1
Flash-flood forecast uncertainty for distributed models using radar data
By
Soni YatheendradasHoshin Gupta
Thorsten WagenerCarl Unkrich
David GoodrichMike SchaffnerAnne Stewart
2
Motivation: Flash flood importance
• What: Basin reaction time < 6 hours (as low as 15 minutes!)
• Where: Usually water-scarce arid/semi-arid regions:
1/3rd of land mass, 1/4th of mainland US, > ½ of Western US
• Loss of life: Significant in US: Highest among natural disasters (80%)
• Flood Fatalities: ~ 146/year for 1972-1991 (NWS, 1992)
• Property/economic loss: Millions of dollars in damages
3
• RFC’s: Continuous, lumped rainfall-runoff models (6 to 1-hourly)
• WFO’s (site-specific hydrologic guidance):
– Local model application (0.5 to 1-hourly )
– Directly from rain using Flash Flood Monitoring and
Prediction (FFMP) for small basins
• Accumulated rain
– Upstream, or @ forecast-pt
– Compared to Flash-Flood
Guidance (FFG) values
• Judgment & experience
• Accuracy improvement potential
in hydrograph timing, magnitude
Current NWS Flood-Forecasting procedures
FFMP
4
• Missing FFMP components:
– dynamically varying soil moisture,
– highly non-linear rainfall-runoff transformation
• Semi-arid hydrology (infiltration excess,
ephemeral channels etc.) different from humid hydrology
• Rain Input: scale, resolution
• Distributed model resolution:
– Interaction scale: storm & basin geometry (Osborn,1964)
– Temporal & spatial variability
Operational flash-flood forecasting difficulty
Summertime storms: convective, high-intensity,
extremely localized, short-duration
Model!
Semi-arid physics!
Distributed input &
structure
Satellite Gage Radar
X?
5
Project hypothesis: “A site-specific model for the Western Region (Western slopes of the Rocky Mountains down to the Pacific Ocean) providing specific hydrologic flash-flood forecasts would greatly improve services of the NWS and reduce potential loss of life and property”
COMET project
+ +University of Arizona Tucson NWS WFO USDA-ARS-SWRC
KINEROS2 model (used with AGWA preprocessor)
( http://www.tucson.ars.ag.gov/kineros/ &http://www.tucson.ars.ag.gov/agwa/)
WSR-88D DHR product (Maddox et al, 2002)
1 km by 1 degree,5-min data from summer 2003
Resources available (model and input)
6Flash-flood forecasting system in action (2006 monsoon)
Sabino Creek - KINEROS Site Specific Forecast Model
3 PM 4 PM 5 PM
09.06.2006
0
1
0
1
2
3
4
5
6
7
8
9
10
0
1
2
0100
1,000
10,000
20,000
0.91 in
3.88 ft (2,298 cfs)
09.06.2006 3:28 PM
Bank FullFlood
Moderate Flood
Major Flood
Problems encountered:
• Frequent parameter set
recalibration
• Especially over severe storms
(e.g. July 31, 2006) (Magirl et al., 2007)
Operational forecast Uncertainty!
7
“Given operational uncertainties in the radar rain estimates,
model parameters and initial conditions, the predictive
uncertainties in the semi-arid flash-flood
forecasting model would be within desirable bounds
that are low enough to significantly improve the timely
issuance and cancellation of flood warnings and alarms.”
Hypothesis
8
• Errors in rainfall estimates dominate modeled uncertainty in semi-
arid runoff uncertainty (Goodrich et al, 1994)
– Inaccurate spatio-temporal representation (e.g. Michaud and Sorooshian, 1994)
– Variability in volume bias of estimates (Faures et al, 1995)
• Radar estimate power law: Z=aRb
• Summertime convective: standard NWS Z-R (a = 300, b = 1.4)
• Bias: NWS Z-R too high (Morin et al, 2005 got a = 655)
• Bias variability: a, b vary across storms
Significant depth bias range of 0.58-1.8 with a = 655
– Hail contamination (truncation @ top intensities)
• Hence:
“What is the influence of errors in rainfall estimates on
model response?”
Research question # 1
9
• Strong non-linear dependence of runoff on initial soil moisture in semi-arid
regions (Nicolau et al, 1996)
• Hence: “What is the influence of initial soil moisture on model
response, and how complex an inter-storm component is required?”
Research questions # 2 & # 3
• Model parameters need adjustment :• Hence: “Which model parameters
strongly influence the model
response?” homog.
