Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling
Nancy Powell (ARCADIS), Haihong Zhao (ARCADIS), Shan Zou (ARCADIS), Hugh Roberts (ARCADIS), Don Resio (University of North Florida), David Johnson (RAND), and Ryan Clark (The Water Institute of the Gulf)
Produced for and Funded by: Coastal Protection & Restoration Authority of Louisiana and Lafourche Basin Levee District
July 8, 2015
INTEGRATING APPLIED RESEARCH | LINKING KNOWLEDGE TO ACTION | BUILDING PARTNERSHIPS
ABOUT THE WATER INSTITUTE OF THE GULF The Water Institute of the Gulf is a not-for-profit, independent research institute dedicated to advancing the understanding of coastal, deltaic, river, and water resource systems, both within the Gulf Coast and around the world. This mission supports the practical application of innovative science and engineering, providing solutions that benefit society. For more information, visit www.thewaterinstitute.org.
SUGGESTED CITATION Powell, N.1, Zhao, H. 1, Zou, S. 1, Roberts. H. 1, Resio, D.2, Johnson, D.3, & Clark, R.4 (2014). Project Development and Implementation Program: Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling. The Water Institute of the Gulf. Funded by the Coastal Protection and Restoration Authority under Task Order 18 Contract No. 2503-12-58. Baton Rouge, LA. 1 ARCADIS 2 University of North Florida 3 RAND Corporation 4 The Water Institute of the Gulf
Preface This study was performed for the Coastal Protection and Restoration Authority of Louisiana (CPRA) and Lafourche Basin Levee District (LBLD) under the Project Development and Implementation Program. The program was proposed in the 2012 Coastal Master Plan to provide a defined process for projects to be added to the analysis of projects for the 2017 Master Plan or be incorporated into the 2012 Master Plan in between the legislatively required 5-year updates and be eligible for funding through the Annual Plan. With limited funding available, any new project that is proposed to be added to the Master Plan must undergo a similar level of analysis and demonstrate a high level of performance. The same strategic approach to decision making used in the 2012 Master Plan will be maintained for any new project being proposed for inclusion in the Master Plan. CPRA recognizes that continued investment in cutting edge technology and further refinement of Master Plan components and projects will be critical to efforts going forward. This effort focuses on the continued analysis of flood risk in the northern extents of Barataria Basin. Flooding caused by both rainfall in the study area and an increased water level in coastal areas south of Highway 90 due to tropical storms and other events (e.g., extratropical or “winter” storms) were studied. The study builds on analyses previously completed as Project Development & Implementation Program: Upper Barataria Basin Risk Reduction (Roberts et al., 2014), which analyzed storm surge in the study area. The analysis will be completed to determine whether proposed project alternatives in the study area should be considered for inclusion in the evaluation for the 2017 Coastal Master Plan. The project team brings together expertise in hydrology, meteorology, hydrodynamic modeling, extremal statistics, and risk assessment. The project team completed the previous analysis, Project Development & Implementation Program: Upper Barataria Basin Risk Reduction (Roberts et al., 2014), and builds on that effort in this study. ARCADIS U. S. (ARCADIS) led the tasks of data collection and review, as well as Hydrology & Hydraulics (H&H) modeling. These tasks were done to support the statistical analysis of rainfall-surge-induced stages led by Professor Resio. The risk assessment was led by RAND. The Water Institute of the Gulf (the Institute) led project management for this analysis, provided an approach for the incorporation of rainfall derived from radar data, and provided quality reviews.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling i
Table of Contents
Preface ...... i List of Figures ...... iv List of Tables ...... v List of Acronyms ...... vi Acknowledgements ...... vii Executive Summary ...... 1 Introduction ...... 5 Data Collection and Review ...... 11 H&H Modeling ...... 16 Model Description ...... 16 Model Updates ...... 16 Future Scenario ...... 18 Model Validation ...... 19 Climatology and Statistics ...... 26 JPM-OS Analysis ...... 26 Antecedent Conditions ...... 27 H&H Model Production Runs ...... 34 Model Setup ...... 34 H&H Modeling Results ...... 34 Limitations of Modeling ...... 44 Flood Risk ...... 45 Damage and Benefits ...... 48 Project Alternatives ...... 48 Cost Estimation ...... 50 Highway 90 ...... 50 Risk Assessment ...... 52 Cost Effectiveness ...... 56 Analysis of Future Large Industrial Projects ...... 57 Uncertainty and Sources of Uncertainty ...... 59
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling ii
Conclusion ...... 60 Appendices ...... 64 Appendix A: Data Inventory ...... 65 Appendix B: Identified Historical Rainfall Events ...... 69 Appendix C: Storage Names and Labels ...... 73 Appendix D: Rainfall Runoff for Existing and Future Landscape ...... 79 Appendix E: Stage Hydrographs ...... 80 Appendix F: Frequency Stages ...... 86 Appendix G: Comparison of HEC-RAS and CLARA Flood Depth Exceedances ...... 106 References ...... 112
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling iii
List of Figures
Figure 1. Study area map, with key place names and locations...... 7 Figure 2: Proposed Ridge Alignment...... 8 Figure 3. Proposed Highway 90 Alignment...... 9 Figure 4. Modeling framework...... 10 Figure 5. Map of water surface elevation gage stations...... 13 Figure 6. Map of rainfall gage locations ...... 14 Figure 7. Example storm event – Hurricane Isaac...... 15 Figure 8. RAS model domain and storage areas ...... 17 Figure 9. Hydrograph comparison for Tropical Storm Allison at the Bayou Des Allemands gage ...... 21 Figure 10. Hydrograph comparison for Gustav-Ike ...... 22 Figure 11. Hydrograph comparison for a nontropical storm (2009) ...... 23 Figure 12. Hydrograph comparison for Isaac ...... 24 Figure 13. Effects of antecedent water level on peak water level at Bayou Des Allemands gage ...... 28 Figure 14. Event diagram- antecedent conditions, rainfall intensity, and synthetic storms ...... 32 Figure 15. Selected synthetic storm tracks ...... 33 Figure 16. HEC-RAS model domain and labeled storage areas...... 35 Figure 17. Peak stage of storm001 associated with the medium rainfall rate 1 and the first antecedent condition of 1.0 ft., NAVD88...... 37 Figure 18. Effects of antecedent conditions (2.0 ft NAVD88 minus 1.0 ft NAVD88) using Storm001 combined with medium rainfall 1...... 39 Figure 19. Effects of rainfall intensity ...... 41 Figure 20. Effects of storm surge (Storm001), difference in peak surge (feet)...... 43 Figure 21: Proposed Project Alignments (Highway 90, Ridge North+East, and Ridge South) and HSDRRS Improvement Reaches...... 49 Figure 22: HSDRRS Improvement Reaches...... 51 Figure 23. Thiessen polygons associated with CLARA v2.0 grid points in study region...... 53 Figure 24. Expected Annual Damage by Parish and Asset Type...... 55 Figure 25. Rainfall runoff for existing and future locations...... 79 Figure 26. Gustav-Ike, August 2008 (gage locations are referred to in Figure 5)...... 81 Figure 27. Nontropical Storm, December 2009 (gage locations are referred to in Figure 5)...... 83 Figure 28. Isaac, August, 2012 (gage locations are referred to in Figure 5)...... 85 Figure 29. Difference in flood depths between RAS and CLARA models (10-year return period)...... 107
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling iv
Figure 30. Difference in flood depths between RAS and CLARA models (25-year return period)...... 108 Figure 31. Difference in flood depths between RAS and CLARA models (50-year return period)...... 109 Figure 32. Difference in flood depths between RAS and CLARA models (100-year return period)...... 110 Figure 33. Difference in flood depths between RAS and CLARA models (500-year return period)...... 111
List of Tables
Table 1. Physical changes for subbasins in Upper Barataria Basin Hydrological Model...... 19 Table 2. Modeled and measured peak stages (feet)...... 25 Table 3. Selected synthetic tropical events...... 30 Table 4. June – November extreme 24-hour rainfall...... 31 Table 5. December – May extreme 24-hour rainfall...... 31 Table 6. Rainfall intensity definitions...... 32 Table 7. Storage areas with revisions...... 46 Table 8. Damage by Return Period and Expected Annual Damage by Portion of Parish in Study Area (No Projects in Place)...... 54 Table 9. Expected Annual Damage (EAD), and Maximum Potential Reduction in EAD by Project (2065, Top-of-Levee Fragility Case)...... 55 Table 10. Cost Effectiveness (Maximum Potential Reduction in EAD per Dollar of Cost) by Alignment...... 56 Table 12. CRMS stage gages...... 65 Table 13. USACE stage gages...... 66 Table 14. Rainfall gages...... 66 Table 15. Historical rainfall events...... 69 Table 16. Storage names and labels...... 73 Table 17. Frequency stages for existing condition assuming no failure...... 86 Table 18. Frequency stages for existing condition assuming 50-year failure...... 91 Table 19. Frequency stages for “future without action” scenario assuming no failure...... 96 Table 20. Frequency stages for "future without action” scenario assuming 50-year failure...... 101
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling v
List of Acronyms
Acronym Term ADCIRC ADvanced CIRCulation AEP Annual Exceedance Probability ARCADIS ARCADIS U.S., Inc. CB&I Chicago Bridge & Iron Company CE Cost Effectiveness CLARA Coastal Louisiana Risk Assessment CN Curve Number CPRA Coastal Protection and Restoration Authority CRMS Coastwide Reference Monitoring System FEMA Federal Emergency Management Agency GIWW Gulf Intracoastal Waterway H&H Hydrology and Hydraulics HEC-HMS Hydrologic Engineering Center – Hydrologic Modeling System HEC-RAS Hydrologic Engineering Center – River Analysis System Highway U.S. Highway HSDRRS Greater New Orleans Hurricane and Storm Damage Risk Reduction System JPM-OS Joint Probability Method-Optimal Sampling JPM Joint Probability Method LBLD Lafourche Basin Levee District LIDAR Light Detection and Ranging RMSE Root Mean Square Error SA Storage Area SCS Soil Conservation Service SLR Sea Level Rise UBB Upper Barataria Basin UnSWAN Unstructured Simulating WAves Nearshore USACE U.S. Army Corps of Engineers WSE Water Surface Elevation
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling vi
Acknowledgements The execution of the project involved several additional personnel including Katelyn Costanza of the Institute, Zach Cobell and Brett McMann of ARCADIS; and Ken Kuhn of RAND. Mark Leadon of CPRA provided project guidance. Lafourche Basin Levee District provided project guidance, the proposed project alignments and attributes, and supporting data. This document was reviewed by Denise Reed and Scott Hemmerling of the Institute.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling vii
Executive Summary This effort―Phase 2 of Barataria Basin Risk Reduction analysis―builds off the previously completed work and determines whether proposed protection project alternatives in the northern extent of Barataria Basin (the study area) should be considered for inclusion in the evaluation for Louisiana’s 2017 Coastal Master Plan. The previously completed work, Phase 1, conducted an analysis that was consistent with the state’s 2012 Coastal Master Plan methodology, focusing on flood hazards and direct economic damage from tropical storm surge (Roberts et al., 2014). The Phase 2 approach builds on the Master Plan methodology, and evaluates flood hazards for a combination of storm surge and rainfall. This includes rainfall from non-tropical rain-making events such as winter cold-front passages and other large storms. The approach also incorporated the use of high-resolution rainfall data derived from weather radar. The study area includes the upper reach of the Barataria Basin, which contains extensive wetlands, bayous and canals, agriculture lands, communities surrounded by local levees, and communities on the high ground on both the western (near Bayou Lafourche) and eastern (near Mississippi River) portions of the basin. Bayou Des Allemands is the major channel conveying water into and out of the study area. U.S. Highway 90 and the Burlington Santa Fe Railroad embankments run east-west through the study area and impede the north-south flow of water into and out of the basin, providing some measure of flood damage reduction for coastal storms. The study area is presented on Figure 1, labeled with critical locations and proposed project alternatives. The proposed project alternatives, further highlighted in Figure 2 and Figure 3 , include the Highway 90 Alignment and the Ridge Alignment from the Phase 1 study (Roberts et al., 2014). The upper reaches of the study area experience less flooding from coastal storm surge; flooding damage in many portions of the basin are dominated by rainfall events which could be tropical storm or non- tropical storm related. Flooding caused by both rainfall in the basin and an increased water surface elevation south of Highway 90, due to tropical events or longer-duration events, have been studied in this phase. Three levee configurations were considered: the Highway 90 Alignment, the North and all East segments of the Ridge Alignment combined, and the Ridge South Alignment. The flood risk in the study area has been analyzed based on simulated storm responses. This requires (1) a model predicting the peak water surface elevation affected by both storm surge and rainfall; (2) a statistical model quantifying the stage-frequency function with multiple variables including rainfall characteristics and tropical storm dynamic parameters; and (3) an economic damage model. The modeling system employed in this study brought together offshore storm surge and wave model outputs, and incorporated an H&H modeling system for predicting rainfall runoff in a rainfall dominant interior basin. The statistical methods have been modified from previous surge-only approaches to include the estimation of exceedance probabilities of rainfall-associated flooding events. This revised methodology also includes the effects of antecedent conditions in the study area, i.e., that the water level in the basin prior to a storm event may be elevated due to water from a previous storm that had not completely drained from the system. Antecedent conditions are a temporary state within dynamic natural and social systems that precedes and influences the onset and magnitude of a hazard and its consequences. Therefore, the antecedent water level is the water level that precedes and influences the onset and magnitude of a hazard and its consequences.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 1
This project focuses on improving the understanding of flood risk without projects in place. Under with- project conditions for the two projects considered in Phase 1, the flood risk is primarily determined by rainfall only in the study area (i.e., not combined with downstream storm surge due to the placement of the project measures). For the purposes of this evaluation, it is assumed that rainfall interior to the basin with projects in place would be managed by the installation of pumps with adequate capacity. Therefore, rather than detailed evaluations of the flood risk under with-project conditions, it will be assumed that flood damages would be fully reduced as a means to determine maximum possible project benefits. The cost effectiveness of each of the three proposed alignments has been investigated. Cost effectiveness of the alignments takes into account the reduction in expected annual damages and construction cost of a given project, relative to the “future without action” case. Future population growth and changes in economic assets were projected using assumptions which were used as the default economic growth scenario in the 2012 Coastal Master Plan and previous iteration of the Barataria Basin analysis. However, the baseline inventory of current economic assets has been updated in CLARA v2.0, subsequent to the prior analyses. Flood risk in the northern extents of Barataria Basin has been reanalyzed for this Phase 2 of the Barataria Basin Risk Reduction analysis. The Phase 2 approach builds on the Master Plan methodology, and evaluates flood hazards for a combination of storm surge and rainfall. The following changes have been incorporated: 1. The statistical joint probability methods have been modified from previous surge-only approaches to also include the estimation of exceedance probabilities of rainfall-associated flooding events. This revised methodology also includes the effects of antecedent conditions in the study area, i.e., that the water level in the basin prior to a storm event may be somewhat elevated due to a previous storm, and not completely drained from the system. 2. HEC-HMS and HEC-RAS models have been used to model rainfall and surge propagation into the UBB. Existing USACE models for Donaldsonville to the Gulf feasibility study have been reviewed; the HEC-RAS model has been updated by reducing the extent of the domain along the north banks of Lake Cataouatche, Lake Salvador, and Company Canal to Lockport, revising the elevation of Highway 90, revising channel cross-sections of the upper reach of Bayou Des Allemands, adding lateral structures along Bayou Des Allemands, and adding connections between developed high-land areas with adjacent low-land areas. The updated UBB HEC-RAS model has been validated using four historical storms: Hurricane Allison in 2001, a combination of Hurricanes Gustav and Ike in 2008 (referred to as Gustav-Ike), a non-tropical storm in December 2009, and Hurricane Isaac in 2012. 3. Future population growth and changes in economic assets were projected using CLARA v2.0. The model starts with an updated baseline inventory of structures, relative to the version of the model which was used in the 2012 Coastal Master Plan and previous iteration of the Barataria Basin analysis. The reference year in which to estimate cost effectiveness has been changed from year 2061 to year 2065. Costs associated with protection system improvements needed to prevent induced flooding within the West bank portion of the HSDRRS were also included.
In the upper portion of the basin, many areas are only affected by rainfall, with little or no variation in peak stage with respect to different synthetic storms at the lower basin boundary. Antecedent conditions generally have little or no effect in the upper portion of the basin and the areas inside local levees. Surge
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 2 effects predominate the lower portion of the basin. In the future, surge effects move further inland, and the risk of flooding inside the local levees increases. Some areas along the Mississippi River and Bayou Lafourche will remain affected by only rainfall. In the “future without action” scenario, damage in both current and future conditions is primarily concentrated in areas with a large number of commercial and residential assets. The majority of damage is geographically located in Lafourche Parish, within communities located on the southern ridge of the study area, and in St. Charles Parish, primarily along the Highway 90 corridor from Des Allemands to Luling. The predicted increase in damage from year 2015 to year 2065 is due to a greater probability of flooding combined with projected population growth and economic development. For two of the three levee configurations considered in the economic analysis, the Highway 90 Alignment and the North and all East segments of the Ridge Alignment combined, (Figures 2 and 3), the large majority of risk reduction occurs in St. Charles Parish (71% for the Highway 90 Alignment and 95% for the North and all East segments of the Ridge Alignment combined). For the Ridge South Alignment, the large majority of risk reduction (99%) occurs in Lafourche Parish. All three of the proposed alignments provide positive results on a Cost Effectiveness (CE) scale. The CE ratios of the Highway 90 Alignment, the North and all East segments of the Ridge Alignment combined, and the Ridge South segments were 2.3, 1.3, and 0.5, respectively. Because this study is intended to determine the upper bound of possible damage reduction, the CE ratios are conservative in nature. Costs for the Highway 90 and Ridge North+East alignments include an assumption that HSDRRS reaches on the west bank of the Mississippi River will be lifted appropriately to counteract any induced damage. This lift was not assumed for the Ridge South alignment, given that it is believed to be unnecessary following the Phase 1 study, which did not find significant induced flooding effects from that project. The cost-effectiveness ratios of projects ultimately selected for inclusion in the 2012 Coastal Master Plan range from 21.3 to -0.1 in the “less optimistic” scenario; projects not included in the Master Plan ranged from 10.3 to -1.2. Some project concepts were not included because they are mutually exclusive from projects with larger ratios that were selected. The conservative CE ratios estimated for the proposed alignments all fall into the low end of these ranges. Estimates of project effects have not yet been updated for the 2017 Coastal Master Plan; the above-quoted values are based on projections using CLARA v1.0. The analysis was designed to estimate an upper bound on the benefits of each proposed levee alignment. As such, the study region assumes approximate project costs to prevent induced damages in areas within HSDRRS on the west bank of the Mississippi River, where induced flooding and damage was found to occur in the previous study (Roberts et al., 2014). Induced flooding was also ignored in other areas in front of the proposed levee alignments. Instead of running a separate analysis of flood depths with and without the alignments in place, it was assumed that each alignment reduced flood depths to zero at all return periods. Pumping systems were assumed to eliminate rainfall-based flooding in each storage area, and the levees and pumping systems were assumed to combine to successfully prevent surge-based flooding. These are strong assumptions, but they result in an estimate of the maximum risk reduction provided by the alignments, which is then used to produce an estimate of their maximum cost effectiveness. The combined effect of these assumptions, and that the Highway 90 Alignment protects not only the same communities as the Ridge North+East and Ridge South projects, but also additional areas in the upper basin, result in the project being associated with the greatest potential risk reduction.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 3
In addition to analyzing risk to the baseline economic inventory in CLARA, the team was requested to examine the impact of the proposed alignments to a small set of large planned industrial projects in the basin. All of these specific large industrial development sites were determined to be at low risk of damaging flooding, even in 2065 under the “less optimistic” environmental scenario assumptions. Even so, it is assumed that the projects will be designed with site-appropriate hardening measures to hedge against future flood risk. As such, it is concluded that the inclusion of these projects in the economic analysis of the previous section would have little to no impact on the cost-effectiveness modeled for each of the proposed levee alignments. FURTHER CONSIDERATION OF THE PROPOSED PROJECTS In order for the alignments discussed in this report to be directly compared to other Master Plan protection projects, the approach used in this study would have to be refined and made adaptable for use throughout coastal Louisiana. This would be necessary to enable consideration of combined rainfall and storm surge flood damages, and projects to address them, in other areas where they are known to influence flood damages. Currently, H&H models are available in Barataria Basin due to the model availability from the previous USACE Donaldsonville to the Gulf Feasibility Study. Although similar models may be available in other areas as a result of localized studies, development may be necessary to replicate the approach used here. A consistent set of tools would need to include H&H models; JPM-OS analysis of rainfall, surge, and antecedent water level conditions for each area being considered; and systematic coupling of the model components to ensure efficient analysis of multiple projects across many scenarios. Due to schedule and budget constraints related to the upcoming 2017 Coastal Master Plan, it is unlikely that such an analysis will be possible in time for projects producing risk reduction damages under the combined effects of rainfall and surge to be considered for inclusion in the Plan. However, this study has identified that the interactive effects of rainfall and surge in poorly draining areas can result in substantial flood damages, and that meeting the objectives of the Coastal Master Plan may require consideration of a broader array of project types.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 4
Introduction The Project Development and Implementation Program was proposed in Louisiana’s 2012 Coastal Master Plan to provide a defined process for consideration of projects proposed between legislatively required five-year updates of the Master Plan. These projects may be added to the list of projects evaluated for the next Master Plan, in this case the 2017 Coastal Master Plan. In order to approximate the benefits of the proposed project alternatives, current and future flood risks must be evaluated. This effort―Phase 2 of Barataria Basin Risk Reduction analysis―builds off the previously completed studies (Roberts et al., 2014) and determines whether proposed project alternatives in the northern extent of Barataria Basin (the study area) should be considered for inclusion in the evaluation for the 2017 Coastal Master Plan. Phase 1 consisted of an analysis consistent with the 2012 Coastal Master Plan methodology, focusing on flood hazards and associated damages from tropical storm surge (Roberts et al., 2014). The Phase 2 approach deviates from the Master Plan methodology and evaluates flood hazards for a combination of storm surge and rainfall. The economic analysis in Phase 2 focuses on identifying an upper bound on potential risk reduction by ignoring the possibility of induced flooding in front of the proposed alignments―including on the west bank of the Greater New Orleans protection system―and by assuming the alignments fully eliminate risk on the project interiors. The study area is the upper reach of the Barataria Basin with extensive wetlands, bayous and canals, agriculture lands, communities surrounded by local levees, and communities on the high ground on both the western (near Bayou Lafourche) and eastern (near Mississippi River) portions of the basin. Bayou Des Allemands is the major channel conveying water into and out of the study area. U.S. Highway (Highway) 90 and the Burlington-Santa Fe Railroad embankments run east-west through the study area and impede the north-south flow of water into and out of the basin, providing some measure of flood damage reduction for coastal storms. The study area is presented in Figure 1, labeled with critical locations and proposed project alternatives. The proposed project alternatives, further highlighted in Figure 2 Figure 3, include the Highway 90 Alignment and the Ridge Alignment from the Phase 1 study (Roberts et al., 2014). The upper reaches of the study area experience less flooding from coastal storm surge. Flooding damage in many portions of the basin are dominated by rainfall events which could be tropical storm or non- tropical storm related. Flooding caused by both rainfall in the basin and an increased water surface elevation south of Highway 90 due to tropical events or longer-duration events, have been studied in this phase. The flood risk in the study area has been analyzed based on individual storm responses, which requires (1) a model predicting the peak water surface elevation affected by both storm surge and rainfall; (2) a statistical model quantifying the stage frequency function with multiple variates including rainfall characteristics and tropical storm dynamic parameters; and (3) an economic model computing AEP of flood damages. Existing ADCIRC and UnSWAN hydrodynamic model systems focus on coastal and oceanic areas where effects of rainfall runoff are minimal. Accordingly, ADCIRC and UnSWAN model simulations are used in this study to inform flooding patterns in the lower basin, while an H&H modeling system has been incorporated in this study for predicting rainfall runoff in a rainfall-dominant interior basin. The H&H models include the Hydrologic Engineering Center – Hydrologic Modeling System
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 5
(HEC-HMS) and HEC-River Analysis System (HEC-RAS). The H&H models developed by USACE for the Donaldsonville to the Gulf levee feasibility study (USACE, 2011) were adapted in this study with moderate model updates to focus on the study area, denoted as the Upper Barataria Basin (UBB) H&H models. Additionally, the joint probability method (JPM) has been modified to incorporate rainfall characteristics into the existing JPM-Optimal Sampling (JPM-OS) approach for estimating exceedance probabilities of rainfall-associated flooding events (Resio, 2007; Resio, Irish, & Cialone 2009; Irish, Resio, & Cialone 2009; Toro et al., 2010). The Coastal Louisiana Risk Assessment (CLARA) model (Johnson et al., 2013) has been used to estimate direct economic damage associated with the flood depths calculated using the modified JPM-OS procedure. Figure 4 illustrates the project method framework, including key components of this project such as statistical analysis, H&H modeling, data collection, and data flow.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 6
Figure 1. Study area map, with key place names and locations.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 7
Figure 2: Proposed Ridge Alignment.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 8
Figure 3. Proposed Highway 90 Alignment.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 9
Rainfall Climatology Interpolated (Radar Data; HEC-HMS & Statistics Rainfall Gage Data)
Water Levels Validation Runoff (Gage Data) Upper & Lateral Boundary
StageFrequency Condition
Maximum Water Levels HEC-RAS 1D Outputs Hydrographs Downstream Boundary
Condition Storm Surge
ADCIRC CLARA (Risk Synthetic Storm Assessment) Simulations
Figure 4. Modeling framework.
