DICKINSON BAYOU WATERSHEDGALVESTON COUNTY, TEXAS
Jason Christian, P.E.
National Flood WorkshopOctober 24-26, 2010 – Houston, Texas
PROBABILISTIC PROBABILISTIC FLOODPLAIN FLOODPLAIN DELINEATIONDELINEATION
Key Points
General characteristics of steady state hydraulic models and current floodplain delineations.
Improvements offered by unsteady hydraulic models.
Improvements offered by probabilistic analysis methods.
Case Study.
Case Study
Dickinson Bayou Watershed Located in Galveston County (south of
Houston). Tributary of Galveston Bay. Coastal Watershed subject to:
Urban development. Intense rainfall patterns. Storm surge.
Approximately 17.5 miles long, covering 95.5 square miles.
Dickinson Bayou Case Study
Characteristics of Steady State Floodplain Models Assumptions of Uniformity:
Uniform design storm hydrology. Normal depth boundary conditions. Constant (usually average) roughness
coefficients.
Characteristics of Steady State Floodplain Models Assumptions of Equilibrium:
Hydrologic systems reach steady state. Timing of events is unimportant. Peak flows occur simultaneously
throughout the collection system.
Characteristics of Steady State Floodplain Models Assumptions of Convenience:
OK to model tributaries separate from main channel (hydraulically disconnected).
Compounded conservative choices are tolerated/encouraged.
Outside the defined 1% floodplain, flooding risk goes to zero.
Updated Floodplain Delineation Methodology Apply unsteady hydraulic models:
Current capabilities released in HEC-RAS version 3.1.
Incorporate temporal characteristics of model parameters.
Apply variability to important parameters: Storm duration, storm movement direction and
speed, outlet boundary conditions, channel roughness coefficients.
Describe floodplain as a probability distribution instead of a binary result: “Floodplain” is plural, not singular.
Unsteady Model Results
Characteristics of Unsteady Floodplain Models Cost for building an unsteady model
is only slightly more than an equivalent steady model (and gets less
with experience). Model should include all important
tributaries into one domain: The flow contribution and the timing of
contribution from the tributaries to the main channel is important.
Characteristics of Probabilistic Floodplain Models Cost for conducting a probabilistic
model is proportional to the amount of variability in the system being studied.
Not every parameter should be considered a probabilistic variable (sensitivity analysis and experience will guide).
Allow important parameters to vary across reasonable distributions & run multiple models.
Dickinson Bayou Case Study Evaluated 96 separate 1% storm
scenarios. Varied the following parameters:
Storm duration, Storm direction and speed, Boundary conditions, Roughness coefficients
Did not vary: Rainfall hyetographs (all were normally
distributed).
Comparison of Floodplain MapsSteady State Model Probabilistic Model
Analysis Results
Profile views of channel show clearly defined hydraulic environments: Surge dominated coastal zones (high
variability). Transitional zone. Inland riverine zone (low variability).
Analysis Results (Dickinson main channel)
Surge Zone
Coastal Zone (tidal)
Transition Zone
Inland Riverine
Analysis Results (Gum Bayou)
Coastal Zone (tidal)Transition
Zone
Inland Riverine
Surge Zone
Analysis Results (Lower Dickinson Bayou)
Applications for Probabilistic Floodplain Mapping Setting flood insurance rates. Prioritizing property buyout
programs. Identification of evacuation routes
with a likelihood of usefulness. Evaluating regional flood
improvement projects. Local land use ordinance
development.
Conclusion
Floodplain maps from steady state hydraulic models can be improved: Analysis error can be quantified. Risk can be differentiated within the
floodplain. Improvements are implemented by
application of unsteady hydraulic models and probabilistic analysis techniques.
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