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US Army Corps of Engineers
BUILDING STRONG®
HYDROLOGIC MODEL (HEC-HMS)
CALIBRATION FOR FLOOD RISK
STUDIESC. Landon Erickson, P.E.,CFM
Water Resources Engineer
USACE, Fort Worth District
April 27th, 2017
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Overview
Flood Risk Study Components/Parameters
Parameter Development
Parameter Calibration
Calibrated Model Results vs Flood
Frequency Analysis Results
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Flood Risk Study Components
Hydrology
►How much water?
Hydraulics
►How deep will the water
get and how far will it spread?
Outcome or Impacts
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Parameters and Common Methods
Losses – Initial Abstraction-Constant Loss
Rate
Transform – Snyder Unit Hydrograph
Routing – Modified-Puls
Baseflow – None or Recession
Rainfall – USGS (2004), NOAA Atlas 14
coming soon (2018).
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Snyder Synthetic Unit Hydrograph
(UH) Method UH methodology (L.K. Sherman 1932)
“Synthetic Unit-Graphs” by Franklin F. Snyder (1938)
Synthetic UH Parameters for Ungaged Watersheds.
Watersheds studied in Appalachian Highlands
Drainage areas from 10-10,000 square miles
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Snyder Synthetic Unit Hydrograph
(UH) Method Unit Hydrograph Definition
► “The basin outflow resulting from one unit of direct runoff generated uniformly over the drainage area at a uniform rainfall rate during a specified period of
rainfall duration.” (Sherman)
𝑡𝑝 = 𝐶𝑡 𝐿𝐿𝑐0.3
𝑈𝑝
𝐴= 640
𝐶𝑝𝑡𝑝
Where
𝑡𝑝 = lag time, (hour)
Cp = UH peaking coefficient (Range seen from 0.4 to 0.8, USACE)
L = Length of the main-stem from the outlet to the drainage divide, (miles)
Lc = Length of the main-stem from the outlet to the point nearest watershed centroid, (miles)
Up = Peak of standard UH, (cubic feet per second)
A = Area, (square miles)
𝐶𝑡 = Basin coefficient accounting for slope and storage (Range seen from 0.4 to 8.0, USACE)
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Snyder Synthetic Unit Hydrograph
(UH) Method Limitations and Conclusions
► Method estimates surface-runoff time distribution and not amount.
► Actual lag tends to increase with storm size.
► Synthetic UH departs from actual UH as basin departs from a typical fan
shape.
► Extreme care should be used in applying UH parameters derived from
ordinary storms to less frequent storms
► The equations and coefficients given are based mainly on fairly
mountainous watersheds and may need adjustment in flatter
areas.
► This procedure can be applied in studies of flood-control, flood-routing,
and flood-forecasting.
► Regional analysis recommended
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USACE Regional Study – Synthetic
Unit Hydrograph Parameters
Nelson, T.L. “Synthetic Unit Hydrographs Relationships, Trinity River
Tributaries, Fort Worth-Dallas Urban Area”, Seminar on Urban Hydrology, Davis, CA, 1970.
Rodman, P.K. “Effects of Urbanization on Various Frequency Peak Discharges”, USACE Water Resources Meeting, Albuquerque, NM,
1977
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Regional Study (Nelson, 1970)
Study Purpose: Develop method for coming up with reasonable
estimates for present and future peak discharges for flood plain management studies for ungaged areas, where funds or study time
are both in short supply.
► Rapid development during 1960s (Pre-NFIP)
► National Flood Insurance Act of 1968
► Large number USACE SWF Flood Studies
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Regional Study (Nelson, 1970)
8 DFW Watersheds
(8-130 sq. mi.)
Urbanization
estimates ranged
between 0-100%
USGS Special
Urban Hydrology
Study (circa [1961-1979])
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Regional Study (Nelson, 1970)
Utilized methods in EM 1110-2-1405 “Flood Hydrograph Analyses
and Computations.”
Correlated lag time to measurable watershed characteristics such as
longest flow length, flow length to centroid, and weighted watershed
slope.
Literature review indicated several studies that concluded that lag
time (tp) correlated well with 𝐿𝐿𝑐𝑎 𝑆0.5
Where
𝑡𝑝 = lag time, (hour)
L = Length of the main-stem from the outlet to the drainage divide, (miles)
Lca = Length of the main-stem from the outlet to the point nearest watershed centroid,
(miles)
𝑆 = stream slope over reach between 10% and 85% of L (feet per mile)
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Regional Study (Nelson, 1970)
Literature Review of
Existing Studies► Slope of line is
consistent for
different areas ( So.
California, Louisville, Houston).
► Scatter in study data was explained by
differences in terrain
type and urbanization
amounts.
► Lag time decreased
as urbanization
amount increased.
Southern California Study
(Linsley, USACE)
Louisville, Kentucky Study
Houston, Texas Study
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Regional Study (Nelson, 1970)
Final DFW
area curves
Houston, Texas Study
0
0
0
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10Percent Urban Values of Study Watersheds
55
100
70
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Regional Study (Nelson, 1970)
Conclusions
► The method accounts for differences in urban development on
adjacent areas and may be used to predict the effect that urban
development might have on a given area.