To execute the components, the following subtasks were performed:
1. Collect and review data to support the evaluation of climatology and the development of flooding stages and frequencies; 2. Collect, update, and test the Donaldsonville to the Gulf H&H models; 3. Based on analysis of observed rainfall and water level data, determine storm conditions which have historically caused flooding in the study area, including the effects of tropical storm surge and rainfall; 4. Identify synthetic storms from the 2012 Coastal Master Plan ADCIRC storm set of 444 storms whose output can be used as downstream boundary conditions in the H&H models; 5. Simulate rainfall storm events associated with storm surge conditions; 6. Perform a JPM analysis to estimate flood stages for a range of return frequencies, e.g., 10-, 50-, and 100-year; and 7. Evaluate the flood risk damage and proposed project alignment benefits in CLARA.
ARCADIS led the tasks of data collection and review, as well as H&H modeling. These tasks supported the statistical analysis of rainfall-surge-induced stages led by Professor Resio. The risk assessment in
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 10
CLARA was led by RAND. The Institute led project management for this analysis, as well as provided quality reviews. The project focuses on improving the understanding of flood risk without projects in place. Under with- project conditions for the two projects considered (the Ridge and Highway 90 Alignments), the damage due to coastal storm surge is fully reduced and the flood risk in the protected areas (the study area) is primarily determined by rainfall only. For the purposes of this evaluation, it is assumed that rainfall interior to the basin with projects in place would be managed by the installation of pumps with adequate capacity. Therefore, rather than detailed evaluations of the flood risk under “With Project” conditions, it will be assumed that flood damages would be fully reduced as a means to determine maximum possible project benefits. The analysis focuses on comparisons of “With Project” and “Without Project” effects on current conditions, as well as conditions projected 50 years into the future.
Data Collection and Review The previous USACE Donaldsonville to the Gulf feasibility study (USACE, 2011) assessed flood risk using rainfall frequency data obtained from National Weather Service (NWS) HYDRO 35, Department of Commerce Technical Paper No. 40 (TP40), and Technical Paper No. 49 (TP49) for a range of return frequencies and storm surge stage frequency data developed for the FEMA flood insurance study (USACE, 2008). A JPM analysis was not conducted to combine the effects of storm surge and rainfall. Instead, the stage for each frequency was determined by adopting the higher value of a rainfall-induced stage frequency curve and a coastal storm induced stage frequency curve at a given frequency. It was assumed that there was no surge effects impacting the flood stages for the more frequent events. For this study, the flood risk is determined using individual storm induced flood elevations in a joint probability analysis that considers tropical events, including the associated rainfall, and rainfall events. The revised method allows for the integration of a comprehensive analysis of rainfall intensity and storm surge to determine increased water surface elevations. The critical flood conditions were determined first via collecting historical rainfall data, rainfall statistics, water surface elevation gage data, bathymetry, and topography. In total, 11 hourly rainfall gages and 14 daily rainfall gages in the study area and vicinity were collected and reviewed. For each gage, the data coverage varied, with a longer record length for daily gages and a shorter record length for hourly gages. In addition, Stage III Gridded Hourly NEXRAD radar rainfall data (2001-2012) were also collected to examine the spatial distribution of rainfall intensity for storm events and to complement gaps in gage records. Water surface elevation is another critical parameter for assessing historical events. Gages maintained by USACE and Louisiana Coastwide Reference Monitoring System (CRMS) were searched, from which 32 CRMS stations and three USACE gages were selected to cover the study area. The USACE gages, e.g., Bayou Des Allemands gage (USACE, 2008), have hourly records since 1999 and daily records throughout the gage record (1950 or 1956 to present). CRMS stations only include records since 2007. The inventory of rainfall gages and stage gages are tabulated in Appendix A. Figure 5 and Figure 6 present maps of water surface elevation (WSE) gage stations and rainfall gage stations, respectively.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 11
The record for the Bayou Des Allemands gage was examined to identify historical events that generated high stages. Using rainfall records and tropical storm histories, the gage record was further refined to create a subset of events for joint probability analysis (see Appendix B). Inspection of both the rainfall records and stage records resulted in the identification of a notable phase lag between peak stage and peak rainfall intensity, as well as a phase lag of peak stages at the seaward end of the basin and the upper basin. For instance, Figure 7 shows the stage hydrographs for Hurricane Isaac (2012) at Bayou Verret (CRMS0217) and the Bayou Des Allemands gage (USACE82700), as well as rainfall intensity at Paradis. However, the actual hours of lag may vary from storm to storm. Focusing on the stage record at the Bayou Des Allemands gage, a significant relationship was found between the antecedent water level at the Bayou Des Allemands gage and the subsequent peak. Antecedent conditions represent a temporary state within dynamic natural and social systems that precedes and influences the onset and magnitude of a hazard and its consequences. Therefore, the antecedent water level is the water level that precedes and influences the onset and magnitude of a hazard and its consequences. The antecedent water level becomes a significant variable in the JPM study.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 12
Figure 5. Map of water surface elevation gage stations.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 13
Figure 6. Map of rainfall gage locations.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 14
Figure 7. Example storm event – Hurricane Isaac. Top: hydrograph at Bayou Verret (CRMS0217); middle: hydrograph at the Bayou Des Allemands gage (USACE87000); and bottom: rainfall intensity at Paradis (USC00167069). Vertical lines indicate the time of peaks.
Topography and bathymetry survey data in the study area were also collected and reviewed for the benefit of H&H modeling. According to recent bathymetry, Bayou Des Allemands channel at Highway 90 is nearly 450 ft wide and nearly 26 ft deep (USACE, 2011). Topographic data indicate the study area is relatively flat in the central basin with ridges defining the water ways and embankments. As discussed in the next section, the USACE H&H model elevations were largely unchanged for this analysis. However, where necessary, such as the Bayou Des Allemands, the H&H models were updated and elevations were defined using available topography and bathymetry survey data.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 15
H&H Modeling
MODEL DESCRIPTION To consider the effects of rainfall-induced flooding in the study area, the one-dimensional H&H models HEC-HMS and HEC-RAS were utilized to compute rainfall runoff and peak stage. The HEC-HMS and HEC-RAS models initially developed by USACE for the Donaldsonville to the Gulf levee feasibility study (USACE, 2011) were adapted in this study with moderate model updates to the HEC-RAS model. The modified H&H models were further tested against observed stage hydrograph data and then applied to simulate peak stage due to combined rainfall and storm surge for flood risk assessment.
Model Updates The USACE rainfall runoff model (HEC-HMS) remained unchanged. However, the USACE interior drainage model (HEC-RAS) was modified. The original USACE model domain extended included the southern and eastern portions of the Barataria Basin in Plaquemines and Jefferson Parishes. In this study, the extent of the domain was trimmed along the north banks of Lake Cataouatche, Lake Salvador, and Company Canal to Lockport. Downstream boundary conditions were applied at the Bayou Des Allemands outlet to Lake Salvador, the confluence of Lake Salvador and Lake Cataouatche, and Sellers Canal, from west to east. Figure 8 presents the updated model and downstream boundary locations. In Figure 8, each storage area (SA) is numbered. SA names are tabulated in Appendix C.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 16
Figure 8. RAS model domain and storage areas.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 17
Inside the trimmed model domain, most of the structures, including levees, canals, ridges, bridges, weirs, and pump stations, remained unchanged except a few critical structures that were identified as features that may affect the model results significantly. First, the original SA-19B is split at Highway 90 into SA- 19B on the south and SA-19B-N on the north. The connection between two SAs is parameterized as a solid weir because recent field survey photographs of Highway 90 culverts, provided by the Lafourche Basin Levee District (LBLD), show little sign of conveyance. The elevation varying along the alignment according to light detection and ranging (LIDAR) data (http://ned.usgs.gov/) was applied to the HEC- RAS model. Second, channel cross-sections of the upper reach of Bayou Des Allemands (from Lac Des Allemands to Highway 90) were revised based on the survey carried out by the USACE New Orleans District in 2003 and the survey performed by CB&I for the Bayou Des Allemands flood gate feasibility study.. Third, lateral structures along Bayou Des Allemands were added to include the connection between the bayou and adjacent SAs. Fourth, some developed high-land areas in the original model have no connection to the adjacent low-land areas, which caused unreasonable buildup of rainfall stages, e.g., St. James, Napoleonville, and Thibodaux. Connections were added to allow storm water drainage to the adjacent low-elevation SAs. The updated UBB HEC-RAS model was tested using four historical storms. The details are described in the model validation section.
Future Scenario The original USACE H&H models were developed in 2006. For the purposes of this study, they are assumed to represent current conditions. To further modify the UBB models for a future scenario requires changes in bathymetry, topography, landscape, and land cover/land use. The future scenario is referred to as the 2012 Coastal Master Plan Less Optimistic Scenario, which includes an estimated sea level rise (SLR) of 1.5 ft by 2061, 1.0 ft land subsidence, and landscape changes derived from wetland morphology and vegetation models (Appendix C in CPRA, 2012). Subsidence and SLR in this future scenario are reflected in the initial water elevations, downstream boundary conditions, and adjusted model geometry. Because the study area is within one of the 2012 Master Plan subsidence polygons, no spatial variation was incorporated. Thus, specifically, the UBB HEC-RAS model was updated to add a constant value of 1.5 ft to existing initial water surface elevations and subtract a constant value of 1.0 ft from existing land elevations, including the local levee elevations. Growing population and land use changes bring biochemical and physical changes to the hydrological system. For example, the loss of pervious surface reduces the rainfall infiltration into soil, an artificial drainage system replaces natural pathways, etc. The typical effects of urbanization or industrialization on the hydrological response of an area are increased runoff and flow, higher recurrence of small floods, and reduced base flow and groundwater recharge (Miller, 2014). These changes have impacts primarily on the rainfall-induced overland flow in the hydrological model system. In order to understand the potential impacts of urbanization on H&H model responses, a sensitivity test was performed to evaluate the impacts of possible future landscape changes. Based on the CPRA Master Plan and future land use of St. James Parish, St. John Parish and St. Charles Parish (CBI, 2014), changes in land cover and land use are expected for the future scenario. The UBB hydrological model was modified based on the assumption that all future land use in five subbasins in St. James, St. John, and St. Charles Parishes would be converted from existing land use to “non-vegetated urban” to account for
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 18 possible development and associated increases in impervious area. The Soil Conservation Service (SCS) Curve Number (CN) method was used to model interception and infiltration (USACE, 2011). SCS CNs were determined by relating land use types to hydrologic soil groups. A composite CN for each watershed was computed by taking an area-weighted average of the different CN for different regions within a watershed (Table 2.7 in USACE, 2011). Table 1 presents the CN and percentage of impervious area within a subbasin (%) in the HEC-HMS model for five subbasins where landscape changes are anticipated.
Table 1. Physical changes for subbasins in Upper Barataria Basin Hydrological Model.
Curve Number Impervious (%) Subbasin ID Existing Future Existing Future 9A 84.2 91.0 2.91 5.82 13A 84.0 91.0 1.40 4.20 14A 84.0 91.0 0.53 1.59 17A 83.6 91.0 4.43 8.86 21A 81.0 88.6 4.75 9.50
For selected subbasins, the modeled rainfall runoff with existing land use conditions was compared with results assuming modified land use in select subbasins. Appendix D contains Figures illustrating the comparison. Model results indicate that the effects of these changes in land use on rainfall runoff in UBB are negligible. Therefore, effects of possible land use changes are not considered in model runs for the future scenario.
MODEL VALIDATION Before further implementing the H&H models, four historical events were selected to validate the HEC- RAS model by comparing modeled stage with measurements: Hurricane Allison in 2001, a combination of Hurricanes Gustav and- Ike in 2008, (referred to as Gustav-Ike), a non-tropical storm in December 2009, and Hurricane Isaac in 2012. For Allison, there was only one stage gage at Bayou Des Allemands, located at approximately the midway point of the model domain. This storm was selected primarily to compare the results from the updated model with results from the original USACE model. Gustav-Ike was a pair of events that contained two consecutive high-intensity tropical storms. Isaac was a hurricane with high-intensity rainfall and a high antecedent stage in the upper basin. The December 2009 storm was the result of a non-tropical weather system that generated heavy rainfall. These events were selected to cover a wide range of event types and to provide comprehensive appreciation of model validation. In the study area, river stage/discharge relationships are complex because many SAs and canals are portions of the forced drainage system affected by levees, gates, and pump stations. Only modeled stage hydrographs were used for model validation. HEC-RAS model stage hydrographs outputs at given locations were compared with field measurements. The HEC-RAS model simulations account for rainfall runoff at lateral and upstream boundary conditions, as well as storm surge hydrographs at downstream boundary conditions (Figure 4).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 19
The rainfall runoff is determined in the HEC-HMS model, which was originally set up by USACE to interpolate rainfall intensity over the basin using data at 10 rainfall gages (Figure 6). However, rainfall gage data are often incomplete. Hence, Stage III gridded hourly NEXRAD radar rainfall data were applied, as the data provide good temporal and spatial coverage. Because the existing HEC-HMS model is not gridded, the gridded radar rainfall data cannot be applied directly. Rather, radar data were interpolated onto the 10 gage locations in the USACS HEC-HMS model setup. For quality control, comparison between radar data and gage observation was carried out. Generally, both data sets are consistent with each other. However, a significant discrepancy exists for some time periods and at some gages, indicating underestimation of rainfall intensity by Radar data. In recent literature, it has been documented that radar rainfall data are likely lower than the actual rainfall intensity due to the imperfection of the radar rainfall estimation algorithm derived from radar-gage pairs (Geoffrey & Baum, 2012). However, to be consistent, all radar-interpolated rainfall data were used in the model validation simulations. In the validation, the downstream water surface elevation boundary condition was determined using field measurements. The downstream boundary is controlled by stage hydrographs at the outlet of Bayou Des Allemands at Lake Salvador (BC #3 in Figure 8) using CRMS3054, the confluence of Lake Salvador and Lake Cataouatche using CRMS0278 (BC #4 in Figure 8), the Sellers Canal using CRMS3169 (BC #5 in Figure 8), and two locations to the west of Bayou Des Allemands reach (BC #1 and BC#2 in Figure 8). Figure 8 shows the CRMS stations where water level records were used as boundary conditions and Figure 5 presents all CRMS stations that were used for model validation data. For each validation storm, HEC-HMS and HEC-RAS were set up in the same manner. The only exception is that the downstream boundary conditions (BC #1 through 5 in Figure 8) for Allison (2001) have all been defined using the Bayou Des Allemands gage measurements because CRMS data were unavailable during that event. The modeled stage hydrograph agrees well with measurements at the Bayou Des Allemands gage for Tropical Storm Allison (2001) (Figure 9). For Gustav-Ike, the non-tropical storm in December 2009 and Isaac, CRMS and USACE measurements are available. The modeled stage hydrographs were compared with measured data at 13 CRMS stations and the Bayou Des Allemands gage. Figure 10Figure 11Figure 12 show stage hydrographs for these three events at selected locations in the upper, middle, and lower portions of the model domain. Additional hydrograph comparison charts are included in Appendix E. Generally, the trend and slope of modeled hydrographs agree well with the measured data, while both over- and under-estimations are observed. Table 2 shows the comparison in peak stages between model results and field measurements, showing the root mean squared error (RMSE) ranging from 0.4 to 0.6 ft.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 20
Figure 9. Hydrograph comparison for Tropical Storm Allison at the Bayou Des Allemands gage.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 21
Figure 10. Hydrograph comparison for Gustav-Ike; upper left: Bake Canal South; upper right: Chackbay; lower left: Brazan Canal; lower right: Bayou Des Allemands gage. Gage locations are referred to in Figure 5.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 22
Figure 11. Hydrograph comparison for a nontropical storm (2009); upper left: Bake Canal South; upper right: Chackbay; lower left: Brazan Canal; lower right: Bayou Des Allemands gage. Gage locations are referred to in Figure 5.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 23
Figure 12. Hydrograph comparison for Isaac; upper left: Bake Canal South; upper right: Chackbay; lower left: Brazan Canal; lower right: Bayou Des Allemands gage. Gage locations are referred to in Figure 5.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 24
Table 2. Modeled and measured peak stages (feet).
Non-tropical Storm Allison (2001) Gustav (2008) Ike (2008) Isaac (2012) Gage (2009) Modeled Measured Modeled Measured Modeled Measured Modeled Measured Modeled Measured Des 2.3 2.3 2.7 2.3 3.7 4.4 2.9 2.2 3.4 3.2 Allemands CRMS0273 2.8 3.1 3.9 4.8 2.7 2.7 3.6 4.2 CRMS3169 2.8 3.1 3.9 5.0 2.7 2.4 3.6 4.5 CRMS0241 2.9 3.1 4.1 4.7 2.8 3.0 3.8 4.1 CRMS0192 2.7 2.5 3.5 3.3 3.0 3.0 3.4 3.8 CRMS0206 2.3 NA 3.2 NA 3.4 2.9 3.5 3.3 CRMS0218 2.3 2.6 3.2 3.0 3.4 3.6 3.5 3.4 CRMS5672 2.5 2.9 3.1 2.9 3.5 4.3 3.6 3.8 CRMS0200 3.0 2.9 3.7 3.1 4.1 4.3 4.2 3.7 CRMS0268 2.2 2.2 3.2 2.8 3.2 3.5 3.4 3.3 CRMS0197 3.0 3.4 3.7 3.4 4.1 4.7 4.2 4.2 CRMS0217 2.7 2.5 3.2 2.7 3.5 4.0 3.4 3.5 CRMS5116 3.0 2.7 3.8 2.8 3.9 4.2 4.0 3.6 CRMS3116 2.2 3.1 3.4 3.3 2.8 2.8 3.4 3.7 RMSE NA 0.4 0.6 0.4 0.4
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 25
Climatology and Statistics
JPM-OS ANALYSIS The JPM-OS methodology applied in the 2012 Coastal Master Plan (Johnson et al., 2013) was developed for coastal storm surge induced flooding. Subsequent discussions with CPRA, LBLD, the Institute, RAND and others identified a need to adjust the stage frequency analysis for UBB to account for flood stages from combined rainfall and storm surge because of the regional climatology and slow-draining terrain in the basin. Two different storm populations generate potentially significant flooding in coastal areas along Gulf Coast of the northeastern United States: storms of extratropical origin and storms of tropical origin. In general, storms occurring from late December through May can be regarded as extratropical in nature, while storms in the remaining months can be a mixed of either tropical storms or extratropical. Thus, three populations of storms need to be considered in flooding analyses for June through November. Given that these three populations can be considered as mutually exclusive (i.e., never occurring simultaneously), the combined cumulative distribution for the exceedance function can be written as
ˆ ˆ ˆ 1. F(comb )1 AEP extr AEP trop 1[1()][1()] F extr F trop where denotes the surge level with subscripts “comb,” “extr,” and “trop” indicating the combined, extratropical and tropical events, respectively, and Fˆ() denotes the exceedance probability.
Three different situations can create flood hazards in the study area: (1) storm surges with no significant rainfall accompanying the storm; (2) rainfall with no significant coincident surges; and (3) rainfall that occurs in conjunction with storm surges along the coast. Since additional surge simulations for extratropical storms were not possible within the scope of this work, it will be assumed that surges from extratropical storms do not significantly affect flooding in the study area. For the case of tropical storms, the conditional probability of rainfall in the summer will be based on a partitioning of the extreme rainfalls during the June through November interval, with 85% of the extreme rainfall coming from tropical systems and 15% of the rainfall coming from extratropical systems. The partitioning of the extreme rainfalls has been determined from evaluating events that cause high stages at the Des Allemands gage.
As with previous JPM developments, the analysis is restricted to only the peak flood conditions. However, the sequences of rainfall are also important for the inland hydrologic modeling. For the case of only rainfall, incorporation of many different simulations with varying spatial and temporal rainfall characteristics would be required to develop the complete statistics. Whereas this might be the approach that can eventually be used for this type of study, presently there is insufficient spatial and temporal information on storms to allow such an analysis within the study area. Instead, the approach here uses 24- hour rainfall to categorize the flooding potential and distributes this rainfall over an interval of time based on a Gaussian distribution of rainfall through time with different standard deviations in the rainfall before and after landfall. The Gaussian functions used to characterize this distribution through time were derived from an analysis of hourly radar rainfall totals integrated over the UBB area. This reduces the probability
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 26
ˆ requirement for rainfall-only dominated events to a representation of F()extr for winter only storms and 15% of the summer storms and produces an event-based scenario for modeling with the surge boundary condition in the hydrologic model.
For flooding also influenced by significant storms surges at the coast, a more general approximation to the joint probability function for local flooding was utilized
2.p( max ) pR ( max | 24 , surge ,,...,)( zzpR 1 n 24 , surge ,,...,) zzR 1 n 24 , surge ,(,...,) zz 1 n where
R24 = 24 hour rainfall
surge = sequence of water level deviations due to storm surge at the hydrologic model boundary; zz1,...., n = other factors that are found to influence flood levels within the study basis.
Antecedent Conditions The antecedent water level, or the water level in the basin three days prior to significant historical flooding events affects the peak water level that occurs during the historical storm event. This antecedent water level before each historic flooding event (tropical or extratropical) was often caused by a major rainfall event or a preceding tropical event, when the basin did not have enough time to drain to average ambient conditions prior to the onset of the significant flooding event. As shown in Figure 13, there is a strong (statistically significant) relationship between the antecedent water level at the Des Allemands gage and the subsequent peak at this gage. A JPM study such as this one for the upper Barataria basin has to jointly account for any variation that affects water levels in the study basin and for correlations among parameters. In this case, the significant correlation implies a causal relationship that is consistent with the observed very slow drainage rate of the upper basin. The slope of the line is actually greater than 1:1, so neglecting the antecedent conditions could lead to a significant misestimate of the water levels in this basin. If the mean value of about 1.7 ft were chosen and applied to all simulations, it would overestimate water levels in the low range of values and underestimate water levels in the high range of values. Picking either the highest or lowest “typical” values would lead to even more significant misestimates at the opposite range of values. Thus, the only physically and mathematically viable course was determined to include this effect by running a range of antecedent conditions, which should produce a natural correlation of the type observed in Figure 13. To cover the relevant range of conditions seen in the historical flooding events in the basin included in this study, 1 ft, 1.5 ft, and 2-ft (NAVD88) antecedent conditions were analyzed.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 27
Figure 13. Effects of antecedent water level on peak water level at Bayou Des Allemands gage. Black squares indicate historical data points; the black line indicates a linear regression of the data; and the orange line indicates a 1:1 line.
The inclusion of antecedent water levels into this analysis means that two additional dimensions exist within the JPM approach due to incorporating both rainfall and antecedent conditions as additional factors significantly affecting flooding levels. Since the initial JPM for hurricane surges contains five dimensions, it already contains 5 storm-related parameters which must be treated probabilistically: storm intensity, storm size, storm landfall location, storm track angle at landfall, and storm forward translation speed, which requires typically 152 -storms or 304 storms to represent in the JPM integration (USACE, 2008). If two additional dimensions are included for all of these individual storms, it would increase the number of runs by a factor of nine (using only 3 antecedent conditions and 3 rainfall conditions). Instead, an approach was used which considered larger probability masses for the hurricane parameters used in this study to cover the range of parameters important to flooding in the probability range 10 years to 500 years and will use fitted extrapolations to extend the results to rarer events. In essence, this was accomplished by examining the storm surge peaks from these storms at Des Allemands and limiting the storms to a subset with exceeded the 10-year surge level and fell below the 500-year level at the Des Allemands site. The basis of storm selection was achieved using a somewhat evenly spaced range of surges at this gage. This is not equivalent to individual probability masses for the original surge events that were modeled, since the number of storm cases are reduced by about a factor of 10. For this study, the 20 storms selected represent the general pattern of storm landfall, intensities and sizes in along the coast in the vicinity of
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 28
Grand Isle. The probability masses for these storms in the JPM-OS method used to define surge probabilities for this area are the same as used in previous JPM-OS studies for this area. These probability masses have to be converted to equivalent probability masses for the larger ranges of parameter increments that they represent, while maintaining their same relative likelihood. This is done by normalizing the relative annual probabilities to sum to one and rewriting the JPM integral as follows:
20 15 3 3. F() p (|)(, RpR ,)(, zpR ,)[() zH ) dRdz maxsurge max 24 24 surge a 24 surge a max 24 1 surge , k k1 00 where
F(max )surge is the cumulative distribution function of flooding when surges are significant Hx( ) is the Heaviside function of x ( ) is the numerical model used to convert parameters to flood levels za is the antecedent water level at the start of an event th surge, k is the increment of complementary probability of the k event.
