1 Brian Cosgrove Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang NOAA/NWS/OHD Brian Cosgrove Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang NOAA/NWS/OHD Distributed Modeling DHM-TF: Monitoring and Predicting Flash Floods with a Distributed Hydrologic Model Eastern Region Flash Flood Conference June 3 rd 2010 Distributed Modeling DHM-TF: Monitoring and Predicting Flash Floods with a Distributed Hydrologic Model Eastern Region Flash Flood Conference June 3 rd 2010 Photo: NOAA
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1 Brian Cosgrove Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang NOAA/NWS/OHD Brian Cosgrove Collaborators: Seann.
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Brian CosgroveCollaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang,
Zhengtao Cui, Ziya Zhang
NOAA/NWS/OHD
Brian CosgroveCollaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang,
Zhengtao Cui, Ziya Zhang
NOAA/NWS/OHD
Distributed ModelingDHM-TF: Monitoring and Predicting Flash Floods with a Distributed Hydrologic
Model
Eastern Region Flash Flood ConferenceJune 3rd 2010
Distributed ModelingDHM-TF: Monitoring and Predicting Flash Floods with a Distributed Hydrologic
Model
Eastern Region Flash Flood ConferenceJune 3rd 2010
Photo: NOAA
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Focus:Focus: Leveraging distributed Leveraging distributed modeling to more effectively modeling to more effectively analyze and predict flash floodinganalyze and predict flash flooding
Hydrologic Modeling: Distributed versus lumped Overview of OHD’s Distributed Hydrologic Model
Threshold Frequency (DHM-TF) flash flood application
DHM-TF Precipitation forcing dataVisualization and interpretation of DHM-TF dataDHM-TF Flash flood case studiesSummary and future plans
Outline:Outline:
1. Rainfall and soil properties averaged over basin
2. Single rainfall/runoff model computation for entire basin
3. Prediction/verification at one point
Lumped Distributed
Lumped Versus Distributed ModelsLumped Versus Distributed Models
1. Rainfall, soil properties vary by grid cell2. Rainfall/runoff model applied separately
to each grid cell3. Prediction/verification at any grid cell4. Advantages over lumped—cell-to-cell
Distributed models are well-suited for flash flood prediction and monitoring, offering high-resolution streamflow at outlet and interior points with ability to route flow
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DHM-TF: An application of distributed DHM-TF: An application of distributed modelingmodeling
What is DHM-TF?◦ A generic approach to leverage strengths of distributed
modeling and statistical processing to monitor and predict flash floods
◦ Provides way to cast flood severity in terms of return period by converting model flow forecasts to frequency (return period)
◦ Similar approach to that used/developed at CBRFC
Why this method?◦ Fills gaps in existing flash flood tools (routing, rapid updates,
interior pts) ◦ Return periods directly relate to existing engineering design
criteria◦ Resistance to uniform bias in modeled flow (only rankings used)
DistributedHydrologic
Model
Frequency Post
Processor
Gridded Frequency(Return Period)
GriddedDischarge
DHM-TF
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0-1 Hour HPN Forecast (mm) 23Z April 21st to 00Z April 22nd 2009
HPE Precipitation (mm) 23Z April 21st to 00Z April 22nd 2009
MPE Precipitation (mm) 23Z April 21st to 00Z April 22nd 2009
1-2 Hour HPN Forecast (mm) 23Z April 21st to 00Z April 22nd 2009
Ob
serv
atio
ns
Fo
reca
sts
DHM-TF Ingests MPE, HPE, and HPN DHM-TF Ingests MPE, HPE, and HPN PrecipitationPrecipitation
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DHM-TF OutputDHM-TF OutputBoth discharge and return period output availableReturn period superior for flash flood depiction
◦ Resistance to bias in flow values versus raw discharge◦ Relates directly to existing engineering design criteria
DHM-TF Return Period (Years)DHM-TF Discharge (m3/s)