► Additional analyses will be required to improve the relationships
developed, to isolate the effect that channel improvement produces,
and to develop an improved method of determining the unit
hydrograph peak.
► For the stated purpose of developing flood peaks for flood plain
management studies for ungagged areas, where funds or study time
are both in short supply, the present method appears to offer
reasonably accurate results.
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Regional Study (Rodman, 1977)
Study Purpose: Develop a method that offers a quick, relatively
consistent and reasonable procedure for estimating the effect of urbanization on the unit hydrograph for a specific watershed in a
specific urban area.
► D-FW Clay Urbanization Curves (22 watersheds, 1-130 sq. mi.)
• 8 watersheds from Nelson (1970) with 14 additional watersheds.
► D-FW Sandy Loam Urbanization Curves (4 watersheds, 27-333 sq. mi.)
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Regional Study (Rodman, 1977) Final Blackland Praire Clay Urbanization Curves
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Regional Study (Rodman, 1977) Final Cross Timbers Sandy-Loam Urbanization Curves
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1
1
1
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Regional Study(Rodman, 1977) Final DFW Urbanization Curve Comparison
A complete change from rural to urban conditions
would reduce the lag time by about 50%.
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Regional Study (Rodman, 1977)
Conclusions
► The urbanization curves offer a quick, relatively consistent and reasonable
procedure for estimating the effect of urbanization on the unit hydrograph
► Recommended peaking coefficient of 0.72 (Cp640=460) for DFW area.
► The urbanization curves have been verified with observed hydrographs.
Limitations
► This study assumed future urbanization practices will approximate those of
the past.
► This study did not separately calibrate all the complicating factors of
urbanization (Percent imperviousness, storm sewers, channelization)
► The data used for the Sandy-Loam Urbanization curves was very limited
and could use additional verification. Only 4 watersheds (Compared to 22
for the Clay curves) ranging from 1-5 percent urban were used to develop
these curves.
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FW District Methodology in HMS
Sand (%) based on Soil Permeability (inches per hour)
► 0 % - Less than 0.06 (Blackland Praire Clay Urbanization Curves)
► 33 % - 0.06 to 0.2
► 66 % - 0.2 to 0.6
► 100 % - 0.6 to 2.0 (Cross Timbers Sandy Loam Urbanization Curves)
Clay (0% Sand) watersheds generally praire/grassland under natural
conditions.
Sandy Loam (100% Sand) generally have more trees and brush to
slow down and attenuate hydrograph peak.
Possible for Clay watershed with heavy brush and trees may
hydrologically act as Sandy watershed.
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FW District Methodology in HMS
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From the Component Editior
FW District Methodology in HMS
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FW District Methodology in HMS
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From the Global Editior
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River Routing Parameters Storage-Discharge Values developed in HEC-RAS (Steady Flow)
Profiles computed for a range of discharges to define the relationship of storage to flow between two channel cross sections.
Subreach estimate should be determined by calibration to streamflow
gages. If unable to calibrate, can be estimated by computation based on flood wave travel time through reach. Subreach value can
approach one for very wide floodplains with heavy attenuation.
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Loss and Baseflow Parameters Initial Abstraction-Constant Loss
► From HMS Technical Reference Manual
Recession Baseflow
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Calibration Purpose Modify initial parameter estimates to improve model’s ability to simulate
actual/physical watershed response to observed storm events.
Develop a single set of representative watershed parameters
Rapid Response (Tall, narrow hydrograph) or slow response (Flat, wide
hydrograph)?
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Calibration Needs Initial Parameter Estimates
NEXRAD Stage III or MPE Precipitation Data► http://dipper.nws.noaa.gov/hdsb/data/nexrad/nexrad.html
USGS Streamflow Data
► https://maps.waterdata.usgs.gov/mapper/index.html
HEC-GridUtil (Precipitation viewing, processing, and analysis tool)
► http://www.hec.usace.army.mil/software/hec-gridutil/
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Calibration Process Baseflow Parameters - Match the magnitude and slope of the flow in the stream before and
after the storm.
Initial Loss – Adjust initial loss to match beginning of runoff.
Constant Loss – Match the volume.
Lag Time – Match the time and magnitude of the peak.
Peaking Coefficient – Match the shape and try to keep peaking coefficient consistent among
subbasins with similar slopes.
Routing Subreaches – Match hydrograph peak attenuation.
Completed from upstream to downstream.
Parameter adjustments made uniformly to all subbasins above a gage, unless there is
strong evidence to adjust individual areas.
Goal is to match peak, shape, timing, and volume of observed hydrograph.
Keep each parameter within its reasonable, expected range. For example, do not use such a short lag time that the water would have to be traveling at > 20 ft/s to reach the outlet.
Parameter calibration is compared to initial parameter estimates and calibrated parameters
from other storms.