Both the rainfall and the antecedent water levels can be treated as continuous variables as shown in the form for this integral; however, the increments for the events are discretized (i.e., a given set of 20 individual storms). When the rainfall is zero, it can be applied to all 20 events. The distribution for the summer rainfalls was obtained from analysis of a long-term (1947-2008) hourly record at Baton Rouge. Table 4 contains the equation for the summer cumulative frequency distribution and estimates. Since New Orleans is more exposed to the direct effects of the sea-to-land transition of the winds and potential associated effects on rainfall, Baton Rouge was selected as the long-term station for analysis even though is slightly farther away from the upper Barataria basin. Since these values are required to be analyzed for separate seasons, they are not available in conventional sources which give the expected rainfall for selected return periods and durations for the entire year. When the surges are not significant, the two remaining contributions to flooding can be quantified from consideration of rainfall with no storms in the summer. As can be seen in Appendix B, twenty two tropical event exceeded a cutoff threshold of 2.5 feet (NAVD-27) at the Des Allemands gage and only 4 non-tropical events exceeded this threshold. Thus, approximately 85% of the summer June-November rainfalls are associated with tropical systems and 15% with non-tropical systems during the June-December interval. It is assumed here that all of the December-May rainfall is produced by non-tropical events, with comparable information to Table 4 given in Table 5). Note that, even if the peak were slightly beyond the time limit for the event, if the water levels exceeded the threshold in the June through November period, they were included into the “June-November” analysis.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 29
Table 3. Selected synthetic tropical events.
Parameters Rainfall Intensity Antecedent PBL Rainfall Conditions (water Cp Radius Vf Rainfall Storm# Track ID intensity elevation, feet, (mbar) (nm) (knots) events (inch) NAVD88) 1 960 11.0 E1(A 00) 11 A1 1.0 2 960 21.0 E1(A 00) 11 No rainfall 0 3 960 35.6 E1(A 00) 11 A2 1.5 Medium 4 930 8.0 E1(A 00) 11 4 rainfall 5 930 17.7 E1(A 00) 11 A3 2.0 High 6 930 25.8 E1(A 00) 11 8 rainfall 11 960 21.0 E2(A 00) 11 A1_future 2.5 Medium 12 960 35.6 E2(A 00) 11 4 rainfall 1 13 930 8.0 E2(A 00) 11 A3_future 3.5 Medium 14 930 17.7 E2(A 00) 11 8 rainfall 2 15 930 25.8 E2(A 00) 11 Medium 16 900 6.0 E2(A 00) 11 8 rainfall 3 412 975 35.6 W4(A 00) 11 Medium 415 975 35.6 W5(A 00) 11 12 rainfall 4 423 975 24.6 W4(A-45) 11 Medium 449 975 17.7 W5(A 00) 17 8 rainfall 5 503 975 35.6 E1(A 00) 11 Medium 506 975 35.6 E2(A 00) 11 12 rainfall 6 517 975 24.6 E1(A-45) 11 521 975 24.6 E3(A-45) 11
The June through November data was very well fit by a Gumbel distribution: ˆ eR ˆ Ra24 0 F() R24 e with R, where a 4.3757 and b 1.635 . b 0 Estimated values of 24-hour rainfall totals for selected returns are shown in Table 4 below:
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 30
Table 4. June – November extreme 24-hour rainfall.
Return Period ( years) Estimated Rainfall (inches) 25 9.61 50 10.76 100 11.90 500 14.53
The December through May data was very well fit by a Gumbel distribution: ˆ eR ˆ Ra24 0 F() R24 e with R, where a 3.4011 and b 1.6062 . b 0 Estimated values of 24-hour rainfall totals for selected returns are shown in Table 5 below:
Table 5. December – May extreme 24-hour rainfall.
Return Period ( years) Estimated Rainfall (inches) 25 8.54 50 9.67 100 10.79 500 13.38
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 31
Flood Risk Due to Combined Rainfall and Storm Surge
RAINFALL ASSOCIATED STORM EVENTS Based on the climate and rainfall statistics described in the previous section, storm events identified to model include 20 synthetic storms with various rainfall intensity including no rainfall condition and rainfall only with the high rainfall rate. Figure 14 is a diagram showing the storm events, defined by varying antecedent conditions, rainfall intensities, and water level conditions in the lower basin related to tropical storms. Table 3 listed the synthetic storms selected from the FEMA flood insurance study modeling analysis (USACE, 2008), which are depicted on Figure 15 showing different tracks and approaching angle to the coast. Table 3 combined with Figure 14 and the rainfall intensity definitions from Table 6 provides the total storm set used in this study.
Figure 14. Event diagram; top: antecedent conditions; middle; rainfall intensity; bottom: synthetic storms. In this Figure, the definition of the different rainfalls is given in Table 6 below.
Table 6. Rainfall intensity definitions.
Rainfall Definition Medium Rainfall 4 inches of rain distributed according to a Gaussian form High Rainfall 8 inches of rain distributed according to a Gaussian form Rainfall intensity 1 4 inches of rain distributed with constant rate Rainfall intensity 2 8 inches of rain distributed with constant rate Rainfall Intensity 3 8 inches of rain distributed with rate 3 times higher before landfall Rainfall Intensity 4 12 inches of rain distributed with rate 3 times higher before landfall Rainfall Intensity 5 8 inches of rain distributed with rate 3 times higher after landfall Rainfall Intensity 6 12 inches of rain distributed with rate 3 times higher after landfall
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 32
31.0 E3(A-45)
E1(A-45) 30.5
30.0 W4(A-45)
29.5
Latitude (dgrees) Latitude 29.0
W4(A 00) 28.5 W5(A 00)
E1(A 00) 28.0 E2(A 00)
-93.0 -92.5 -92.0 -91.5 -91.0 -90.5 -90.0 -89.5 -89.0 Longitude (dgrees)
Figure 15. Selected synthetic storm tracks
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 33
H&H MODEL PRODUCTION RUNS
Model Setup The validated H&H models were utilized to determine peak stages at 127 locations for each modeled event. In this analysis, 20 synthetic storms and nine rainfall rates including “no rainfall” condition were considered for three antecedent conditions. In all, 71 storm events for each antecedent condition were simulated for a total of 213 storm events simulated to support the JPM-OS analysis for existing conditions. The hydrographs at the downstream boundary locations were extracted from corresponding synthetic storms (USACE, 2008). The ADCIRC output from the synthetic storms was modified as needed to extend the duration of the output and incorporate antecedent conditions. Each storm event is named with three markers including the FEMA study storm number, rainfall rate label, and antecedent condition label. Batch processing of H&H models was set up to reduce human interaction with the modeling process, and thus to reduce both model production time and human error. Model outputs including hydrographs and peak stage elevations were reviewed for quality control. For future scenario simulations, the same model setup was applied, with adjustments for subsidence and SLR. However, only the first and third antecedent conditions were employed as prefilled initial water levels to provide upper and lower bounds for flood responses. Hydrographs at the HEC-RAS model downstream boundary were extracted from the 2012 Coastal Master Plan Less Optimistic Scenario synthetic storm simulation results. In total, 142 storm events were simulated for the “future without action” scenario. Peak stages for the second antecedent condition were interpolated from the first and third antecedent future scenario HEC-RAS model runs and incorporated into the statistical analysis.
H&H Modeling Results The peak stage calculated in the HEC-RAS model is uniform for each storage area. Hence, the model SAs were initially defined by USACE and revised for this study to define areas where limited variation of the peak water surface elevation is expected. Figure 16 shows each storage area used to tabulate HEC-RAS model output and JPM analysis results.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 34
Figure 16. HEC-RAS model domain and labeled storage areas.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 35
Figure 17 shows an example model output of peak water surface elevation for existing conditions considering synthetic storm 001 combined with medium rainfall 1 and an antecedent condition of 1 ft. Peak stages in the model domain generally range from 0.5 ft to 7.5 ft except areas inside local levees. Areas inside local levees have low- to negative peak stages (e.g. blue areas in Figure 17). The storage areas with the highest peak stages are on the seaward end of the model domain and in the upper basin, e.g. the north corner by the railroad and US 3127 (Figure 1 for locations) and the south-west corner in Labadieville and Thibodaux. It is intuitive that a high stage at the seaward end of the domain is associated with the coastal storm surge. However, the peak stages in the some storage areas in the upper basin appear to be unrealistic when compared to ground elevations and are likely caused by the limitations of the model. Some connections between storage areas in the USACE HEC-RAS model were absent, or multiple connections were aggregated into one connection; for instance, storage areas 9a, 9b, 9c, 9d, 9e and 4C-1. Those areas in the north eastern corner presenting high stage, e.g. orange to red areas in Figure 17 are significantly higher in ground elevation than the adjacent wetlands and may not be flooded under actual real world conditions. But due to limitations of the model output, it is difficult to determine the actual flooding conditions in these limited areas with certainty. Those areas are not affected by coastal storm surge, but experience only rainfall runoff. Because of the missing connections from these high- areas to channels and storage areas, rainfall accumulated with no outflow. The water levels from the adjacent storage area were substituted in the flood risk analysis. Model limitations are discussed in more detail in the “Limitations of Modeling” and “Flood Risk” sections.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 36
Figure 17. Peak stage of storm001 associated with the medium rainfall rate 1 and the first antecedent condition of 1.0 ft., NAVD88.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 37
The effect of antecedent water levels on peak stages varies within the model domain. With a higher antecedent water level, the peak stage may be higher or remain the same. Figure 18 shows the difference between antecedent water levels 1.0 ft and 2.0 ft under existing conditions for the synthetic storm 001 with Medium Rainfall 1. Positive values represent an increase in peak stage due to a higher antecedent water level of 2.0 ft. Generally, storage areas in the upper portion of the basin, inside local levees, and at the seaward end of the model domain display little or no increase in water level as a result of higher antecedent conditions. The middle basin shows an increase in peak stage by 0.2-0.6 ft. The exception is SA21Bb, which may be caused by model limitations.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 38
Figure 18. Effects of antecedent conditions (2.0 ft NAVD88 minus 1.0 ft NAVD88) using Storm001 combined with medium rainfall 1.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 39
The HEC-RAS model results show that in the upper portion of the basin, many areas are only affected by rainfall, with little or no variation in peak stage with respect to different synthetic storms at the lower basin boundary. Using synthetic storm 001 as an example, Figure 19 illustrates the effects of rainfall. The top panel shows the stage difference between storm events with and without rainfall. The positive values represent an increase in peak stage due to the inclusion of rainfall. The bottom panel shows the stage difference between two rainfall-intensities associated with one surge event, synthetic storm 001. Positive values indicate the increase in peak stage due to a higher rainfall intensity. As illustrated in the Figure, the north to north-east portion of the basin is most affected by rainfall, while the effects on other areas are moderate. The remarkable increase in peak stage in some portions of the lower basin may be due to limited connectivity to surrounding storage areas.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 40
Figure 19. Effects of rainfall intensity. Top: surge combined with medium rainfall 1 minus no rainfall (surge only); bottom: surge combined medium rainfall 2 minus medium rainfall 1.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 41
To examine the effects of coastal storm surge on flood risk within the study area, the difference in peak stage between with and without coastal storm surge boundary conditions was calculated and is presented in Figure 20. Positive values represent higher peak stages due to storm surge and rainfall in comparison to the case of rainfall only. Figure 20 shows that the areas south of Highway 90 are significantly affected by coastal storm surge, as is expected. Inspection of the model results like those in Figure 20 indicated that the interior basin is affected by both surge and rainfall, the upper portion of the basin and the surrounding high-elevation areas are dominated by rainfall, and the effects of storm surge in the lower portion of the basin are predominant.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 42
Figure 20. Effects of storm surge (Storm001), difference in peak surge (feet). Peak stage due to storm surge and high intensity rainfall minus peak stage due to high intensity rainfall only.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 43
Limitations of Modeling Several limitations are noted in the UBB HEC-RAS model:
1. HEC-RAS is a one-dimensional model that does not consider wind effects. Wind and wave set up along the ridges and levees within the study area is not factored into the model results. This could underestimate flood depths.
2. In the original Donaldsonville to the Gulf HEC-RAS model and the modified UBB HEC- RAS model, most of the levees in the model are set at an infinite elevation with no overtopping or levee failure is modeled and breaching is assumed not to occur. This could underestimate flood depths inside the storage area as the model prevents any overtopping from occurring. The frequency curve for a storage area was adjusted when overtopping or levee failure was projected to occur.
3. Several connections between storage areas in the USACE HEC-RAS model were absent, or multiple connections were aggregated into one connection. Some connections may have been added during model validation for model stability. The treatment of connections in the USACE model may influence model output, e.g. absent connection may cause an unrealistic high stage for a storage area, which would not be the case should the connection be implemented. Lack of connections would overestimate flood depths within a storage area and possibly underestimate flood depths in the downstream storage area. If the connections were too big, they would underestimate flood depths within the storage area and possibly overestimate flood depths in the downstream storage area. Where it was evident that there were connection issues for a storage area, the frequency information from adjacent storage areas was used. 4. Antecedent conditions may not be completely represented in the modeling. The RAS model downstream boundary may still experience effects of antecedent conditions. Using ADCIRC simulation results as the boundary condition misses the signal of rainfall runoff from the upstream, which could raise the surge level at the boundary higher in comparison to the ADCIRC surge-only simulation results. However, synthetic storms were not rerun with the inclusion of the antecedent condition in the ADCIRC model for this study; boundary conditions were not revised for antecedent conditions. Thus the effects of antecedent conditions were not fully realized by the UBB RAS model. This may have underestimated the flood depth.
5. For some of the storms modeled in ADCIRC, the duration of the event was insufficient to completely capture the timing of the peak stage in the study area. The ADCIRC hydrographs were extended as necessary to develop the HEC-RAS downstream boundary stage hydrograph. This may have underestimated the flood depth. Where it was evident that the storm hydrograph duration was insufficient, it was extended.
6. The modified JPM-OS procedure was applied using a small number of storms producing storm surge. Further, the joint rainfall and surge analysis only produced estimates of flood depth exceedances from the 5- to 200-year return periods; extrapolation was used beyond the 200-year return period. In addition, the JPM analysis only considers positive values in its computation. Frequency curves were revised to account for the limitation.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 44
7. The new modeling approach used here generally produced greater flood depths at a given return period than have been produced using the 2012 Master Plan CLARA v2.0 model without consideration of rainfall, but we cannot confidently conclude that this is due to the inclusion of rainfall. A full comparison of differences in model procedures, storm set inputs, etc., is beyond the scope of this study. In some locations, the modified JPM-OS produced lower exceedance values; see Appendix G for more detailed comparisons of flood depths between the two approaches.
FLOOD RISK HEC-RAS model results for existing and future without action conditions were included in the JPM analysis. Frequency stages for frequencies ranging from 5- to 200-years were estimated. The frequency stages directly output from the JPM-OS analysis increased in a stepwise fashion. They were then fitted to a logarithm function for a continuous frequency stage curve for all 127 storage locations in the study area. However, for some areas, the frequency stages did not increase for the 5- to 200-year return periods and cannot be significantly fitted to a logarithm function because of both the HEC-RAS model limitations and JPM model limitations. For these storage areas, stage frequency curves from adjacent areas were substituted. It was also apparent, from review of JPM and HEC-RAS model results, that the issues with USACE HEC-RAS model affected the frequency curves for several storage areas. For example, storage area 4C-1 had no connection with adjacent channels or storage areas. Rainfall accumulated with no outflow; the 5-year stage was over 3 ft higher than the ground elevation. These areas required revisions based on best engineering judgement and information from adjacent storage areas. For the areas into which water should be moving, the stage is underestimated since they are downstream of the overestimated high ground upper stream areas. However those areas are also connected to their downstream areas as a more extensive receiving basin, and would be affected by coastal storm surge as well. Without fine modeling it is not practical to adjust the stage but reasonable to note the limitations of the model. For the leveed reaches within the UBB model where the elevation of the levee is set infinitely high, there are instances where the peak exterior water level from an adjacent storage area within the model exceeds the actual elevation of the top of the levee present in the study area. . Assessing the water level inside storage areas where existing local levees are present can be difficult. The volume of water entering the leveed reach depends on the height of the exterior water level, the presence of waves, and whether breaching occurs. Both updating the model treatment of existing local levees and computing the fragility of the levees to determine timing and extent of breaching is beyond this scope. Two sets of frequency curves have been developed for the local levee reaches for both the existing and future conditions. For the first curve, it is assumed that surge enters the leveed reach only when the exterior still water level is higher than the levee height. When the exterior still water level is below the top of the levee, wave overtopping is not considered to contribute to the interior water level. The second curve is based on the assumptions for overtopping/failure used by USACE in the Donaldsonville to the Gulf feasibility study (USACE, 2011). The frequency curves presented in the USACE report assume overtopping or failure at the exterior stage associated with the 50-year return period. Exterior frequency values are used beginning at the 50-year return period for the upper portion of the frequency curve.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 45
For the portion of the two curves where there is no overtopping, rainfall was used as the source of water within the leveed reach. USACE modeled all of the rainfall return periods, from 1-year through 500-year. Many of the leveed reaches have ground elevations below zero, and peak stages from rainfall may be negative values. Since the JPM analysis cannot manage negative values, the USACE rainfall frequency curve is adopted as a more accurate representation of flood hazard with no overtopping within the local levee reaches than the results of the JPM analysis. Therefore, the USACE rainfall frequency values were adopted for the portion of the frequency curves where there is no overtopping. Table 7 lists the storage areas that were revised using the values from the exterior basins, from the adjacent basin, or using USACE (2011) results.
Table 7. Storage areas with revisions.
SA revised Existing condition Future scenario Revision Note Revision Note 11a2 use Brazan Canal reach use Brazan Canal reach 11a3 use Brazan Canal reach use Brazan Canal reach 11c use Brazan Canal reach use Brazan Canal reach 4C-1 use 4C-3 use 4C-3 4C-10 use SA C-1 use SA C-1 4C-20 use 4C-2 use 4C-2 9a use differences from 9e applied to existing conditions 9b between highway 3127 and railroad, use use differences from 9e applied to existing 9a conditions 9c use 4C-2, ground elevation - 5.64 use differences from 9e applied to existing conditions 9e between highway 3127 and railroad, use use 4C-2 4C-2 9f between highway 3127 and railroad, use between highway 3127 and railroad, use 4C-2 Verret LB-01 Use Lake Boeuf use Lake Boeuf R11 bounded, high ground = 4.7 bounded, high ground in future = 3.7 RL-1 levee = 5.5, use SB-04 levee = 4.5 future, use SB-04 RL-10 levee = 3.5, assume near RL-8, use levee = 2.5 future, use updated RL-8, updated RL-8 RL-2 levee = 5.5; use SB02 for 50-year under; levee = 4.5 future, use SB03 use USACE (2011) for the rest RL-3 levee = 4, use GB-05 for 200-year under; levee = 3 ft future, use GB-05 use USACE (2011) for the rest RL-4 levee = 4; use SB-10 for 200-year under; levee = 3 future, Use SB-10 use USACE (2011) for the rest RL-5 levee = 4; use SB10 for 200-year under; levee = 3, Use SB-10 use USACE (2011) for the rest RL-6 levee = 4; use GB-07 for 200-year under; levee = 3 future, Use GB-07, use USACE (2011) for the rest RL-7 levee = 5.5; levee = 4.5 future, use GB-04
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 46
SA revised Existing condition Future scenario Revision Note Revision Note RL-8 levee = 3.5; use GB-06 for 30-year under; levee = 2.5 future, use GB-06 use USACE (2011) for the rest RL-9 levee = 3.5, use GB-08 35-year under; use levee = 2.5 future, Use GB-08, USACE (2011) for the rest SA22A levee=4.5, change 250-year under; use levee=4.5 future, Use sA19E1 USACE (2011) for the rest SA23A-01 levee=5, change 400-year under; use levee=4 future, Use SA23B, USACE (2011) for the rest SA23A-02 levee = 5.5 no overtopping, revised to levee = 4.5 future, use SA23B, USACE SA23A-03 levee=6.5; no overtopping; revised to be levee=5.5 future, use SA23B > 5.5 in line with USACE SA23A-04N levee = 6; no overtopping; revised to be in levee = 5 future, use SA23B > 5 line with USACE SA23A-04S levee=4.25; revised to USACE when levee=3.25 future, actual results < 4.31ft SB-01 use SB-02 use SB-02 SB-03 use SB-10 use SB-10 SB-04 use SB-10 use SB-10 SB-05 use SB-10 use SB-10 SB-07 use SB-10 use SB-10 SB-08 use SB-06 use SB-06
In 2065, the two frequency curves produce very similar values in all storage areas. For this reason, and because it is more conceptually realistic to assume failure based on depths reaching a particular point relative to the levee crest rather than a particular point in the flood depth probability distribution, damage results are presented only for the first frequency curve. The Tables F-1 and Table F-2 in Appendix F tabulate the existing condition frequency information for two overtopping scenarios previously described. Each row includes frequency stage information for a storage area; shaded rows contain frequency information that has been revised. Table F-3 and Table F-4 show the return stages for the future scenario.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 47
Damage and Benefits
PROJECT ALTERNATIVES The Phase 1 study assessed multiple project alternatives by simulating future with and without project conditions and directly comparing damages, costs, and benefits (Roberts et al., 2014). Based on project performance in Phase 1 and anticipated performance based on the combined rainfall and storm surge flood hazards, three of the alternatives have been carried forward to this analysis. The three alternatives include Highway 90, Ridge North + East, and Ridge South, each with some adjustments to alignments and costs following the Phase 1 study. Alternative alignment, design elevation and cost adjustments are discussed in detail in the following section. The project alternatives are shown in Figure 21. This analysis differs from the Phase 1 study not only by including rainfall in the flood hazard assessment but also in the cost effectiveness evaluation; this study was designed to estimate an upper bound on the benefits of project alternatives rather than a direct assessment of project performance. An upper bound has been determined by assuming maximum benefits in the protected area of each alignment; the damages for assets in protected areas are evaluated assuming that that all future damages could be prevented for some flood hazard return periods. Project alternative assumptions for this phase include that pumping systems will eliminate rainfall-based flooding in each storage area and that the project levees and pumping systems combine to successfully eliminate surge-based flooding in the areas they are designed to protect. Additionally, the project alternatives include costs to lift existing levees and prevent induced damages within HSDRRS on the west bank of the Mississippi River, where induced flooding was found to occur in Roberts et al., (2014). The HSDRRS levee reaches that require lifts are shown in Figure 21. Though the project benefits as a result of these assumptions are not fully achievable, the assumptions allow for the estimation of the maximum risk reduction and maximum cost effectiveness associated with the alignments.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 48
Figure 21: Proposed Project Alignments (Highway 90, Ridge North+East, and Ridge South) and HSDRRS Improvement Reaches.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 49
COST ESTIMATION For Highway 90, Ridge North+East, and Ridge South, the same cost estimation assumptions as described in the Phase 1 analysis were applied here, and applied to future conditions (Roberts, et al., 2014). Each of the three alternatives was modified as follows.
Highway 90 The revised cost estimate for this analysis includes a lift of certain existing West Bank HSDRRS levees. The section of levee requiring a 1 foot lift runs from the Western Tie-In of the HSDRRS system to the Mississippi River and Tributaries (MR&T) West Bank levee at the Union Pacific Railroad Crossing, which is West Bank and Vicinity (WBV) project No. 77 and continues east, crossing the BNSF Rail Road (WBV-75), US Highway 90 (WBV-73), a navigation sector gate (WBV-74) and ending nearly at the Highway 90 pump station (WBV-76). This length of earthen levee includes WBV-71 and WBV-72 for approximately 19,950 linear feet of earthen levee. Non-earthen levee features such as the road, railroad, and navigation gates, plus their concrete t-wall tie-in structures, were originally built with several feet of structural superiority, and thus, no elevation of those structures is assumed for the purposes of this analysis. The section of levee requiring a 0.5 foot lift runs from WBV-76 east to the Lake Catahouatche Pump Station (WBV-15b.2), and includes the earthen levee segments of WBV-17.2 and WBV-18.2 for approximately 14,400 linear feet of earthen levee. The 1.0 foot and 0.5 lift sections are shown in Figure 22. The year 100-year elevation along these reaches is 15.5 feet NAVD88 according to Master Plan future (year 50) condition levee elevations as well as the Army Corps’ lift schedules. As-built cross sections from USACE were assessed and typical representative sections were used as the existing cross sections upon which the incremental lift height would be added. The existing USACE lift schedules were used for these reaches and altered to represent the additional height needed to address any inducements since no 2012 Master Plan methodology existed for calculating or assuming overbuild for relatively small lifts. In general, these reaches of earthen levee are comprised of stability berms on the protected side with shallow 1V:20H slopes, 1V:3H on the front and back slopes, 10 foot crown width, and a flood-side wave berm of a 1V:24-32H slope. There are no gates or concrete wall elevation costs included in the estimate described in this document. It was additionally assumed that a straddle lift would be used to accomplish the lift. High Performance Turf Reinforcement Map (HPTRM) armor was assumed as part of the lift cost for the first lift only. It was assumed to extend from 2 feet below the crown on the flood side to 15 feet beyond the back slope/stability berm inflection point on the protected side. It was assumed that Southeast Louisiana Flood protection Authority-West (SLFPA-W) would cover future costs of removing and replacing HPTRM armoring during future scheduled lift events. No additional line item costs such as clearing and grubbing, land rights, wetland mitigation, or property acquisition were assumed, instead a 30% contingency is added to the estimated construction cost as is normal for 2012 Master Plan methodology. The change is an additional cost of $53,000,000 in addition to the $366,000,000 Highway 90 total cost described in the Phase 1 analysis. The Phase 2 total coast for the Highway 90 alternative is $419,000,000.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 50
Figure 22: HSDRRS Improvement Reaches.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 51
Ridge North+East Similar to the Highway 90 alignment, the revised estimate for this analysis includes a lift of certain existing West Bank HSDRRS levees. The lengths, lift height, and governing assumptions are all identical to those for the Highway 90 alternative. The change is an additional cost of $53,000,000 in addition to the $194,000,000 (Ridge North) and $307,000,000 (Ridge East) described in the previous analysis. The Phase 2 total coast for the Ridge North+East alternative is $554,000,000. Ridge South The alignment originally recommended by CB&I in the Phase 1 study was used for the purposes of this study. It has a total length of approximately 296,000 feet in the ArcMap shape files provided. Based on the HEC-RAS modeling results, the 100-year stillwater elevation is approximately 5.5 feet along the reach. The 5.5 foot 100-year elevations plus the necessary freeboard is necessary for the majority of the alignment (262,000 linear feet), with the exception of the portion of alternative that requires a 10 foot design elevation (34,000 linear feet) as described in the Phase 1 document (Roberts, et al., 2014). The CB&I-provided design elevation of 8.5 feet was used for cost estimation purposes, as it was deemed to contain sufficient freeboard to address sea level rise, wave runup and overtopping considerations. The only changes made from the Phase 1 study to create this alternative cost were to increase the design elevation from 4.5 feet (previous) to 8.5 feet and to calculate the additional earthen fill volume that would be required. No additional changes were necessary based on the 2012 Master Plan cost estimation methodology. The adjusted alignment and design elevations result in an alternative costs of $326,000,000 compared to $132,000,000 described in the previous analysis. Note the large increase is due to both the length of the alignment reverting back to longer alignment in CB&I’s original proposal (resulting in more protected assets in the damage assessment) and the increase in design elevation described above. As part of the cost estimation, both the Phase 1 and Phase 2 costs include the 2012 Master Plan methodology calculation of Engineering and Design (E&D), Mobilization and Demobilization (Mob/Demob), and Construction Management (CM) as percentages of the total construction cost. Since the total construction cost increased in Phase 2 due to the additional length of the alignment and higher design elevations, the percentage-based costs increased accordingly. The Phase 2 total coast for the Ridge South alternative is $326,000,000.
