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.