Download lettenmaier_gssp_goddard_jun_2002

Runoff generation and its
representation in land surface models
Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
University of Washington
for presentation at
GSSP Seminar Series
NASA/GSFC
June 14, 2002
OUTLINE OF THIS TALK
1. Runoff generation processes
2. Spatially distributed modeling
3. Macroscale modeling
a) Strategy
b) Testing and evaluation
c) Implementation
4. Example 1 – Puget Sound flood forecast system
5. Example 2 – Seasonal ensemble forecasting
6. Example 3 – Climate change assessment
1. Runoff generation processes
Darcy’s Equation (fundamental equation of motion in subsurface,
applies to both saturated and unsaturated zones):
h
q  K
z
where
q = flow per unit cross-sectional area (units L/T)
K = hydraulic conductivity (L/T)
h   z
Definitions:
 = volume of water/total volume
η = porosity (volume of voids/total volume
 = suction head (height to which moisture is drawn above free
surface
h   z
 (  z )
 d  
q  K
 K 
 1
z
 d z

let
d
DK
d
From continuity
= diffusivity
 q

0
t z
Combining,

  

 D
K
t z  z

(Richard’s equation)
Complications in the application of
Richards Equation
• Applies at point scale, “well behaved” porous medium
• K is highly nonlinear spatially varying function of suction
head, moisture
• K varies over orders of magnitude due to variations in
soil properties at meter scales (much less than typical
scale of application)
• Direct estimation of K difficult even at small scale (and
scale complications in interpretation of measurements)
• Methods of estimating K from e.g. mapable soil
properties are highly approximate, and subject to scale
complications
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)
Seasonal contraction of pre-storm saturated areas,
Sleepers River VT (source: Dunne and Leopold)
2. Spatially distributed modeling
Distributed Hydrology Soil Vegetation
Model (DHSVM)
Explicit Representation of Downslope
Moisture Redistribution
Lumped Conceptual (Processes parameterized)
DHSVM Snow Accumulation and Melt Model
Distributed vs Spatially Lumped Hyrologic Models
Lumped
Conceptual
•Streamflow (at
predetermined
points)
•Predictive skill
limited to
calibration
conditions
Suitable for
flood forecasting
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.
Suitable for flood forecasting
and a wide range of water
resource related issues
3. Macroscale modeling
a: strategy
Traditional “bottom up”
hydrologic modeling
approach (subbasin by
subbasin)
Macroscale modeling approach (“top
down”)
1 Northwest
2 California
3 Great Basin
4 Colorado
5 Rio Grande
6 Missouri
7 Arkansas-Red
8 Gulf
9 Great Lakes
10 Upper Mississippi
11 Lower Mississippi
12 Ohio
13 East Coast
3. Macroscale hydrologic models,
b: Testing and evaluation
Investigation of forest canopy effects on
snow accumulation and melt
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
SWE (mm)
250
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 shelterwood = 7 mm
Zo below-canopy = 20 cm
4/1/97
Albedo based on
exponential decay
with age; fitted to
spot observations
of albedo
5/1/97
Summer 1994 - Mean Diurnal Cycle
Point Evaluation of a Surface Hydrology Model for BOREAS
SSA Mature Black Spruce
300
NSA Mature Black Spruce
SSA Mature Jack Pine
Rnet
Rnet
Rnet
H
H
H
LE
LE
LE
Flux (W/m2)
100
-100
250
150
50
-50
120
60
0
0
3
6
9
12 15 18 21 24
0
3
6
9
12 15
18 21
24 0
3
6
9
12 15 18 21 24