RISK ASSESSMENT Impacts for each proposed alignment have been assessed using CLARA v2.0’s economic module. The study region was restricted to 4,637 grid points that lie within the RAS model domain; points that fall between RAS storage area boundaries were assigned flood depth exceedances from the nearest storage area. This set of grid points is shown in Figure 23 below, with grid points representing permanently wet areas like Lake Salvador excluded.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 52
Figure 23. Thiessen polygons associated with CLARA v2.0 grid points in study region.
As stated in the Introduction, this analysis was designed to estimate an upper bound on the benefits of each proposed levee alignment. As such, the study region excludes areas within HSDRRS on the west bank of the Mississippi River, where induced flooding was found to occur in Roberts et al., (2014). Including HSDRRS in the study region would require a different treatment of levee fragility than was used in assessing failure of local levees in the RAS model (see Flood Risk section). Induced flooding was also ignored in other areas in front of the proposed levee alignments. Instead of running a separate analysis of flood depths with and without the alignments in place, it was assumed that each alignment reduced flood depths to zero at all return periods. Pumping systems were assumed to eliminate rainfall-based flooding in each storage area, and the levees and pumping systems were assumed to combine to successfully prevent surge-based flooding. These are strong assumptions, but they result in an estimate of the maximum risk reduction provided by the alignments, which is then used to produce an estimate of their maximum cost effectiveness. Future population growth and changes in economic assets were projected using CLARA v2.0. The model starts with an updated baseline inventory of structures, relative to the version of the model which was used in the 2012 Coastal Master Plan and previous iteration of the Barataria Basin analysis. The spatial unit of analysis, as shown in Figure 21, is a variable-resolution mesh represented by grid points spaced at most 1 kilometer from each other; where census blocks are smaller than 1 square kilometer, the grid consists of census block centroids, such that each census block is represented by at least one grid point. Estimated structure counts are allocated among grid points using a Landscan data set of daytime and nighttime population counts at a 100-meter resolution; nighttime populations are used for estimating the locations of residential structures, while daytime populations were used for non-residential assets. See Fischbach, et al. (2015) for a full description of the CLARA v2.0 model changes and how economic inventories are calculated. Changes in assets are based on the default population growth scenario used in
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 53
the 2012 Coastal Master Plan, corresponding to a 0.67% average annual growth rate and no change in the proportion of urban and rural populations. Table 8 shows the breakdown of damage by return period for points within the study region; expected annual damage (EAD) is also included in the last column. Total values over the entire study region are also shown; total EAD in 2015 is projected to be $241 million, growing to $993 million by 2065. Note that the study region does not encompass all of each parish, so the table does not illustrate the total risk to each parish under current conditions or a future without action. It only represents a reference point for comparison when discussing project effects. The damage estimates are based on the flood depths resulting from the fragility scenario assuming levee failures when surge reaches the top of the levees.
Table 8. Damage by Return Period and Expected Annual Damage by Portion of Parish in Study Area (No Projects in Place).
Without the proposed levee alignments in place, damage in both current and future conditions is primarily concentrated among commercial and residential assets, as shown in Figure 24. The majority of damage is geographically located in Lafourche Parish, among communities located on the southern ridge of the study area, and in St. Charles Parish, primarily along the Highway 90 corridor from Des Allemands to Luling. The predicted increase in damage from year 2015 to year 2065 is due to a greater probability of flooding combined with projected population growth and economic development.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 54
Figure 24. Expected Annual Damage by Parish and Asset Type.
Three levee configurations were considered: the Highway 90 alignment, the North and all East segments of the Ridge alignment combined, and the Ridge South alignment (see Figure 2 and Figure 3). Risk reduction, expressed as the change in EAD with the levee alignments in place, is summarized by parish in Table 9. The baseline EAD values in the Future Without Action are also provided for reference. With the Highway 90 and Ridge North+East alignments, the large majority of risk reduction occurs in St. Charles Parish. For the Ridge South alignment, risk reduction is almost entirely contained within Lafourche Parish.
Table 9. Expected Annual Damage (EAD), and Maximum Potential Reduction in EAD by Project (2065, Top-of-Levee Fragility Case).
Because the study assumed that each alignment fully reduces risk in the areas they are designed to protect, Table 9 only represents the maximum potential reduction in EAD associated with the project. Assessing
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 55
the probability and consequences of breaches or overtopping was beyond the scope of this study, as was assessing the impact of any induced flooding on damage in front of the levees. These assumptions mean that the Highway 90 project is associated with the greatest potential risk reduction, because it protects the same communities as the Ridge North+East and Ridge South projects, plus additional areas along the southern ridge and upper basin. The Ridge North+East alignment provides greater potential risk reduction compared to the Ridge East alignment alone, but the baseline risk is smaller in the areas not protected by the East reaches (primarily in St. James and St. John the Baptist Parishes). Further, the lack of a suitable point for the Ridge East alignment to tie in to high ground on its western edge means that without it being built in conjunction with the Ridge North levee, the Des Allemands area could be subject to surge runaround that would reduce benefits; a full analysis of potential runaround was beyond the scope of this work, as it cannot be studied using the HEC models.
Cost Effectiveness Table 10 outlines the maximum potential cost effectiveness for each proposed alignment under the 2065 (Year 50) case run with Top-of-Levee fragility assumptions. Cost effectiveness is expressed as the reduction in EAD produced by a given project, relative to the FWOA case, divided by its cost. Because benefits accrue over time, rather than in the single year modeled here, the actual net present value (NPV) of each project is not estimated in this analysis; NPV would also depend heavily on financing mechanisms, the time of implementation, and any nonstructural risk reduction efforts performed in conjunction with the structural projects. As noted previously, costs for the Highway 90 and Ridge North+East alignments include an assumption that HSDRRS reaches on the west bank of the Mississippi River will be lifted appropriately to counteract any induced damage. This lift was not assumed for the Ridge South alignment, given that the previous study of Barataria Basin did not find significant induced flooding effects from that project.
Table 10. Cost Effectiveness (Maximum Potential Reduction in EAD per Dollar of Cost) by Alignment.
Highway Ridge Ridge 90 North+East South Max EAD Reduction $946M $705M $151M Total Cost $419M $554M $326M Cost Effectiveness 2.3 1.3 0.5
The previous study of the Barataria Basin (Roberts et al., 2014) estimated a negative cost effectiveness ratio for the Highway 90 project, due to induced flooding on the West Bank of HSDRRS causing greater EAD with the project than without it. This study does not rule out that possibility. Median estimates of flood depth exceedances on the West Bank of HSDRRS produced by CLARA v2.0 are generally lower than the corresponding exceedances produced by CLARA v1.0 during the 2012 Coastal Master Plan analysis, but induced flooding may still be a concern. Another change from the previous study is the choice of reference year in which to estimate cost effectiveness. The previous effort projected damage in the year 2061, whereas the updated estimates in Table 10 are based on the year 2065, representing four additional years of population growth and economic development.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 56
The cost-effectiveness ratios of projects ultimately selected for inclusion in the 2012 Coastal Master Plan range from 21.3 to -0.1 in the Less Optimistic Scenario; projects not included in the Master Plan ranged from 10.3 to -1.2. Some project concepts were not included because they are mutually exclusive from projects with larger ratios that were selected. The ratios estimated for the proposed alignments all fall into the low end of these ranges. Note, however, that during the 2012 Master Plan process, project selection was based on cost-effectiveness ratios calculated using results from the plan’s Moderate Scenario, which was not run in this analysis. The Less Optimistic Scenario was used for this study. Estimates of project effects have not yet been updated for the 2017 Coastal Master Plan; the above-quoted values are based on projections using CLARA v1.0.
Analysis of Future Large Industrial Projects To assist with the economic analysis, Lafourche Basin Levee District supplied The Water Institute with a list of existing and future large industrial projects. The future projects represented announced investments with planned locations, primarily along the Mississippi River, in the affected basin. LBLD’s request was to incorporate these large projects into the assets at risk projected for future risk calculations. Our analysis focused on the ten “high-value” future projects for which LBLD provided press releases and other accompanying documentation. These projects represent major potential investments in industry in the Upper Barataria Basin. Lists of other existing and future projects provided by LBLD were not incorporated explicitly into this separate analysis, as a) existing projects were assumed to be already incorporated into the CLARA economic asset inventory, b) the names of the future projects not on the “high-value” list generally suggested agriculture or other assets representing relatively little value compared to existing assets, or c) no additional information was available to assess the construction cost or economic value of the non-agriculture future projects. Five of the ten “high-value” projects were removed from consideration in the analysis. Two, the “CF Industries” and “Deep Water Access Megasite” projects, were located outside of the study region. One, the “Mosaic” plant expansion project, was found to have been cancelled in 2013. Another, the “Monsanto Co.” plant expansion project, was found to have been completed in 2010. Finally, no estimates of the cost or value of planned facilities at the “Gavilon Agriculture, LLC” project site could be found; the provided press release only assigned a dollar value to the land purchase. The five remaining projects—the “Yuhuang Chemical, Inc.”, “South Louisiana Methanol,” “NuStar,” “AM Agrigen Industries,” and “Petroplex International, LLC and others”—total $5.3 billion in estimated cost. Each project’s location was assigned to the nearest CLARA grid point, as estimated using the land use map provided by LBLD. All five of the projects are located in the region protected by the Highway 90 alignment. The AM Agrigen and Petroplex projects are located in the region protected by the Ridge North+East alignment. None of the projects is located in the region protected by the Ridge South alignment. The grid points associated with each project were then cross-referenced in the flood depth exceedance data. No flooding was projected in 2015 or 2065 at four of the five sites for any of the return periods explicitly estimated (up to the 200-year return period). At the site of the AM Agrigen Industries project, a small amount of positive flooding was predicted at the 150-year exceedance and beyond. The 500-year flood depth is estimated to be 1 foot.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 57
The study concludes that each of the future project sites is at low risk of damaging flooding, even in 2065 under the Less Optimistic environmental scenario assumptions. It assumes that, even so, the projects will be designed with site-appropriate hardening measures to hedge against future flood risk. As such, it is concluded that the inclusion of these projects in the economic analysis of the previous section would have little to no impact on the cost-effectiveness modeled for each of the proposed levee alignments.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 58
Uncertainty and Sources of Uncertainty Uncertainty is commonly classified into two categories: Aleatoric uncertainty generally applies to statistical uncertainty due to the natural randomness in a process. For discrete variables, the randomness is parameterized by the probability of each possible value. For continuous variables, the randomness is parameterized by the probability density function. Regarding this study, aleatoric uncertainty can be attributed to the JPM statistical analysis. Epistemic uncertainty refers to uncertainty in measurements and models. There are several sources of model uncertainty for this study. HEC-HMS is a mathematical model consisting of equations representing the response of a hydrologic system component to a change in hydrometeorological conditions. HEC- RAS is a one-dimensional open channel flow model consisting of channels and storage areas. Both models are used to represent complex meteorological, hydrologic, and hydraulic conditions across the highly variable and changing coastal landscape of the UBB. The models are limited by their equations and inputs, such as elevation data, rainfall data, and ADCIRC model output used as the downstream boundary. In this study, epistemic uncertainty was not added, as it is not well known for two primary reasons: (1) a number of careful studies over the entire range of extreme values has not been conducted and (2) the characteristics of uncertainty in an areas with discontinuities in the distributions (due to levees being overtopped or failing) cannot be characterized by a continuous function as is typically done for most uncertainty estimates for surges and waves. Given the relatively low variations in water levels with respect to the return period, it is expected that the conventional treatment of epistemic uncertainty would create an increase in the levels of perhaps 0.1 to 0.3 ft in the areas outside of the levees. This uncertainty would have to be considered separately for each different levee in different locations to transform this into a direct impact of this uncertainty on the flooding inside the leveed areas. Another source of uncertainty is the reliability of the local levees. Two scenarios have been considered; the first scenario assumes overtopping/failure occurs at the top of levee, and a second scenario assumes overtopping/failure occurs at the elevation of the existing conditions exterior water level with a 50-year return period. These assumptions can affect the economic analysis. A full engineering assessment of the fragility of the levees is needed to determine the performance of the levees, identify mechanisms for failure, and calculate timing and extent of breaching. As documented in the 2012 Louisiana Coast Master Plan, ADCIRC and UnSWAN uncertainty was quantified by comparing model output to measured data for the two storms used in the model validation, i.e., Hurricane Gustav and Hurricane Ike. For the two storms, the difference in modeled peak water levels and high water marks was in the +/‐2 ft range (2012 Master Plan). For this study, HEC-RAS model output was compared to measured data for the four storms used in the model validation. As noted in Table 2, the difference in model output and measured data at 14 gage locations lie within a +/- 1.1 ft range. Sensitivity tests are a means to assess the effects of uncertainty on the results of the analysis. Should additional analysis be performed for the UBB, sensitivity tests are recommended. Uncertainty also exists in the economic modeling, particularly in projections of future populations and economic assets. As of May 2015, methods and scenarios for projecting future development in the 2017
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 59
Coastal Master Plan have not yet been finalized, so this analysis instead has used assumptions about future development that are consistent with the assumptions made in the previous study of Barataria Basin and the 2012 Coastal Master Plan. The baseline economic inventory of current assets has been updated, but the risk reduction and cost-effectiveness metrics in 2065 have been projected using the same set of assumptions about future growth as were used before.
Conclusion Flood risk in the northern extents of Barataria Basin has been reanalyzed for this Phase 2 of the Barataria Basin Risk Reduction analysis. The Phase 2 approach builds on the Master Plan methodology, and evaluates flood hazards for a combination of storm surge and rainfall. The following changes have been incorporated: 1. The statistical joint probability methods have been modified from previous surge-only approaches to also include the estimation of exceedance probabilities of rainfall-associated flooding events. This revised methodology also includes the effects of antecedent conditions in the study area, i.e., that the water level in the basin prior to a storm event may be somewhat elevated due to a previous storm, and not completely drained from the system. 2. HEC-HMS and HEC-RAS models have been used to model rainfall and surge propagation into the UBB. Existing USACE models for Donaldsonville to the Gulf feasibility study have been reviewed; the HEC-RAS model has been updated by reducing the extent of the domain along the north banks of Lake Cataouatche, Lake Salvador, and Company Canal to Lockport, revising the elevation of Highway 90, revising channel cross-sections of the upper reach of Bayou Des Allemands, adding lateral structures along Bayou Des Allemands, and adding connections between developed high-land areas with adjacent low-land areas. The updated UBB HEC-RAS model has been validated using four historical storms: Hurricane Allison in 2001, a combination of Hurricanes Gustav and Ike in 2008 (referred to as Gustav-Ike), a non-tropical storm in December 2009, and Hurricane Isaac in 2012. 3. Future population growth and changes in economic assets were projected using CLARA v2.0. The model starts with an updated baseline inventory of structures, relative to the version of the model which was used in the 2012 Coastal Master Plan and previous iteration of the Barataria Basin analysis. The reference year in which to estimate cost effectiveness has been changed from year 2061 to year 2065. Costs associated with protection system improvements needed to prevent induced flooding within the West bank portion of the HSDRRS were also included.
In the upper portion of the basin, many areas are only affected by rainfall, with little or no variation in peak stage with respect to different synthetic storms at the lower basin boundary. Antecedent conditions generally have little or no effect in the upper portion of the basin and the areas inside local levees. Surge effects predominate the lower portion of the basin. In the future, surge effects move further inland, and the risk of flooding inside the local levees increases. Some areas along the Mississippi River and Bayou Lafourche will remain affected by only rainfall. In the “future without action” scenario, damage in both current and future conditions is primarily concentrated in areas with a large number of commercial and residential assets. The majority of damage is geographically located in Lafourche Parish, within communities located on the southern ridge of the study area, and in St. Charles Parish, primarily along the Highway 90 corridor from Des Allemands to Luling.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 60
The predicted increase in damage from year 2015 to year 2065 is due to a greater probability of flooding combined with projected population growth and economic development. For two of the three levee configurations considered in the economic analysis, the Highway 90 Alignment and the North and all East segments of the Ridge Alignment combined, (Figures 2 and 3), the large majority of risk reduction occurs in St. Charles Parish (71% for the Highway 90 Alignment and 95% for the North and all East segments of the Ridge Alignment combined). For the Ridge South Alignment, the large majority of risk reduction (99%) occurs in Lafourche Parish. All three of the proposed alignments provide positive results on a Cost Effectiveness (CE) scale. The CE ratios of the Highway 90 Alignment, the North and all East segments of the Ridge Alignment combined, and the Ridge South segments were 2.3, 1.3, and 0.5, respectively. Because this study is intended to determine the upper bound of possible damage reduction, the CE ratios are conservative in nature. Costs for the Highway 90 and Ridge North+East alignments include an assumption that HSDRRS reaches on the west bank of the Mississippi River will be lifted appropriately to counteract any induced damage. This lift was not assumed for the Ridge South alignment, given that it is believed to be unnecessary following the Phase 1 study, which did not find significant induced flooding effects from that project. The cost-effectiveness ratios of projects ultimately selected for inclusion in the 2012 Coastal Master Plan range from 21.3 to -0.1 in the “less optimistic” scenario; projects not included in the Master Plan ranged from 10.3 to -1.2. Some project concepts were not included because they are mutually exclusive from projects with larger ratios that were selected. The conservative CE ratios estimated for the proposed alignments all fall into the low end of these ranges. Estimates of project effects have not yet been updated for the 2017 Coastal Master Plan; the above-quoted values are based on projections using CLARA v1.0. The analysis was designed to estimate an upper bound on the benefits of each proposed levee alignment. As such, the study region assumes approximate project costs to prevent induced damages in areas within HSDRRS on the west bank of the Mississippi River, where induced flooding and damage was found to occur in the previous study (Roberts et al., 2014). Induced flooding was also ignored in other areas in front of the proposed levee alignments. Instead of running a separate analysis of flood depths with and without the alignments in place, it was assumed that each alignment reduced flood depths to zero at all return periods. Pumping systems were assumed to eliminate rainfall-based flooding in each storage area, and the levees and pumping systems were assumed to combine to successfully prevent surge-based flooding. These are strong assumptions, but they result in an estimate of the maximum risk reduction provided by the alignments, which is then used to produce an estimate of their maximum cost effectiveness. The combined effect of these assumptions, and that the Highway 90 Alignment protects not only the same communities as the Ridge North+East and Ridge South projects, but also additional areas in the upper basin, result in the project being associated with the greatest potential risk reduction. In addition to analyzing risk to the baseline economic inventory in CLARA, the team was requested to examine the impact of the proposed alignments to a small set of large planned industrial projects in the basin. All of these specific large industrial development sites were determined to be at low risk of damaging flooding, even in 2065 under the “less optimistic” environmental scenario assumptions. Even so, it is assumed that the projects will be designed with site-appropriate hardening measures to hedge against future flood risk. As such, it is concluded that the inclusion of these projects in the economic analysis of the previous section would have little to no impact on the cost-effectiveness modeled for each of the proposed levee alignments.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 61
FURTHER CONSIDERATION OF THE PROPOSED PROJECTS In order for the alignments discussed in this report to be directly compared to other Master Plan protection projects, the approach used in this study would have to be refined and made adaptable for use throughout coastal Louisiana. This would be necessary to enable consideration of combined rainfall and storm surge flood damages, and projects to address them, in other areas where they are known to influence flood damages. Currently, H&H models are available in Barataria Basin due to the model availability from the previous USACE Donaldsonville to the Gulf Feasibility Study. Although similar models may be available in other areas as a result of localized studies, development may be necessary to replicate the approach used here. A consistent set of tools would need to include H&H models; JPM-OS analysis of rainfall, surge, and antecedent water level conditions for each area being considered; and systematic coupling of the model components to ensure efficient analysis of multiple projects across many scenarios. Due to schedule and budget constraints related to the upcoming 2017 Coastal Master Plan, it is unlikely that such an analysis will be possible in time for projects producing risk reduction damages under the combined effects of rainfall and surge to be considered for inclusion in the Plan. However, this study has identified that the interactive effects of rainfall and surge in poorly draining areas can result in substantial flood damages, and that meeting the objectives of the Coastal Master Plan may require consideration of a broader array of project types.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 62
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 63
Appendices
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 64
APPENDIX A: DATA INVENTORY
Table 11. CRMS stage gages.
CRMS Stage Gages Station ID Longitude Latitude Period of Record Minimum Mean Maximum Stage Stage Stage (ft NAVD88) (ft NAVD88) (ft NAVD88) CRMS0188 -90.13487 29.7928 Aug 2006 - Sep 2014 -0.89 1.08 4.73 CRMS0189 -90.26462 29.80881 May 2007 - Jul 2014 0.58 1.35 5.00 CRMS0190 -90.26473 29.53987 Feb 2010 - Oct 2014 -0.86 1.15 5.89 CRMS0192 -90.51794 29.8625 Mar 2010 - Aug 2014 -0.28 1.50 4.55 CRMS0194 -90.94046 29.99168 May 2008 - Oct 2014 2.18 3.61 5.72 CRMS0197 -90.94585 29.91164 Apr 2008 - Nov 2014 1.07 2.56 4.77 CRMS0200 -90.982 29.96975 Apr 2008 - Nov 2014 0.79 2.33 4.48 CRMS0206 -90.66672 29.85056 Mar 2008 - Nov 2014 -0.53 1.26 3.29 CRMS0211 -90.46285 29.79218 Mar 2010 - Oct 2014 -0.37 1.43 4.52 CRMS0217 -90.87729 29.91662 Apr 2008 - Nov 2014 -0.19 1.67 4.12 CRMS0218 -90.69962 29.76986 Mar 2010 - Oct 2014 0.21 1.72 3.42 CRMS0219 -90.31889 29.80683 Aug 2006 - Feb 2014 0.22 1.28 4.61 CRMS0234 -90.10299 29.79624 Jan 2008 - Sep 2014 1.14 1.71 4.54 CRMS0241 -90.50809 29.71299 Mar 2008 - Sep 2014 0.22 2.09 3.78 CRMS0248 -90.07075 29.60258 May 2007 - Sep 2014 -0.29 1.16 6.38 CRMS0261 -90.10466 29.60199 May 2007 - Sep 2014 -0.46 1.17 5.87 CRMS0268 -90.60291 29.79737 Jul 2006 - Nov 2014 -0.23 1.39 3.60 CRMS0273 -90.31916 29.81229 Sep 2011 - Mar 2013 -0.34 1.08 4.25 CRMS0276 -89.94484 29.61948 Mar 2008 - Sep 2014 0.38 1.36 6.71 CRMS0278 -90.22716 29.81014 May 2007 - Aug 2014 0.07 1.10 5.00 CRMS0287 -90.01427 29.68565 Feb 2008 - Aug 2014 0.66 1.32 7.09 CRMS3054 -90.35783 29.72186 Dec 2007 - Aug 2014 -0.84 1.08 4.70 CRMS3136 -90.63869 29.94953 Apr 2008 - Sep 2014 -0.17 1.27 3.67 CRMS3166 -90.28905 29.85889 Mar 2008 - Sep 2014 -0.69 1.43 5.12 CRMS3169 -90.27183 29.88895 Mar 2008 - Sep 2014 -0.87 1.62 4.97 CRMS3601 -89.9476 29.57419 Jun 2008 - Sep 2014 -0.36 1.33 6.64
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 65
CRMS Stage Gages Station ID Longitude Latitude Period of Record Minimum Mean Maximum Stage Stage Stage (ft NAVD88) (ft NAVD88) (ft NAVD88) CRMS3985 -90.14993 29.71819 May 2008 - Aug 2014 -0.84 1.17 5.09 CRMS4103 -90.03873 29.65773 Feb 2008 - Sep 2014 -0.46 1.23 6.86 CRMS4218 -90.16483 29.56343 Feb 2008 - Sep 2014 0.20 1.13 6.05 CRMS4245 -90.13514 29.67232 May 2008 - Aug 2014 -0.56 1.20 5.91 CRMS5116 -90.91173 29.97345 May 2008 - Nov 2014 0.95 2.61 4.34 CRMS5672 -90.84644 29.86870 Apr 2008 - Nov2014 -0.29 2.04 4.28
Table 12. USACE stage gages.
