a statistical-distributed hydrologic model for flash flood forecasting international workshop on...
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A Statistical-Distributed Hydrologic Model for Flash Flood Forecasting
International Workshop on Flash Flood ForecastingMarch 13, 2006
Seann Reed1, John Schaake1, Ziya Zhang1,3
1Hydrology Laboratory, Office of Hydrologic DevelopmentNOAA National Weather Service, Silver Spring, Maryland
2Consultant to Office of Hydrologic Development, Annapolis, MD
3University Corporation for Atmospheric Research
Flash Flood Forecasting Goals and Strategies
• Goals– Improve accuracy– Improve lead times
• Hydrologic Modeling Strategies – Investigate a statistical-distributed hydrologic model
• Understand model errors at flash flood scales• Compare distributed model results to FFG results• Validate inherent bias correction of the statistical-distributed
model
– Investigate the use of high resolution, short-term QPF grids to force the statistical-distributed model
• Force the model with grids from the Multisensor Precipitation Nowcaster (MPN)
NWS Flash Flood Guidance (FFG)
TR
FFGWRainfall Depth
Run
off
Dep
th
FFGD
Wet
Dry1650 km2
800 km2
285 km2
(1) River Forecast Center (RFC)
Maintains 6 hr Lumped Model
Forecast points
(2) RFC Runs Flash Flood Guidance System
1 hr Gridded FFG
(3) RFC transmits FFG to Weather Forecast Offices
(WFO)(4) Forecaster compares mean areal basin rainfall (ABR) to FFG in in small, flashy basins (5 - 260 km2).
TR = Threshold runoff
Scale mismatch!
0
20
40
60
80
10 100 1000 10000
Area (km2)
Ave
rag
e %
A
bs.
Pe
ak
Flo
w E
rro
rs
0.0
0.3
0.6
0.9
1.2
Rq
High Resolution Modeling Brings Potential Benefits but Also Increased Uncertainty
• FFG system uses lumped (260 – 4000 km2) soil moisture states.
• A distributed hydrologic model can make computations at spatial and temporal scales consistent with flash flooding.
• Model errors tend to increase at smaller modeling scales.
• Will increased model errors in small basins mask the benefits of making calculations at the appropriate scales?
Flash floods
260
Distributed model (uncalibrated). Each point is an average peak flow error from approximately 25 events over an eight year study period.
Scaling relationship for an uncertainty index (Rq) from Carpenter and Georgakakos (2004) (secondary axis)
Log-linear regression for distributed model data
Forecastfrequencies
A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations
HistoricalReal-time
simulated historical
peaks (Qsp)
Simulated peaks distribution (Qsp) (unique for each
cell)
Archived
QPE
Initial hydro model states
StatisticalPost-processor
Distributed hydrologic
model
Distributed hydrologic
model
Real-time
QPE/QPF
Max forecastpeaks
• The statistical-distributed model produces gridded flood frequency forecasts.
• We express flood frequencies in terms of the Average Recurrence Interval (ARI) associated with the annual maximum flood.
Local/regional knowledge
Frequencythresholds
Compare
Why a frequency-based approach?
• Frequency grids provide a well-understood historical context for characterizing flood severity; values relate to engineering design criteria for culverts, detention ponds, etc.
• Computation of frequencies using model-based statistical distributions can inherently correct for model biases.