(xeff,eff)
input
input
output
outputiden
tical
iden
tical
heterog.
measurement
Real world
10
Research question # 4
Rain estimate uncertainty
Initial soil moisture uncertainty
Model parameter uncertainty
Hence: “How reliable is such a flash-flood
forecasting model in presence of
compounding effect of these uncertainties?”
11
Legend
Hillslope
elements
Channels
Watershed
Outlet
Rain Gages
Stock pond
outlet
Radar Pixels
Small test basin setup: Walnut Gulch Flume 11 (WG11)
• 6.5 km2 area• Almost spatially homogenous AGWA-based a priori parameters
12Methodology and Analytical tools
• Gage/radar rainfall merging• A priori parameter evaluation runs• Monte-Carlo simulations
– Spatial modifiers on 24 factors– 2 sets of runs
• Generalized Likelihood Uncertainty Estimation (GLUE) framework
• Variance-based Global sensitivity analysis• Multi-objective information in hydrograph
Rain + Initial condition + parameters
Initial condition + parameters
13Results obtained: Rainfall intercomparison
0
1
2
3
4
5
6
Dis
charg
e (
m3 /s
)
Event 6 Gage Rain Input
05:0
0
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06:0
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06:3
0
07:0
0
07:3
0
08:0
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08:3
0
09:0
0
UTC time on Jul. 29, 2006
Simulations
Observed
0
1
2
3
4
5
6Event 6 Depth Bias-adjusted Radar Rain Input
05:0
0
05:3
0
06:0
0
06:3
0
07:0
0
07:3
0
08:0
0
08:3
0
09:0
0
UTC time on Jul. 29, 2006
Dis
charg
e (
m3 /s
)
0
1
2
3
4
5
6Event 6 Merged Rain Input
05:0
0
05:3
0
06:0
0
06:3
0
07:0
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07:3
0
08:0
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08:3
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09:0
0
UTC time on Jul. 29, 2006
Dis
charg
e (
m3 /s
)
Uncertainty in spatial distribution
Gage
Radar
Merged • More behavioral factor set consistency across peaks• Better replication of peaks
Functional inconsistency
Structural inconsistency
?
14
Results obtained: Sensitivity Analysis (SA)Rain influence on response
Sta
rt t
ime
Peak m
ag
.
volu
me
Peak t
ime
En
d t
ime
Mu
lti-
ob
j.
NS
E
Beh
avio
ral
15Results obtained: Sensitivity Analysis (SA)Relative rain influence on response (1st set of runs)
NSE Multi-objective Behavioral
16Results obtained: Sensitivity Analysis (SA)
Answer to research question # 1:
“While merging of high spatial resolution gage and radar
estimates might potentially provide better simulations
than either one by itself, the influence of rain
depth/volume bias uncertainty clearly almost completely
dominates the model response.”
17Results obtained: Sensitivity Analysis (SA)Model parameters’ influence on response (2nd set of runs)
NSE Multi-objective Behavioral
Hill
slop
e p
ara
mete
rs
Chann
el
para
mete
rs
Hill
slop
e p
ara
mete
rs
Hill
slop
e p
ara
mete
rs
Chann
el
para
mete
rs
Chann
el
para
mete
rs
Init
ial co
ndit
ions
Init
ial co
ndit
ions
Init
ial co
ndit
ions
18Results obtained: Sensitivity Analysis (SA)
Answer to research question # 2:“In general, uncertainties in the hillslope model parameters
impact predictions more than those in the channel
parameters for small basins. The most influential hillslope
parameter uncertainties stem from the soil saturated
hydraulic conductivity, the soil volumetric rock fraction, and
the soil surface roughness, while those for the channel are
from the soil surface roughness and the Woolhiser coefficient.
”
19Results obtained: Sensitivity Analysis (SA)Initial condition influence on response (2nd set of runs)
Hill
slop
e init
ial
Soil
mois
ture
Hill
slop
e init
ial
Soil
mois
ture
Hill
slop
e init
ial
Soil
mois
ture
NSE Multi-objective Behavioral
Hill
slop
e p
ara
mete
rs
Chann
el
para
mete
rs
Hill
slop
e p
ara
mete
rs
Hill
slop
e p
ara
mete
rs
Chann
el
para
mete
rs
Chann
el
para
mete
rs
20Results obtained: Sensitivity Analysis (SA)
Answer to research question # 3:
“Initial hillslope soil moisture can have a dominant effect
on the predicted response. Hence, sophisticated inter-
storm model components are required to track it with a
high degree of accuracy.”
