Adapting Land Use and Water Management Plans to …...Adapting Land Use and Water Management Plans...

Adapting Land Use and Water Management Plans to a Changing Climate in Miami-Dade and Broward Counties, FloridaDavid G. Groves, Debra Knopman, Neil Berg, Craig A. Bond, James Syme,

Robert J. Lempert

C O R P O R A T I O N

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iii

Preface

Cities and metropolitan regions are at the forefront of efforts to understand and respond to cur-rent and future climate change impacts, develop resiliency and adaptation capacity in response to climate change, and reduce greenhouse gas emissions. Through the generous support of the John D. and Catherine T. MacArthur Foundation, the RAND Corporation has been help-ing advance these efforts by bringing new analytical and planning capabilities into a regional urban context. This effort has included pilot engagements in three metropolitan regions. One of the regions RAND selected for engagement was Southeast Florida, specifically the area including the counties of Miami-Dade and Broward, two of the four counties that formed the Southeast Florida Regional Climate Change Compact in 2010.

The primary audience for this work includes local and regional officials, planners, engi-neers, and residents of Southeast Florida. The report should also be relevant to other coastal regions in the United States and elsewhere that are seeking to better understand their vulner-abilities to a changing climate and make progress on identifying and implementing measures to reduce risk while fostering economic development, ecosystem protection, water quality, and other public values.

Readers of this report may also be interested in other RAND work on this and related topics:

• Jordan R. Fischbach, Kyle Siler-Evans, Devin Tierney, Michael T. Wilson, Lauren M. Cook, and Linnea Warren May, Robust Stormwater Management in the Pittsburgh Region: A Pilot Study, Santa Monica, Calif.: RAND Corporation, RR-1673-MCF, 2017.

• Debra Knopman and Robert J. Lempert, Urban Responses to Climate Change: Framework for Decisionmaking and Supporting Indicators, Santa Monica, Calif.: RAND Corporation, RR-1144-MCF, 2016.

• David G. Groves, Jordan R. Fischbach, Debra Knopman, David R. Johnson, and Kate Giglio, Strengthening Coastal Planning: How Coastal Regions Could Benefit from Louisi-ana’s Planning and Analysis Framework, Santa Monica, Calif.: RAND Corporation, RR-437-RC, 2014b.

• David G. Groves, Jordan R. Fischbach, Nidhi Kalra, Edmundo Molina-Perez, David Yates, David Purkey, Amanda Fencl, Vishal K. Mehta, Ben Wright, and Grantley Pyke, Developing Robust Strategies for Climate Change and Other Risks: A Water Utility Frame-work, Santa Monica, Calif.: RAND Corporation, RR-977-WRF, 2014a.

iv Adapting Land Use and Water Management Plans to a Changing Climate

About RAND Infrastructure Resilience and Environmental Policy

The research reported here was conducted in the RAND Infrastructure Resilience and Envi-ronmental Policy Program, which performs analyses on urbanization and other stresses. This includes research on infrastructure development; infrastructure financing; energy policy; urban planning and the role of public-private partnerships; transportation policy; climate response, mitigation, and adaptation; environmental sustainability; and water resource management and coastal protection. Program research is supported by government agencies, foundations, and the private sector.

RAND Justice, Infrastructure, and Environment (JIE) conducts research and analysis in civil and criminal justice, infrastructure development and financing, environmental policy, transportation planning and technology, immigration and border protection, public and occu-pational safety, energy policy, science and innovation policy, space, telecommunications, and trends and implications of artificial intelligence and other computational technologies.

Questions or comments about this report should be sent to Debra Knopman ([email protected]). For more information about the Infrastructure Resilience and Environmental Policy Program, see www.rand.org/jie or contact the director at [email protected].

http://www.rand.org/jie

mailto:[email protected]

v

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixSummary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii

CHAPTER ONE

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Water Management Challenges Faced in Southeast Florida . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Analytical Needs in the Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4How This Report Is Organized . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

CHAPTER TWO

Approach to Analysis and Decision Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Climate Adaptation Planning Using Deliberation with Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Overview of Robust Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Scope of the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Decision Support Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14How This Approach Differs from Other Efforts in the Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

CHAPTER THREE

Integrated Modeling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Overview of Modeling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Urban Miami-Dade Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Broward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Linkage of Economic Metrics to Hydrologic Model Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Observations About the Hydrologic Modeling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