Compare DHM-TF Return Period Map -with- Return Period Threshold Map
Flooding judged to occur in grid cells which exceed two year return period
Flooding judged to occur in grid cells which exceed values on varying threshold map-or-
1.5
2
5
20
Spatially Varying Values
(Generated from local knowledge,
engineering design criteria)
Superior Choice:Better-reflects actual channel conditions
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DHM-TF PerformanceDHM-TF PerformanceFactors leading to good DHM-TF simulations:
◦ Temporally static (or zero) model flow bias◦ Hydrologic model which accurately represents flow
distribution◦ Adequate length of underlying precipitation record (need ≥
10 years)◦ High-quality precipitation forcing data◦ Good fit of Log Pearson Type III distribution to actual flow
values◦ Few instances of water regulation in simulation domain
Skill of end-user◦ Interpretation of return period map affected by local
knowledge Low water crossings Vulnerable infrastructure Well-protected / highly engineered areas Water regulation structures
Photo credit: NOAA APRFC
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How is DHM-TF currently implemented?◦ Sacramento model with kinematic wave routing…but generic approach which
can be applied to any distributed model
◦ Executed with and without cell-to-cell routing DHM-TF pilot studies are underway in coordination with NWS Weather
Forecast Offices (WFOs) and River Forecast Centers (RFCs)◦ DHM-TF executed over Baltimore/Washington WFO domain on OHD server◦ Pittsburgh WFO domain DHM-TF simulation run on Pittsburgh WFO server◦ Imminent expansion to Binghamton WFO domain (on BGM server)
Current Status of DHM-TFCurrent Status of DHM-TF
Pittsburgh, Binghamton, and Balt/Wash WFO Domains
89,000 km2
57,500 km2
11,000 km2Pittsburgh
Binghamton
Balt/Wash
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OHRFC MPE(4km, high confidence)
OHRFC MPE or PBZ HPE
No Precipitation
T-24 hrs T-23 hrs Present T+3 hrs
DHM-TF Run 1(state update)
DHM-TF Run 2(forecast run)
Mod
el S
tate
sS
aved
*Cycle automatically repeated every hour in current setup
DHM-TF VerificationDHM-TF VerificationTwo flash flood case studies from the Pittsburgh
WFO◦ August 9th-10th, 2007: 25 flash flood warnings issued,
large event with two waves of rain ◦ March 22nd-23rd, 2010: 4 flash flood warnings issued,
smaller eventFollowing slides will detail several comparisons:
◦ Location of spotter-reports versus DHM-TF output◦ DHM-TF output with and without cell-to-cell routing◦ Model-produced flow versus measured USGS stream
gauge flow ◦ DHM-TF timing versus timing of WFO flash flood warnings
Highlights:◦ Good overall results versus observations◦ Cell-to-cell and local routing each have unique strengths
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Standard Cell-to-Cell Routing Local Routing (only internal cell routing)
Maximum DHM-TF Return Period Values (Years) 12Z 8/9/07 through Maximum DHM-TF Return Period Values (Years) 12Z 8/9/07 through 12Z 8/10/07)12Z 8/10/07)
Overall, good match between areas of high DHM-TF return periods and spotter-reported events (wave symbols)
Local routing performs slightly better than cell-to-cell routing Difficult to determine storm report location
DHM-TF Verification: August 9DHM-TF Verification: August 9thth, 2007 Flash , 2007 Flash FloodFlood
Reported Flash Floods
Pittsburgh Area DHM-TF maximum event return period difference plot (years) over 12Z 8/9 to 12Z 8/10 time period
Computed as: Local Routing minus Cell-to-Cell Routing
DHM-TF Verification: August 9DHM-TF Verification: August 9thth, 2007 , 2007 Flash FloodFlash Flood
Local routing yields higher return periods over main stem rivers, better representing flash floods in pixels that include large channels
Reported Flash Floods
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Girty's Run Discharge and Rainfall 10Z 8/9/07 to 06Z 8/10/07