– This hypothesis is validated through probability matching at gauged locations (results in slide 10)
Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM)
• This implementation of HL-RDHM uses:– 2 km grid cell resolution
– 8 years of hourly, 4 km QPE and QPF grids are resampled to 2 km (nearest neighbor resampling)
– Gridded SAC-SMA
– Hillslope routing within each model cell
– Cell-to-cell channel routing
– Uncalibrated, a-priori parameters for Sacramento (SAC-SMA) and channel routing models (Koren et al., 2004)
• Similar HL-RDHM implementations showed good performance in the Distributed Model Inter-comparison Project (DMIP) (Smith et al., 2004; Reed et al., 2004)
• An operational prototype version of HL-RDHM is running at two NWS River Forecast Centers (slated for official delivery in Fall 2006)
Study Basins
OK
AR
INX Radar
SRX Radar
N
No Short Station Name Area Period of record Time toName (km2) (hourly flow) peak (hrs)
1 SPRINGT Flint Ck at Springtown AR 36.8 6/1993-9/2004 3
2 SSILOAM Sager Ck nr W. Siloam Springs OK 48.9 9/1996-9/2004 3
3 CHRISTI Peacheater Ck at Christie OK 64.7 5/1993 - 9/2003 6
4 CAVESP Osage Ck near Cave Springs AR 89.9 4/2000-9/2004 4
5 DUTCH Baron Fork at Dutch Mills AR 105.1 10/1992-9/2004 2
6 KNSO2 Flint Ck near Kansas OK 284.9 6/1993- 9/2004 6
7 ELMSP Osage Ck near Elm Springs AR 336.7 10/1995-9/2004 7
8 ELDO2 Baron Fork at Eldon OK 795.1 10/1992 - 9/2004 13
9 ISILOAM Illinois R. South of Siloam Springs AR 1489.2 7/1995 - 9/2004 17
10 TALO2 Illinois R. near Tahlequah OK 2483.7 6/1993-9/2004 37
Interior,Flash floodbasins
Basins are well covered by either the INX or SRX radar
0102030405060708090
SPRINGT
SSILO
AM
CHRISTI
CAVESP
DUTCHA
vera
ge
Pe
rce
nt A
bso
lute
P
ea
k F
low
Err
or
Lumped States (FFG-Like) Distributed (Uncalib.)
• Peak flow errors are averages from approximately 25 events over an eight year study period. Peak flow errors are computed regardless of time.
• Correlation coefficients are based on the same events.
Distributed Model Simulations Compared to FFG-Like Simulationsfor the 5 Smallest Basins
(for events from Oct. 1996 – Sept. 2004)
Correlation coefficientsAverage absolute percent peak
flow errors
(37 km2) (49 km2) (65 km2) (105 km2)(90 km2)
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
SPRING
T
SSILO
AM
CHRISTI
CAVESP
DUTCH
Cor
rela
tion
Coe
ffic
ient
for
Pea
ks
Inherent Bias Adjustment
• We suggest that the comparing model-calculated frequencies to frequency-based thresholds can produce an inherent bias correction.
• To validate this concept, we compute inherent adjustments at validation points using probability matching. This adjustment is only done for validation as we do not have the techniques and data to make explicit adjustments at ungauged locations.
DUTCH
0
0.2
0.4
0.6
0.8
1
1 10 100 1000
Flow (cms)
Pro
b. o
f Occ
urre
nce
Simulated161 cms
Adjusted271 cms
Simulated
Observed
DUTCH
0
0.2
0.4
0.6
0.8
1
1 10 100 1000
Flow (cms)
Pro
b. o
f Occ
urre
nce
Simulated161 cms
Adjusted271 cms
Simulated
Observed
Simulated
Observed
0
10
20
30
40
50
60
70
SPRINGT
SSILOAM
CHRISTI
CAVESP
DUTCH
Ave
rage
Per
cent
Abs
olut
e P
eak
Err
or
Distributed, Uncalibrated
Distributed, Uncalibrated w/ Adjustment
SPRINGT
0
50
100
150
0 50 100 150
Obs Qpeak (cms)
Sim
Qpe
ak (
cms)
Worst basin: inherent adjustment degrades peak results by 1% on average
Best basin: inherent adjustment improves peak results by 14% on average
Gain from Inherent Bias Adjustment
One inconsistently simulated event has a big impact
DUTCH
0
200
400
600
0 200 400 600
Obs Qpeak (cms)
Sim
Qpe
ak (
cms)
Distributed, Uncalibrated w/ Adjustment
Distributed, Uncalibrated
2 year flood flow
14 UTC 15 UTC
16 UTC 17 UTC
Maximum Forecast Frequencies at 4 Times on 1/4/1998 (Generated in hindcast mode using QPE up to the forecast time and 1 hr
nowcast QPF beyond)
In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.
Conclusions
• At scales down to 40 km2, results show gains from the distributed model over the current FFG method even from an uncalibrated distributed model
• Inherent bias adjustment in the statistical-distributed model further improves results
• Even further gains are possible with distributed model calibration (not shown here)
• In forecast mode, gridded QPF data from MPN can be used to force the model and gain lead time
– We have begun evaluating forecast case studies using both QPE and QPF (not shown here)
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