Photo: NOAA
Distributed Modeling
DHM-TF: Monitoring and Predicting Flash
Floods with a Distributed Hydrologic Model
Eastern Region Flash Flood Conference
rd 2010
June
June 33rd
Brian Cosgrove
Collaborators:
Collaborators: Seann
Seann Reed,
Reed, Michael
Michael Smith,
Smith, Feng
Feng Ding,
Ding, Yu
Yu Zhang,
Zhang,
Zhengtao
Zhengtao Cui,
Cui, Ziya
Ziya Zhang
Zhang
NOAA/NWS/OHD
NOAA/NWS/OHD
1
Focus: Leveraging distributed
modeling to more effectively analyze and
predict flash flooding
Outline:






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
2
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
1.
Rainfall and soil properties averaged
over basin
2. Single rainfall/runoff model
computation for entire basin
3. Prediction/verification at one point
1.
2.
Rainfall, soil properties vary by grid cell
Rainfall/runoff model applied separately to
each grid cell
3. Prediction/verification at any grid cell
4. Advantages over lumped—cell-to-cell routing,
higher resolution, ingest gridded observations
3
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
DHM-TF
Distributed
Hydrologic
Model

Gridded
Discharge
Frequency
Post
Processor
Gridded Frequency
(Return Period)
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)
4
DHM-TF Ingests MPE, HPE, and HPN Precipitation
HPE Precipitation (mm)
23Z April 21st to 00Z April 22nd 2009
0-1 Hour HPN Forecast (mm)
23Z April 21st to 00Z April 22nd 2009
1-2 Hour HPN Forecast (mm)
23Z April 21st to 00Z April 22nd 2009
Forecasts
Observations
MPE Precipitation (mm)
23Z April 21st to 00Z April 22nd 2009
5
DHM-TF Output
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
DHM-TF Discharge (m3/s)
DHM-TF Return Period (Years)
6
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
20
5
2
1.5
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

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
Photo credit: NOAA APRFC
8
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
Pittsburgh
Balt/Wash
11,000
km2
9
Real-time Pittsburgh DHM-TF Prototype
Return Periods Calculated
OHRFC MPE
(4km, high confidence)
OHRFC MPE or
PBZ HPE
T-23 hrs
DHM-TF Run 1
(state update)
HPN
No
Precipitation
Switch Time
Model States
Saved
T-24 hrs
Optional
Present T+1 hr
T+3 hrs
DHM-TF Run 2
(forecast run)
*Cycle automatically repeated every hour in current setup
10
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
11
DHM-TF Verification: August 9th, 2007 Flash Flood
Maximum DHM-TF Return Period Values (Years) 12Z 8/9/07 through 12Z 8/10/07)
Standard Cell-to-Cell Routing
Local Routing (only internal cell routing)
Reported Flash Floods



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
12
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
Girty's Run Discharge and Rainfall 10Z 8/9/07 to 06Z 8/10/07
30
4.5
Girty’s Run
discharge
with input
precipitation
derived with
standard Z-R
relationship
20
3
2.5
15
2
10
1.5
1
5
USGS Gauge
DHMTF Local
DHMTF Std
Rain at Gauge
Rain Upstream
0.5
0
0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
Hour of Day
1
2
3
4
5
6
Local = Only internal cell routing
Std = Standard cell-to-cell routing
Precipitation forcing greatly impacts modeled flows
Girty’s Run
discharge with
input
precipitation
derived with
tropical Z-R
relationship
80
18
70
16
60
14
12
50
10
40
8
30
6
20
4
10
2
0
Precipitation (mm/15min)
Girty's Run Discharge and Rainfall 10Z 8/9/07 to 06Z 8/10/07
Discharge (CMS)
Discharge (CMS)
3.5
Precipitation (mm/15min)
4
25
USGS Gauge
DHMTF Std
Rain at Gauge
Rain Upstream
0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
Hour of Day
1
2
3
4
5
6
Local = Only internal cell routing
14
Std = Standard cell-to-cell routing
14
DHM-TF Verification: August 9th, 2007 Flash Flood
Allegheny County Maximum DHM-TF Return Period
Standard Cell-to-Cell Routing and Local Routing
8
13
7
11
Return Period (Years)
Return Period (Years)
Westmoreland County Maximum DHM-TF Return Period
Standard Cell-to-Cell Routing and Local Routing
6
5
4
3
9
7
5
2
3
1
1
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
NWS FF Warning
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
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
15

