A StatisticalDistributed Hydrologic Model for Flash Flood Forecasting

1
A Statistical-Distributed Hydrologic Model for
Flash Flood Forecasting International Workshop
on Flash Flood Forecasting March 13, 2006 Seann
Reed1, John Schaake1, Ziya Zhang1,3 1Hydrology
Laboratory, Office of Hydrologic Development NOAA
National Weather Service, Silver Spring,
Maryland 2Consultant to Office of Hydrologic
Development, Annapolis, MD   3University
Corporation for Atmospheric Research
2
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)

3
NWS Flash Flood Guidance (FFG)
(1) River Forecast Center (RFC) Maintains 6 hr
Lumped Model
TR Threshold runoff
(2) RFC Runs Flash Flood Guidance System
Forecast points
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).
4
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
5
A Statistical-Distributed Model for Flash Flood
Forecasting at Ungauged Locations
Historical
Real-time

• 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.

Distributed hydrologic model
Distributed hydrologic model
Simulated peaks distribution (Qsp) (unique for
each cell)
Max forecast peaks
Statistical Post-processor
Local/regional knowledge
Compare
Forecast frequencies
6
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)

7
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)

8
Study Basins
INX Radar
AR
OK
SRX Radar
Basins are well covered by either the INX or SRX
radar
Interior, Flash flood basins
9
Distributed Model Simulations Compared to
FFG-Like Simulations for the 5 Smallest
Basins (for events from Oct. 1996 Sept. 2004)
Average absolute percent peak flow errors
Correlation coefficients
(37 km2)
(49 km2)
(65 km2)
(105 km2)
(90 km2)

• 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.

10
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.

11
Gain from Inherent Bias Adjustment
Best basin inherent adjustment improves peak
results by 14 on average
Worst basin inherent adjustment degrades peak
results by 1 on average
One inconsistently simulated event has a big
impact
12
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)
14 UTC
15 UTC
In these examples, frequencies are derived from
routed flows, demonstrating the capability to
forecast floods in locations downstream of where
the rainfall occurred.
16 UTC
17 UTC
13
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)