challenges and advancements in regional climate and ... and advancements in regional climate and...
TRANSCRIPT
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Challenges and Advancements in Regional Climate and Hydrological
Modelings for Water Resources Planning and Management
Xu LiangDepartment of Civil and Environmental
Engineering, UC Berkeley
L. Ruby LeungPacific Northwest National Laboratory
Richland, WA
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Acknowledgements
Jianzhong Guo Maoyi Huang Laura M. Parada
Funding Agencies: NASA, NOAA, and the Center of California Water Resources
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Distribution of fresh water use. Note the large percentage of irrigation use in many countries (From Sorooshian et al., 2002).
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0 5 10 15 20 2 5 305
10
15
20
25
30
35
40
45
50
55
Run
off(Q
)
T im e (t)
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Main Challenges
> Model Inputs• Precipitation and other inputs
from climate model under climate change conditions
> Model Structures • Representation of hydrological
processes> Data Assimilations > Scaling Issues, …
Schematic representation of
a hydrological model
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Observed Precipitation (DJF)Daily Extreme (95%)Seasonal Mean
1/8 degree data from UW
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Observed Precipitation Transects (DJF)
Sierra Nevada
Coastal Range
OlympicMtn
CascadeRange
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Elevation Dependence and Seasonality
Precipitation-Elevation Relationships
012345678
0 500 1000 1500 2000 2500 3000
Elevation (m)
P (m
m/d
ay)
Sierra N. Rockies
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Interannual Variability (CA)DJF
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Interannual Variability (Temperature)
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Snowpack
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PCM RCM
Absolute Bias (mm/day)
Win
ter
(DJF
)Su
mm
er (J
JA)
Model Precipitation Biases
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Precipitation Skill Score
PCMRCM
0
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9 10 11 120
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9 10 11 120
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9 10 11 120
0.1
0.2
0.3
0.4
0.5
1 2 3 4 5 6 7 8 9 10 11 12Equ
itabl
e T
hrea
t Sco
re
Precipitation Threshold (mm/day) Precipitation Threshold (mm/day)
Columbia River Basin Sacramento-San Joaquin Basin
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Seasonal Mean and Daily Extreme Precipitation (mm/day)
Observation Simulated
DJF
Mea
nD
JF D
aily
Ext
rem
e
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Observed and Simulated El Nino Precipitation Anomaly
Needs to predict changes in circulation and represent orographic effects
Observation RCM Simulation NCEP Reanalyses
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Sierra
Cascades
Moist
Dry
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Simulated Changes in Circulation During El Nino Years
A shift towards southwesterly during El Nino
CaliforniaPacific Northwest
SouthwesterlySouthwesterly
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Observed Streamflow Variability
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Global and Regional Simulations of Snowpack
GCM under-predicted and misplaced snowRegional Simulation Global Simulation
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Extreme Precipitation/Snowpack ChangesLead to significant changes in streamflow affecting hydropower
production, irrigation, flood control, and fish protection
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Can we simulate soil moisture Can we simulate soil moisture field and field and streamflowstreamflow well?well?
