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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 Flas h Floods with a Distributed Hydrologic Model Eastern Region Flash Flood Conference June 3 rd 2010. Photo: NOAA.

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Brian cosgrove

Brian Cosgrove

Collaborators: 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 ModelEastern Region Flash Flood ConferenceJune 3rd 2010

Photo: NOAA

Distributed ModelingDHM-TF: Monitoring and Predicting Flash Floods with a Distributed Hydrologic ModelEastern Region Flash Flood ConferenceJune 3rd 2010

Brian Cosgrove

Collaborators: Seann Reed, Michael Smith, Feng Ding, Yu Zhang, Zhengtao Cui, Ziya Zhang

NOAA/NWS/OHD


Focus leveraging distributed modeling to more effectively analyze and predict flash flooding

Focus:Leveraging distributed modeling to more effectively analyze 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 data

  • Visualization and interpretation of DHM-TF data

  • DHM-TF Flash flood case studies

  • Summary and future plans

Outline:


Brian cosgrove

Lumped Versus Distributed Models

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

Distributed

Lumped

  • Rainfall, soil properties vary by grid cell

  • Rainfall/runoff model applied separately to each grid cell

  • Prediction/verification at any grid cell

  • Advantages over lumped—cell-to-cell routing, higher resolution, ingest gridded observations

  • Rainfall and soil properties averaged over basin

  • Single rainfall/runoff model computation for entire basin

  • Prediction/verification at one point

3


Dhm tf an application of distributed modeling

DHM-TF: An application of distributed modeling

  • 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)

DHM-TF

Distributed

Hydrologic

Model

Frequency

Post

Processor

Gridded

Discharge

Gridded Frequency

(Return Period)


Brian cosgrove

DHM-TF Ingests MPE, HPE, and HPN Precipitation

MPE Precipitation (mm)

23Z April 21st to 00Z April 22nd 2009

HPE Precipitation (mm)

23Z April 21st to 00Z April 22nd 2009

Observations

1-2 Hour HPN Forecast (mm)

23Z April 21st to 00Z April 22nd 2009

0-1 Hour HPN Forecast (mm)

23Z April 21st to 00Z April 22nd 2009

Forecasts


Dhm tf output

DHM-TF Output

DHM-TF Discharge (m3/s)

DHM-TF Return Period (Years)

  • Both discharge and return period output available

  • Return period superior for flash flood depiction

    • Resistance to bias in flow values versus raw discharge

    • Relates directly to existing engineering design criteria


Brian cosgrove

Interpreting DHM-TF Output

Compare DHM-TF Return Period Map

-with-

Return Period Threshold Map

Return Period (Years)

DHM-TF Output

Return Period (Years)

Uniform 2-Year Value

Spatially Varying Values

(Generated from local knowledge, engineering design criteria)

Superior Choice:

Better-reflects actual channel conditions

Flooding judged to occur in grid cells which exceed two year return period

-or-

Flooding judged to occur in grid cells which exceed values on varying threshold map

7


Dhm tf performance

DHM-TF Performance

Photo credit: NOAA APRFC

  • Factors 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

8


Current status of dhm tf

Current Status of DHM-TF

  • 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)

Pittsburgh, Binghamton, and Balt/Wash WFO Domains

Binghamton

57,500 km2

89,000 km2

Balt/Wash

Pittsburgh

11,000 km2


Real time pittsburgh dhm tf prototype

Real-time Pittsburgh DHM-TF Prototype

Return Periods Calculated

Optional

HPN

No

Precipitation

OHRFC MPE

(4km, high confidence)

OHRFC MPE or

PBZ HPE

Model States

Saved

Switch Time

T-24 hrs

T-23 hrs

Present

T+3 hrs

T+1 hr

DHM-TF Run 2

(forecast run)

DHM-TF Run 1

(state update)

*Cycle automatically repeated every hour in current setup


Brian cosgrove

DHM-TF Verification

  • Two 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 event

  • Following 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


Maximum dhm tf return period values years 12z 8 9 07 through 12z 8 10 07

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

Maximum DHM-TF Return Period Values (Years) 12Z 8/9/07 through 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

Standard Cell-to-Cell Routing

Local Routing (only internal cell routing)

Reported Flash Floods


Brian cosgrove

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

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

Reported Flash Floods

Local routing yields higher return periods over main stem rivers, better representing flash floods in pixels that include large channels

13


Brian cosgrove

Girty’s Run dischargewith input precipitation derived with standard Z-R relationship

Local = Only internal cell routing

Std = Standard cell-to-cell routing

Precipitation forcing greatly impacts modeled flows

Girty’s Run discharge withinput precipitation derived with tropical Z-R relationship

