
• Weak links and some thoughts on the path
forward
The role of hydrology in Earth
system science
“Where is the water, where is it going and
coming from and at what rate, and what
controls its movement?”
The land hydrologic cycle in a modeling context
A classical hydrological problem: Predicting
runoff and streamflow given precipitation
Runoff generation mechanisms
1) Infiltration excess – precipitation rate exceeds local
(vertical) hydraulic conductivity -- typically occurs over
low permeability surfaces, e.g., arid areas with soil
crusting, frozen soils
2) Saturation excess – “fast” runoff response over saturated
areas, which are dynamic during storms and seasonally
(defined by interception of the water table with the
surface)
Infiltration excess flow (source: Dunne and Leopold)
Runoff generation mechanisms on a hillslope
(source: Dunne and Leopold)
Saturated area (source: Dunne and Leopold)
Seasonal contraction of saturated area at Sleepers River,
VT following snowmelt (source: Dunne and Leopold)
Expansion of saturated area during a storm (source: Dunne
and Leopold)
What are the “grand challenges” in
hydrology?
• From Science (2006) 125th Anniversary issue (of eight in
Environmental Sciences): Hydrologic forecasting –
floods, droughts, and contamination
• From the CUAHSI Science and Implementation Plan (2007):
… a more comprehensive and … systematic
understanding of continental water dynamics …
• From the USGCRP Water Cycle Study Group, 2001
(Hornberger Report): [understanding] the causes of
water cycle variations on global and regional scales,
to what extent [they] are predictable, [and] how …
water and nutrient cycles [are] linked?
Important problems all, but I will argue instead (in
addition) that understanding hydrologic sensitivities
to global change should rise to the level of a grand
challenge to the community.
In an era of
global change
…
•
What are the
impacts of land use
and land cover
change on river
basin hydrology?
•
What is the
climatic sensitivity
of runoff?
•
What are the
impacts of water
management on the
water cycle?
Understanding hydrologic change: The
Puget Sound basin as a case study
Topography of the Puget Sound basin
The role of changing land cover – 1880 v. 2002
1880
2002
The role of changing climate, 1950-2000
source: Mote et al (2005)
Insert lan’s temperature plots here
Understanding hydrologic change: The
modeling context
Fundamental premises
a) Simulation modeling must play a central role,
because we rarely have enough observations to
diagnose change on the basis of observations alone
(and in the future, the “experiment” hasn’t yet been
performed)
b) If the hydrological processes are changing, we need
to represent those processes
c) Hence, prediction approaches that are “trained” to
observations won’t work well
Distributed vs Spatially Lumped Hydrologic Models
Lumped
Conceptual
•Streamflow (at
predetermined
points)
•Predictive skill
limited to
calibration
conditions
Smaller Sub-watersheds
More realistic Processes
Fully Distributed
Physically-based
Streamflow
Snow Runoff
Soil Moisture,
etc at all
points and
areas in the
basin
Predictive
Skill Outside
Calibration
Conditions.
Visual courtesy Andrew Wood
The Distributed Hydrology-SoilVegetation Model (DHSVM)
Explicit Representation of Downslope
Moisture Redistribution
Lumped Conceptual (Processes parameterized)
DHSVM Snow Accumulation and Melt Model
Representing urbanization effects in DHSVM
Hydrologically relevant features of urbanization
not found in “natural” watersheds:
1) surface components such as streets, rooftops, ditches
2) subsurface components such as pipes and other
manmade stormwater drainage conduits
3) In fully urbanized catchments, these elements are
linked through street curb inlets and manholes
4) In partially urbanized catchments, these urban
drainage elements are often mixed with the natural
channel drainage system
Modifications of DHSVM for urban areas
• For pixels with land cover category “urban”, a fraction of
impervious surface area is specified.
• For the fraction that is not impervious, DHSVM handles infiltration
using the same parameterizations as for non-urban pixels.
• A second parameter, the fraction of water stored in flood detention,
was also added. Runoff generated from impervious surfaces is
assumed to be diverted to detention storage.
• The runoff diverted to detention storage is allowed to drain as a
linear reservoir, and re-enters the channel system in the pixel from
which it is diverted.
• Surface runoff that is not diverted is assumed to enter the channel
system directly, i.e., all urban channels are connected directly to the
channel system
• We assume that the natural channel system remains intact, and we
retain the support area concept that defines the connectivity of
pixels to first order channels. However, impervious surface runoff
(and drainage from detention reservoirs) is assumed to be
connected to the nearest stream channel directly
• Once impervious surface runoff has entered a stream channel, it
follows the “standard” DHSVM channel flow routing processes.
Springbrook Creek catchment
Springbrook Creek simulations (left) and
errors (right) with and without urban module
No impervious
or detention
Impervious,
no detention
impervious
and detention
Springbrook Creek mean seasonal cycle
simulated current land cover and all mature forest
Forest cover change effects
Measurement of Canopy Processes
via two 25 m2 weighing lysimeters
(shown here) and additional
lysimeters in an adjacent clear-cut.