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P
( )
⋅−−−−−∆+
×+
=−∆+
∫∆+
dtEQRpttt
tnttt
tt
ttb
es
)()(
)(1)()(
θθ
θαα
K( )D( ) sin k( z,t )t z z zθ θ θθ∂ ∂ ∂ ∂ = − − ∂ ∂ ∂ ∂
Change of soil moisture
Diffusion term
Drainage term
Change of water table depth
Change of total soil moisture in the unsaturated zone
Net water recharge to the groundwater body
θs porosityne(t) effective porosity
Surface and Groundwater Interactions
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VIC considers partially …
• sub-grid spatial variability of precipitation and soil heterogeneity
• Horton and Dunne flow regimes under the context of sub-grid spatial variabilities
• Snow and Frozen soil processes
Liang et al. (1994, 1996a, 1996b, 1999); Cherkauer & Lettenmaier (1999); Liang & Xie (2001)
Spatial scales: 1km2 ~104km2
(for each computational grid)
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Usadievskiy, Valdai, Russia, 0.36km2
Source: http://www.envsci.rutgers.edu/~luo/
O: SWEGroundwater tableSoil moisture
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Precipitation and
Evaporation
1967 1968 1969 1970 1971 1972 1973 19740
2
4
6
8
years (1966-1983)
Eva
pora
tion
(mm
/day
)
s imulatedes timated bas ed on waterbalance
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 830
500
1000
year
mm
Annual prec ipitation 1967-1983
1 2 3 4 5 6 7 8 9 10 11 1220
40
60
80
100
month
mm
/mon
th
Averaged mo nthly prec ipitation over 1967-1983
Area: 0.36km2
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Soil Column Configuration:
Top layer: 0.1m
Upper layer: 0.4m
Lower layer: 2.5m
Water table,ValdaiArea: 0.36km2
0100200300
1975
0100200300
1976
0100200300
1977
0100200300
1978
0100200300
1979
0100200300
1980
1 2 3 4 5 6 7 8 9 1011120
100200300
month o f the year
1981
1 2 3 4 5 6 7 8 9 1011120
100200300
mo nth o f the year
1982
0100200300
1967
0100200300
1968
0100200300
dept
h to
the
wat
erta
ble
(cm
)
1969
0100200300
1970
0100200300
1971
0100200300
1972
1 2 3 4 5 6 7 8 9 1011120
100200300
mo nth o f the year
1973
1 2 3 4 5 6 7 8 9 1011120
100200300
mo nth o f the year
1974
Simulated daily groundwater table
Observed groundwater table every five days*
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Soil Moisture in the Top Layer (0.1m) , Valdai
Area: 0.36km2
0
0.25
0.5 1975
0
0.25
0.5 1976
0
0.25
0.5 1977
0
0.25
0.5 1978
0
0.25
0.5 1979
0
0.25
0.5 1980
1 2 3 4 5 6 7 8 9 1011120
0.25
0.5 1981
month o f the year1 2 3 4 5 6 7 8 9 101112
0
0.25
0.5 1982
mo nth o f the year
0
0.25
0.5 1967
0
0.25
0.5 1968
0
0.25
0.5 1969
0
0.25
0.5 1970
0
0.25
0.5 1971
Moi
st. i
n La
yer 1
0
0.25
0.5 1972
1 2 3 4 5 6 7 8 9 1011120
0.25
0.5 1973
month o f the year1 2 3 4 5 6 7 8 9 101112
0
0.25
0.5 1974
month o f the year
Soil Column Configuration:
Top layer: 0.1m
Upper layer: 0.4m
Lower layer: 2.5m
Simulated daily soil moisture
Observed soil moisture, once to three times per month
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Soil Moisture in the Upper Layer (0.4m), Valdai
Area: 0.36km2
0
0.25
0.5 1967
0
0.25
0.5 1968
0
0.25
0.5 1969
0
0.25
0.5 1970
0
0.25
0.5 1971
Moi
st. i
n La
yer 2
0
0.25
0.5 1972
1 2 3 4 5 6 7 8 9 1011120
0.25
0.5 1973
month o f the year1 2 3 4 5 6 7 8 9 101112
0
0.25
0.5 1974
month o f the year
0
0.25
0.5 1975
0
0.25
0.5 1976
0
0.25
0.5 1977
0
0.25
0.5 1978
0
0.25
0.5 1979
0
0.25
0.5 1980
1 2 3 4 5 6 7 8 9 1011120
0.25
0.5 1981
mo nth o f the year1 2 3 4 5 6 7 8 9 101112
0
0.25
0.5 1982
month o f the year
Soil Column Configuration:
Top layer: 0.1m
Upper layer: 0.4m
Lower layer: 2.5m
Simulated daily soil moisture
Observed soil moisture, once to three times per month
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Tulpehocken Creek, PA, Drainage Area:
172 km2
05/01/9205/01/9305/01/9405/01/9505/01/9605/01/9705/01/980
50
100
150
a
Prec
ipita
tion
(mm
/day
)
05/01/9205/01/9305/01/9405/01/9505/01/9605/01/9705/01/980
100
200
300
400
500b
Days (Oc t. 1,1991-Se pt. 30,1998)
Dep
th to
wat
erta
ble
(cm
)
VIC-g ro und (multi-la ye rs)Obse rva tion
Soil Column Config:
Top layer: 0.1m