21Results obtained: Model predictive uncertainty
0
2
4
6
8
10
12
14
16
18
20
Dis
char
ge (m
3 /s)
Uncertainty from prior events on E6P1: 90% confidence
05:0
0
05:3
0
06:0
0
06:3
0
07:0
0
07:3
0
08:0
0
08:3
0
09:0
0 UTC time on Jul. 29, 2006
Confidence Limits
Observed
0
2
4
6
8
10
12
14
16
18
20
Dis
char
ge (m
3 /s)
Uncertainty from all events on E6P1: 90% confidence
05:0
0
05:3
0
06:0
0
06:3
0
07:0
0
07:3
0
08:0
0
08:3
0
09:0
0 UTC time on Jul. 29, 2006
Confidence Limits
Observed
(a)(b)
22Results obtained: Model predictive uncertainty
Answer to research question # 4:
“Given current uncertainties in the radar rain estimates,
model parameters and initial conditions, the predictive
uncertainties in flash-flood forecasting can be much higher
than desired. Using a model is still a valuable tool for
forecasters, giving a reliable quantitative risk-based tool
for timely issuance and cancellation of flood warnings and
alarms.”
23Unresolved issues
• Behavioral factor set inconsistency
• No reduction in factor ranges
• Causes: Model structural error? Distributed model nonlinearity? Inadequate factor space sampling? Data errors?
– Synthetic study required!!
24
Sensitivity comparison with real data case: 23 factors
25
• Factor influences similar to those in real data study
• Behavioral factor set inconsistency due to inadequate
factor space sampling
• No reduction in factor set ranges due to high
distributed model non-linearity and parameter
interactions
Synthetic study conclusions
26Overall Study Results
• Methodology for operational flash-flood forecasting
with uncertainty• Applicable even when behavioral factor set
inconsistency exists• Uncertainty in bias of rain estimates from radar
recognized as biggest source of uncertainty• Influential yet poorly defined model parameters • Sophisticated inter-storm model component required
for continuous operational forecasting mode• Possibly similar results if strict behavioral constraints
set on humid hydrology models also: towards reducing
predictive uncertainty
27
“This is what we mean by science. That both question and answer are tied up with uncertainty, and that they are painful. But that there is no way around them. And that you hide nothing; instead, everything is brought out in the open.” -- Peter Høeg (1995), Borderliners
Acknowledgements:• COMET program• SAHRA NSF-STC• Soren Scott, University of Nebraska-Lincoln• Paul Jendrowski, NOAA-NWS• Stefano Tarantola, Joint Research Center, Italy
28
Conceptualization
Mathematical Representation (Equations)
System Invariants (Parameters)
U(t) f
Model(Prognostic Variables)
E(t)
X0
Output(Diagnostic Variables)
Y(t)
Forcing(Input Variables)
State(Prognostic Variables)
KINEROS2 Deterministic Simulation
[Rain]
[Initial Soil Moisture]
[Outflow]
[Soil moisture]
[Kinematic surface flow, Parlange 3-parameter infiltration etc.]
From HWR642
29
Results obtainedResults obtained
30
Results obtained: Behavioral ranges & correlations in Factors Factor correlation structures
pKsM vs. RainM (0.6-0.7)
• RainM in infiltrability equation
• Scaling: r*=(r - Ksmodel) / Ks
model
pKsM vs. pRocM (0.45-0.62)
• In model constraining equations
• Gr = ( (1- Ø) * (1- Vr) ) / (1- Ø * (1- Vr) )
• Ksrock = Ks
text * Gr
• Ksmodel = Ks
rock * (e0.0105*CI)
• Ksmodel=Ks
text *((1- Ø)*(1-Vr))/(1-Ø*(1- Vr))(e0.0105*CI)
+ve: quotient or difference
Difference: +ve corr.
Quotient: +ve corr.
Quotient: +ve corr.
No strong directional components in PCA
31
Results obtained: Sensitivity Analysis (SA)Rain influence on response
Sta
rt t
ime
Peak m
ag
.
volu
me
Peak t
ime
En
d t
ime
Mu
lti-
ob
j.