USACE Stage Gages 82700 Des -90.47667 29.82389 Nov 1999 - Nov 2014 -2.67 0.47 4.37 Allemands 82875 -90.11056 29.66944 Sep 1999 - Nov 2014 -1.25 0.45 4.85 Lafitte 82720 -90.27306 29.9125 Aug 1999 - Nov 2014 -1.74 0.68 4.11 Sellers Canal
Table 13. Rainfall gages.
Rainfall Gages Station Name Elevation Latitude Longitude Data Data Data Period Gap Type THIBODAUX 4 SE LA 4.6 29.75470 -90.77480 20120101- hourly US (169013) 20131001 DONALDSONVILLE 4 9.1 30.06667 -91.03333 19880601- hourly SW LA US (162534) 20131001 NEW ORLEANS 2.7 30.04944 -90.02889 20040701- hourly LAKEFRONT 20130930 AIRPORT LA US (166667) NEW ORLEANS 3.0 29.93333 -90.13333 19630101- hourly AUDUBON LA US 20131001 (166664)
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 66
Rainfall Gages Station Name Elevation Latitude Longitude Data Data Data Period Gap Type NEW ORLEANS 0.9 29.98611 -90.25556 19471001- hourly INTERNATIONAL 19531230 AIRPORT LA US (166295) NEW ORLEANS 2.7 29.95000 -90.06667 19471001- hourly WSFO CITY LA US 19611231 (166659) HOUMA LA US 4.6 29.58333 -90.73333 19960501- hourly (164407) 20061230 GONZALES LA US 3.0 30.23333 -90.91667 19691001- hourly (163695) 19820201 HOUMA 2 LA US 3.0 29.58333 -90.73333 19471001- hourly (164410) 19540418 NEW ORLEANS 0.9 29.98611 -90.25556 19540101- hourly INTERNATIONAL 20130930 AIRPORT LA US (166660) BATON ROUGE 21.0 30.53333 -91.15000 19471001- hourly RYAN AIRPORT LA 20130930 US (160549) NEW ORLEANS 3.7 29.95000 -90.06667 18930101- daily AUDUBON LA US 20150109 (USW00012930) THIBODAUX 4 SE LA 6.1 29.80000 -90.81667 19741001- 2008- daily US (USC00169013) 20150111 2011 DONALDSONVILLE 4 5.2 30.09917 -90.92694 19961101- daily E LA US 20110725 (USC00162536) LOUISIANA NATURE 0.0 30.05000 -89.96667 19810801- 1990- daily CENTER LA US 20050831 1996 (USC00165610) NEW ORLEANS 0.0 29.93333 -90.03333 19460701- 1992 daily ALGIERS LA US 20130702 (USC00166666) GALLIANO LA US 1.5 29.43333 -90.30000 19680201- daily (USC00163433) 20150111 THIBODAUX 6.1 29.80000 -90.81667 19920101- daily NUMBER 2 LA US 19930630 (USC00169027) RESERVE LA US 3.0 30.06667 -90.56667 19460701- 2009- daily (USC00167767) 20150111 2011 PARADIS 7 S LA US 3.0 29.78333 -90.43333 19460701- daily (USC00167096) 20111109
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 67
Rainfall Gages Station Name Elevation Latitude Longitude Data Data Data Period Gap Type ST BERNARD LA US 3.0 29.86667 -89.83333 19660208- daily (USC00168108) 20050828 NEW ORLEANS -0.3 29.83333 -90.01667 19560601- 1999 daily ALVIN CALLENDER 20150111 FIELD LA US (USW00012958) THIBODAUX 4 SE LA 6.1 29.80000 -90.81667 19800101- 2008- daily US (USC00169013) 20150111 2011 HOUMA LA US 3.0 29.58333 -90.73333 19600101- 2007- daily (USC00164407) 20130816 2011 LUTCHER LA US 6.1 30.06667 -90.70000 19860101- daily (USC00163755) 20150123
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 68
APPENDIX B: IDENTIFIED HISTORICAL RAINFALL EVENTS
Table 14. Historical rainfall events.
Peak Stage Annual # Year Event Event Dates feet gage Day Peak Note 1950 Feb 13 - Feb 14 2.28 Feb 13 yes 1950 Hurricane Baker Aug 20 - Aug 31 2.00 Sep 2 8am 1951 Mar 28 - Apr 3 2.09 Mar 29 yes 1952 Apr 12 - Apr 15 2.25 Apr 12 yes 1953 none listed Jun 29 - Jul 2 2.23 Jun 29 yes 1954 Tropical Storm Barbara Jul 27 - Jul 30 1.70 Jul 30 8am 1954 none listed Sep 17 - Sep 19 2.15 Sep 18 yes 1955 Feb 6 2.00 Feb 6 yes 1955 Tropical Storm Brenda Jul 31 - Aug 3 NR 1955 Tropical Storm 5 Aug 23 - Aug 30 1.60 Aug 30 8am 1956 Tropical Storm 1 Jun 12 - Jun 15 2.10 Jun 16 8am 1956 Hurricane Flossy Sep 21 - Sep 30 2.37 Sep 24 yes 1957 Apr 3 - Apr 9 2.45 Apr 5 yes 1-1 1957 Hurricane Audrey Jun 25 - Jun 28 2.27 Jun 29 8am 1957 Tropical Storm Bertha Aug 8 - Aug 11 1.80 Aug 11 8am 1957 Tropical Storm Esther Sep 16 - Sep 19 2.40 Sep 19 8am 1958 Tropical Storm Gerda Sep 13 - Sep 15 2.42 Sep 21 yes 2-1 1959 Tropical Storm Arlene May 28 - Jun 2 2.81 Jun 8 yes 1960 May 7 2.12 May 7 yes 1960 Hurricane Ethel Sep 14 - 15 1.80 Sep 11 8am 3-1 1961 Hurricane Carla Sep 3 - Sep 13 3.28 Sep 14 yes 1962 May 1 2.08 May 1 yes 1963 none listed Nov 22 2.06 Nov 22 4-1 1964 Hurricane Hilda Sep 28 - Oct 4 2.50 Oct 4 5-1 1965 Hurricane Betsy Aug 27 - Sep 12 2.84 Sep 10 6-W 1966 May 20 - May 29 2.60 May 22 yes 1966 unnamed depression Jul 24 - Jul 26 1.40 Jul 28 8am 1967 none listed Jun 1 2.34 Jun 1 yes 1968 Apr 4 - Apr 5 2.20 Apr 4 yes 1969 May 6 - May 10 2.45 May 8 yes 1969 Hurricane Camille Aug 14 - Aug 22 1.88 Aug 29 8am 1970 Hurricane Cecilia Jul 31 - Aug 5 2.02 Aug 4 8am 1970 none listed Oct 27 - Oct 30 2.40 Oct 27 yes 1971 Tropical Depression 2 Jul 6 - Jul 8 1.70 Jul 11 8am 1971 Tropical Depression 5 Aug 5 - Aug 8 1.73 Aug 6 8am
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 69
Peak Stage Annual # Year Event Event Dates feet gage Day Peak Note see 1971 Tropical Depression 11 Aug 28 - Sep 1 8am below 7-1 1971 Hurricane Fern Sep 3 - Sep 13 2.51 Sep 12 8am 8-1 1971 Hurricane Edith Sep 5 - Sep 18 2.83 Sep 17 yes 9-W 1972 30 Apr - 21 May 2.92 May 12 yes 10-W 1973 16 Apr - 28 Apr 3.74 Apr 22 yes 11-1 1973 Tropical Storm Delia Sep 1 - Sep 7 3.09 (IR) Sep 7 8am 1973 Tropical Depression 11 Sep 6 - Sep 12 ditto 8am 12-W 1974 May 22 - May 28 2.99 May 22 yes 13-1 1974 Hurricane Carmen Aug 29 - Sep 10 2.86 (IR) Sep 11 8am 14-W 1975 30 Apr - 1 May 3.11 Apr 30 yes 1975 Tropical Depression 9 Jul 27 - Jul 31 2.59(IR) Aug 5 8am 1975 Tropical Depression 22 Oct 14 - Oct 17 2.59 Oct 17 8am 1976 Apr 18 - Apr 20 1.94 Apr 18 yes 15-1 1977 Hurricane Babe Sep 3 - Sep 6 3.12 Sep 6 yes 1978 May 4 - May 10 2.51 May 7 yes 1978 unnamed storm Aug 9 - Aug 10 1.48 (IR) Aug 10 8am 1978 Tropical Storm Debra Aug 26 - Aug 29 2.17(IR) Aug 30 8am 1979 Hurricane Bob Jul 9 - Jul 16 1.85 Jul 11 8am 1979 Tropical Storm Claudette Jul 16 - Jul 29 2.57 Jul 26 yes 1979 Hurricane Frederic Aug 29 - Sep 14 1.95 Aug 31 8am 16-W 1980 Apr 13 - Apr 16 3.45 Apr 13 yes 1980 Tropical Storm Danielle Sep 4 - Sep 7 1.98 Sep 7 8am 1981 none listed Jun 10 - Jun 16 2.14 Jun 15 yes 1982 Tropical Storm Chris Sep 9 - Sep 12 1.80 (IR) Sep 12 8am 17-2 1982 Dec 3 - Dec 9 2.73 Dec 4 yes 1983 Apr 6 - Apr 11 2.57 Apr 7 yes 1983 Hurricane Alicia Aug 15 - Aug 21 1.78(IR) Aug 18 8am 1984 Tropical Depression 17 Oct 25 - Oct 28 2.57 Oct 23 yes 1985 Hurricane Danny Aug 12 - Aug 18 2.24 Aug 17 8an 18-1 1985 Hurricane Juan Oct 26 - Nov 1 3.92 Oct 29 yes 1986 Hurricane Bonnie Jun 23 - Jun 28 1.94 Jun 27 8am 1986 Nov 24 - Nov 26 2.28 Nov 24 yes 1987 Mar 23 - Mar 30 2.54 Mar 23 yes 1987 Tropical Storm 2 Aug 9 -Aug 17 1.77 Aug 11 8am 1988 Apr 2 - Apr 7 2.57 Apr 3 yes 1988 Tropical Storm Beryl Aug 8 - Aug 10 1.48 Aug 11 8am 1988 Tropical Depression 10 Sep 3 - Sep 4 NR
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 70
Peak Stage Annual # Year Event Event Dates feet gage Day Peak Note 1988 Hurricane Florence Sep 7 - Sep 11 NR 1989 Tropical Storm Allison Jun 24 - Jun 27 1.95(IR) Jun 27 8am 1989 Hurricane Chantal Jul 30 - Aug 3 2.03 Aug 2 yes 1990 Mar 12 - Mar 18 2.39 Mar 15 yes 19-W 1991 May 7 - May 25 3.38 May 25 yes 20-1 1992 Hurricane Andrew Aug 16 - Aug 28 2.81 Aug 26 yes 1993 no storms listed NR 1994 no storms listed NR 1995 no storms listed NR 1996 Tropical Storm Josephine Oct 4 - Oct 8 NR 1997 Hurricane Danny Jul 16 - Jul 26 NR 1998 Tropical Storm Charley Aug 21 - Aug 24 NR 1998 Hurricane Georges Sep 15 - Oct 1 NR 1998 Tropical Storm Hermine Sep 17 - Sep 20 NR 1999 no storms listed NR 2000 Tropical Depression 9 Sep 7 - Sep 9 1.94 Sep 10 yes 21-1 2001 Tropical Storm Allison Jun 5 - Jun 17 3.24 Jun 11 yes 23 2001 Tropical Storm Barry Aug 2 - Aug 7 1.85 24 2002 Tropical Storm Bertha Aug 4 - Aug 9 1.80 Aug 10 25 2002 Tropical Storm Fay Sep 5 - Sep 8 2.27 26 2002 Tropical Storm Hannah Sep 12 - Sep 15 2.01 22-1 2002 Hurricane Isidore Sep 14 - Sep 27 3.32 Sep 26 23-1 2002 Hurricane Lili Sep 21 - Oct 4 3.54 Oct 4 yes 29 2003 Tropical Storm Bill Jun 28 - Jul 2 2.68 Jun 30 yes 30 2003 Hurricane Claudette Jul 8 - Jul 17 2.18 31 2003 Tropical Storm Grace Aug 30 - Sep 2 2.31 32 2004 Apr 24 - Apr 26 2.24 Apr 25 24-W 2004 May 10 - May 19 2.93 May 18 34 2004 Hurricane Ivan Sep 2 - Sep 24 2.27 Sep 24 25-1 2004 Tropical Storm Matthew Oct 8 - Oct 10 3.35 Oct 10 yes 2005 Feb 3 - Feb 5 2.46 Feb 3 36 2005 Tropical Storm Arlene Jun 8 - Jun 16 1.01 Jun 14 37 2005 Hurricane Cindy Jul 3 - Jul 7 1.26 Jul 6 38 2005 Hurricane Dennis Jul 4 - Jul 10 0.83 Jul 13 39 2005 Hurricane Katrina Aug 23 - Aug 30 1.69 Aug 29 26-1 2005 Hurricane Rita Sep 18 - Sep 26 2.55 Sep 25 yes 41 2006 none listed Oct 14 - Oct 22 2.68 Oct 17 yes 42 2007 Hurricane Humberto Sep 12 - Sep 14 2.27 Sep 12
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 71
Peak Stage Annual # Year Event Event Dates feet gage Day Peak Note 43 2007 Tropical Depression 10 Sep 21 - Sep 22 ditto 27-2 2007 none listed Sep 22 - Oct 25 3.02 Oct 21 yes 45 2008 Tropical Storm Eduoard Aug 3 - Aug 6 2.48 Aug 5 28-1 2008 Hurricane Gustav Aug 25 - Sep 4 3.36 Sep 3 29-1 2008 Hurricane Ike Sep 1 - Sep 14 5.43 Sep 14 yes 48 2009 Hurricane Ida Nov 4 - Nov 10 2.56 Nov 9 30-W 2009 8 Dec - 2 Jan 3.29 Dec 15 yes 31-2 2010 none listed Jul 7 - Jul 10 3.36 Jul 7 yes 51 2010 Tropical Storm Bonnie Jul 22 - Jul 24 2.65 Jul 22 52 2010 Tropical Depression 5 Aug 10 - Aug 11 2.35 Aug 13 32-1 2011 Tropical Storm Lee Sep 1 - Sep 6 3.86 Sep 5 yes 33-1 2012 Hurricane Isaac Aug 21 - Sep 1 4.28 Aug 31 yes 34-1 2013 Tropical Storm Karen Oct 3 - Oct 6 3.03 Oct 5 yes 35-1 2014 27 May - 3 Jun 3.27 May 31 yes
Green shaded: Non-tropical events Gray shaded and numbered: events selected for JPM analysis #-1 = tropical (Jun-Dec) #-2 = non-tropical (Jun-Dec) #-W = non-tropical (Dec-May) IR: Incomplete record NR: No record 8am: Reading at 8 am, peak may be higher
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 72
APPENDIX C: STORAGE NAMES AND LABELS
Table 15. Storage names and labels.
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown 1 11a -90.6749 29.9211 no 0 2 11a2 -90.6622 29.9263 no 0 3 11a3 -90.6496 29.9245 3.8 3.8 11a3 to 11c become levee 2.2-2.0 3.8 ft ft 4 11b -90.6238 29.9087 no 0 5 11c -90.6663 29.9346 3.6 3.8 11c lateral to become Vacherie canal 3.6ft; 1.1-1.8ft; 11c become lateral to 3.7ft and Brazan 0.9-2ft 4.3ft 6 12A -90.6905 29.9936 no 0 7 12b -90.7023 29.9572 no 0 8 13 -90.6790 30.0216 no 0 9 14A -90.6283 30.0122 no 0 10 15A -90.5927 30.0346 no 0 11 15B -90.5956 30.0184 no 0 12 15C -90.5900 29.9808 no 0 13 4A1 -90.9634 29.8714 no 0 14 4A2 -90.9463 29.8657 no 0 15 4A3 -90.9315 29.8943 no 0 16 4A4 -90.9229 29.8945 no 0 17 4A5 -90.9125 29.8969 no 0 18 4A6 -90.8965 29.9040 no 0 19 4A7 -90.8795 29.9027 no 0 20 4A8 -90.8978 29.8888 no 0 21 4A9 -90.9116 29.8833 no 0 22 4C-1 -90.8342 29.9861 no 0 23 4C-10 -90.8836 29.9810 no 0 24 4C-11 -90.8449 29.9578 no 0 25 4C-12 -90.8020 29.9446 no 0
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 73
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown 26 4C-13 -90.7663 29.9368 no 0 27 4C-14 -90.7488 29.9162 no 0 28 4C-15 -90.7346 29.9062 no 0 29 4C-16 -90.7214 29.8987 no 0 30 4C-17 -90.7023 29.8865 no 0 31 4C-18 -90.6624 29.8734 no 0 32 4C-19 -90.6260 29.8805 no 0 33 4C-2 -90.8380 29.9732 no 0 34 4C-20 -90.8569 29.9704 no 0 35 4C-21 -90.8153 29.9570 no 0 36 4C-3 -90.7676 29.9953 no 0 37 4C-4 -90.7637 29.9750 no 0 38 4C-5 -90.7661 29.9527 no 0 39 4C-6 -90.7311 29.9460 no 0 40 4C-7 -90.7103 29.9202 no 0 41 4C-8 -90.6849 29.9100 no 0 42 4C-9 -90.6503 29.9002 no 0 43 5A -90.9719 29.8961 no 0 44 5B -90.9523 29.9033 no 0 45 5C -90.9400 29.9034 no 0 46 9a -90.8879 30.0470 no 0 47 9b -90.9175 30.0387 no 0 48 9c -90.9364 30.0410 no 0 49 9d -90.8543 30.0164 no 0 50 9e -90.8681 29.9951 no 0 51 9f -90.8970 30.0142 no 0 52 BB-01 -90.5907 29.8042 no 0 53 GB-01 -90.8520 29.8876 no 0 54 GB-02 -90.8602 29.8720 no 0 55 GB-03 -90.7874 29.9038 no 0 56 GB-04 -90.7352 29.8776 no 0 57 GB-05 -90.7523 29.8725 no 0
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 74
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown 58 GB-06 -90.6510 29.8550 no 0 59 GB-07 -90.6645 29.8401 no 0 60 GB-08 -90.6156 29.8615 no 0 61 HC-01 -90.6545 29.7988 no 0 62 HC-02 -90.6643 29.7592 no 0 63 Lac Des -90.5778 29.9222 no 0 Allem 64 Lake -90.6230 29.7959 no 0 Boeuf 65 LB-01 -90.6410 29.7721 no 0 66 LB-02 -90.6189 29.7806 no 0 67 MC-01 -90.6099 29.7644 no 0 68 R11 -90.6913 29.9334 4.7 0 Bounded (no re update explicit levee) to 4.7 to 11a2 69 RL-1 -90.8360 29.8574 5.5 0 Bounded and re update 3ft high short to 5.5 connection to SB04 70 RL-10 -90.5930 29.8678 3.5 1 Bounded with no connection 71 RL-2 -90.8060 29.8758 5.5 1 Bounded with no connection 72 RL-3 -90.7673 29.8704 4 1 Bounded with no connection 73 RL-4 -90.7523 29.8530 4 1 Bounded with no connection 74 RL-5 -90.7399 29.8513 4 0 Bounded and Update to 3ft high short 4 connection to SB10 75 RL-6 -90.7067 29.8427 4 1 Bounded with no connection 76 RL-7 -90.7352 29.8892 5.5 1 Bounded with no connection 77 RL-8 -90.6594 29.8576 3.5 1 Bounded with no connection
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 75
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown 78 RL-9 -90.6229 29.8662 3.5 1 Bounded with no connection 79 SA C-1 -90.8827 29.9691 no 0 80 SA C- -90.8440 29.9076 no 0 10 81 SA C- -90.9060 29.9151 no 0 11 82 SA C- -90.9321 29.9097 no 0 12 83 SA C-2 -90.8765 29.9566 no 0 84 SA C-3 -90.8744 29.9355 no 0 85 SA C-4 -90.8409 29.9435 no 0 86 SA C-5 -90.8337 29.9311 no 0 87 SA C-6 -90.8051 29.9270 no 0 88 SA C-7 -90.7787 29.9317 no 0 89 SA C-8 -90.7681 29.9209 no 0 90 SA C-9 -90.7828 29.9147 no 0 91 SA-1A -90.5687 29.8372 no 0 92 SA16 -90.5701 30.0324 no 0 93 SA17A -90.5004 30.0043 no 0 94 SA17B -90.5246 29.9497 no 0 95 SA19- -90.4845 29.8930 8.1 1 bounded with re update E-1 connection to 8.1 the south and east with an elevation of 2 ft 96 SA19- -90.3657 29.8017 no 0 E-2 97 SA19A -90.5483 29.7009 no 0 98 SA19B -90.4625 29.7429 no 0 99 SA19B- -90.5277 29.8283 no 0 Split from N SA19B 100 SA21A -90.4096 29.9487 5 1 its northwest re-update side is high to adding ground; its two
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 76
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown southwest side opening is of a (60ft connection of wide; 80 ft 5 ft high wide; 0.5 (road) ft ele) 101 SA21B -90.4318 29.9347 no 0 102 SA21Bb -90.4487 29.9091 no 0 103 SA21Bc -90.4078 29.8792 6.5 1 bounded with re update connect to 6.5 seaward basin of 3ft high 104 SA22A -90.4366 29.8345 4.5 1 bounded with connection to the north storage area SA19-E-1 with a varying elevation determined by road height 105 SA23A- -90.3409 29.9229 5 1 Bounded with 01 no connection 106 SA23A- -90.3632 29.9119 5.5 1 Bounded with 02 no connection 107 SA23A- -90.3414 29.8952 6.5 1 Bounded with 03 no connection 108 SA23A- -90.3323 29.9175 6 1 Bounded with 04N no connection 109 SA23A- -90.3340 29.8857 4.25 0 Connected to re update 04S SA23B with 4.25 an elevation of 3 ft 110 SA23B -90.3051 29.8848 no 0 111 SA25B -90.2985 29.7812 no 0 w 112 SB-01 -90.9096 29.8464 no 0 113 SB-02 -90.8551 29.8301 no 0 114 SB-03 -90.8116 29.8639 no 0 115 SB-04 -90.8098 29.8515 no 0 116 SB-05 -90.7822 29.8599 no 0 117 SB-06 -90.8158 29.8151 no 0 118 SB-07 -90.7791 29.8380 no 0
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 77
Storage Area Center Node Label Storage POINT_ POINT_Y Levee or Pump Levee in Updates Area X high Stations, HEC-RAS ground, if 1=yes yes, elevation is shown 119 SB-08 -90.7361 29.7800 no 0 120 SB-09 -90.7346 29.8551 no 0 121 SB-10 -90.7150 29.8097 no 0 122 TC-01 -90.6232 29.7420 1 Bounded with no connection 123 Baker -90.9855 29.9602 no 0 Canal N. 124 Verret -90.9285 29.9818 no 0 Canal 125 Brazan -90.6577 29.9461 no 0 Canal 126 Becnel -90.6451 29.9749 no 0 Canal 127 Lasseig -90.5607 29.9995 no 0 ne
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 78
APPENDIX D: RAINFALL RUNOFF FOR EXISTING AND FUTURE LANDSCAPE
Figure 25. Rainfall runoff for existing and future locations.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 79
APPENDIX E: STAGE HYDROGRAPHS Gustav-Ike, August 2008 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 80
Figure 26. Gustav-Ike, August 2008 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 81
Nontropical Storm, December 2009 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 82
Figure 27. Nontropical Storm, December 2009 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 83
Isaac, August, 2012 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 84
Figure 28. Isaac, August, 2012 (gage locations are referred to in Figure 5).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 85
APPENDIX F: FREQUENCY STAGES
Table 16. Frequency stages for existing condition assuming no failure.