Upper layer: 0.4m
Lower layer: 4.5m
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Tulpehocken Creek, PA, 172 km2
07/01/96 07/01/97 07/01/98 07/01/990
0.10.20.30.40.5
b
Moi
st. i
n La
yer
2
VIC-groundVIC-3L
07/01/96 07/01/97 07/01/98 07/01/990
0.10.20.30.40.5
c
Days (July 1,1995-S e pt. 30,1998)
Moi
st. i
n La
yer
3
VIC-groundVIC-3L
07/01/96 07/01/97 07/01/98 07/01/99
-0.2
0
0.2a
Moi
st. d
iff. i
n La
yer
1
"VIC-ground"-"VIC-3L"
0 10 20 30 40 50 60 70 800
20
40
60a
Prec
ipita
tion
(mm
/day
)
0 10 20 30 40 50 60 70 800
5
10
15b
Days (Marc h 7,1996-May.26,1996)
Tota
l Run
off (
mm
/day
)
VIC-g ro undVIC-3LObs e rv atio n
07/01/95 07/01/96 07/01/97 07/01/980
2
4
6
8a
Evap
orat
ion
(mm
/day
)
VIC-ground (3 laye rs)
07/01/95 07/01/96 07/01/97 07/01/98-0.5
0
0.5
Days (July 1,1995-Se pt. 30,1998)
ET d
iffer
ence
(mm
/day
)
bVIC-ground (3 laye rs)-VIC-3L
Soil Column Configuration:
Top layer: 0.1m
Upper layer: 0.4m
Lower layer: 4.5m
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Relative Absolute Differences between VIC-ground (3-layers) and VIC-3L
12121161Mean annual precip. (mm/yr)
7%4%Soil moisture in lower layer (4.5m)
13%13%Soil moisture in upper layer (0.4m)
38%37%Soil moisture in top layer (0.1m)
3%5%Evapotranspiration
20%34%Total runoff
West Conewago
Tulpehocken
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Can we simulate soil moisture Can we simulate soil moisture field well?field well?
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Data Assimilation of Soil Moisture for Hydrological Models Using Remote Sensing Information
Data Assimilation Soil moisture <= microwave sensorsData assimilation: a
procedure to provide time-dependent spatially distributed estimates of a system that can be updatedwhenever new observationsbecome available.
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SGP97 Study Region•TB Soil moisture
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Study Region & Data Sources (Cont.)
Study Region:Little Washita watershed, OK34.75o to 35o N; 97.9375o to 98.1875o W.
Calibration: (7/12/1994 to 4/30/1997)1/8 degree resolution1/8 degree meteorological, soil and vegetation data available from UW [Maurer et al. 2002]Daily streamflow observations from USGS
Assimilation simulation: (4/1/1997 to 3/31/1998)1/32 degree resolutionSoil and vegetation data available from UWMeteorological data from NESOB97 hourly surface composite800-m resolution near-surface soil moisture observations derived from ESTAR (SGP97) [Jackson et al. 1999]
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Assimilation Results for 6/18/97:Spatial Structure of Soil Moisture Fields
•BASE: VIC base run prediction (no assimilation)•PRIOR: VIC prediction for the current time given previous assimilations •POSTERIOR: Optimal estimates for current time
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Assimilation Results for 6/19/97:Spatial Structure of Soil Moisture Fields
•BASE: VIC base run prediction (no assimilation)•PRIOR: VIC prediction for the current time given previous assimilations •POSTERIOR: Optimal estimates for current time
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Are there dominant effective scales Are there dominant effective scales in hydrological modeling? in hydrological modeling?
What is the nature of variability at What is the nature of variability at the the subgrid subgrid scales for basin scales for basin physical characteristics andphysical characteristics andforcingsforcings??
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Conclusions• For the mountain areas in California, precipitation
due to orographic effects cannot be appropriately captured by GCM, but reasonably simulated by RCM.
• Climate change could significantly affect snow distribution in Sierra areas which could result in significant impacts on water resources planning and managements.
• Improvements of parameterizations of hydrological processes and understanding of scale-related issues are prominently important for better hydrological predictions with reduced uncertainties.