CHAPTER FOUR

Plausible Future Conditions Affecting Southeast Florida’s Built Environment . . . . . . . . . . . . . . . . . . 23Climate Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Land Use and Asset Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

vi Adapting Land Use and Water Management Plans to a Changing Climate

CHAPTER FIVE

Vulnerability of Assets to Groundwater Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Baseline Groundwater Flooding Potential Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Future Groundwater Flooding Hazards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Asset Values at Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Drivers of Vulnerability to Groundwater Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55Potential Strategies to Mitigate Vulnerability to Groundwater Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

CHAPTER SIX

Vulnerability of Wells to Saltwater Intrusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Broward Wells and Saltwater Intrusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Miami-Dade Wells and Saltwater Intrusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

CHAPTER SEVEN

Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Summary of Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

APPENDIXES

A. Simulating Future Land Use in the Broward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75B. Standardizing Land Use Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79C. Grid-Cell Density Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

vii

Figures

S.1. Integrated Modeling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii 1.1. Areal Extent of the Biscayne Aquifer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2.1. Steps to Robust Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2. Integrated Modeling Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1. Boundaries of USGS Groundwater Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.1. Current and Future Monthly Tides as Represented in the UMD Model . . . . . . . . . . . . . . . . . . . 25 4.2. Current and Future Monthly Tides as Represented in the Broward Model . . . . . . . . . . . . . . . . 25 4.3. Current and Future Monthly Rainfall Averaged over the UMD and Broward

Model Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.4. Simple Example of Converting Parcel-Based Land Use and Value Information to

Model Grid Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.5. Maps of Standardized Current and Future Land Use Across the Groundwater

Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.6. Land Use Change from the Present to 2030 for Miami-Dade and 2040 for

Broward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.7. Regions Used for Reporting of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.8. Current Land Use Values, 2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.9. Projected Change in Valuation Under the Equal-Value Elevation Method for

Broward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.10. Projected Valuation Change for Broward Using the TAZ Method . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.11. Projected Change in Valuation Under the Equal-Value Elevation Method. . . . . . . . . . . . . . . . 38 4.12. Projected Change in Asset Values for Broward County, 2015–2035 . . . . . . . . . . . . . . . . . . . . . . . . 39 4.13. Projected Change in Asset Values for Miami-Dade County, 2015–2035 . . . . . . . . . . . . . . . . . . 40 5.1. Groundwater Depth Under Baseline Conditions for SLR and Precipitation in

the Wet Season for Broward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2. Groundwater Depth Under Baseline Conditions for SLR and Precipitation in

the Wet Season for UMD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.3. Changes in Groundwater Depths for Broward for Middle SLR and Average

Precipitation in the Wet Season, Through 2040–2054 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.4. Changes in Groundwater Depths for UMD for Middle SLR and Average

Precipitation in the Wet Season, Through 2040–2054 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.5. Groundwater Depths Under High SLR and Wettest Precipitation in the Wet

Season for Broward, 2040–2054 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.6. Groundwater Depths Under High SLR and Wettest Precipitation in the Wet

Season for UMD, 2040–2054 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.7. Summary of Broward Vulnerabilities to Groundwater Flooding . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.8. Summary of Miami-Dade’s Vulnerabilities to Groundwater Flooding . . . . . . . . . . . . . . . . . . . . . 51 5.9. Summary of Assets Vulnerable to Groundwater Flooding in Broward . . . . . . . . . . . . . . . . . . . . . 52

viii Adapting Land Use and Water Management Plans to a Changing Climate

5.10. Value of Vulnerable Assets in Broward for High SLR and Wettest Precipitation Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5.11. Summary of Assets Vulnerable to Groundwater Flooding in UMD . . . . . . . . . . . . . . . . . . . . . . . 54 5.12. Value of Vulnerable Assets in UMD for Low SLR and Driest Precipitation

Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

5.13. Value of Vulnerable Assets in UMD for High SLR and Wettest Precipitation Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.14. Total Value Vulnerable Across SLR and Precipitation Scenarios in UMD Using the Elevation Method, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.15. Disaggregated Groundwater Flooding Vulnerability for the Elevation Method . . . . . . . . . . . 58 5.16. Disaggregated Groundwater Flooding Vulnerability for Two Broward Regions

and Two Asset Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.17. Drivers of Vulnerability to Groundwater Flooding for Two Futures for Broward . . . . . . . . 60 5.18. Drivers of Vulnerability to Groundwater Flooding for Two Futures for UMD . . . . . . . . . . . . 61 5.19. Asset-Dominated Vulnerabilities in Broward for Average Precipitation and