Girty’s Run Girty’s Run discharge discharge withwithinput input precipitatioprecipitation derived n derived with with tropical tropical Z-R Z-R relationshiprelationship
Girty’s Run Girty’s Run dischargedischargewith input with input precipitation precipitation derived with derived with standard Z-Rstandard Z-R relationshiprelationship
Girty's Run Discharge and Rainfall 10Z 8/9/07 to 06Z 8/10/07
Pittsburgh WFO-Issued Warnings and Spotter-Reported Flash Floods
DHM-TF Return Periods (Years) at 12Z on March 23rd, 2010
FF
FF
FF
FF
AF
FF = Flash Flood Warning AF = Areal Flood Warning
Local Routing Option
DHM-TF Verification: March 22-23, 2010 Flash FloodDHM-TF Verification: March 22-23, 2010 Flash Flood
PBZ WFO: Use of cell-to-cell routing enabled accurate depiction of flood extent
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DHM-TF: Summary and Future DHM-TF: Summary and Future WorkWork
Summary◦ DHM-TF: Combines distributed hydrologic model with
threshold frequency post-processor return periods◦ Capitalizes on strengths of distributed modeling◦ Fills gaps in existing flash flood tools (routing, rapid
updates, interior pts)◦ Collaborative development and promising assessment
effortFuture Work
◦ Validation and deployment at additional field locations◦ Operation at higher temporal and spatial resolutions◦ In-depth validation using NSSL SHAVE data◦ Collaborative Assessment…Further refine DHM-TF to
better match the needs of forecasters
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Thank YouThank You
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Extra slides that follow are only for reference if needed
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Return Period CalculationReturn Period Calculation Distributed model outputs flow within each grid cell (m3/s) Method needed to translate flow into return period DHM-TF uses Log Pearson Type III (LP3) procedure
◦ Established procedure with good availability of supporting data sets
◦ Create probability distribution curve for each grid cell from log of annual max flow values (over ≥ 10 years)
◦ Mean, standard deviation, and skew of flow data control shape of curve
◦ Use cumulative probability distribution and flow for each grid cell to compute annual exceedance probability (AEP) and return period (1/AEP)
◦ Procedure is automated within DHM-TF subroutines
1.0
0.90.80.7
0.60.5
0.4
0.3
0.20.1
0
yln (flow)
p(y)
prob
abi
lity
10 20 30 40 50 60 70 80 90 100 110
LP3 Probability Distribution1.0
0.90.80.7
0.60.5
0.4
0.3
0.20.1
010 20 30 40 50 60 70 80 90 100 110
cumulativeprobability (yy)
ln (flow)
Cumulative LP3 Probability Distribution1.0
0.90.80.7
0.60.5
0.4
0.3
0.20.1
010 20 30 40 50 60 70 80 90 100 110
cumulativeprobability (yy)
ln (flow)
1.0
0.90.80.7
0.60.5
0.40.3
0.20.1
0
yln (flow)
p(y)
prob
abili
ty
10 20 30 40 50 60 70 80 90 100 110
probability p(y)
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Sacramento Soil Moisture Model
Cell-to-Cell Channel Routing
Snow17 Snow Model
PrecipitationTemperature
Potential Evaporation
surface/impervious/direct runoff
rain + melt
Flows and State Variables
base flow / interflow
Hillslope Routing (delays within-cell flow into channel)
*** Currently, full version only available as separate package from OHD (not within AWIPS) but will eventually be integrated in upcoming Community Hydrologic Prediction System (CHPS).
Specifics: OHD Research Distributed Hydrologic Specifics: OHD Research Distributed Hydrologic ModelModel (RDHM)(RDHM)
Optional DHM-TF Flash Flood Post Processor
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= Basin boundary
= Model grid cell
= Channel network
= Outlet Point = Interior Point = Headwater Point
Various types of output locations
RDHM ingests temperature, precip, and PE and produces discharge, soil temperature and soil moisture at each cell
Routes flow between cells via channel network
Accurately reflects impact on flow (timing/magnitude) of non-uniform precipitation
Produces verifiable discharge values at any location (including interior points.)