Local time (hours)
Observed Fluxes
Rnet
Net Radiation
Simulated Fluxes
H
Sensible Heat Flux
LE
Latent Heat Flux
Range in Snow Cover Extent
Observed and Simulated
6
km2)
snow cover extent (10
Eurasia
North America
20
10
16
8
12
6
8
4
4
2
0
0
J F MAM J J A SO N D J
J F MA MJ J A S O ND J
Month
Month
Observed
Simulated
UPPER LAYER SOIL MOISTURE
SOIL MOISTURE (%)
0.40
0.30
Illinois soil moisture comparison
TOPLATS regional
ESTAR distributed
X TOPLATS distributed
X
0.20
X
X
X
0.10
X
X
X X
X
X
X
X
X
X X
June 18th-July 20th, 1997
11:00 CST JUNE 20, 1997
ESTAR
11:00 CST JULY 12 1997
50
50
10
10
TOPLATS
ESTAR TOPLATS
Mean Normalized Observed and Simulated Soil Moisture
Normalized
Soil Moisture (mm)
Central Eurasia, 1980-1985
A
200
B
C
D
100
J F MA M J J A S O N D J J F MA M J J A S O N D J J F MA M J J A S O N D J J F MA M J J A S O N D J
Normalized
Soil Moisture (mm)
0
E
200
F
G
H
100
0
J F MA M J J A S O N D J J F MA M J J A S O N D J J F MA M J J A S O N D J J F MA M J J A S O N D J
Observed
Simulated
60°N
60°N
50°N
50°N
40°N
40°N
20°E
30°E
40°E
50°E
60°E
70°E
80°E
90°E
100°E
110°E
120°E
130°E
140°E
Cold Season Parameterization -- Frozen Soils
Key
Observed
Simulated
5-100 cm layer
0-5 cm layer
3. Macroscale hydrologic models,
c: Implementation
Shasta
Reservoir
inflows
5. Example 1 – Puget Sound flood
forecasting
Data Requirements for applying DHSVM.
• Terrain - 150
m. aggregated
from 10 m.
resolution DEM
• Land Cover 19 classes
aggregated from
over 200 GAP
classes
• Soils - 3 layers
aggregated from
13 layers (31
different
classes); variable
soil depth from
1-3 meters
• Stream
Network - based
on 0.25 km2
source area
Calibration-Validation
with all available meteorological observations (50 sites)
Validation 1991-1996
Calibration (Snohomish River)
From 1987-1991
(USGS gauges at
Gold Bar and Carnation only )
DHSVM Calibration (Snoqualmie at Carnation)
Flood of record
Principal calibration locations were the
Skykomish at Gold Bar and the
Snoqualmie at Carnation
Calibration Location (Snoqualmie)
2500
2000
Testing: Cedar
1500
1000
500
0
20-Nov
•Calibration to two USGS
sites
•Split sample validation at
over 60 sites
•Parameters transfer
extremely well to other
watersheds without
recalibration
4-Dec
18-Dec
1-Jan
15-Jan
29-Jan
12-Feb
2000/2001
Real-time
Streamflow
Forecast System
26 basins
48,896 km2
2,173,155 pixels
@ 150 m
resolution
The average relative absolute error in peak runoff forecast for
six events during water year 1999 (Westrick et al 2002).
100%
90%
Obs-based
80%
MM5
70%
MM5 no
bias
60%
50%
40%
RFC
30%
20%
10%
0%
Sauk
Skykomish N.F. Snoq M.F. Snoq Snoq
Cedar
5. Example 2 – Seasonal ensemble
streamflow forecasting
General Approach
climate model forecast  hydrologic (VIC)  streamflow,
meteorological outputs
model inputs
soil moisture,
snowpack,
• 1/8-1/4 degree resolution
• ~1.9 degree resolution (T62)
runoff
• monthly total P, avg T
Use 3 step approach:
• daily P, Tmin, Tmax
1) statistical bias correction
2) downscaling
3) hydrologic simulation
Models: Global Spectral Model (GSM)
ensemble forecasts from NCEP/EMC
•
forecast ensembles available near beginning of each
month, extend 6 months beginning in following month
•
each month:
• 210 ensemble members define GSM climatology for
monthly Ptot & Tavg
• 20 ensemble members define GSM forecast
One Way Coupling of GSM and VIC models
Temperature
30
25
TOBS
TGSM
20
15
10
5
0
0
Probability
a.
