1. DICKINSON BAYOU WATERSHED GALVESTON COUNTY, TEXAS Jason Christian, P.E. National Flood Workshop October 24-26, 2010 – Houston, Texas PROBABILISTIC FLOODPLAIN DELINEATION

2. 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.

3. 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.

4. Dickinson Bayou Case Study

5. Characteristics of Steady State Floodplain Models  Assumptions of Uniformity:  Uniform design storm hydrology.  Normal depth boundary conditions.  Constant (usually average) roughness coefficients.

6. 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.

7. 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.

8. 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.

9. Unsteady Model Results

10. 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.

11. 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.

12. 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).

13. Comparison of Floodplain Maps Steady State ModelProbabilistic Model

14. Analysis Results  Profile views of channel show clearly defined hydraulic environments:  Surge dominated coastal zones (high variability).  Transitional zone.  Inland riverine zone (low variability).

15. Analysis Results (Dickinson main channel) Surge Zone Coastal Zone (tidal) Transition Zone Inland Riverine

16. Analysis Results (Gum Bayou) Coastal Zone (tidal) Transition Zone Inland Riverine Surge Zone

17. Analysis Results (Lower Dickinson Bayou)

18. 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.

19. 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.