NS
E
Beh
avio
ral
32Results obtained: Sensitivity Analysis (SA)
Answer to research question # 2:“In general, uncertainties in the hillslope model parameters
impact predictions more than those in the channel
parameters for small basins. The most influential hillslope
parameter uncertainties stem from the soil saturated
hydraulic conductivity, the soil volumetric rock fraction, and
the soil surface roughness, while those for the channel are
from the soil surface roughness and the Woolhiser coefficient.
”
• Note: factor identifiability factor sensitivity
• Already identified factors: hillslope soil saturated hydraulic
conductivity, hillslope soil surface roughness, channel soil surface
roughness, channel Woolhiser coefficient
33Research Motivation: Future importance of flash floods
• Future climate: Increasingly drier, more variable
• More arid regions: Due to global warming?
• Extreme precipitation: Increased global incidence (e.g., IPCC, 2001),
changing hydrological cycle (Oki & Kanae, 2006)
34
Task 1: Model Recoding and Task 1: Model Recoding and operational model performance operational model performance
35
Recoding of KINEROS2
• Outer space, inner time (Space-time) loop
• Procedural paradigm-based Fortran 77 code
• Well-defined, but dependent components of a
monolithic code
Earlier version
• Outer time, inner space (Time-space) loop for forecasting
• Object-oriented library of self-contained modules
• Easy future extensibility
• Code improved (applicable now to large basins)
New version
• Real-time forecasting requirement
• Current advances in computing
resources
• Object-oriented paradigm in Fortran
90/95
Changes
36
A PRIORI ESTIMATION VIEW:
IF a watershed model is theory-based & structurally consistent with reality & available distributed data
THEN all parameters specifiable directly from data & no calibration required.
CALIBRATION VIEW:
Watershed models are perceptual & conceptual simplifications of reality, so
certain model invariants will not correspond directly to observable
properties
THEN some “calibration” necessary.
Reality generally somewhere in between
Do apriori estimation where possible and calibrate the
remaining parameters
AGWA a priori estimates not working well
Manual calibration by
USDA-ARS-SWRC
From HWR642
A priori estimation vs. calibration
37
Task 2: Investigating operational Task 2: Investigating operational flash-flood forecasting flash-flood forecasting
uncertaintyuncertainty
38
• NWS: Reliable model-based flash-flood forecasts with acceptable
uncertainty levels
• Scientific: – Refining uncertainty in parameter estimates (Philosophy of
progressive uncertainty reduction)
• Consequent narrower forecast uncertainty
• Future regionalization efforts: ungaged basins
– Sensitivity/uncertainty analysis
• Uncertainties in Dominant but poorly identified/known
parameters needing reduction (field investigations)
• Redundant parameters fixable to reduce calibration burden
• Modifications needed in parameter-corresponding model
processes
Objectives
40
• Radar hail threshold– NWS: 103.8 mm/hr
– Can be 75-150 mm/hr (Fulton et al, 1998)
– Five-min max.:
• Gage data : ~ 250 mm/hr (Mendez et al,, 2003)
• Radar data max. : > 103.8 mm/hr (Morin et al, 2006)
– Flash-flood forecasting: • high-intensity, short return period (~10 yrs)
• Merging gage & radar– Gage data depth “more reliable” (0.01” accuracy)
Methodology and Analytical tools: Gage-radar merging
146.3 mm/hr (Mendez et al,, 2003)
Areal gage data
‘Point’ gage data
Thiessen weighting
Bulk adjustment
Gage-radar time lag
adjustment
Areal radar data
Adjusted radar data
Averaging Merged
rain
45Methodology and Analytical Tools: Multi-objective informationFormulating objective functions (OFs): Minimization problem
obs
obssim
magnitudePeak
magnitudePeakmagnitudePeakFQPge
)(
)()(.,.
Normalized:
46Methodology and Analytical Tools: Multi-objective informationFormulating likelihood functions (LFs): Maximization problem
LFtemp = (OF)max-
OF
LFtemp
Min
Max
LFtemp2
0.1
1.0
• 0 is non-behavioral
LF = LFtemp2 / sum
( LFtemp2 )• Summation to 1
Hence, e.g.:
Shape descriptor: Peak magnitude
OF: FQP
LF: LFQP
47Methodology and Analytical Tools: Multi-objective information
Initial Feasible factor
Uncertainty space
Overall behavioral factor
Uncertainty space
“Original” shape-based LFs
Overall behavioral LFB information
“Enhanced” shape-based LFs: combinedInformation
Overall behavioral: Monte Carlo Set-Membership Methodology (MCSM)(Van Straten & Keesman, 1991)
• Upper and lower behavioral constraint around observations• On a combination of basic shape descriptors here: LFB likelihood function
Uncertainty BoundsUncertainty BoundsQ
t t
QNon-behavioral factor sets
Behavioral factor setsNon-behavioral factor sets
Behavioral factor sets
Illustrations by Koray Yilmaz
49Results obtained: Rainfall intercomparison
Gage vs. radar
50Results obtained: Behavioral ranges in Factors
Behavioral classification:
• Resultant uncertainty reduction : None !
