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

Acknowledgements

Jianzhong Guo Maoyi Huang Laura M. Parada

Funding Agencies: NASA, NOAA, and the Center of California Water Resources

Distribution of fresh water use. Note the large percentage of irrigation use in many countries (From Sorooshian et al., 2002).

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)

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

Observed Precipitation (DJF)Daily Extreme (95%)Seasonal Mean

1/8 degree data from UW

Observed Precipitation Transects (DJF)

Sierra Nevada

Coastal Range

OlympicMtn

CascadeRange

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

Interannual Variability (CA)DJF

Interannual Variability (Temperature)

Snowpack

PCM RCM

Absolute Bias (mm/day)

Win

ter

(DJF

)Su

mm

er (J

JA)

Model Precipitation Biases

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

Seasonal Mean and Daily Extreme Precipitation (mm/day)

Observation Simulated

DJF

Mea

nD

JF D

aily

Ext

rem

e

Observed and Simulated El Nino Precipitation Anomaly

Needs to predict changes in circulation and represent orographic effects

Observation RCM Simulation NCEP Reanalyses

Sierra

Cascades

Moist

Dry

Simulated Changes in Circulation During El Nino Years

A shift towards southwesterly during El Nino

CaliforniaPacific Northwest

SouthwesterlySouthwesterly

Observed Streamflow Variability

Global and Regional Simulations of Snowpack

GCM under-predicted and misplaced snowRegional Simulation Global Simulation

Extreme Precipitation/Snowpack ChangesLead to significant changes in streamflow affecting hydropower

production, irrigation, flood control, and fish protection

Can we simulate soil moisture Can we simulate soil moisture field and field and streamflowstreamflow well?well?

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

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)

Usadievskiy, Valdai, Russia, 0.36km2

Source: http://www.envsci.rutgers.edu/~luo/

O: SWEGroundwater tableSoil moisture

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

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*

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

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

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

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

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

Can we simulate soil moisture Can we simulate soil moisture field well?field well?

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.

SGP97 Study Region•TB Soil moisture

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]

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

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

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??

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.