HRAP (16km2) resolution most common, but ½ and ¼ HRAP are future possibilities
Distributed Distributed Model OverviewModel Overview
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Application of OHD Distributed Model to Blue River, OK April 3, 1999
ModelParameters Rainfall
SurfaceRunoff
FlowDirection
Heavy Rain
Distributed Modeling for Improved River Distributed Modeling for Improved River ForecastsForecasts
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Hydrologic Response at Different Points in the Blue River Basin
Lumped model output limited to basin outlet, distributed model able to output at interior points
Lumped model underestimates and delays peak at outlet due to basin averaged precip
Distributed model captures spatial variability and produces better simulation
Hydrograph at Location A
Hydrograph at Location B
Hydrographs at Basin Outlet (O)
Distributed Modeling for Improved River Distributed Modeling for Improved River ForecastsForecasts
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Current DHM-TF Current DHM-TF RequirementsRequirements Model operation
◦ OHD RDHM software package (obtain from NWS LAD) Operating System: Red Hat Enterprise Linux 4.0 Compiler: GNU GCC/G++ 3.4.6 or later and PGF90 4.1-2 Software Libraries
C++ BOOST library 1.36.x GNU Scientific Library (GSL) 1.6 or later
Miscellaneous Autoconf 2.13 Automake 1.4-p5 GNU Make 3.79.1
◦ RDHM Supporting data sets Meteorological: Precipitation (long-term ~10 years, quality controlled),
potential evaporation (can use monthly climatology), temperature (if using Snow17)
Parameters: Can often use pre-defined a priori data sets as solid starting point
Visualization of output◦ Google Earth (KML)
Google Earth software (runs best on PC, Pro version ingests shapefiles)
xmrgtoasc and a2png conversion utilities, luxisr.ttf font, Linux zip utility
DHM-TF with cell-to-cell routing currently running in real-time on OHD server over LWX WFO domain
Analyzed June and September 2009 flash flood events with both cell-to-cell and local routing simulations
Monitoring real-time DHM-TF simulations
Sterling WFO DHM-TF Domain
Baltimore
Washington DC
Baltimore
Washington DC
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DHM-TF Verification: August 9DHM-TF Verification: August 9thth, 2007 Flash Flood, 2007 Flash Flood
Three mesoscale convective systems caused widespread flooding over Ohio, Pennsylvania, West Virginia, and Maryland◦ 25 Flash flood warnings issued by Pittsburgh WFO 12Z 8/9 to 02Z
8/10◦ 24 Reported flash flood events◦ 10 Flash flood warnings with no corresponding reported event in
county Verification: Difficult to determine storm report location
Warned counties outlined in greenWave symbol indicates reported flash flood
PBZ WFO CWA outlined in redWarned counties outlined in green DHM-TF domain covers shaded area
MPE Precipitation (mm)12Z 8/9 to 12Z 8/10
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Girty’s Run DischargeGirty’s Run Discharge
Modeled flows (using local and cell-to-cell routing options) are too small in magnitude
Precipitation input was too small (PBZ WFO has provided updated precipitation)
Two HRAP pixels cover Girty’s Run (upstream pixel and pixel at gauge)
Girty's Run Discharge (CMS) and 15-min Rainfall (mm) 10Z 8/9/07 to 06Z 8/10/07
◦ Produced in AWIPS environment at each field office◦ HPE leverages recent MPE gauge/radar bias information to
automatically generate radar-based rainfall and rain rate products statistically corrected for bias
◦ A user-defined radar mask determines how overlapping radars will be blended for each pixel within domain of interest
Characteristics◦ No manual quality control◦ Low latency, and high
spatial/temporal resolution makes real-time use practical for flash flood forecasting
~72 pixels within City of Baltimore
HPE Precipitation (mm) 23Z April 21st to 00Z April 22nd 2009
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DHM-TF Precipitation Forcing: High DHM-TF Precipitation Forcing: High Resolution Precipitation Nowcaster Resolution Precipitation Nowcaster (HPN)(HPN) Description
◦ Sub-hourly temporal resolution, 4km spatial resolution, 1 hour (operational) or 2 hour (research) forecast lead time
Production◦ Dependent on HPE, produced in AWIPS environment at each field office
◦ Local motion vectors are derived through a comparison of radar rain rates spaced 15 minutes apart, and are used to project current radar echoes forward in time out to two hours
◦ Rain rates are then variably smoothed by a method based on the observed changes in echo structure over the past 15 minutes, as well as the current observed rain rate field
Characteristics◦ High spatial/temporal
resolution well-suited for flash flood forecasting
HPN 15 minute precipitation forecasts (mm) out to 2 hours
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Monocacy at Jug Bridge (2116 km2)
Cumulative Bias, Monocacy River at Jug Bridge (2100 km2)
Bias Correction of Bias Correction of PrecipitationPrecipitation
Time-changing bias detected in MARFC MPE archives prior to 2004
Bias corrected precipitation needed to support unbiased simulation statistics for a reasonable historical period (~10 years)
Analysis of Monocacy River flow shows reduction in cumulative bias and improved consistency when bias corrected precipitation is used
Consistent bias can be removed through calibration or addressed through DHM-TF approach