DHM-TF Verification: March 22-23, 2010 Flash Flood
DHM-TF Return Periods (Years)
at 12Z on March 23rd, 2010
Standard Routing Option
Pittsburgh WFO-Issued Warnings and
Spotter-Reported Flash Floods
Local Routing Option
PBZ WFO:
Use of cell-to-cell
routing enabled
accurate depiction
of flood extent
FF 3/22 23:59Z – 3/23 03:00Z
FF 3/22 23:42Z – 3/23 02:45Z
FF 3/22 23:42Z – 3/23 03:45Z
FF 3/23 01:09Z – 3/23 07:15Z
AF 3/23 13:48Z – 3/23 22:45Z
Reported Flash Floods
FF = Flash Flood Warning AF = Areal Flood Warning
16
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
17
Thank You
18
Extra slides that follow are only for reference if needed
19
Return 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
0.9
0.8 Probability
LP3
0.7
probability
1.0
probability
1.0
0.4
0.3
0.2
0.1
0.6
0.5
0.4
0.3
0.2
0.1
0
Cumulative LP3
0.9 Probability Distribution
0.6
p(y)
probability p(y)
0.5
0.9
0.8
0.7
p(y)
1.0
Distribution
0
10
20
30
cumulative
probability (yy)
10
40
20
50
30
40
60
70
y
ln (flow)
50
80
60
70
y
ln (flow)
90
100
80
110
0.9
0.8
0.7
cumulative
probability (yy)
0
10
0.6
0.5
0.4
0.3
0.2
0.1
0.6
0.5
0.4
90 0.3
100
0.2
0.1
0.8
0.7
110
0
20
30
40
10
50
20
30
40
50
60
70
ln (flow)
80
90
60
70
ln (flow)
100
110
20
8
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
Flows and State Variables
Optional DHM-TF
Flash Flood Post Processor
*** 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).21
Distributed Model
Overview
= Basin boundary
= Model grid cell
= Channel network
Various types of output locations
= Outlet Point
= Interior Point
= Headwater Point

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
22
Distributed Modeling for Improved River Forecasts
Model
Parameters
Rainfall
Heavy
Rain
Application of OHD Distributed Model to Blue River, OK April 3, 1999
Surface
Runoff
Flow
Direction
23
Distributed Modeling for Improved River Forecasts
Hydrologic Response at Different Points in the Blue River Basin
200
Flow (CMS)
Hydrograph at Location A
Distributed
160
Lumped
120
80
Observed
40
0
4/3/99 0:00
A
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

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
200
Hydrograph at Location B
Heaviest
Rain
Flow (CMS)
160
B
120
80
40
O
0
4/3/99 0:00
Blue River, Oklahoma
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
Hydrographs at Basin Outlet (O)
Flow (CMS)
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
24
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
25
Sterling 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
ia
an
v
l
d
sy
nn ylan
e
r
P
a
M
Baltimore
Washington DC
Domain = 11,000 km2
26
DHM-TF Verification: August 9th, 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
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
27
Girty’s Run Discharge
Girty's Run Discharge (CMS) and 15-min Rainfall (mm) 10Z 8/9/07 to 06Z 8/10/07
30

Discharge (CMS)
25
USGS Gauge
20
DHMTF Local
15
DHMTF Std
Two HRAP pixels
cover Girty’s Run
(upstream pixel
and pixel at gauge)
Rain at Gauge
10
Rain Upstream
USGS Gauge at Millvale
5
0
10 11 12 13 14 15 16 17 18 19 20 21 22 23 0
1
2
3
4
5
6
Hour of Day

Modeled flows (using local and cell-tocell routing options) are too small in
magnitude

Precipitation input was too small (PBZ
WFO has provided updated
precipitation)
28
DHM-TF Precipitation Forcing: Multisensor
Precipitation Estimator (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
MPE Precipitation (mm) 23Z April 21st to 00Z April 22 2009
nd

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
~18 pixels within City of Baltimore
29
DHM-TF Precipitation Forcing: High 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

HPE Precipitation (mm) 23Z April 21st to 00Z April 22nd 2009
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
30
DHM-TF Precipitation Forcing: High Resolution
Precipitation Nowcaster (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 wellsuited for flash flood forecasting
HPN 15 minute precipitation
forecasts (mm) out to 2 hours
31
Bias Correction of Precipitation

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)
Cumulative Bias, Monocacy River at Jug Bridge (2100 km 2)
200
0
0
Sim - Obs Runoff (mm)

20
40
60
80
100
120
-200
-400
bias corrected
original xmrg
-600
-800
-1000
-1200
Time (months)


Monocacy at Jug
Bridge (2116 km2)
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
32
Bias Correction of Precipitation
Monthly RFC MPE
Precipitation 03/97 (mm)
RFC Hourly MPE
Precipitation
03/01/97 12z (mm)
Monthly Bias (ratio, log scale)
Monthly PRISM
Precipitation 3/97 (mm)
Adjusted RFC Hourly
MPE Precipitation
03/01/97 12z (mm)
Key end result: time-changing, inconsistent precipitation biases are removed
33