Storage Return Period, years, and Stages, ft NAVD88 Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 11a 3.37 3.64 3.80 3.91 3.99 4.07 4.13 4.18 4.22 4.27 4.30 4.34 4.37 4.40 4.42 4.45 4.47 4.49 4.52 4.54 4.62 4.69 4.75 4.81 11a2 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 11a3 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 11b 3.37 3.64 3.80 3.91 3.99 4.07 4.13 4.18 4.22 4.27 4.30 4.34 4.37 4.40 4.42 4.45 4.47 4.49 4.52 4.54 4.62 4.69 4.75 4.81 11c 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 12A 4.28 4.72 4.98 5.16 5.30 5.41 5.51 5.59 5.67 5.74 5.80 5.85 5.90 5.95 5.99 6.03 6.07 6.11 6.14 6.17 6.31 6.43 6.52 6.61 12b 3.64 4.02 4.24 4.40 4.52 4.62 4.71 4.78 4.84 4.90 4.95 5.00 5.04 5.09 5.12 5.16 5.19 5.22 5.25 5.28 5.40 5.50 5.59 5.66 13 5.43 5.93 6.23 6.44 6.60 6.73 6.84 6.94 7.02 7.10 7.17 7.23 7.29 7.34 7.39 7.44 7.48 7.53 7.57 7.60 7.76 7.90 8.01 8.10 14A 3.60 3.80 3.91 4.00 4.06 4.11 4.16 4.19 4.23 4.26 4.29 4.31 4.33 4.36 4.38 4.39 4.41 4.43 4.44 4.46 4.52 4.58 4.62 4.66 15A 3.53 3.89 4.10 4.25 4.37 4.46 4.54 4.61 4.67 4.73 4.78 4.82 4.86 4.90 4.94 4.97 5.00 5.03 5.06 5.09 5.20 5.30 5.38 5.44 15B 3.45 3.68 3.82 3.91 3.99 4.05 4.10 4.15 4.18 4.22 4.25 4.28 4.31 4.33 4.36 4.38 4.40 4.42 4.43 4.45 4.53 4.59 4.64 4.68 15C 3.41 3.67 3.83 3.94 4.03 4.10 4.16 4.21 4.25 4.29 4.33 4.36 4.40 4.42 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 4A1 5.47 5.88 6.12 6.28 6.42 6.52 6.61 6.69 6.76 6.82 6.88 6.93 6.98 7.02 7.06 7.10 7.14 7.17 7.20 7.23 7.36 7.47 7.56 7.64 4A2 5.47 5.88 6.12 6.29 6.42 6.53 6.62 6.70 6.77 6.83 6.88 6.93 6.98 7.03 7.07 7.10 7.14 7.17 7.20 7.23 7.37 7.47 7.56 7.64 4A3 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.13 4.15 4.18 4.19 4.21 4.23 4.31 4.37 4.42 4.46 4A4 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.13 4.15 4.18 4.19 4.21 4.23 4.31 4.37 4.42 4.46 4A5 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 3.99 4.03 4.06 4.08 4.11 4.13 4.15 4.17 4.19 4.21 4.23 4.30 4.36 4.41 4.46 4A6 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A7 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A8 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A9 3.24 3.47 3.60 3.70 3.77 3.83 3.88 3.92 3.96 3.99 4.03 4.05 4.08 4.10 4.13 4.15 4.17 4.19 4.20 4.22 4.29 4.35 4.40 4.45 4C-1 8.22 8.55 8.75 8.88 8.99 9.08 9.15 9.22 9.27 9.32 9.37 9.41 9.45 9.48 9.52 9.55 9.58 9.60 9.63 9.65 9.76 9.85 9.92 9.98 4C-10 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 86
Storage Return Period, years, and Stages, ft NAVD88 Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 4C-11 3.00 3.28 3.45 3.56 3.66 3.73 3.79 3.85 3.89 3.94 3.97 4.01 4.04 4.07 4.10 4.13 4.15 4.17 4.20 4.22 4.31 4.38 4.44 4.50 4C-12 2.98 3.26 3.43 3.55 3.64 3.72 3.78 3.83 3.88 3.93 3.96 4.00 4.03 4.06 4.09 4.12 4.14 4.17 4.19 4.21 4.30 4.38 4.44 4.50 4C-13 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-14 3.07 3.33 3.48 3.59 3.68 3.75 3.80 3.86 3.90 3.94 3.98 4.01 4.04 4.07 4.09 4.12 4.14 4.16 4.18 4.20 4.29 4.36 4.41 4.47 4C-15 3.17 3.43 3.58 3.69 3.77 3.84 3.90 3.95 3.99 4.03 4.06 4.10 4.13 4.15 4.18 4.20 4.23 4.25 4.27 4.29 4.37 4.44 4.49 4.54 4C-16 3.18 3.45 3.60 3.72 3.80 3.87 3.93 3.98 4.03 4.07 4.10 4.14 4.17 4.20 4.22 4.25 4.27 4.29 4.31 4.33 4.42 4.49 4.55 4.60 4C-17 3.32 3.57 3.71 3.82 3.90 3.96 4.02 4.07 4.11 4.15 4.18 4.22 4.25 4.27 4.30 4.32 4.34 4.36 4.38 4.40 4.48 4.55 4.60 4.65 4C-18 3.42 3.67 3.82 3.92 4.01 4.07 4.13 4.18 4.22 4.26 4.29 4.33 4.35 4.38 4.41 4.43 4.45 4.47 4.49 4.51 4.59 4.66 4.72 4.77 4C-19 3.42 3.68 3.82 3.93 4.01 4.08 4.13 4.18 4.22 4.26 4.30 4.33 4.36 4.38 4.41 4.43 4.45 4.48 4.50 4.51 4.60 4.66 4.72 4.77 4C-2 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 4C-20 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 4C-21 2.98 3.26 3.43 3.55 3.64 3.72 3.78 3.83 3.88 3.93 3.96 4.00 4.03 4.06 4.09 4.12 4.14 4.17 4.19 4.21 4.30 4.38 4.44 4.50 4C-3 8.22 8.55 8.75 8.88 8.99 9.08 9.15 9.22 9.27 9.32 9.37 9.41 9.45 9.48 9.52 9.55 9.58 9.60 9.63 9.65 9.76 9.85 9.92 9.98 4C-4 5.10 5.55 5.81 5.99 6.14 6.25 6.35 6.44 6.51 6.58 6.64 6.70 6.75 6.80 6.84 6.88 6.92 6.96 6.99 7.03 7.17 7.29 7.39 7.47 4C-5 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-6 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-7 3.17 3.43 3.58 3.69 3.77 3.84 3.90 3.95 3.99 4.03 4.07 4.10 4.13 4.16 4.18 4.21 4.23 4.25 4.27 4.29 4.37 4.44 4.50 4.55 4C-8 3.32 3.57 3.71 3.82 3.90 3.96 4.02 4.07 4.11 4.15 4.18 4.22 4.25 4.27 4.30 4.32 4.34 4.36 4.38 4.40 4.48 4.55 4.60 4.65 4C-9 3.42 3.68 3.82 3.93 4.01 4.08 4.13 4.18 4.22 4.26 4.30 4.33 4.36 4.38 4.41 4.43 4.45 4.48 4.50 4.51 4.60 4.66 4.72 4.77 5A 3.85 4.23 4.45 4.61 4.73 4.83 4.92 4.99 5.05 5.11 5.16 5.21 5.26 5.30 5.33 5.37 5.40 5.44 5.46 5.49 5.62 5.72 5.80 5.87 5B 3.56 3.86 4.04 4.17 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.69 4.72 4.75 4.78 4.81 4.83 4.85 4.88 4.98 5.06 5.12 5.18 5C 3.56 3.86 4.04 4.17 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.69 4.72 4.75 4.78 4.81 4.83 4.85 4.88 4.98 5.06 5.12 5.18 9a 8.94 9.45 9.75 9.96 10.12 10.25 10.37 10.46 10.55 10.63 10.70 10.76 10.82 10.88 10.93 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65 9b 8.94 9.45 9.75 9.96 10.12 10.25 10.37 10.46 10.55 10.63 10.70 10.76 10.82 10.88 10.93 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65 9c 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 9d 8.93 9.44 9.74 9.95 10.11 10.25 10.36 10.46 10.55 10.62 10.69 10.76 10.82 10.87 10.92 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 87
Storage Return Period, years, and Stages, ft NAVD88 Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 9e 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 9f 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 BB-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 GB-01 3.06 3.34 3.50 3.62 3.71 3.78 3.84 3.90 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.18 4.20 4.23 4.25 4.27 4.36 4.43 4.49 4.55 GB-02 3.04 3.33 3.49 3.61 3.70 3.77 3.84 3.89 3.94 3.98 4.02 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.26 4.36 4.43 4.49 4.55 GB-03 3.40 3.52 3.60 3.65 3.69 3.72 3.75 3.77 3.80 3.81 3.83 3.85 3.86 3.88 3.89 3.90 3.91 3.92 3.93 3.94 3.98 4.01 4.04 4.07 GB-04 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-05 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-06 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-07 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-08 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 HC-01 3.01 3.28 3.43 3.55 3.63 3.71 3.77 3.82 3.86 3.90 3.94 3.98 4.01 4.04 4.06 4.09 4.11 4.13 4.16 4.18 4.26 4.33 4.39 4.45 HC-02 3.01 3.28 3.43 3.55 3.63 3.71 3.77 3.82 3.86 3.90 3.94 3.98 4.01 4.04 4.06 4.09 4.11 4.13 4.16 4.18 4.26 4.33 4.39 4.45 Lac Des Allemands 3.41 3.68 3.83 3.94 4.03 4.10 4.16 4.21 4.26 4.30 4.33 4.37 4.40 4.43 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 Lake Boeuf 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 LB-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 LB-02 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 MC-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 R11 3.90 4.51 4.86 5.12 5.31 5.47 5.60 5.72 5.82 5.92 6.00 6.08 6.15 6.21 6.27 6.33 6.38 6.43 6.48 6.52 6.72 6.88 7.01 7.13 RL-1 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-10 1.01 1.04 1.07 1.10 1.12 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-2 1.94 2.04 2.08 2.11 2.15 2.17 2.19 2.21 2.23 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-3 2.05 2.12 2.17 2.22 2.27 2.28 2.28 2.29 2.29 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-4 2.15 2.22 2.26 2.30 2.35 2.36 2.38 2.40 2.42 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 RL-5 2.92 3.04 3.05 3.06 3.07 3.08 3.08 3.09 3.09 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 RL-6 1.38 1.44 1.47 1.50 1.52 1.53 1.55 1.56 1.57 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 88
Storage Return Period, years, and Stages, ft NAVD88 Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 RL-7 1.13 1.54 1.78 1.95 2.09 2.20 2.29 2.37 2.44 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-8 1.01 1.04 1.07 1.10 1.12 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-9 -1.33 -1.20 -0.80 -0.30 0.20 0.40 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 SA C-1 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-10 3.13 3.38 3.53 3.63 3.71 3.78 3.83 3.88 3.92 3.96 3.99 4.03 4.06 4.08 4.11 4.13 4.15 4.17 4.19 4.21 4.29 4.36 4.41 4.46 SA C-11 3.32 3.58 3.74 3.85 3.93 4.00 4.06 4.11 4.16 4.20 4.24 4.27 4.30 4.33 4.36 4.38 4.40 4.43 4.45 4.47 4.55 4.62 4.68 4.73 SA C-12 3.40 3.67 3.83 3.94 4.03 4.10 4.16 4.22 4.26 4.31 4.34 4.38 4.41 4.44 4.47 4.49 4.51 4.54 4.56 4.58 4.67 4.74 4.80 4.85 SA C-2 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-3 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-4 2.99 3.28 3.45 3.57 3.66 3.74 3.80 3.86 3.91 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.33 4.41 4.47 4.53 SA C-5 2.99 3.28 3.45 3.57 3.66 3.74 3.80 3.86 3.91 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.33 4.41 4.47 4.53 SA C-6 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-7 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-8 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-9 3.01 3.30 3.47 3.59 3.68 3.75 3.82 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.16 4.18 4.20 4.23 4.25 4.34 4.41 4.48 4.53 SA-1A 3.41 3.68 3.83 3.94 4.03 4.10 4.16 4.21 4.26 4.30 4.33 4.37 4.40 4.43 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 SA16 3.35 3.63 3.79 3.90 3.99 4.06 4.12 4.18 4.22 4.26 4.30 4.34 4.37 4.40 4.43 4.45 4.47 4.50 4.52 4.54 4.63 4.70 4.76 4.81 SA17A 3.10 3.34 3.48 3.58 3.66 3.72 3.78 3.82 3.86 3.90 3.93 3.96 3.99 4.02 4.04 4.06 4.08 4.10 4.12 4.14 4.22 4.28 4.33 4.38 SA17B 1.89 2.41 2.72 2.94 3.10 3.24 3.36 3.46 3.55 3.63 3.70 3.77 3.83 3.88 3.93 3.98 4.03 4.07 4.11 4.15 4.32 4.46 4.58 4.68 SA19-E-1 2.48 2.92 3.18 3.36 3.50 3.61 3.71 3.80 3.87 3.94 4.00 4.05 4.11 4.15 4.20 4.24 4.28 4.31 4.35 4.38 4.52 4.64 4.73 4.82 SA19-E-2 3.99 4.25 4.40 4.51 4.60 4.67 4.72 4.77 4.82 4.86 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.12 5.20 5.27 5.33 5.38 SA19A 3.99 4.26 4.41 4.52 4.61 4.68 4.74 4.79 4.84 4.88 4.92 4.95 4.98 5.01 5.04 5.06 5.08 5.11 5.13 5.15 5.23 5.30 5.36 5.41 SA19B 3.97 4.24 4.40 4.51 4.60 4.67 4.73 4.78 4.83 4.87 4.91 4.94 4.98 5.01 5.03 5.06 5.08 5.10 5.13 5.15 5.23 5.31 5.37 5.42 SA19B-N 3.97 4.09 4.16 4.21 4.25 4.28 4.31 4.33 4.35 4.37 4.39 4.40 4.42 4.43 4.44 4.45 4.46 4.47 4.48 4.49 4.53 4.56 4.59 4.61 SA21A 2.33 2.81 3.09 3.29 3.44 3.57 3.67 3.76 3.84 3.92 3.98 4.04 4.10 4.15 4.20 4.24 4.28 4.32 4.36 4.39 4.55 4.67 4.78 4.87 SA21B 2.30 2.75 3.02 3.21 3.35 3.47 3.57 3.66 3.73 3.80 3.87 3.92 3.97 4.02 4.07 4.11 4.15 4.19 4.22 4.25 4.40 4.52 4.62 4.71
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 89
Storage Return Period, years, and Stages, ft NAVD88 Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 SA21Bb 2.29 2.62 2.81 2.95 3.06 3.14 3.22 3.28 3.34 3.39 3.43 3.47 3.51 3.55 3.58 3.61 3.64 3.67 3.69 3.72 3.82 3.91 3.98 4.05 SA21Bc 3.58 3.81 3.95 4.04 4.12 4.18 4.23 4.27 4.31 4.35 4.38 4.41 4.44 4.46 4.48 4.50 4.52 4.54 4.56 4.58 4.65 4.71 4.77 4.81 SA22A -5.50 -5.00 -4.60 -4.30 -4.00 -3.48 -2.98 -2.46 -1.96 3.94 4.00 4.05 4.11 4.15 4.20 4.24 4.28 4.31 4.35 4.38 4.52 4.64 4.73 4.82 SA23A-01 0.05 0.11 0.17 0.22 0.28 0.33 0.38 0.43 0.49 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA23A-02 0.01 0.15 0.90 1.50 2.06 2.26 2.46 2.66 2.86 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA23A-03 0.53 0.57 0.59 0.62 0.65 0.67 0.69 0.71 0.73 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA23A-04N -0.98 -0.96 -0.94 -0.92 -0.89 -0.86 -0.84 -0.82 -0.80 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA23A-04S 2.41 2.46 2.56 2.66 4.35 4.38 4.40 4.42 4.44 4.45 4.46 4.48 4.49 4.50 4.51 4.52 4.53 4.53 4.54 4.55 4.58 4.61 4.63 4.65 SA23B 3.67 3.90 4.04 4.14 4.21 4.27 4.32 4.37 4.41 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA25Bw 3.67 3.90 4.04 4.14 4.21 4.27 4.32 4.37 4.41 4.44 4.47 4.50 4.53 4.56 4.58 4.60 4.62 4.64 4.66 4.67 4.75 4.81 4.86 4.91 SB-01 4.87 5.16 5.34 5.46 5.55 5.63 5.70 5.76 5.81 5.85 5.89 5.93 5.96 5.99 6.02 6.05 6.08 6.10 6.12 6.15 6.24 6.32 6.39 6.44 SB-02 4.87 5.16 5.34 5.46 5.55 5.63 5.70 5.76 5.81 5.85 5.89 5.93 5.96 5.99 6.02 6.05 6.08 6.10 6.12 6.15 6.24 6.32 6.39 6.44 SB-03 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-04 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-05 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-06 4.53 4.77 4.92 5.02 5.10 5.16 5.22 5.26 5.31 5.34 5.38 5.41 5.44 5.46 5.49 5.51 5.53 5.55 5.57 5.59 5.67 5.73 5.79 5.84 SB-07 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-08 4.53 4.77 4.92 5.02 5.10 5.16 5.22 5.26 5.31 5.34 5.38 5.41 5.44 5.46 5.49 5.51 5.53 5.55 5.57 5.59 5.67 5.73 5.79 5.84 SB-09 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-10 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 TC-01 1.32 1.62 1.79 1.91 2.01 2.09 2.15 2.21 2.26 2.30 2.34 2.38 2.42 2.45 2.48 2.50 2.53 2.55 2.58 2.60 2.69 2.77 2.84 2.89 B.CNort28203 3.58 3.88 4.05 4.18 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.68 4.72 4.75 4.77 4.80 4.82 4.85 4.87 4.97 5.05 5.11 5.17 Verret10863 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 Brazan1.36363 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 Becnel10065 3.40 3.68 3.84 3.96 4.05 4.12 4.19 4.24 4.29 4.33 4.37 4.40 4.44 4.47 4.49 4.52 4.55 4.57 4.59 4.61 4.70 4.78 4.84 4.89 Lasseigne9157991 3.39 3.66 3.81 3.92 4.01 4.08 4.14 4.19 4.24 4.28 4.32 4.35 4.38 4.41 4.44 4.46 4.48 4.51 4.53 4.55 4.63 4.70 4.76 4.82
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 90
Table 17. Frequency stages for existing condition assuming 50-year failure.