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Calibration Process Baseflow Parameters - Match flow in the stream before storm.
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Initial baseflow too high
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Calibration Process Initial Loss – Adjust initial loss to match beginning of runoff.
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Initial loss
too high.
Initial runoff
beginning
too late
Initial loss
too low.
Initial runoff
beginning
too soon
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Calibration Process Constant Loss – Match the volume.
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Constant loss
too high.
Constant loss
too low.
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Calibration Process Lag Time – Match the time and magnitude of the peak.
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Lag time too high.
Peak is late and
hydrograph is
wider
Lag time too low.
Peak is early and
hydrograph is
narrower
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Calibration Process Peaking Coefficient – Match the shape.
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Peaking coefficient
too low. Hydrograph
is flatter than
observed
Peaking coefficient is
usually set to the
same value for all
storm events
Peaking coefficient
too high. Hydrograph
is steeper than
observed
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Calibration Process Routing Subreaches – Match hydrograph peak attenuation.
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Routing subreach
values too high.
Hydrograph is steeper
than observed
Routing subreach
values too low.
Hydrograph is flatter
than observed
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Calibrating only to peak discharge. Many different ways to match peak.
Not comparing calibration results between different events.
Common Calibration Mistakes
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Parameter/Storm Initial Est. 1998 2002 2004 2007 2015
Lag Time 5.1 5.1 2.4 5.5 2.2 4.7
Peaking Coefficient 0.7 0.7 0.4 0.7 0.3 0.7
Parameter/Storm Initial Est. 1998 2002 2004 2007 2015
Lag Time 5.1 5.1 4.5 5.5 5.7 4.7
Peaking Coefficient 0.7 0.7 0.7 0.7 0.7 0.7
Losses
decreased to
match peak, but
volume and
shape are off.
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Final Parameter Selection
Can use weighting based on subbasin runoff volume or peak discharge to select representative parameter for the subbasin.
Avoid using or assign low weighting to calibration parameters that are very different from other events where the simulated hydrograph has a
poor match to the observed hydrograph.
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Comparison with Statistical Analysis
Interagency Flood Risk Management (InFRM) Hydrology Report for the San Marcos River Basin (Sept 2016)
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Comparison with Statistical Analysis
Blanco River at Wimberley, TX (355 sq. mi., length of record – 91 years)
Peak Discharge Frequency Curves Change of 100-yr Peak Discharge Over Time
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95 % Confidence Limit
Peak Discharges for 100-yr (1% annual chance)
Statistical Analysis – 154,000 cfs
Calibrated HMS Model – 152,600 cfs
*Wimberley systematic record began in 1925
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Comparison with Statistical Analysis
San Marcos River at Luling, TX (838 sq. mi., length of record – 79 years)
Peak Discharge Frequency Curves Change of 100-yr Peak Discharge Over Time
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95 % Confidence Limit
Peak Discharges for 100-yr (1% annual chance)
Statistical Analysis – 143,600 cfs
Calibrated HMS Model – 142,400 cfs
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Statistical analysis results change
significantly over
time (+33% to -38%).
Calibrated numerical
modeling results are
relatively stable over time (+6% to -12%).
95 % Confidence Limit (Statistical Resutlts)
Uncertainty in Flood Risk Estimates
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205,000
95,000
*Wimberley systematic record began in 1925
135,000
Calibration Event Sensitivity163,000
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Summary
The USACE Fort Worth District has developed urbanization curves
which account for different terrain conditions and the affects of urbanization. These curves produce reasonably accurate and
consistent flood risk estimates for a given area where funds and time
are in short supply.
The uncertainty in flood risk estimates has the potential of affecting
people’s property and lives. Model calibration is an effective way of reducing the uncertainty in our flood risk estimates.
.
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References
Interagency Flood Risk Management (InFRM) Team, “Hydrology Report for San
Marcos River Basin”, Submitted to FEMA Region VI, Sept. 15, 2016.
Nelson, T.L. “Synthetic Unit Hydrographs Relationships, Trinity River Tributaries, Fort
Worth-Dallas Urban Area”, Seminar on Urban Hydrology, Davis, CA, 1970.
Rodman, P.K. “Effects of Urbanization on Various Frequency Peak Discharges”, USACE Water Resources Meeting, Albuquerque, NM, 1977.
US Army Corps of Engineers, EM1110-2-1405, “FLOOD-HYDROGRAPH ANALYSES
AND COMPUTATIONS”, August 31, 1959.
US Army Corps of Engineers, EM 1110-2-1417, “FLOOD-RUNOFF ANALYSES”,
August 31, 1994.
US Army Corps or Engineers, Hydrologic Engineering Center, “HMS Technical
Reference Manual”, Davis, CA, 2010.
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Questions?
C. Landon Erickson, P.E.,CFM
Water Resources Engineer
(817) 886-1692 [email protected]
U.S. Army Corps of Engineers
Fort Worth District (SWF)
819 Taylor Street
Fort Worth, TX 76102