• % of behavioral simulations: (<1%) !
• Only some restructuring tendency of some
distributions away from uniform (identifiability):
• PKsM (hillslope hydraulic conductivity)
• PnM (hillslope roughness)
• CnM (channel roughness)
• CWCoM (channel Woolhiser coefficient:
wetting)
• RainM (rain depth)
0.5 1 1.5 2
1
2x 10
-5
D(P
KsM
)
0.5 1 1.5 2
1
2
3x 10
-5
D(P
nM)
ParPostDis:WG11_23Par: BehLeast
0.5 1 1.5
1
2
x 10-5
D(P
CV
M)
0.8 1 1.2 1.4
0.5
1
1.5
x 10-5
D(P
GM
)
0.8 0.9 1 1.1 1.20.5
1
1.5
2
2.5x 10
-5
D(P
Roc
M)
0.8 0.9 1 1.1 1.20.60.8
11.21.41.6
x 10-5
D(P
IntM
)
0.8 0.9 1 1.1 1.2
1
2x 10
-5
D(P
Dis
tM)
0.8 0.9 1 1.1 1.20.60.8
11.21.41.61.8
x 10-5
D(P
Por
M)
0.02 0.04 0.06
1
2x 10
-5
D(P
Rill
DA
)
0.05 0.1 0.15 0.2 0.25
0.5
1
1.5
x 10-5
D(P
Rill
SA
)
0.8 0.9 1 1.1 1.20.5
1
1.5
x 10-5
D(P
Can
M)
0.85 0.9 0.95 1 1.050.5
1
1.5
x 10-5
D(C
KsM
)
0.5 1 1.5 2
0.51
1.52
2.5x 10
-5
D(C
nM)
0.5 1 1.50.5
1
1.5
x 10-5
D(C
CV
A)
0.5 1 1.5 2
1
2x 10
-5
D(C
GM
)
0.02 0.04 0.06 0.08
1
1.5
2
x 10-5
D(C
Roc
A)
0.8 1 1.2 1.40.60.8
11.21.41.61.8
x 10-5
D(C
Dis
tM)
0.9 0.95 1 1.05 1.10.5
11.5
22.5
x 10-5
D(C
Por
M)
0.5 1 1.5 2 2.5
1
2
3x 10
-5
D(C
WC
oM)
0.4 0.5 0.60.5
1
1.5
2
x 10-5
D(C
Wid
M)
0.960.98 1 1.021.041.061.08
1
2x 10
-5
D(C
Tor
tF)
0.3 0.4 0.5
1
2
x 10-5
D(P
SM
IA)
0.3 0.4 0.5
1
2x 10
-5
D(C
SM
IA)
0.5 1 1.5 2
1
2x 10
-5
D(P
KsM
)
0.5 1 1.5 2
1
2
3x 10
-5
D(P
nM)
ParPostDis:WG11_23Par: BehLeast
0.5 1 1.5
1
2
x 10-5
D(P
CV
M)
0.8 1 1.2 1.4
0.5
1
1.5
x 10-5
D(P
GM
)
0.8 0.9 1 1.1 1.20.5
1
1.5
2
2.5x 10
-5
D(P
Roc
M)
0.8 0.9 1 1.1 1.20.60.8
11.21.41.6
x 10-5
D(P
IntM
)0.8 0.9 1 1.1 1.2
1
2x 10
-5
D(P
Dis
tM)
0.8 0.9 1 1.1 1.20.60.8
11.21.41.61.8
x 10-5
D(P
Por
M)
0.02 0.04 0.06
1
2x 10
-5
D(P
Rill
DA
)
0.05 0.1 0.15 0.2 0.25
0.5
1
1.5
x 10-5
D(P
Rill
SA
)
0.8 0.9 1 1.1 1.20.5
1
1.5
x 10-5
D(P
Can
M)
0.85 0.9 0.95 1 1.050.5
1
1.5
x 10-5
D(C
KsM
)
0.5 1 1.5 2
0.51
1.52
2.5x 10
-5
D(C
nM)
0.5 1 1.50.5
1
1.5
x 10-5
D(C
CV
A)
0.5 1 1.5 2
1
2x 10
-5
D(C
GM
)0.02 0.04 0.06 0.08
1
1.5
2
x 10-5
D(C
Roc
A)
0.8 1 1.2 1.40.60.8
11.21.41.61.8
x 10-5
D(C
Dis
tM)
0.9 0.95 1 1.05 1.10.5
11.5
22.5
x 10-5
D(C
Por
M)
0.5 1 1.5 2 2.5
1
2
3x 10
-5
D(C
WC
oM)
0.4 0.5 0.60.5
1
1.5
2
x 10-5
D(C
Wid
M)
0.960.98 1 1.021.041.061.08
1
2x 10
-5
D(C
Tor
tF)
0.3 0.4 0.5
1
2
x 10-5
D(P
SM
IA)
0.3 0.4 0.5
1
2x 10
-5
D(C
SM
IA)
Entire Range
Restructuring
No restructuring
51Results obtained: A priori run evaluation on Walnut Gulch
52