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 11a 3.37 3.64 3.80 3.91 3.99 4.07 4.13 4.18 4.22 4.27 4.30 4.34 4.37 4.40 4.42 4.45 4.47 4.49 4.52 4.54 4.62 4.69 4.75 4.81 11a2 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 11a3 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 11b 3.37 3.64 3.80 3.91 3.99 4.07 4.13 4.18 4.22 4.27 4.30 4.34 4.37 4.40 4.42 4.45 4.47 4.49 4.52 4.54 4.62 4.69 4.75 4.81 11c 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 12A 4.28 4.72 4.98 5.16 5.30 5.41 5.51 5.59 5.67 5.74 5.80 5.85 5.90 5.95 5.99 6.03 6.07 6.11 6.14 6.17 6.31 6.43 6.52 6.61 12b 3.64 4.02 4.24 4.40 4.52 4.62 4.71 4.78 4.84 4.90 4.95 5.00 5.04 5.09 5.12 5.16 5.19 5.22 5.25 5.28 5.40 5.50 5.59 5.66 13 5.43 5.93 6.23 6.44 6.60 6.73 6.84 6.94 7.02 7.10 7.17 7.23 7.29 7.34 7.39 7.44 7.48 7.53 7.57 7.60 7.76 7.90 8.01 8.10 14A 3.60 3.80 3.91 4.00 4.06 4.11 4.16 4.19 4.23 4.26 4.29 4.31 4.33 4.36 4.38 4.39 4.41 4.43 4.44 4.46 4.52 4.58 4.62 4.66 15A 3.53 3.89 4.10 4.25 4.37 4.46 4.54 4.61 4.67 4.73 4.78 4.82 4.86 4.90 4.94 4.97 5.00 5.03 5.06 5.09 5.20 5.30 5.38 5.44 15B 3.45 3.68 3.82 3.91 3.99 4.05 4.10 4.15 4.18 4.22 4.25 4.28 4.31 4.33 4.36 4.38 4.40 4.42 4.43 4.45 4.53 4.59 4.64 4.68 15C 3.41 3.67 3.83 3.94 4.03 4.10 4.16 4.21 4.25 4.29 4.33 4.36 4.40 4.42 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 4A1 5.47 5.88 6.12 6.28 6.42 6.52 6.61 6.69 6.76 6.82 6.88 6.93 6.98 7.02 7.06 7.10 7.14 7.17 7.20 7.23 7.36 7.47 7.56 7.64 4A2 5.47 5.88 6.12 6.29 6.42 6.53 6.62 6.70 6.77 6.83 6.88 6.93 6.98 7.03 7.07 7.10 7.14 7.17 7.20 7.23 7.37 7.47 7.56 7.64 4A3 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.13 4.15 4.18 4.19 4.21 4.23 4.31 4.37 4.42 4.46 4A4 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.13 4.15 4.18 4.19 4.21 4.23 4.31 4.37 4.42 4.46 4A5 3.22 3.46 3.59 3.69 3.76 3.82 3.88 3.92 3.96 3.99 4.03 4.06 4.08 4.11 4.13 4.15 4.17 4.19 4.21 4.23 4.30 4.36 4.41 4.46 4A6 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A7 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A8 3.18 3.42 3.56 3.66 3.74 3.80 3.85 3.90 3.94 3.98 4.01 4.04 4.07 4.10 4.12 4.14 4.16 4.18 4.20 4.22 4.30 4.36 4.41 4.46 4A9 3.24 3.47 3.60 3.70 3.77 3.83 3.88 3.92 3.96 3.99 4.03 4.05 4.08 4.10 4.13 4.15 4.17 4.19 4.20 4.22 4.29 4.35 4.40 4.45 4C-1 8.22 8.55 8.75 8.88 8.99 9.08 9.15 9.22 9.27 9.32 9.37 9.41 9.45 9.48 9.52 9.55 9.58 9.60 9.63 9.65 9.76 9.85 9.92 9.98 4C-10 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 4C-11 3.00 3.28 3.45 3.56 3.66 3.73 3.79 3.85 3.89 3.94 3.97 4.01 4.04 4.07 4.10 4.13 4.15 4.17 4.20 4.22 4.31 4.38 4.44 4.50
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 91
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 4C-12 2.98 3.26 3.43 3.55 3.64 3.72 3.78 3.83 3.88 3.93 3.96 4.00 4.03 4.06 4.09 4.12 4.14 4.17 4.19 4.21 4.30 4.38 4.44 4.50 4C-13 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-14 3.07 3.33 3.48 3.59 3.68 3.75 3.80 3.86 3.90 3.94 3.98 4.01 4.04 4.07 4.09 4.12 4.14 4.16 4.18 4.20 4.29 4.36 4.41 4.47 4C-15 3.17 3.43 3.58 3.69 3.77 3.84 3.90 3.95 3.99 4.03 4.06 4.10 4.13 4.15 4.18 4.20 4.23 4.25 4.27 4.29 4.37 4.44 4.49 4.54 4C-16 3.18 3.45 3.60 3.72 3.80 3.87 3.93 3.98 4.03 4.07 4.10 4.14 4.17 4.20 4.22 4.25 4.27 4.29 4.31 4.33 4.42 4.49 4.55 4.60 4C-17 3.32 3.57 3.71 3.82 3.90 3.96 4.02 4.07 4.11 4.15 4.18 4.22 4.25 4.27 4.30 4.32 4.34 4.36 4.38 4.40 4.48 4.55 4.60 4.65 4C-18 3.42 3.67 3.82 3.92 4.01 4.07 4.13 4.18 4.22 4.26 4.29 4.33 4.35 4.38 4.41 4.43 4.45 4.47 4.49 4.51 4.59 4.66 4.72 4.77 4C-19 3.42 3.68 3.82 3.93 4.01 4.08 4.13 4.18 4.22 4.26 4.30 4.33 4.36 4.38 4.41 4.43 4.45 4.48 4.50 4.51 4.60 4.66 4.72 4.77 4C-2 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 4C-20 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 4C-21 2.98 3.26 3.43 3.55 3.64 3.72 3.78 3.83 3.88 3.93 3.96 4.00 4.03 4.06 4.09 4.12 4.14 4.17 4.19 4.21 4.30 4.38 4.44 4.50 4C-3 8.22 8.55 8.75 8.88 8.99 9.08 9.15 9.22 9.27 9.32 9.37 9.41 9.45 9.48 9.52 9.55 9.58 9.60 9.63 9.65 9.76 9.85 9.92 9.98 4C-4 5.10 5.55 5.81 5.99 6.14 6.25 6.35 6.44 6.51 6.58 6.64 6.70 6.75 6.80 6.84 6.88 6.92 6.96 6.99 7.03 7.17 7.29 7.39 7.47 4C-5 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-6 2.98 3.26 3.43 3.55 3.64 3.71 3.77 3.83 3.88 3.92 3.96 4.00 4.03 4.06 4.09 4.11 4.14 4.16 4.18 4.20 4.30 4.37 4.43 4.49 4C-7 3.17 3.43 3.58 3.69 3.77 3.84 3.90 3.95 3.99 4.03 4.07 4.10 4.13 4.16 4.18 4.21 4.23 4.25 4.27 4.29 4.37 4.44 4.50 4.55 4C-8 3.32 3.57 3.71 3.82 3.90 3.96 4.02 4.07 4.11 4.15 4.18 4.22 4.25 4.27 4.30 4.32 4.34 4.36 4.38 4.40 4.48 4.55 4.60 4.65 4C-9 3.42 3.68 3.82 3.93 4.01 4.08 4.13 4.18 4.22 4.26 4.30 4.33 4.36 4.38 4.41 4.43 4.45 4.48 4.50 4.51 4.60 4.66 4.72 4.77 5A 3.85 4.23 4.45 4.61 4.73 4.83 4.92 4.99 5.05 5.11 5.16 5.21 5.26 5.30 5.33 5.37 5.40 5.44 5.46 5.49 5.62 5.72 5.80 5.87 5B 3.56 3.86 4.04 4.17 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.69 4.72 4.75 4.78 4.81 4.83 4.85 4.88 4.98 5.06 5.12 5.18 5C 3.56 3.86 4.04 4.17 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.69 4.72 4.75 4.78 4.81 4.83 4.85 4.88 4.98 5.06 5.12 5.18 9a 8.94 9.45 9.75 9.96 10.12 10.25 10.37 10.46 10.55 10.63 10.70 10.76 10.82 10.88 10.93 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65 9b 8.94 9.45 9.75 9.96 10.12 10.25 10.37 10.46 10.55 10.63 10.70 10.76 10.82 10.88 10.93 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65 9c 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38 9d 8.93 9.44 9.74 9.95 10.11 10.25 10.36 10.46 10.55 10.62 10.69 10.76 10.82 10.87 10.92 10.97 11.02 11.06 11.10 11.14 11.30 11.43 11.55 11.65 9e 5.31 5.70 5.92 6.09 6.21 6.31 6.40 6.47 6.54 6.60 6.65 6.70 6.75 6.79 6.83 6.86 6.90 6.93 6.96 6.99 7.11 7.22 7.30 7.38
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 92
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 9f 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 BB-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 GB-01 3.06 3.34 3.50 3.62 3.71 3.78 3.84 3.90 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.18 4.20 4.23 4.25 4.27 4.36 4.43 4.49 4.55 GB-02 3.04 3.33 3.49 3.61 3.70 3.77 3.84 3.89 3.94 3.98 4.02 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.26 4.36 4.43 4.49 4.55 GB-03 3.40 3.52 3.60 3.65 3.69 3.72 3.75 3.77 3.80 3.81 3.83 3.85 3.86 3.88 3.89 3.90 3.91 3.92 3.93 3.94 3.98 4.01 4.04 4.07 GB-04 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-05 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-06 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-07 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 GB-08 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 HC-01 3.01 3.28 3.43 3.55 3.63 3.71 3.77 3.82 3.86 3.90 3.94 3.98 4.01 4.04 4.06 4.09 4.11 4.13 4.16 4.18 4.26 4.33 4.39 4.45 HC-02 3.01 3.28 3.43 3.55 3.63 3.71 3.77 3.82 3.86 3.90 3.94 3.98 4.01 4.04 4.06 4.09 4.11 4.13 4.16 4.18 4.26 4.33 4.39 4.45 Lac Des Allemands 3.41 3.68 3.83 3.94 4.03 4.10 4.16 4.21 4.26 4.30 4.33 4.37 4.40 4.43 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 Lake Boeuf 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 LB-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 LB-02 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 MC-01 3.02 3.29 3.45 3.56 3.65 3.72 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 R11 3.90 4.51 4.86 5.12 5.31 5.47 5.60 5.72 5.82 5.92 6.00 6.08 6.15 6.21 6.27 6.33 6.38 6.43 6.48 6.52 6.72 6.88 7.01 7.13 RL-1 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-10 1.01 1.04 1.07 1.10 1.12 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-2 1.94 2.04 2.08 2.11 2.15 2.17 2.19 2.21 2.23 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-3 2.05 2.12 2.17 2.22 2.27 2.28 2.28 2.29 2.29 2.30 2.31 2.31 2.32 2.32 2.33 2.33 2.34 2.34 2.35 2.36 2.38 2.40 2.42 4.54 RL-4 2.15 2.22 2.26 2.30 2.35 2.36 2.38 2.40 2.42 2.44 2.44 2.44 2.44 2.45 2.45 2.45 2.45 2.45 2.45 2.46 2.46 2.47 2.47 4.54 RL-5 2.92 3.04 3.05 3.06 3.07 3.08 3.08 3.09 3.09 3.10 3.15 3.20 3.25 3.30 3.35 3.40 3.45 3.50 3.55 3.60 3.65 3.70 3.75 4.54 RL-6 1.38 1.44 1.47 1.50 1.52 1.53 1.55 1.56 1.57 1.58 1.59 1.59 1.60 1.60 1.61 1.62 1.62 1.63 1.63 1.64 1.65 1.66 1.68 4.54 RL-7 1.13 1.54 1.78 1.95 2.09 2.20 2.29 2.37 2.44 2.50 2.56 2.61 2.66 2.70 2.74 2.78 2.82 2.85 2.88 2.91 3.05 3.16 3.25 3.33
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 93
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 RL-8 1.01 1.04 1.07 1.10 1.12 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 RL-9 -1.33 -1.20 -0.80 -0.30 0.20 0.40 3.78 3.83 3.88 3.92 3.95 3.99 4.02 4.05 4.08 4.10 4.12 4.15 4.17 4.19 4.27 4.35 4.41 4.46 SA C-1 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-10 3.13 3.38 3.53 3.63 3.71 3.78 3.83 3.88 3.92 3.96 3.99 4.03 4.06 4.08 4.11 4.13 4.15 4.17 4.19 4.21 4.29 4.36 4.41 4.46 SA C-11 3.32 3.58 3.74 3.85 3.93 4.00 4.06 4.11 4.16 4.20 4.24 4.27 4.30 4.33 4.36 4.38 4.40 4.43 4.45 4.47 4.55 4.62 4.68 4.73 SA C-12 3.40 3.67 3.83 3.94 4.03 4.10 4.16 4.22 4.26 4.31 4.34 4.38 4.41 4.44 4.47 4.49 4.51 4.54 4.56 4.58 4.67 4.74 4.80 4.85 SA C-2 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-3 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 SA C-4 2.99 3.28 3.45 3.57 3.66 3.74 3.80 3.86 3.91 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.33 4.41 4.47 4.53 SA C-5 2.99 3.28 3.45 3.57 3.66 3.74 3.80 3.86 3.91 3.95 3.99 4.03 4.06 4.09 4.12 4.15 4.17 4.20 4.22 4.24 4.33 4.41 4.47 4.53 SA C-6 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-7 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-8 3.02 3.30 3.47 3.58 3.68 3.75 3.81 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.15 4.18 4.20 4.22 4.25 4.34 4.41 4.47 4.53 SA C-9 3.01 3.30 3.47 3.59 3.68 3.75 3.82 3.87 3.92 3.96 4.00 4.04 4.07 4.10 4.13 4.16 4.18 4.20 4.23 4.25 4.34 4.41 4.48 4.53 SA-1A 3.41 3.68 3.83 3.94 4.03 4.10 4.16 4.21 4.26 4.30 4.33 4.37 4.40 4.43 4.45 4.48 4.50 4.52 4.54 4.56 4.65 4.72 4.78 4.83 SA16 3.35 3.63 3.79 3.90 3.99 4.06 4.12 4.18 4.22 4.26 4.30 4.34 4.37 4.40 4.43 4.45 4.47 4.50 4.52 4.54 4.63 4.70 4.76 4.81 SA17A 3.10 3.34 3.48 3.58 3.66 3.72 3.78 3.82 3.86 3.90 3.93 3.96 3.99 4.02 4.04 4.06 4.08 4.10 4.12 4.14 4.22 4.28 4.33 4.38 SA17B 1.89 2.41 2.72 2.94 3.10 3.24 3.36 3.46 3.55 3.63 3.70 3.77 3.83 3.88 3.93 3.98 4.03 4.07 4.11 4.15 4.32 4.46 4.58 4.68 SA19-E-1 2.48 2.92 3.18 3.36 3.50 3.61 3.71 3.80 3.87 3.94 4.00 4.05 4.11 4.15 4.20 4.24 4.28 4.31 4.35 4.38 4.52 4.64 4.73 4.82 SA19-E-2 3.99 4.25 4.40 4.51 4.60 4.67 4.72 4.77 4.82 4.86 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.12 5.20 5.27 5.33 5.38 SA19A 3.99 4.26 4.41 4.52 4.61 4.68 4.74 4.79 4.84 4.88 4.92 4.95 4.98 5.01 5.04 5.06 5.08 5.11 5.13 5.15 5.23 5.30 5.36 5.41 SA19B 3.97 4.24 4.40 4.51 4.60 4.67 4.73 4.78 4.83 4.87 4.91 4.94 4.98 5.01 5.03 5.06 5.08 5.10 5.13 5.15 5.23 5.31 5.37 5.42 SA19B-N 3.97 4.09 4.16 4.21 4.25 4.28 4.31 4.33 4.35 4.37 4.39 4.40 4.42 4.43 4.44 4.45 4.46 4.47 4.48 4.49 4.53 4.56 4.59 4.61 SA21A 2.33 2.81 3.09 3.29 3.44 3.57 3.67 3.76 3.84 3.92 3.98 4.04 4.10 4.15 4.20 4.24 4.28 4.32 4.36 4.39 4.55 4.67 4.78 4.87 SA21B 2.30 2.75 3.02 3.21 3.35 3.47 3.57 3.66 3.73 3.80 3.87 3.92 3.97 4.02 4.07 4.11 4.15 4.19 4.22 4.25 4.40 4.52 4.62 4.71 SA21Bb 2.29 2.62 2.81 2.95 3.06 3.14 3.22 3.28 3.34 3.39 3.43 3.47 3.51 3.55 3.58 3.61 3.64 3.67 3.69 3.72 3.82 3.91 3.98 4.05
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 94
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 SA21Bc 3.58 3.81 3.95 4.04 4.12 4.18 4.23 4.27 4.31 4.35 4.38 4.41 4.44 4.46 4.48 4.50 4.52 4.54 4.56 4.58 4.65 4.71 4.77 4.81 SA22A -5.50 -5.00 -4.60 -4.30 -4.00 -3.48 -2.98 -2.46 -1.96 -1.37 -1.32 -1.27 -1.21 -1.15 -1.09 -1.03 -0.96 -0.89 -0.82 -0.75 -0.71 -0.67 -0.63 -0.60 SA23A-01 0.05 0.11 0.17 0.22 0.28 0.33 0.38 0.43 0.49 0.56 0.61 0.66 0.71 0.76 0.81 0.86 0.91 0.96 1.01 1.09 1.12 1.15 1.18 1.21 SA23A-02 0.01 0.15 0.90 1.50 2.06 2.26 2.46 2.66 2.86 3.09 3.15 3.21 3.27 3.33 3.39 3.45 3.51 3.57 3.64 3.72 3.88 4.04 4.20 4.37 SA23A-03 0.53 0.57 0.59 0.62 0.65 0.67 0.69 0.71 0.73 0.75 0.77 0.79 0.81 0.83 0.86 0.89 0.92 0.95 0.98 1.01 1.04 1.07 1.10 1.13 SA23A-04N -0.98 -0.96 -0.94 -0.92 -0.89 -0.86 -0.84 -0.82 -0.80 -0.78 -0.76 -0.74 -0.72 -0.70 -0.68 -0.66 -0.64 -0.62 -0.60 -0.57 -0.56 -0.54 -0.52 -0.50 SA23A-04S 2.41 2.46 2.56 2.66 4.35 4.38 4.40 4.42 4.44 4.45 4.46 4.48 4.49 4.50 4.51 4.52 4.53 4.53 4.54 4.55 4.58 4.61 4.63 4.65 SA23B 3.67 3.90 4.04 4.14 4.21 4.27 4.32 4.37 4.41 4.44 4.47 4.50 4.53 4.55 4.58 4.60 4.62 4.64 4.65 4.67 4.75 4.81 4.86 4.90 SA25Bw 3.67 3.90 4.04 4.14 4.21 4.27 4.32 4.37 4.41 4.44 4.47 4.50 4.53 4.56 4.58 4.60 4.62 4.64 4.66 4.67 4.75 4.81 4.86 4.91 SB-01 4.87 5.16 5.34 5.46 5.55 5.63 5.70 5.76 5.81 5.85 5.89 5.93 5.96 5.99 6.02 6.05 6.08 6.10 6.12 6.15 6.24 6.32 6.39 6.44 SB-02 4.87 5.16 5.34 5.46 5.55 5.63 5.70 5.76 5.81 5.85 5.89 5.93 5.96 5.99 6.02 6.05 6.08 6.10 6.12 6.15 6.24 6.32 6.39 6.44 SB-03 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-04 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-05 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-06 4.53 4.77 4.92 5.02 5.10 5.16 5.22 5.26 5.31 5.34 5.38 5.41 5.44 5.46 5.49 5.51 5.53 5.55 5.57 5.59 5.67 5.73 5.79 5.84 SB-07 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-08 4.53 4.77 4.92 5.02 5.10 5.16 5.22 5.26 5.31 5.34 5.38 5.41 5.44 5.46 5.49 5.51 5.53 5.55 5.57 5.59 5.67 5.73 5.79 5.84 SB-09 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 SB-10 3.03 3.32 3.48 3.60 3.69 3.77 3.83 3.88 3.93 3.98 4.02 4.05 4.08 4.11 4.14 4.17 4.19 4.22 4.24 4.26 4.35 4.43 4.49 4.54 TC-01 1.32 1.62 1.79 1.91 2.01 2.09 2.15 2.21 2.26 2.30 2.34 2.38 2.42 2.45 2.48 2.50 2.53 2.55 2.58 2.60 2.69 2.77 2.84 2.89 B.CNort28203 3.58 3.88 4.05 4.18 4.27 4.35 4.42 4.47 4.53 4.57 4.61 4.65 4.68 4.72 4.75 4.77 4.80 4.82 4.85 4.87 4.97 5.05 5.11 5.17 Verret10863 3.50 3.79 3.96 4.09 4.18 4.26 4.32 4.38 4.43 4.47 4.51 4.55 4.58 4.61 4.64 4.67 4.70 4.72 4.74 4.77 4.86 4.94 5.00 5.06 Brazan1.36363 3.39 3.69 3.87 3.99 4.09 4.17 4.24 4.30 4.35 4.39 4.43 4.47 4.51 4.54 4.57 4.60 4.63 4.65 4.67 4.70 4.79 4.87 4.94 5.00 Becnel10065 3.40 3.68 3.84 3.96 4.05 4.12 4.19 4.24 4.29 4.33 4.37 4.40 4.44 4.47 4.49 4.52 4.55 4.57 4.59 4.61 4.70 4.78 4.84 4.89 Lasseigne9157991 3.39 3.66 3.81 3.92 4.01 4.08 4.14 4.19 4.24 4.28 4.32 4.35 4.38 4.41 4.44 4.46 4.48 4.51 4.53 4.55 4.63 4.70 4.76 4.82
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 95
Table 18. Frequency stages for “future without action” scenario assuming no failure.
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 11a 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 11a2 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 11a3 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 11b 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 11c 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 12A 5.90 6.12 6.25 6.35 6.42 6.47 6.52 6.57 6.60 6.64 6.67 6.70 6.72 6.74 6.77 6.79 6.81 6.82 6.84 6.86 6.93 6.99 7.04 7.08 12b 5.50 5.71 5.84 5.93 6.00 6.06 6.11 6.15 6.19 6.22 6.25 6.28 6.31 6.33 6.35 6.37 6.39 6.41 6.43 6.44 6.51 6.57 6.62 6.66 13 6.44 6.54 6.60 6.64 6.67 6.70 6.72 6.74 6.76 6.77 6.79 6.80 6.81 6.82 6.83 6.84 6.85 6.86 6.87 6.87 6.91 6.93 6.96 6.98 14A 5.77 6.08 6.26 6.39 6.49 6.57 6.64 6.69 6.75 6.79 6.84 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 15A 5.56 5.91 6.12 6.26 6.37 6.47 6.54 6.61 6.67 6.72 6.77 6.82 6.86 6.89 6.93 6.96 6.99 7.02 7.05 7.08 7.19 7.28 7.36 7.43 15B 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 15C 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 4A1 4.55 4.94 5.17 5.33 5.46 5.56 5.65 5.72 5.79 5.85 5.90 5.95 6.00 6.04 6.08 6.12 6.15 6.18 6.21 6.24 6.37 6.47 6.56 6.63 4A2 4.53 4.93 5.16 5.32 5.45 5.55 5.64 5.72 5.79 5.85 5.90 5.95 6.00 6.04 6.08 6.11 6.15 6.18 6.21 6.24 6.37 6.47 6.56 6.64 4A3 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A4 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A5 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A6 4.02 4.17 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.68 4.69 4.74 4.78 4.81 4.84 4A7 4.02 4.17 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.68 4.69 4.74 4.78 4.81 4.84 4A8 4.02 4.18 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.67 4.68 4.73 4.77 4.81 4.84 4A9 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4C-1 7.46 7.76 7.93 8.05 8.14 8.22 8.28 8.34 8.39 8.43 8.47 8.51 8.54 8.57 8.60 8.63 8.66 8.68 8.70 8.72 8.82 8.90 8.96 9.02 4C-10 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 4C-11 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 96
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 4C-12 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-13 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-14 4.98 5.19 5.31 5.40 5.46 5.52 5.57 5.61 5.64 5.67 5.70 5.73 5.75 5.77 5.79 5.81 5.83 5.85 5.87 5.88 5.95 6.00 6.05 6.09 4C-15 5.45 5.71 5.86 5.97 6.05 6.12 6.18 6.23 6.27 6.31 6.35 6.38 6.41 6.44 6.46 6.49 6.51 6.53 6.55 6.57 6.66 6.72 6.78 6.83 4C-16 5.46 5.72 5.87 5.98 6.06 6.13 6.19 6.23 6.28 6.32 6.35 6.39 6.42 6.44 6.47 6.49 6.52 6.54 6.56 6.58 6.66 6.73 6.78 6.83 4C-17 5.62 5.90 6.07 6.18 6.28 6.35 6.41 6.47 6.52 6.56 6.60 6.63 6.67 6.70 6.72 6.75 6.78 6.80 6.82 6.84 6.93 7.01 7.07 7.13 4C-18 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 4C-19 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 4C-2 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 4C-20 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 4C-21 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36 4C-3 7.46 7.76 7.93 8.05 8.14 8.22 8.28 8.34 8.39 8.43 8.47 8.51 8.54 8.57 8.60 8.63 8.66 8.68 8.70 8.72 8.82 8.90 8.96 9.02 4C-4 5.69 5.90 6.02 6.11 6.18 6.23 6.28 6.32 6.36 6.39 6.42 6.45 6.47 6.49 6.51 6.53 6.55 6.57 6.59 6.60 6.67 6.73 6.77 6.81 4C-5 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-6 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36 4C-7 5.45 5.71 5.87 5.98 6.06 6.13 6.19 6.24 6.28 6.32 6.36 6.39 6.42 6.45 6.48 6.50 6.53 6.55 6.57 6.59 6.67 6.74 6.80 6.85 4C-8 5.62 5.90 6.07 6.19 6.28 6.35 6.41 6.47 6.51 6.56 6.60 6.63 6.66 6.69 6.72 6.75 6.77 6.80 6.82 6.84 6.93 7.00 7.07 7.12 4C-9 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 5A 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 5B 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 5C 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 9a 8.91 9.28 9.49 9.64 9.76 9.85 9.94 10.01 10.07 10.12 10.17 10.22 10.26 10.30 10.34 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9b 8.91 9.28 9.49 9.64 9.76 9.85 9.94 10.01 10.07 10.12 10.17 10.22 10.26 10.30 10.34 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9c 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 9d 8.90 9.27 9.48 9.63 9.75 9.85 9.93 10.00 10.06 10.12 10.17 10.22 10.26 10.30 10.33 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9e 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 97
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 9f 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 BB-01 4.49 4.68 4.79 4.87 4.93 4.98 5.03 5.06 5.10 5.12 5.15 5.17 5.20 5.22 5.24 5.25 5.27 5.29 5.30 5.32 5.38 5.43 5.47 5.51 GB-01 3.94 4.04 4.09 4.14 4.17 4.19 4.21 4.23 4.25 4.26 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.35 4.36 4.39 4.42 4.44 4.46 GB-02 3.94 4.04 4.09 4.14 4.17 4.19 4.21 4.23 4.25 4.26 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.35 4.36 4.39 4.42 4.44 4.46 GB-03 4.42 4.61 4.73 4.81 4.87 4.92 4.97 5.01 5.04 5.07 5.10 5.12 5.14 5.16 5.18 5.20 5.22 5.24 5.25 5.26 5.33 5.38 5.42 5.46 GB-04 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-05 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-06 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-07 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-08 4.46 4.66 4.78 4.87 4.93 4.98 5.03 5.07 5.10 5.13 5.16 5.19 5.21 5.23 5.25 5.27 5.29 5.30 5.32 5.33 5.40 5.45 5.50 5.54 HC-01 4.46 4.67 4.79 4.87 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.51 5.55 HC-02 4.46 4.67 4.79 4.87 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.51 5.55 Lac Des Allemands 6.51 6.70 6.82 6.90 6.96 7.01 7.05 7.09 7.12 7.15 7.18 7.20 7.23 7.25 7.27 7.28 7.30 7.32 7.33 7.35 7.41 7.46 7.50 7.54 Lake Boeuf 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 LB-01 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 LB-02 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 MC-01 4.48 4.68 4.80 4.88 4.95 5.00 5.05 5.08 5.12 5.15 5.18 5.20 5.22 5.25 5.27 5.28 5.30 5.32 5.33 5.35 5.41 5.47 5.51 5.55 R11 3.26 3.80 4.11 4.34 4.51 4.65 4.77 4.87 4.96 5.05 5.12 5.19 5.25 5.31 5.36 5.41 5.46 5.50 5.54 5.58 5.76 5.90 6.02 6.12 RL-1 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-10 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-2 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-3 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-4 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-5 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-6 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-7 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 98
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 RL-8 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-9 4.46 4.66 4.78 4.87 4.93 4.98 5.03 5.07 5.10 5.13 5.16 5.19 5.21 5.23 5.25 5.27 5.29 5.30 5.32 5.33 5.40 5.45 5.50 5.54 SA C-1 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-10 4.04 4.20 4.29 4.36 4.41 4.45 4.48 4.51 4.54 4.57 4.59 4.61 4.63 4.64 4.66 4.67 4.69 4.70 4.71 4.72 4.78 4.82 4.85 4.88 SA C-11 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-12 4.02 4.19 4.29 4.36 4.41 4.46 4.50 4.53 4.56 4.58 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.75 4.81 4.85 4.89 4.92 SA C-2 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-3 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-4 4.20 4.38 4.48 4.56 4.62 4.66 4.70 4.74 4.77 4.79 4.82 4.84 4.86 4.88 4.90 4.91 4.93 4.94 4.96 4.97 5.03 5.08 5.11 5.15 SA C-5 4.20 4.38 4.48 4.56 4.62 4.66 4.70 4.74 4.77 4.79 4.82 4.84 4.86 4.88 4.90 4.91 4.93 4.94 4.96 4.97 5.03 5.08 5.11 5.15 SA C-6 4.04 4.20 4.29 4.35 4.41 4.45 4.48 4.51 4.54 4.56 4.59 4.61 4.62 4.64 4.66 4.67 4.68 4.70 4.71 4.72 4.77 4.81 4.85 4.88 SA C-7 4.04 4.20 4.29 4.36 4.41 4.45 4.49 4.52 4.55 4.57 4.59 4.61 4.63 4.65 4.66 4.68 4.69 4.71 4.72 4.73 4.78 4.82 4.86 4.89 SA C-8 4.04 4.20 4.29 4.36 4.41 4.45 4.49 4.52 4.55 4.57 4.59 4.61 4.63 4.65 4.66 4.68 4.69 4.71 4.72 4.73 4.78 4.82 4.86 4.89 SA C-9 4.04 4.20 4.29 4.35 4.41 4.45 4.48 4.51 4.54 4.56 4.59 4.61 4.62 4.64 4.66 4.67 4.68 4.70 4.71 4.72 4.77 4.81 4.85 4.88 SA-1A 5.85 6.14 6.31 6.43 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.05 7.07 7.09 7.12 7.21 7.29 7.35 7.41 SA16 5.77 6.06 6.24 6.36 6.46 6.54 6.60 6.66 6.71 6.76 6.80 6.84 6.87 6.90 6.93 6.96 6.99 7.01 7.04 7.06 7.15 7.23 7.30 7.36 SA17A 5.55 5.81 5.97 6.07 6.16 6.23 6.29 6.34 6.38 6.42 6.46 6.49 6.52 6.55 6.57 6.60 6.62 6.64 6.66 6.68 6.77 6.84 6.89 6.94 SA17B 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA19-E-1 5.84 6.14 6.31 6.43 6.53 6.60 6.67 6.72 6.77 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.29 7.35 7.41 SA19-E-2 5.85 6.14 6.31 6.44 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.94 6.97 7.00 7.02 7.05 7.07 7.10 7.12 7.21 7.29 7.36 7.41 SA19A 5.85 6.14 6.31 6.44 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.94 6.97 7.00 7.02 7.05 7.07 7.10 7.12 7.21 7.29 7.36 7.41 SA19B 5.85 6.15 6.32 6.44 6.53 6.61 6.68 6.73 6.78 6.83 6.87 6.90 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.12 7.21 7.29 7.36 7.41 SA19B-N 5.85 6.14 6.31 6.43 6.53 6.60 6.67 6.73 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.28 7.35 7.41 SA21A 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09 SA21B 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09 SA21Bb 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 99
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 SA21Bc 5.44 5.70 5.85 5.95 6.04 6.10 6.16 6.21 6.25 6.29 6.33 6.36 6.39 6.41 6.44 6.46 6.49 6.51 6.53 6.54 6.63 6.69 6.75 6.80 SA22A 5.84 6.14 6.31 6.43 6.53 6.60 6.67 6.72 6.77 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.29 7.35 7.41 SA23A-01 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-02 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-03 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-04N 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-04S 5.37 5.60 5.74 5.84 5.92 5.98 6.03 6.08 6.12 6.16 6.19 6.22 6.25 6.27 6.30 6.32 6.34 6.36 6.38 6.39 6.47 6.53 6.59 6.63 SA23B 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA25Bw 5.67 5.94 6.10 6.21 6.29 6.36 6.42 6.47 6.52 6.56 6.59 6.63 6.66 6.69 6.71 6.74 6.76 6.78 6.80 6.82 6.91 6.98 7.04 7.09 SB-01 4.48 4.68 4.80 4.88 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.21 5.24 5.26 5.27 5.29 5.31 5.32 5.34 5.40 5.45 5.50 5.54 SB-02 4.48 4.68 4.80 4.88 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.21 5.24 5.26 5.27 5.29 5.31 5.32 5.34 5.40 5.45 5.50 5.54 SB-03 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-04 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-05 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-06 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-07 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-08 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-09 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-10 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 TC-01 3.85 4.36 4.66 4.87 5.04 5.17 5.29 5.39 5.47 5.55 5.62 5.69 5.74 5.80 5.85 5.90 5.94 5.99 6.03 6.06 6.23 6.36 6.48 6.58 B.CNort28203 4.03 4.20 4.31 4.38 4.44 4.49 4.53 4.56 4.59 4.62 4.64 4.66 4.69 4.70 4.72 4.74 4.75 4.77 4.78 4.80 4.85 4.90 4.94 4.97 Verret10863 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 Brazan1.36363 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 Becnel10065 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 Lasseigne9157991 5.76 6.06 6.24 6.37 6.47 6.55 6.62 6.68 6.73 6.77 6.82 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.18 7.26 7.33 7.39
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 100
Table 19. Frequency stages for "future without action” scenario assuming 50-year failure.
