1

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

2

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

routing, higher resolution, ingest gridded observations

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

3

4

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

5

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

6

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)

Interpreting DHM-TF Interpreting DHM-TF OutputOutput

Return Period (Years)

Uniform 2-Year Value

DHM-TF OutputReturn Period (Years)

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

7

8

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

8

9

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

10

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

Sw

itch T

ime

Return Periods Calculated

Real-time Pittsburgh DHM-TF Real-time Pittsburgh DHM-TF PrototypePrototype

OptionalHPN

T+1 hr

11

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

12

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

13

14

Girty's Run Discharge and Rainfall 10Z 8/9/07 to 06Z 8/10/07

0

5

10

15

20

25

30

10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6

Hour of Day

Dis

ch

arg

e (

CM

S)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Pre

cip

itat

ion

(m

m/1

5min

)

USGS Gauge

DHMTF Local

DHMTF Std

Rain at Gauge

Rain Upstream

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

0

10

20

30

40

50

60

70

80

10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6

Hour of Day

Dis

ch

arg

e (

CM

S)

0

2

4

6

8

10

12

14

16

18

Pre

cip

itati

on

(m

m/1

5m

in)

USGS Gauge

DHMTF Std

Rain at Gauge

Rain Upstream

Precipitation forcing greatly impacts modeled flows

Local = Only internal cell routingStd = Standard cell-to-cell routing

Local = Only internal cell routingStd = Standard cell-to-cell routing14

15

Allegheny County Maximum DHM-TF Return PeriodStandard Cell-to-Cell Routing and Local Routing

1

3

5

7

9

11

13

12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12

Hour of Day

Ret

urn

Per

iod

(Yea

rs)

Westmoreland County Maximum DHM-TF Return PeriodStandard Cell-to-Cell Routing and Local Routing

1

2

3

4

5

6

7

8

12 13 14 15 16 1718 19 20 21 22 23 0 1 2 3 4 5 6 7 8 9 10 11 12

Hour of Day

Ret

urn

Per

iod

(Y

ears

)

NWS FF Warning NWS FF Warning

DHM-TF Verification: August 9th, 2007 Flash FloodDHM-TF Verification: August 9th, 2007 Flash Flood

County-wide comparison of DHM-TF with FF warnings Simulations used MPE data NWS Flash flood warnings

◦ Westmoreland County (3 issued, 3rd not verified)◦ Allegheny County (4 issued, 4th not verified)

DHM-TF peaks (and time above 2 year return period threshold) agree well with verified warning periods

Local routing performs better toward end of event

16

Standard Routing Option

3/22 23:59Z – 3/23 03:00Z 3/23 13:48Z – 3/23 22:45Z

3/23 01:09Z – 3/23 07:15Z Reported Flash Floods

3/22 23:42Z – 3/23 02:45Z

3/22 23:42Z – 3/23 03:45Z

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

17

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

18

Thank YouThank You

19

Extra slides that follow are only for reference if needed

20

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)

21

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

22

= 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

23

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

24

Hydrologic Response at Different Points in the Blue River Basin

0

40

80

120

160

200

4/3/99 0:00 4/3/99 12:00 4/4/99 0:00 4/4/99 12:00 4/5/99 0:00 4/5/99 12:00 4/6/99 0:00

Flo

w (

CM

S)

0

40

80

120

160

200

4/3/99 0:00 4/3/99 12:00 4/4/99 0:00 4/4/99 12:00 4/5/99 0:00 4/5/99 12:00 4/6/99 0:00

Flo

w (

CM

S)

Distributed

Lumped

Observed

Flo

w (

CM

S)

0

40

80

120

160

200

4/3/99 0:00 4/3/99 12:00 4/4/99 0:00 4/4/99 12:00 4/5/99 0:00 4/5/99 12:00 4/6/99 0:00

B

A

Blue River, Oklahoma

HeaviestRain O

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

25

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

◦ Simple PNG image GRASS GIS

26

Domain = 11,000 km2

Sterling WFO DHM-TF PrototypeSterling WFO DHM-TF Prototype

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

27

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

28

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

0

5

10

15

20

25

30

10 11 12 13 14 15 16 17 18 19 20 21 22 23 0 1 2 3 4 5 6

Hour of Day

Dis

char

ge (C

MS

) USGS Gauge

DHMTF Local

DHMTF Std

Rain at Gauge

Rain Upstream

USGS Gauge at Millvale

29

DHM-TF Precipitation Forcing: DHM-TF Precipitation Forcing: Multisensor Precipitation Estimator Multisensor Precipitation Estimator (MPE) Data(MPE) Data Description

◦ One hour temporal resolution, 4km spatial resolution, > 1 hour latency

◦ Uses a combination of radar, gauge, and satellite rainfall estimates

Production◦ Produced in AWIPS environment by each field office◦ Bias correction factors developed from a comparison of radar

and gauge data ◦ Bias-corrected radar blended with gauge-only field to produce

merged radar/gauge product

~18 pixels within City of Baltimore

MPE Precipitation (mm) 23Z April 21st to 00Z April 22nd 2009

Characteristics◦ Several hour latency time

may exist due to repeated manual adjustments and quality control of input fields as additional gauge reports are received

◦ Latency makes real-time use in flash flood forecasting impractical

30

DHM-TF Precipitation Forcing: High DHM-TF Precipitation Forcing: High Resolution Precipitation Estimator (HPE)Resolution Precipitation Estimator (HPE) Description

◦ Sub-hourly temporal resolution, 1km spatial resolution, < 1 hour latency

◦ Uses radar rainfall estimates Production

◦ 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

31

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

32

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

33

Monthly RFC MPE Precipitation 03/97 (mm)

Monthly PRISM Precipitation 3/97 (mm)

Monthly Bias (ratio, log scale)

RFC Hourly MPE Precipitation

03/01/97 12z (mm)

Adjusted RFC Hourly MPE Precipitation 03/01/97 12z (mm)

Bias Correction of PrecipitationBias Correction of Precipitation

Key end result: time-changing, inconsistent precipitation biases are removed