1
b.
c.
a) bias correction:
climate model climatology  observed climatology
b) spatial interpolation:
GSM (1.8-1.9 deg.)  VIC (1/8 deg)
c) temporal disaggregation (via resampling of observed
patterns):
monthly  daily
GSM Regional
Bias: a spatial
example
Bias is removed at the monthly
GSM-scale from the
meteorological forecasts
(so 3rd column ~= 1st column)
Downscaling Test
1.
2.
3.
4.
Start with GSM-scale
monthly observed met
data for 21 years
Downscale into a daily
VIC-scale timeseries
Force hydrology model to
produce streamflow
Is observed streamflow
reproduced?
GSM forecast and climatology
ensembles
10 member climatology ensembles (21 sets)
35
15
25
35
15
25
5
-5
5
deg C
25
deg C
deg C
35
from 1979 SSTs
from 1980 SSTs
from 1981 SSTs
15
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
5
-5
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
-5
35
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
deg C
20 member forecast ensemble
deg C
25
15
35
5
25
-5
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
15
5
-5
Mon 1 Mon 2 Mon 3 Mon 4 Mon 5 Mon 6
from current SSTs
from 1999 SSTs
Simulations
start of month 0
1-2 years back
end of month 6
VIC forecast ensemble
VIC model spin-up
VIC climatology ensemble
data
sources
NCDC met.
station obs.
up to 2-4
months
from
current
LDAS/other
met. forcings
for
remaining
spin-up
climate
forecast
information
(from GSM)
A
B
C
Forecast Products
streamflow
soil moisture
runoff
snowpack
CRB
Initial
Conditions
late-May
SWE &
water
balance
CRB
Initial
Conditions
(percentile)
CRB: May forecast
forecast
observed
forecast
medians
CRB: May forecast
forecast
hindcast
“observed”
forecast medians
CRB May forecast
forecast
hindcast “observed”
forecast
medians
CRB May
forecast
basin avg. soil
moisture
CRB
May Forecast
Streamflow
CRB:
sequential streamflow
forecasts
Forecasts of Columbia River Flow @ The Dalles, 2001
500000
Mar fcast
Mar clim
Apr fcast
Apr clim
May fcast
May clim
Hindcast
450000
400000
climatologies
350000
cfs
300000
250000
200000
150000
100000
50000
0
Apr
forecasts
hindcast
May
Jun
Jul
Aug
Sep
Oct
Nov
ensemble medians
CRB
May
Forecast
cumulative
flow
averages
forecast
medians
6. Example 3 – Climate change
assessment
Accelerated
Climate Prediction
Initiative (ACPI) –
NCAR/DOE
Parallel Climate
Model (PCM) grid
over western U.S.
Regional
Climate Model
(RCM) grid and
hydrologic
model domains
Climate Change Scenarios
PCM Simulations
Historical
B06.22 (greenhouse CO2+aerosols forcing)
1870-2000
Climate Control
B06.45 (CO2+aerosols at 1995 levels)
1995-2048
Climate Change
Climate Change
Climate Change
B06.44 (BAU6, future scenario forcing)
B06.46 (BAU6, future scenario forcing)
B06.47 (BAU6, future scenario forcing)
1995-2099
1995-2099
1995-2099
PNNL Regional Climate Model (RCM)
Simulations
Climate Control
B06.45 derived-subset
1995-2015
Climate Change
B06.44 derived-subset
2040-2060
ACPI: PCMclimate change
scenarios, historic
simulation v air
temperature
observations
ACPI: PCMclimate change
scenarios, historic
simulation v
precipitation
observations
Bias Correction and Downscaling
Approach
climate model scenario  hydrologic model  snowpack
meteorological outputs
inputs
runoff
streamflow
•2.8 (T42)/0.5 degree
resolution
•monthly total P, avg. T
• 1/8-1/4 degree resolution
• daily P, Tmin, Tmax
Bias Correction
bias-corrected climate scenario
month m
raw climate scenario
from NCDC observations
month m
from PCM historical run
Note: future scenario temperature trend (relative to control run)
removed before, and replaced after, bias-correction step.
