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Model Parameter Estimation Experimant (MOPEX)
Science Issues
• What models are most appropriate for different climatic and physiographic regions?
• What are the most robust parameter estimation methods?
• What are the uncertainty bounds for ungauged basin applications?
• What are the most robust calibration methods?
Participants
• NWS (SWB & SAC)• Meteo France
(ISBA)• Russian Academy of
Science (SWAP)• UC Berkeley /
Princeton (VIC)• Cemagref, France
(GR4J)• NCEP (NOAH)• USGS (PRMS)
• Yamanashi University (BTOPMC)
• Swedish Meteor. and Hydro. Institute, Sweden (HBV)
• University of Alberta, Canada (SAC)
•University of Arizona
•University of Newcastle, Australia
•Centre for Ecology and Hydrology, UK
•Oregon State University
•Wageningen University, The Netherlands
•National Institute of Hydrology, Canada
•University of New Hampshire
Location of MOPEX Basins
-100 -95 -90 -85 -80 -7528
30
32
34
36
38
40
42Location of 12 Basins for 2nd MOPEX workshop in Tucson
Climatic Hydrologic RatiosEvaporation Efficiency vs Climate Index
0
0.2
0.4
0.6
0.8
1
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00
Climate Index (P/PE)
Evap
orat
ion
Effic
ienc
y (A
E/PE
)
Obs Oldekop Schrieber Turc-Pike
Runoff Coefficient vs Climate Index
0.00
0.20
0.40
0.60
0.80
1.00
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00
Climate Index (P/PE)
Runo
ff Co
effic
ient
(Q/P
)
Obs Oldekop Schrieber Turc-Pike
desert steppe tundraforest
Annual runoff estimates
A Priori Runoff Estimates - 1960-1998
0
100
200
300
400
500
600
700
800
900
0 100 200 300 400 500 600 700 800 900
Observed Runoff (mm)E
stim
ate
d R
un
off
(m
m)
A
B
C
D
E
F
G
H
Linear
Calibration Runoff Estimates - 1960-1998
0
100
200
300
400
500
600
700
800
900
0 100 200 300 400 500 600 700 800 900
Observed Runoff (mm)
Es
tim
ate
d R
un
off
(m
m)
A
B
C
D
E
F
G
H
Linear (=)
A Priori Results-
Average and Standard
Deviation of Daily
Efficiency
Average Daily Nash-Sutcliffe EfficiencyA Priori - 1960-1998
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
A B C D E F G H
ModelE
ffic
ien
cy
Inter-Basin Standard Deviation of Daily Nash-Sutcliffe Efficiency
A Priori Results 1960-1998
0
0.2
0.4
0.6
0.8
1
1.2
1.4
A B C D E F G H
Model
Sta
nd
ard
Devia
tio
n o
f E
ffic
ien
cy
A Priori Results-
Average Daily Efficiency of 6-Best & Worst
Basins
Average Daily Nash-Sutcliffe EfficiencyA Priori Results 1960 - 1998 Best 6 Basins
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
A B C D E F G H
ModelE
ffic
ien
cy
Average Daily Nash-Sutcliffe Effice incyA Priori Results 1960 - 1998 Worst 6 Basins
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
A B C D E F G H
Model
Eff
icie
nc
y
Calibration- Average and
Standard Deviation of
Daily Efficiency
Average Daily Nash-Sutcliff EfficiencyCalibration - 1960-1998
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
A B C D E F G H
Model
Eff
icie
nc
y
Inter-Basin Standard Deviation of Daily Nash-Sutcliffe EfficiencyCalibration Rsults 1960 - 1988
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
A B C D E F G H
M odel
Sta
nd
ard
De
via
tio
n o
f E
ffic
ien
cy
Calibration-Average Daily Efficiency of
6-Best & Worst Basins
Average Daily Nash-Sutcliffe EfficencyCalibration Results 1960-1998 Best 6 Basins
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
A B C D E F G H
M odel
Eff
icie
nc
y
Average Daily Nash-Sutlciffe EfficencyCalibration Resu;lts 1960 - 1998 Worst 6 Basins
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
A B C D E F G H
Model
Eff
icie
nc
y
1
14
4
13
8 9
7
9
16
6
5
3
6
5
3
4
6
1
12 2
15
18
11
2
18
16
10
5
4
2
2
9
10
9
12
4
3
6
5
10
18
16
6
6
6
8
6
16
18
106
5
8
14 8
17
6
2
4
6
14
719
5
712
12
82
3
19
12
5 8
5
11
8
37
2
61
7
2
17
15
1511
11
17
15
1110
17
19
17
18
16
17
18
17
12
11
18
17
•A statistical clustering (20 clusters) of the factors that define hydrologic landscapes
•Among-region variability in the factors is maximized and within-region variability is minimized
Hydrologic landscape regions
Hydrologic landscapes:
A combination of natural factors (climate, geology, and terrain) expected to affect hydrologic transport processes
Direction of surface runoff surfasurfacesffl orunof
Stream VALLEY SIDE
LOWLAND Water table
UPLAND
Direction of ground- water flow
EVAPOTRANSPIRATION
PRECIPITATION
Factors used to define hydrologic landscape regions
Precip – Potential evapotranspiration Percent sand
Bedrock permeability Topography
Expanded a priori Parameter Estimation Study
Selected Basins
Hydrologic Landscape Regions
GIS WEASEL
Vegetation Type (USFS)
Vegetation Density (USFS)
Land Use-Land Cover (USGS)
DIGITAL DATABASESDIGITAL DATABASES (1 km2 resolution)
STATSGO - Soils Data, 1 km2 (USDA)
SW Solar RadiationClimatological monthly means
Interpolated (multiple linear regression)
PET Maps
Climatological Monthly Means
(NOAA)
Nash Sutliffe vs % vol error
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50
% vol error (abs value)
Nas
h S
utc
liff
e C
E
fitted
a priori
MOPEX Basins MOPEX Strategy
Nash Sutcliffe vs % vol error
-4
-3
-2
-1
0
1
0 50 100 150 200 250 300
percent vol error (abs value)
Nas
h S
utc
liff
e C
E
apriori
20 Hydrologic Landscape Regions(61 basins)
Nash Sutcliffe vs % vol error
00.10.20.30.40.50.60.70.8
0 20 40 60 80 100
percent vol error (abs value)
Nas
h S
utc
liff
e C
E
a priori
20 Hydrologic Landscape Regions(46 basins)
Nash Sutcliffe vs % vol error
00.10.20.30.40.50.60.70.8
0 20 40 60 80 100
percent vol error (abs value)
Nas
h S
utc
liff
e C
E
a priori
20 Hydrologic Landscape Regions(46 basins)
18
15
7
16
9
16
438 U.S. Basins Meet Criteria for MOPEX Basin Selection
Criteria: Number of Precipitation Gages > 0.6 * Area**0.3
100
101
102
103
104
105
10-1
100
101
102
Basin Area (km2)
Gage Density(km2/gage)
Gage Density vs Basin Size Location of basins with Adequate gage density
International Contributed Data Sets (58 basins to date)
• Australia – University of Melbourne• U.K. – CEH• France – Meteo France• China
Possible Additional Basins• Germany• Sweden• Austria• Tanzania• Global monthly (UNH)• Canada• Japan• Brazil• Others?