Mid-SLR Future, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.20. Hazard-Dominated Vulnerabilities in Broward for All but the Wettest

Precipitation Scenario, 2040. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.21. Regions of Vulnerability Increases for Miami-Dade Based on All but the

Wettest Precipitation Scenario, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.1. Well Locations, Replacement Costs, and Pumping Capacities for Broward

County . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.2. Well Locations and Level of Saltwater Intrusion Across Broward County, 2040 . . . . . . . . . . 67 6.3. Vulnerable Wells Across Futures in Broward County; 2040 Conditions and

Current Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 6.4. Well Locations and Salinity for the High SLR, Average Precipitation Future in

Miami-Dade County, 2040 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

ix

Tables

2.1. XLRM Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2. Potential Goals and Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.1. Monthly Scaling Factors for the Five Future Rainfall Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2. Standardized Land Use Categories Used in the Current and Future Periods . . . . . . . . . . . . . . 29 5.1. Strategies to Mitigate Groundwater Flooding for Broward and Miami-Dade . . . . . . . . . . . . . 61 A.1. Allowable Land Use Categories and Corresponding Depth to the Root Zone in the

Broward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 A.2. Calibrated Extinction Depth Multipliers Used to Simulate Historical Land Use

Changes in the Broward Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 B.1. Translating DOR Land Use Categories to Standardized Categories . . . . . . . . . . . . . . . . . . . . . . . . 79 B.2. Translating Miami-Dade 2030 CDMP Land Use Categories to Standardized

Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 B.3. Translating Broward Future Land Use Plan Categories to Standardized Categories . . . . . . 83 C.1. Persons per Cell and Value per Cell for Broward County by Future Residential

Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 C.2. Persons per Cell and Value per Cell for Miami-Dade County by Future

Residential Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

xi

Summary

Southeast Florida is particularly vulnerable to a changing climate. The highest land elevations in the region are about 12 feet above sea level. Further, the region relies on a complex network of interconnected canals and storm water management systems to manage flooding for both inland and coastal communities. These systems depend heavily on gravity to maintain posi-tive flows and discharges to coastal waters for flood relief. As sea levels rise, discharge capacity at major control structures and storm water outfalls will be reduced if not entirely eliminated. As a result, systemwide flooding will become increasingly frequent and problematic in associa-tion with high tides, storm surge, and even moderate rainfall. Flooding will be exacerbated by rising groundwater levels as rising seas saturate the limestone aquifer that underlies Southeast Florida and thereby reduce soil storage capacity. If not well planned and coordinated, land use and transportation decisions at the local and regional levels could exacerbate impacts of a changing climate.

Southeast Florida communities already experience tidal waters spilling over seawalls, pushing up through stormwater systems, and bubbling from the ground. Over the years, groundwater levels have risen more than a foot at some locations, such that drainage wells no longer operate as designed and flood conditions appear to be more frequent and extensive. Approximately 18 salinity control structures on major canals are operating within 6 inches of their original design capacity, and at some locations, extreme high tides result in the retention of floodwaters behind closed gates. Severe rainfall events during the historically drier winter months have also produced intense flooding, delivering as much as 22 inches of rainfall in fewer than 24 hours.

Objectives

Our goals in this research were to help improve the region’s capacity to adapt to both a chang-ing climate and changes in land use and to better understand the costs of both action and inaction across a wide range of futures. Drawing on experience in Louisiana and other coastal environments, we set out to build a transparent, interactive, and technically credible approach to decision support to assess vulnerabilities and gain insights into the potential strategies to reduce vulnerabilities under a range of climate and land use futures. Our work builds on the strong base of leadership and technical capacity already present in the region.

Our analysis addressed four questions:

xii Adapting Land Use and Water Management Plans to a Changing Climate

• How vulnerable is current and planned future urban development in Broward County and the urban portion of Miami-Dade County (referred to as UMD) to groundwater flooding?

• What are the key drivers of future risks, including sea-level rise (SLR), precipitation, and change in economic assets?

• What areas in Broward and UMD are most at risk?• How can different adaptation measures reduce risk?