Downscaling
monthly PCM
anomaly (T42)
interpolated to
VIC scale
VIC-scale
monthly simulation
observed
mean fields
(1/8-1/4 degree)
BAU 3-run average
historical (1950-99)
control (2000-2048)
PCM
Business-as-Usual
scenarios
Columbia River Basin
(Basin Averages)
PCM BAU B06.44
RCM BAU B06.44
control (2000-2048)
historical (1950-99)
RCM
Business-as-Usual
scenarios
Columbia River Basin
(Basin Averages)
BAU 3-run average
historical (1950-99)
control (2000-2048)
PCM
Business-as-Usual
scenarios
California
(Basin Average)
BAU 3-run average
historical (1950-99)
control (2000-2048)
PCM
Business-as-Usual
scenarios
Colorado
(Basin Average)
PCM
Business-as-Usual Scenarios
Snowpack Changes
Columbia River Basin
April 1 SWE
PCM
Business-as-Usual Scenarios
Snowpack Changes
California
April 1 SWE
PCM
Business-AsUsual
Mean
Monthly
Hydrographs
Columbia
River Basin
@ The Dalles,
OR
1
month
12
1
month
12
PCM
Business-AsUsual
Mean
Monthly
Hydrographs
Shasta
Reservoir
Inflows
1
month
12
1
month
12
CRB Operation Alternative 1 (early
refill)
Total End of Month System Storage (acre-feet)
55,000,000
50,000,000
45,000,000
40,000,000
35,000,000
30,000,000
Max Storage
Control
Base Climate Change
Change (Alt. 1)
25,000,000
Dead Pool
20,000,000
15,000,000
O
N
D
J
F
M
A
M
J
J
A
S
CRB Operation Alternative 2 (reduce
flood storage by 20%)
End of Month Total System Storage (acre-feet)
55,000,000
50,000,000
45,000,000
40,000,000
35,000,000
30,000,000
Max Storage
Control
Base Climate Change
Change (Alt. 2)
Dead Pool
25,000,000
20,000,000
15,000,000
O
N
D
J
F
M
A
M
J
J
A
S
Columbia River Basin Water Resource Sensitivity
to PCM Climate Change Scenarios
120%
Reliability (%, monthly based)
100%
80%
Control
Period 1
Period 2
60%
Period 3
RCM
40%
20%
0%
PortlandPortlandAutumn Firm % of Control
Vancouver
Vancouver
Power
Hydropower
Spring Flood Winter Flood Reliability
Revenues
Control
Control
(November)
Reliability
Reliability
McNary
Instream
Target
Reliability
(AprilAugust)
Middle Snake Grand Coulee
Agricultural
Recreation
Withdrawal
Reliability
Reliability
Average Monthly Deficit at the
McNary Dam Target (cfs)
1 2 0 ,0 0 0
Monthly Reliability at the
McNary Dam Target
1 00 %
Co n tr o l
Cu r r e n t O p e r a ti o n s
90 %
Re f i ll 2 w e e ks e a r lie r
80 %
Re f i ll 1 m o n th e a r lie r
70 %
1 0 0 ,0 0 0
Period 1
8 0 ,0 0 0
60 %
50 %
6 0 ,0 0 0
40 %