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 11a 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 11a2 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 11a3 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 11b 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 11c 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 12A 5.90 6.12 6.25 6.35 6.42 6.47 6.52 6.57 6.60 6.64 6.67 6.70 6.72 6.74 6.77 6.79 6.81 6.82 6.84 6.86 6.93 6.99 7.04 7.08 12b 5.50 5.71 5.84 5.93 6.00 6.06 6.11 6.15 6.19 6.22 6.25 6.28 6.31 6.33 6.35 6.37 6.39 6.41 6.43 6.44 6.51 6.57 6.62 6.66 13 6.44 6.54 6.60 6.64 6.67 6.70 6.72 6.74 6.76 6.77 6.79 6.80 6.81 6.82 6.83 6.84 6.85 6.86 6.87 6.87 6.91 6.93 6.96 6.98 14A 5.77 6.08 6.26 6.39 6.49 6.57 6.64 6.69 6.75 6.79 6.84 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 15A 5.56 5.91 6.12 6.26 6.37 6.47 6.54 6.61 6.67 6.72 6.77 6.82 6.86 6.89 6.93 6.96 6.99 7.02 7.05 7.08 7.19 7.28 7.36 7.43 15B 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 15C 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 4A1 4.55 4.94 5.17 5.33 5.46 5.56 5.65 5.72 5.79 5.85 5.90 5.95 6.00 6.04 6.08 6.12 6.15 6.18 6.21 6.24 6.37 6.47 6.56 6.63 4A2 4.53 4.93 5.16 5.32 5.45 5.55 5.64 5.72 5.79 5.85 5.90 5.95 6.00 6.04 6.08 6.11 6.15 6.18 6.21 6.24 6.37 6.47 6.56 6.64 4A3 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A4 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A5 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4A6 4.02 4.17 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.68 4.69 4.74 4.78 4.81 4.84 4A7 4.02 4.17 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.68 4.69 4.74 4.78 4.81 4.84 4A8 4.02 4.18 4.27 4.33 4.38 4.42 4.45 4.48 4.51 4.53 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.66 4.67 4.68 4.73 4.77 4.81 4.84 4A9 4.01 4.17 4.27 4.33 4.39 4.43 4.46 4.49 4.52 4.55 4.57 4.59 4.61 4.62 4.64 4.65 4.67 4.68 4.69 4.70 4.76 4.80 4.83 4.86 4C-1 7.46 7.76 7.93 8.05 8.14 8.22 8.28 8.34 8.39 8.43 8.47 8.51 8.54 8.57 8.60 8.63 8.66 8.68 8.70 8.72 8.82 8.90 8.96 9.02 4C-10 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 4C-11 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 101
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 4C-12 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-13 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-14 4.98 5.19 5.31 5.40 5.46 5.52 5.57 5.61 5.64 5.67 5.70 5.73 5.75 5.77 5.79 5.81 5.83 5.85 5.87 5.88 5.95 6.00 6.05 6.09 4C-15 5.45 5.71 5.86 5.97 6.05 6.12 6.18 6.23 6.27 6.31 6.35 6.38 6.41 6.44 6.46 6.49 6.51 6.53 6.55 6.57 6.66 6.72 6.78 6.83 4C-16 5.46 5.72 5.87 5.98 6.06 6.13 6.19 6.23 6.28 6.32 6.35 6.39 6.42 6.44 6.47 6.49 6.52 6.54 6.56 6.58 6.66 6.73 6.78 6.83 4C-17 5.62 5.90 6.07 6.18 6.28 6.35 6.41 6.47 6.52 6.56 6.60 6.63 6.67 6.70 6.72 6.75 6.78 6.80 6.82 6.84 6.93 7.01 7.07 7.13 4C-18 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 4C-19 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 4C-2 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 4C-20 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 4C-21 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36 4C-3 7.46 7.76 7.93 8.05 8.14 8.22 8.28 8.34 8.39 8.43 8.47 8.51 8.54 8.57 8.60 8.63 8.66 8.68 8.70 8.72 8.82 8.90 8.96 9.02 4C-4 5.69 5.90 6.02 6.11 6.18 6.23 6.28 6.32 6.36 6.39 6.42 6.45 6.47 6.49 6.51 6.53 6.55 6.57 6.59 6.60 6.67 6.73 6.77 6.81 4C-5 4.39 4.57 4.67 4.75 4.80 4.85 4.89 4.92 4.95 4.98 5.01 5.03 5.05 5.07 5.08 5.10 5.12 5.13 5.15 5.16 5.22 5.26 5.30 5.34 4C-6 4.37 4.56 4.67 4.74 4.80 4.85 4.89 4.93 4.96 4.99 5.01 5.04 5.06 5.08 5.10 5.11 5.13 5.15 5.16 5.17 5.23 5.28 5.32 5.36 4C-7 5.45 5.71 5.87 5.98 6.06 6.13 6.19 6.24 6.28 6.32 6.36 6.39 6.42 6.45 6.48 6.50 6.53 6.55 6.57 6.59 6.67 6.74 6.80 6.85 4C-8 5.62 5.90 6.07 6.19 6.28 6.35 6.41 6.47 6.51 6.56 6.60 6.63 6.66 6.69 6.72 6.75 6.77 6.80 6.82 6.84 6.93 7.00 7.07 7.12 4C-9 5.76 6.07 6.25 6.37 6.47 6.55 6.62 6.68 6.74 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.19 7.27 7.34 7.40 5A 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 5B 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 5C 4.03 4.20 4.30 4.37 4.43 4.47 4.51 4.54 4.57 4.60 4.62 4.64 4.66 4.68 4.70 4.72 4.73 4.75 4.76 4.77 4.83 4.87 4.91 4.94 9a 8.91 9.28 9.49 9.64 9.76 9.85 9.94 10.01 10.07 10.12 10.17 10.22 10.26 10.30 10.34 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9b 8.91 9.28 9.49 9.64 9.76 9.85 9.94 10.01 10.07 10.12 10.17 10.22 10.26 10.30 10.34 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9c 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58 9d 8.90 9.27 9.48 9.63 9.75 9.85 9.93 10.00 10.06 10.12 10.17 10.22 10.26 10.30 10.33 10.37 10.40 10.43 10.46 10.49 10.60 10.70 10.78 10.85 9e 5.28 5.53 5.67 5.77 5.85 5.91 5.97 6.02 6.06 6.09 6.13 6.16 6.19 6.21 6.24 6.26 6.28 6.30 6.32 6.34 6.42 6.48 6.54 6.58
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 102
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 9f 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 BB-01 4.49 4.68 4.79 4.87 4.93 4.98 5.03 5.06 5.10 5.12 5.15 5.17 5.20 5.22 5.24 5.25 5.27 5.29 5.30 5.32 5.38 5.43 5.47 5.51 GB-01 3.94 4.04 4.09 4.14 4.17 4.19 4.21 4.23 4.25 4.26 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.35 4.36 4.39 4.42 4.44 4.46 GB-02 3.94 4.04 4.09 4.14 4.17 4.19 4.21 4.23 4.25 4.26 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.35 4.36 4.39 4.42 4.44 4.46 GB-03 4.42 4.61 4.73 4.81 4.87 4.92 4.97 5.01 5.04 5.07 5.10 5.12 5.14 5.16 5.18 5.20 5.22 5.24 5.25 5.26 5.33 5.38 5.42 5.46 GB-04 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-05 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-06 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-07 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 GB-08 4.46 4.66 4.78 4.87 4.93 4.98 5.03 5.07 5.10 5.13 5.16 5.19 5.21 5.23 5.25 5.27 5.29 5.30 5.32 5.33 5.40 5.45 5.50 5.54 HC-01 4.46 4.67 4.79 4.87 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.51 5.55 HC-02 4.46 4.67 4.79 4.87 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.51 5.55 Lac Des Allemands 6.51 6.70 6.82 6.90 6.96 7.01 7.05 7.09 7.12 7.15 7.18 7.20 7.23 7.25 7.27 7.28 7.30 7.32 7.33 7.35 7.41 7.46 7.50 7.54 Lake Boeuf 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 LB-01 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 LB-02 4.48 4.68 4.80 4.88 4.94 5.00 5.04 5.08 5.11 5.14 5.17 5.20 5.22 5.24 5.26 5.28 5.30 5.31 5.33 5.34 5.41 5.46 5.50 5.54 MC-01 4.48 4.68 4.80 4.88 4.95 5.00 5.05 5.08 5.12 5.15 5.18 5.20 5.22 5.25 5.27 5.28 5.30 5.32 5.33 5.35 5.41 5.47 5.51 5.55 R11 3.26 3.80 4.11 4.34 4.51 4.65 4.77 4.87 4.96 5.05 5.12 5.19 5.25 5.31 5.36 5.41 5.46 5.50 5.54 5.58 5.76 5.90 6.02 6.12 RL-1 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-10 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-2 1.94 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-3 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-4 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-5 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-6 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-7 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 103
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 RL-8 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 RL-9 4.46 4.66 4.78 4.87 4.93 4.98 5.03 5.07 5.10 5.13 5.16 5.19 5.21 5.23 5.25 5.27 5.29 5.30 5.32 5.33 5.40 5.45 5.50 5.54 SA C-1 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-10 4.04 4.20 4.29 4.36 4.41 4.45 4.48 4.51 4.54 4.57 4.59 4.61 4.63 4.64 4.66 4.67 4.69 4.70 4.71 4.72 4.78 4.82 4.85 4.88 SA C-11 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-12 4.02 4.19 4.29 4.36 4.41 4.46 4.50 4.53 4.56 4.58 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.75 4.81 4.85 4.89 4.92 SA C-2 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-3 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 SA C-4 4.20 4.38 4.48 4.56 4.62 4.66 4.70 4.74 4.77 4.79 4.82 4.84 4.86 4.88 4.90 4.91 4.93 4.94 4.96 4.97 5.03 5.08 5.11 5.15 SA C-5 4.20 4.38 4.48 4.56 4.62 4.66 4.70 4.74 4.77 4.79 4.82 4.84 4.86 4.88 4.90 4.91 4.93 4.94 4.96 4.97 5.03 5.08 5.11 5.15 SA C-6 4.04 4.20 4.29 4.35 4.41 4.45 4.48 4.51 4.54 4.56 4.59 4.61 4.62 4.64 4.66 4.67 4.68 4.70 4.71 4.72 4.77 4.81 4.85 4.88 SA C-7 4.04 4.20 4.29 4.36 4.41 4.45 4.49 4.52 4.55 4.57 4.59 4.61 4.63 4.65 4.66 4.68 4.69 4.71 4.72 4.73 4.78 4.82 4.86 4.89 SA C-8 4.04 4.20 4.29 4.36 4.41 4.45 4.49 4.52 4.55 4.57 4.59 4.61 4.63 4.65 4.66 4.68 4.69 4.71 4.72 4.73 4.78 4.82 4.86 4.89 SA C-9 4.04 4.20 4.29 4.35 4.41 4.45 4.48 4.51 4.54 4.56 4.59 4.61 4.62 4.64 4.66 4.67 4.68 4.70 4.71 4.72 4.77 4.81 4.85 4.88 SA-1A 5.85 6.14 6.31 6.43 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.05 7.07 7.09 7.12 7.21 7.29 7.35 7.41 SA16 5.77 6.06 6.24 6.36 6.46 6.54 6.60 6.66 6.71 6.76 6.80 6.84 6.87 6.90 6.93 6.96 6.99 7.01 7.04 7.06 7.15 7.23 7.30 7.36 SA17A 5.55 5.81 5.97 6.07 6.16 6.23 6.29 6.34 6.38 6.42 6.46 6.49 6.52 6.55 6.57 6.60 6.62 6.64 6.66 6.68 6.77 6.84 6.89 6.94 SA17B 5.77 6.07 6.26 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA19-E-1 5.84 6.14 6.31 6.43 6.53 6.60 6.67 6.72 6.77 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.29 7.35 7.41 SA19-E-2 5.85 6.14 6.31 6.44 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.94 6.97 7.00 7.02 7.05 7.07 7.10 7.12 7.21 7.29 7.36 7.41 SA19A 5.85 6.14 6.31 6.44 6.53 6.61 6.67 6.73 6.78 6.82 6.86 6.90 6.94 6.97 7.00 7.02 7.05 7.07 7.10 7.12 7.21 7.29 7.36 7.41 SA19B 5.85 6.15 6.32 6.44 6.53 6.61 6.68 6.73 6.78 6.83 6.87 6.90 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.12 7.21 7.29 7.36 7.41 SA19B-N 5.85 6.14 6.31 6.43 6.53 6.60 6.67 6.73 6.78 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.28 7.35 7.41 SA21A 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09 SA21B 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09 SA21Bb 5.10 5.29 5.39 5.47 5.53 5.58 5.62 5.66 5.69 5.72 5.74 5.77 5.79 5.81 5.83 5.84 5.86 5.88 5.89 5.90 5.96 6.01 6.05 6.09
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 104
Storage Frequencies and Stages Area 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 125 150 175 200 SA21Bc 5.44 5.70 5.85 5.95 6.04 6.10 6.16 6.21 6.25 6.29 6.33 6.36 6.39 6.41 6.44 6.46 6.49 6.51 6.53 6.54 6.63 6.69 6.75 6.80 SA22A 5.84 6.14 6.31 6.43 6.53 6.60 6.67 6.72 6.77 6.82 6.86 6.90 6.93 6.96 6.99 7.02 7.04 7.07 7.09 7.11 7.21 7.29 7.35 7.41 SA23A-01 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-02 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-03 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-04N 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA23A-04S 5.37 5.60 5.74 5.84 5.92 5.98 6.03 6.08 6.12 6.16 6.19 6.22 6.25 6.27 6.30 6.32 6.34 6.36 6.38 6.39 6.47 6.53 6.59 6.63 SA23B 5.76 6.07 6.25 6.38 6.48 6.56 6.63 6.69 6.74 6.79 6.83 6.87 6.91 6.94 6.97 7.00 7.03 7.05 7.08 7.10 7.20 7.28 7.35 7.41 SA25Bw 5.67 5.94 6.10 6.21 6.29 6.36 6.42 6.47 6.52 6.56 6.59 6.63 6.66 6.69 6.71 6.74 6.76 6.78 6.80 6.82 6.91 6.98 7.04 7.09 SB-01 4.48 4.68 4.80 4.88 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.21 5.24 5.26 5.27 5.29 5.31 5.32 5.34 5.40 5.45 5.50 5.54 SB-02 4.48 4.68 4.80 4.88 4.94 4.99 5.04 5.08 5.11 5.14 5.17 5.19 5.21 5.24 5.26 5.27 5.29 5.31 5.32 5.34 5.40 5.45 5.50 5.54 SB-03 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-04 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-05 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-06 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-07 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-08 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-09 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 SB-10 4.41 4.61 4.73 4.81 4.87 4.93 4.97 5.01 5.04 5.07 5.10 5.12 5.15 5.17 5.19 5.21 5.22 5.24 5.26 5.27 5.33 5.39 5.43 5.47 TC-01 3.85 4.36 4.66 4.87 5.04 5.17 5.29 5.39 5.47 5.55 5.62 5.69 5.74 5.80 5.85 5.90 5.94 5.99 6.03 6.06 6.23 6.36 6.48 6.58 B.CNort28203 4.03 4.20 4.31 4.38 4.44 4.49 4.53 4.56 4.59 4.62 4.64 4.66 4.69 4.70 4.72 4.74 4.75 4.77 4.78 4.80 4.85 4.90 4.94 4.97 Verret10863 3.97 4.15 4.25 4.33 4.38 4.43 4.47 4.51 4.54 4.56 4.59 4.61 4.63 4.65 4.67 4.68 4.70 4.71 4.73 4.74 4.80 4.85 4.89 4.92 Brazan1.36363 5.45 5.67 5.80 5.89 5.96 6.02 6.07 6.11 6.15 6.18 6.21 6.24 6.27 6.29 6.31 6.33 6.35 6.37 6.39 6.40 6.47 6.53 6.58 6.63 Becnel10065 5.84 6.13 6.30 6.42 6.52 6.60 6.66 6.72 6.77 6.81 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.10 7.20 7.27 7.34 7.39 Lasseigne9157991 5.76 6.06 6.24 6.37 6.47 6.55 6.62 6.68 6.73 6.77 6.82 6.85 6.89 6.92 6.95 6.98 7.01 7.03 7.06 7.08 7.18 7.26 7.33 7.39
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 105
APPENDIX G: COMPARISON OF HEC-RAS AND CLARA FLOOD DEPTH EXCEEDANCES Flood depths and damage estimates from the main body of this report are based on the combined rainfall and surge-based flood modeling done in the HEC-RAS model, as described in previous sections. Modeling in support of CPRA’s 2017 Coastal Master Plan is based on the CLARA model’s flood modules, which carry out a modified JPM-OS approach that primarily addresses surge-based flooding (rainfall from tropical cyclones is predicted in CLARA but only applied in areas enclosed by a federal ring levee system like HSDRRS). The joint rainfall and surge analysis is intended to produce more accurate statistical results at the higher- frequency end of the flood depth probability distribution; the flood dynamics of lower-frequency, more extreme storms are generally dominated by surge. However, in the upper end of the basin where it is difficult for surge to penetrate, the addition of rainfall may have a significant impact at all parts of the curve. This appendix shows the difference in estimated flood depths between the HEC-RAS and CLARA models at the 10-, 25-, 50-, 100-, and 500-year return periods. Across these Figures, several patterns are readily apparent. West of Highway 90, the new RAS method generally produces higher flood depth exceedances than the CLARA model, even at low-frequency return periods (including the 500-year return period, which was extrapolated from the RAS results rather than being explicitly estimated). At the 10- and 25-year return periods, this reflects that CLARA predicts little flooding in the area from storm surge, but the same pattern holds even at points and return periods where CLARA and HEC-RAS both predict flooding. The exception to this pattern is in Lac Des Allemands, where the CLARA model produced greater values. In the wetlands and wildlife areas east of Highway 90, at the eastern edge of the study region, the CLARA model generally estimates greater flood depth exceedances. In populated Des Allemands, however, the new method produces greater values at the higher-frequency 10- and 25-year return periods, but lower values at the 100- and 500-year return periods. Because the Highway 90 and Ridge Alignments both protect Des Allemands and the communities west of Highway 90, damage calculations are generally based on higher flood depth exceedances than those produced by CLARA v2.0. While many of the points in the middle of the basin are unpopulated and contain few economic assets, this still has the effect of producing greater EAD, and thus greater risk reduction, than would be produced using CLARA in the same type of analysis used to support the Master Plan development.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 106
Figure 29. Difference in flood depths between RAS and CLARA models (10-year return period).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 107
Figure 30. Difference in flood depths between RAS and CLARA models (25-year return period).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 108
Figure 31. Difference in flood depths between RAS and CLARA models (50-year return period).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 109
Figure 32. Difference in flood depths between RAS and CLARA models (100-year return period).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 110
Figure 33. Difference in flood depths between RAS and CLARA models (500-year return period).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 111
References Coastal Protection and Restoration Authority of Louisiana. (2012). Louisiana’s Comprehensive Master Plan for a Sustainable Coast. Baton Rouge, LA: CPRA. Baum, G. (2012). Effects of radar and rain gage representations of precipitation on the flood modeling of the remnants of Tropical Storm Henri in the Red Clay Creek Watershed. (Thesis). University of Delaware, USA. Evans, D. L., Gudes, S., & Kelly, J. J. (2001). Tropical Storm Allison Heavy Rains and Floods Texas and Louisiana, June 2001. (Retrieved from http://www.nws.noaa.gov/om/assessments/pdfs/allison.pdf) Irish, J. L., & D. T. Resio, et al. (2009). A surge response function approach to coastal hazard assessment. Part 2: Quantification of spatial attributes of response functions. Natural Hazards 51(1), 183-205. Johnson, D. R., Fischbach, J. R. & Ortiz, D. S. (2013). Estimating Surge-Based Flood Risk with the Coastal Louisiana Risk Assessment Model. In: N. Peyronnin, & D. Reed (Eds.), Louisiana’s 2012 Coastal Master Plan Technical Analysis. Journal of Coastal Research (Special Issue), No. 67. Miller, James D., Hyeonjun, Kim, Kjeldsen, Thomas R., Packman, John, Grebby, Stephen, & Dearden, Rachel. (2014). Assessing the impact of urbanization on storm runoff in a peri-urban catchment using historical change in impervious cover. Journal of Hydrology, Vol. 515, 59-70. Resio, D. T. (2007). White Paper on Estimating Hurricane Inundation Probabilities. Interagency Performance Evaluation Taskforce. New Orleans, LA, VIII-2 (R2007). Resio, D. T., & Irish, J. L., et al. (2009). A surge response function approach to coastal hazard assessment. Part 1: Basic concepts. Natural Hazards, 51(1), 163-182. Roberts, H.J., Johnson, D.R., & Clark, F.R. (2014). Project Development & Implementation Program: Upper Barataria Basin Risk Reduction. The Water Institute of the Gulf. Funded by the Coastal Protection and Restoration Authority under Task Order 18, Contract No. 2503-12-58. Baton Rouge, LA. Toro, G. R., & Resio, D.T., et al. (2010). Efficient joint-probability methods for hurricane surge frequency analysis. Ocean Engineering 37(1), 125-134. USACE. (2008). Flood Insurance Study: Southeastern Parishes, Louisiana. Intermediate Submission 2: Offshore Water Levels and Waves. Vicksburg, MS: USACE, 152p. USACE. (2011). Donaldsonville, Louisiana to the Gulf of Mexico, Flood Control-Mississippi River and Tributaries Feasibility Study. Engineering Appendix, January 2011, Vol. 2, U.S. Army Corps of Engineers. New Orleans: LA. NOAA Technical Memorandum. (nd). NWS HYDRO-35: Five- to 60-Minute Precipitation Frequency for the Eastern and Central United States. (Retrieved from http://www.nws.noaa.gov/oh/hdsc/PF_documents/TechnicalMemo_HYDRO35.pdf). U.S. Department of Commerce. (nd). Technical Paper 49: Two- to Ten-day Precipitation for Return Periods of 2 to 100 Years in the Contiguous United States. (Retrieved from http://www.nws.noaa.gov/oh/hdsc/PF_documents/Technical Paper _No 49.pdf).
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 112
U.S. Department of Commerce. (1961). Technical Paper 40: Rainfall Frequency Atlas of the United States for Durations from 30 Minutes to 24 Hours and Return Periods from 1 to 100 Years. (Retrieved from http://www.nws.noaa.gov/oh/hdsc/PF_documents/TechnicalPaper_No40.pdf.
Upper Barataria Basin Risk Reduction Modeling: Phase 2 – Rainfall & Storm Surge Combined Effects Modeling 113
INTEGRATING APPLIED RESEARCH | LINKING KNOWLEDGE TO ACTION | BUILDING PARTNERSHIPS
301 NORTH MAIN STREET, SUITE 2000 BATON ROUGE, LA 70825
(225) 448-2813 WWW.THEWATERINSTITUTE.ORG WWW.THEWATERINSTITUTE.ORG