Scope of the Analysis

Our approach began with the existing land use and economic development plans in the region. We then evaluated how a changing climate and distribution of expected population and asset growth could affect the performance of these plans. We considered alternative futures consis-tent with the land use plans and adaptive strategies that could reduce the identified vulner-abilities under a range of SLR and precipitation scenarios. For this process, we have drawn on the expertise within the counties’ planning departments to ensure development of realistic alternative land use scenarios.

We applied the Robust Decision Making method to this analysis. We worked with stake-holders to structure an analysis of vulnerabilities to a changing climate and increased economic growth, followed by an analysis of alternative approaches to land use that could possibly miti-gate at least some of these vulnerabilities.

Planning Goals and Metrics

Discussions with officials in Miami-Dade and Broward counties led to selection of the follow-ing key metrics:

• average and 90th percentile wet-season (May–October) groundwater depth• average and 90th percentile dry-season (November–April) groundwater depth• salinity at the lower boundary of the Biscayne Aquifer• asset values at risk from inland groundwater flooding• replacement costs of groundwater wells rendered unusable from saltwater intrusion.

We focus on current conditions (based on 2015) and a future state reflecting plausible SLR conditions in the 2040–2054 time frame, coupled with future assets based on 2035 popu-lation projections. To reconcile the lack of consistency among these different data sets, we use the year 2040 to represent “the future.”

Uncertainties

We identified three key uncertainties driving future climate vulnerabilities in Southeast Flor-ida: SLR, rainfall amounts, and distribution of future population growth. We developed three SLR scenarios based on Southeast Florida Regional Climate Change Compact (2015) and five rainfall scenarios. Scenarios were derived from future rainfall patterns across 112 projections provided by the South Florida Water Management District. Last, we tested three different methods of distributing future population and asset growth across the region:

Summary xiii

1. Random distribution: Future assets are distributed randomly on cells zoned for urbandevelopment.

2. Elevation: Future assets are preferentially distributed to high-elevation cells zoned forurban development.

3. Traffic analysis zone: Future assets are preferentially distributed to traffic corridors.

Relationships

Simulation models were used to evaluate the selected performance metrics by running them under a consistent set of assumptions about the future. This project developed an integrated modeling approach to

• simulate rainfall, drainage, infiltration, and recharge across the region, tracking flows andwater levels at key locations (e.g., all key drainage and discharge points)

• represent the ability of retrofitted and new facilities to control salinity, reduce drainagevolumes and flooding, and allow exploration of alternative portfolios of such strategies

• allow evaluation of climate-modified precipitation and temperature scenarios, preferablyrunning the model in a “batch” mode to simulate many plausible futures

• modify land cover and land use assumptions for alternative scenarios.

Figure S.1 summarizes the relationships among the physical and economic elements of anintegrated system of models. This approach projects incremental changes in water supply, water quality, and financial metrics over time. Two U.S. Geological Survey hydrological models simulate for each county groundwater elevations and the freshwater-saltwater interface under

Figure S.1Integrated Modeling Framework

RAND RR1932-S.1

Climatescenarios

Update inputs tohydrology models:

• Rainfall• Sea level

Hydrology models

Key metrics to analyze:• Groundwater levels• Freshwater-saltwater

interface

Quantify economic risk to flooding and saltwater intrusion

Reallocatepopulation and

assets

Land use plansand policy levers

and strategies

xiv Adapting Land Use and Water Management Plans to a Changing Climate

different assumptions about future climate conditions. The dollar value of economic assets exposed to flooding and saltwater intrusion risks were quantified using the groundwater eleva-tions and freshwater-saltwater interface outputs from the hydrological models.

Strategies and Levers

Guided by future land use plans the counties have developed, we explored how different growth and development patterns could reduce the assets exposed to risks of groundwater flooding and saltwater intrusion.

Decision Support Tool

A decision support tool was developed to synthesize the significant amounts of data produced by the integrated model and to show the key findings from the analysis. The tool enabled the project team, Broward and Miami-Dade partners, and key stakeholders to interact with the analysis throughout this research. The integrated modeling framework developed for this work is intended for the continued and extended use of our partners in Southeast Florida.

How This Approach Differs from Other Efforts in the Region

Our work built on decades of observational and model-based studies to examine how ground-water in Southeast Florida may respond to a changing climate. The modeling framework sum-marized in Figure S.1 advances the methods of previous studies in three key aspects: (1) simu-lating additional climate uncertainties, (2) including future land use as an adaptation option, and (3) connecting hydrological output with economic data for a novel hydrological-economic vulnerability analysis.