4 0 ,0 0 0
30 %
20 %
2 0 ,0 0 0
10 %
0%
Apr
M ay
Ju n
J ul
Apr
A ug
Ma y
J un
Ju l
Aug
J un
Ju l
A ug
1 00%
1 2 0 ,0 0 0
Co n tr o l
90%
Cu r r e n t O p e r a ti o n s
1 0 0 ,0 0 0
Re f ill 2 w e e ks e a r li e r
80%
Re f ill 1 mo n th e a r lie r
70%
Period 2
8 0 ,0 0 0
60%
50%
6 0 ,0 0 0
40%
4 0 ,0 0 0
30%
20%
2 0 ,0 0 0
10%
0%
Apr
M ay
Ju n
J ul
A pr
A ug
1 00%
1 2 0 ,0 0 0
Co n tr o l
Cu r r e n t O p e r a tio n s
1 0 0 ,0 0 0
90%
Re f i ll 2 w e e ks e a r lie r
80%
Re f i ll 1 m o n t h e a r lie r
70%
Period 3
8 0 ,0 0 0
60%
6 0 ,0 0 0
50%
40%
4 0 ,0 0 0
30%
20%
2 0 ,0 0 0
10%
-
0%
Ma y
- $40
$ 1 .0
- $60
$ 0 .8
- $80
$ 0 .6
-$ 100
$ 0 .4
M e a n p ow e r r e ve nue
c han ge
-$ 120
$ 0 .2
$-
-$ 140
C urre nt
2 W e e ks
1 M ont h
$1. 8
$1 00
$1. 4
$1. 2
$ 60
$1. 0
$ 40
$0. 8
$0. 6
$ 20
$0. 4
$0
M e an pow e r r e v e nu e
cha ng e
$-
-$ 20
Change in Ann ua Pow er Revenu e
(m illions)
Cu r r e n t
Period 3
$0. 2
Aver age Annual Flood Damage (million)
$1. 6
$ 80
2 W e e ks
1 M ont h
$0
$ 1 .8
-$ 10
$ 1 .6
-$ 20
$ 1 .4
-$ 30
$ 1 .2
-$ 40
$ 1 .0
M e a n a n n u a l f lo o d d a ma g e
-$ 50
$ 0 .8
-$ 60
$ 0 .6
-$ 70
$ 0 .4
M e an po w e r r e ve n ue
-$ 80
$ 0 .2
ch an ge
$-
-$ 90
Cu r r e n t
2 W e e ks
1 M o n th
Av erage Annual F lood Damage ( million)
Change in Ann ual Pow er Revenu e
(millions)
Period 2
Me a n a n n u a l f lo o d d a m a g e
% o f Value Observed Und er Con trol Clim ate &
O peratio ns Scenario
$ 1 .2
% o f Value Observed Und er C on trol Clim ate &
O peratio ns Scenario
- $20
1 60%
1 60%
% o f Value Ob served Und er Con trol Clim at e &
O peratio ns Scenario
Change in Ann ual Pow er Revenu e
(m illions)
Period 1
M e a n a n n u a l f lo o d d a ma g e
Aver age Annual Flood Damage (million)
$ 1 .4
$0
1 50%
C h a n g e in c u mmu la tiv e a n n u a l d e f ic i t a t Mc N a r y
1 40%
1 30%
1 20%
1 10%
Ch a n g e in s u s ta in a b le f ir m
1 00%
90%
80%
Cu r r e n t
2 W e e ks
1 M o n th
1 50%
1 40%
Ch a n g e in c u m mu la tiv e a n n u a l d e f ic it a t M c Na r y
1 30%
1 20%
1 10%
Ch a n g e in s u s ta in a b le f ir m p o w e r
1 00%
90%
80%
Cu r r e n t
2 W e e ks
1 M o n th
1 60%
1 50%
1 40%
1 30%
Ch a n g e in c u m mu l a tiv e a n n u a l d e f ic it a t M c Na r y
1 20%
1 10%
C h a n g e in s u s ta in a b le f ir m p o w e r
1 00%
90%
80%
Cu r r e n t
2 W e e ks
1 M o n th