Local = Only internal cell routing

Std = Standard cell-to-cell routing

14


Dhm tf verification august 9th 2007 flash flood

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

NWS FF Warning

NWS FF Warning

  • 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


Brian cosgrove

DHM-TF Verification: March 22-23, 2010 Flash Flood

DHM-TF Return Periods (Years) at 12Z on March 23rd, 2010

Pittsburgh WFO-Issued Warnings and

Spotter-Reported Flash Floods

Standard Routing Option

Local Routing Option

PBZ WFO:

Use of cell-to-cell routing enabled accurate depiction of flood extent

FF

FF

3/22 23:59Z – 3/23 03:00Z

3/22 23:42Z – 3/23 02:45Z

3/23 13:48Z – 3/23 22:45Z

AF

FF

FF

3/22 23:42Z – 3/23 03:45Z

3/23 01:09Z – 3/23 07:15Z

Reported Flash Floods

FF = Flash Flood Warning AF = Areal Flood Warning


Dhm tf summary and future work

DHM-TF: Summary and Future Work

  • 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 effort

  • Future 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


Thank you

Thank You


Brian cosgrove

Extra slides that follow are only for reference if needed


Return period calculation

Return Period Calculation

probability p(y)

LP3 Probability Distribution

Cumulative LP3 Probability Distribution

  • 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


Brian cosgrove

Specifics: OHD Research Distributed Hydrologic Model

(RDHM)

Precipitation

Temperature

Potential Evaporation

Snow17 Snow Model

rain + melt

Sacramento Soil Moisture Model

surface/impervious/direct runoff

base flow / interflow

Hillslope Routing

(delays within-cell flow into channel)

Cell-to-Cell Channel Routing

*** 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).

Flows and State Variables

Optional DHM-TF

Flash Flood Post Processor


Brian cosgrove

Distributed Model Overview

= Basin boundary

= Model grid cell

  • 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

= Channel network

Various types of output locations

= Outlet Point

= Interior Point

= Headwater Point


Brian cosgrove

Distributed Modeling for Improved River Forecasts

Model

Parameters

Heavy

Rain

Rainfall

Application of OHD Distributed Model to Blue River, OK April 3, 1999

Surface

Runoff

Flow

Direction


Brian cosgrove

Distributed Modeling for Improved River Forecasts

200

160

120

80

40

0

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

200

160

A

120

80

40

B

0

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

200

Blue River, Oklahoma

160

120

80

40

0

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

Hydrologic Response at Different Points in the Blue River Basin

Distributed

Lumped

Observed

Hydrograph at Location A

Flow (CMS)

  • 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 B

Flow (CMS)

Heaviest

Rain

O

Hydrographs at Basin Outlet (O)

Flow (CMS)


Current dhm tf requirements

Current DHM-TF Requirements

  • 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


Sterling wfo dhm tf prototype

Sterling WFO DHM-TF Prototype

Sterling WFO DHM-TF Domain

Domain = 11,000 km2

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


Dhm tf verification august 9 th 2007 flash flood

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

PBZ WFO CWA outlined in red

Warned counties outlined in green

DHM-TF domain covers shaded area

MPE Precipitation (mm)

12Z 8/9 to 12Z 8/10

Warned counties outlined in green

Wave symbol indicates reported 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


Girty s run discharge

Girty’s Run Discharge

USGS Gauge at Millvale

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)


Dhm tf precipitation forcing multisensor precipitation estimator mpe data

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

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

  • 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


Dhm tf precipitation forcing high resolution precipitation estimator hpe

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

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

  • 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


    Dhm tf precipitation forcing high resolution precipitation nowcaster hpn

    DHM-TF Precipitation Forcing: High Resolution Precipitation Nowcaster (HPN)

    HPN 15 minute precipitation forecasts (mm) out to 2 hours

    • 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


  • Bias correction of precipitation

    Bias Correction of Precipitation

    Monocacy at Jug

    Bridge (2116 km2)

    Cumulative Bias, Monocacy River at Jug Bridge (2100 km2)

    • Time-changing bias detected in MARFCMPE 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


    Brian cosgrove

    Bias Correction of Precipitation

    Monthly RFCMPE

    Precipitation 03/97 (mm)

    Monthly PRISM

    Precipitation 3/97 (mm)

    RFC Hourly MPE

    Precipitation

    03/01/97 12z (mm)

    Adjusted RFC Hourly

    MPE Precipitation

    03/01/97 12z (mm)

    Monthly Bias (ratio, log scale)

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


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