Direct measurement
of snow interception
Calibration of an energy balance model of canopy effects on snow accumulation and melt
to the weighing lysimeter data. (Model was tested against two additional years of data)
350
300
Observed
Predicted
250
SWE (mm)
Shelterwood
200
150
100
Below-canopy
50
0
11/1/96
12/1/96
Tmin = 0.4 C
Tmax = 0.5 C
1/1/97
2/1/97
3/1/97
Zo shelterw ood = 7 mm
Zo below -canopy = 20 cm
4/1/97
Albedo based on
exponential decay
w ith age; f itted to
spot observations
of albedo
5/1/97
Understanding the effects of historical land
cover and climate change on the Puget Sound
basin – modeling and analysis
Study Areas
Puget Sound basin,
Washington State, USA
Temperate marine climate,
Precipitation falls in October – March
Snow in the highland,
rare snow in the lowland
Targeted sub-basins
Tmin at
selected
Puget
Sound basin
stations,
1916-2003
Tmin at
selected
Puget Sound
basin
stations,
1916-2003
Tmin at
selected
Puget
Sound
basin
stations,
1916-2003
Model Calibration
Land cover
change
effects on
seasonal
streamflow
for eastern
(Cascade)
upland
gages
Land cover
change
effects on
seasonal
streamflow
for western
(Olympic)
upland
gages
Land cover
change
effects on
seasonal
streamflow
at selected
eastern
lowland
(Greater
Seattle
area) gages
Land
cover
change
effects by
region
Land cover
change
effects on
annual
maximum
flow at
eastern
(Cascade)
upland gages
Land cover
change
effects on
annual
maximum
flow at
western
(Olympic)
upland
gages
Land cover
change effects
on annual
maximum flows
at selected
eastern lowland
gages (greater
Seattle area)
Predicted
temperature
change
effects on
seasonal
streamflow at
eastern
(Cascade)
upland gages
Predicted
temperature
change
effects on
seasonal
streamflow
at western
(Olympic)
upland
gages
Predicted
temperature
change
effects on
seasonal
streamflow at
selected
eastern
lowland gages
(greater
Seattle area)
Predicted
temperature
change
effects on
seasonal
streamflow
by region
Predicted
temperature
change effects
on annual
maximum flow
at eastern
(Cascade)
upland gages
Predicted
temperature
change
effects on
annual
maximum
flows for
upland
gages in the
western
(Olympic)
region
Predicted
temperature
change effects
on annnual
maximum flow
for selected
lowland gages
in the greater
Seattle area
Comparison of
temperature
change and
land cover
change effects
on annual
maximum flows,
selected upland
and lowland
basins
Model/observed residuals analysis of selected basins with
long gauge records, annual maximum and annual mean flows
Model/observed residuals analysis of
selected basins with long gauge records,
annual maximum and annual mean flows
Projected future climate change: GCMs
Models
Institutions
BCCR
Univ. of Bergen, Norway
CCSM3
NCAR, USA
CGCM 3.1_t47
CCCma, Canada
CGCM3.1_t63
CCCma, Canada
CNRM_CM3
CNRM, France
CSIRO_MK3
CSIRO, Australia
ECHAM5
MPI, Germany
ECHO_G
Max Plank Institute for Mathematics, Germany
GFDL_CM2_1
Geophysical Fluid Dynamic Laboratory, USA
GISS_AOM
NASA/GISS, (Goddard Institute for Space Studies) USA
HADCM
Met Office, UK
HADGEM1
Hadley Center Global Environment Model, v 1., UK
INMCM3_0
Institute Numerical Mathematics, Russia
IPSL_CM4
IPSL (Institute Pierre Simon Laplace, Paris, France
MIROC_3.2
CCSR/NIES/FRCGC, Japan
PCM1
NCAR, USA
Projected Future Climate Conditions A1B
Scenario
Basins
Tmin/Year
(˚ C)
Tmax/Year
(˚ C)
Prcp/Year
(%)
Tmin hist
vs. future
(˚ C)
Tmax hist
vs. future
(˚C)
Prcp hist
vs. future
(%)
Deschutes
0.03
0.03
2.02
2.12
2.13
4.53
Cedar
0.04
0.04
1.90
1.88
1.91
3.24
Skokomish
0.03
0.04
2.16
2.04
2.05
6.59
Dosewallips 0.04
0.04
2.00
2.09
2.10
6.63
Lowlandwest
0.04
2.03
2.11
2.12
6.45
0.04
Annual change rate in 2000 – 2099;
Historical vs. future change: 2000 – 2099 vs. 1960 – 1999.
Average of Models: Hadgem1, Echam5, Cnrm_cm3, Hadcm, Cgcm3.1_t47,
Ipsl_cm4
Future climate change: multi-model ensemble
simulation: A1B scenario
Summary
• Upland basin mean flow sensitivity to land cover
change is mostly as a result of changes in snow
accumulation and ablation, and lower ET associated
with reduced vegetation.
• However, overall upland basin seasonal flows
distribution, especially in the transient snow zone,
are much more sensitive to temperature change
effects – both to mean and peak flows – than to land
cover change
• Lowland basin mean flows are much more sensitive
to land cover change than are upland basins,
especially in the most urbanized basins.
• Temperature change effects on peak flows in upland
basins tend to be more modest than are changes in
seasonal flows (suggests rain on snow effect on
peak flows may be modest).
Weak links and some thoughts on
the path forward
• (Over?) Reliance on models – the ideal design
for land cover change studies is paired
catchments, but …
• Are the data up to the challenge?
• Need to understand whether the model
sensitivities are correct (not necessarily
evidenced by calibration/verification errors)
• How do we use evolving (e.g. remote sensing)
data sources in methods that are highly
dependent on long record lengths?