Results obtained: Correlations in LFs
• Correlations in 5 original basic LFs :
• LFQP vs. LFVU (peak magnitude vs. volume)
• Hence:
• No correlations in 5 basic enhanced LFsU
pdati
ng
LFM
LFQP
LFTP
LFTE
Peak magnitude
Peak timing
End timing
Entire factor space
LFTSStart timing
53
• Enhanced LF SA indices similar to LFB indices
(behavioral)
– Low Power : Low % of behavioral simulations
– From here, only LFM (Multi-objective) & LFB
(behavioral) used.
Results obtained: Sensitivity Analysis (SA)
55
Task 3: Synthetic study of Task 3: Synthetic study of uncertainty in flash-flood model uncertainty in flash-flood model
predictions predictions
56
1 J ul. 29 '03 21:27 1 3.70 65822 Aug. 25 '03 19:35 1 2.68 64863 Aug. 28 '03 0:26 1 8.28 175734* Aug. 09 '05 23:09 1 1.271 2357
5 J ul. 29 '06 6:29 2
7.48,
4.99 245336* J ul. 30 '06 14:24 1 2.46 55347* J ul. 31 '06 11:51 1 2.27 4433
* Rain modifier 1.3
#
peaks
Peak
flows
(cms)
Flow
volume
(cu. m.)
Event
# Date (UTC)
Flow
start
time
(UTC)
Events used
57Reduced factor range behavioral consistency
21 22 23
2N
Behavioral factor set consistency over reduced factor
ranges -> Sampling density inadequate over original range
58
Sensitivity comparison with reduced number of factors
59Results obtained: Predictive uncertainty comparison
0
2
4
6
8
10
12
14
16
18
20
Dis
char
ge
(m3 /s
)
Uncertainty from prior events for 23 factors: 90% confidence
05:0
0
05:3
0
06:0
0
06:3
0
07:0
0
07:3
0
08:0
0
08:3
0
09:0
0
UTC time on Jul. 29, 2006
Confidence Limits
Observed
0
2
4
6
8
10
12
14
16
18
20
Dis
char
ge
(m3 /s
)
Uncertainty from prior events for 8 factors: 90% confidence
05:0
0
05:3
0
06:0
0
06:3
0
07:0
0
07:3
0
08:0
0
08:3
0
09:0
0 UTC time on Jul. 29, 2006
Confidence Limits
Observed
23 factors 8 factors
60Spatial factor sensitivity
Hillslope soil saturated hydraulic conductivity: 10% perturbation
61
Digital Elevation model (DEM)
Watershed Discretization
+
Parameters: Grid to elemental
Soil
Land Cover
Model elemental parameter
s
AMBER GISRadar grid shapefile
AMBER code or DHR decoder
Pixel weights in
model elements
Pixel reflectivities from DHR
Pixel rains
KINEROS2 rain preprocessor module
KINEROS2 code execution
Model elemental rains
AGWA
KINEROS2
Results
Single model run setup
62
Time
Dis
char
ge
Event hydrograph
Methodology and Analytical Tools: Multi-objective information
Peak magnitude
Peak timing
Volume
Start timing
End timing
Basic shape descriptorsInflection point