While most prior work focused only on SLR, we jointly examined the effects of future SLR and rainfall changes to groundwater elevations and salinities. To our knowledge, this research represents the first time that the UMD and Broward models have been simulated with future rainfall scenarios. Moreover, we advanced previous work by incorporating updated SLR projections into the groundwater models. We also integrated future land use plans into the hydrological modeling framework and data visualizations. This allowed us to explore how land use plans could strategically reorient development in Southeast Florida to mitigate inland flood risks. Finally, we developed novel techniques to merge economic asset valuation data with groundwater variables for a new hydrological-economic perspective on inland flood and saltwater intrusion risk in the region.

Our interest in developing an integrated modeling framework is to use it to gain insight into the vulnerability of South Florida’s built environment to a range of possible future climate conditions, land uses, and asset values.

Summary of Key Findings

Both counties are currently vulnerable to coastal flooding, and their vulnerability could increase in the future (i.e., 2040 time frame) under one or more SLR scenarios. Many inland locations are also vulnerable to flooding and could be more so under one or more future rain-fall scenarios. Understanding the heterogeneity across the region should help planners and

Summary xv

other decisionmakers better focus their efforts and funding on the areas most vulnerable to flooding and the wells most likely to be threatened by saltwater intrusion.

Our results for Broward showed the following:

• Broward is currently vulnerable to flooding in which groundwater is lifted to the surface by high tides as a consequence of low surface elevations.

• In the future (i.e., the 2040 time frame), changes in precipitation patterns could increase or decrease depth to groundwater broadly across the county. SLR will reduce depth to groundwater in coastal areas, with the highest SLR scenario leading to decreases of up to 2 feet in some areas.

• Broward has more than $12 billion of assets that are potentially vulnerable under current conditions. The value of these vulnerable assets is expected to increase between 22 per-cent and 45 percent by around 2040, both because of future development in currently impacted areas and because of increased hazards due to SLR and precipitation pattern changes.

• Orienting future development toward traffic analysis zones reduces future vulnerable assets by 45 percent over a development scenario that only considers elevation ($2.0 bil-lion versus $3.7 billion).

• The remaining vulnerability due to asset growth is concentrated in three to four areas of the county, providing an opportunity for adaptation to accompany future development in these areas. Vulnerability to increased groundwater flooding hazards could affect more than $2 billion of future assets; however, $900 million of these assets would be impacted only at the highest rate of SLR evaluated.

• Saltwater intrusion has the potential to impact many groundwater wells in Broward County. Under the most favorable future (low SLR with very wet precipitation patterns), wells with replacement costs of about $40 million could be impacted by groundwater salinities that exceed 1,000 mg/L. Under the least favorable future (high SLR, very dry precipitation), wells with a total replacement cost of more than $250 million could be impacted by high-salinity groundwater.

Our results for Miami-Dade showed the following:

• UMD is much less vulnerable to tidally induced groundwater flooding than Broward, with only a few areas currently experiencing average depths to groundwater less than 1 foot, and areas along UMD’s ridge not exhibiting any vulnerability within the time frame of the analysis.1

• In the future (i.e., 2040 time frame), changes in precipitation patterns could increase or decrease depth to groundwater broadly across the county, and SLR will reduce depth to groundwater in coastal areas. These changes would lead to low depths to groundwater, primarily in the coastal regions and far-western areas of UMD.

• About $2.1 billion of assets in UMD are potentially vulnerable under current condi-tions—a significantly lower value than in Broward. Increases in vulnerable assets by around 2040 of between about 25 percent and 270 percent of current levels are expected,

1 These areas are still vulnerable to rainfall event flooding, tropical storms, and locally there are pockets of highly vulner-able areas based on the local topography.

xvi Adapting Land Use and Water Management Plans to a Changing Climate

both because of future development in currently impacted areas and because of increased hazards due to SLR and precipitation pattern changes.

• Directing future growth toward areas of naturally higher ground decreases future vul-nerable assets by 19 percent over a development scenario that does not consider elevation ($497 million versus $614 million) (in the 2040 time frame).

• The remaining vulnerability due to asset growth is concentrated in two areas of UMD—downtown Miami and Doral—providing an opportunity for adaptation to accompany future development in these areas. About one-half of the vulnerability to increased groundwater flooding hazards would occur only under the high SLR scenario evaluated through the 2040 time frame, per the research assumptions.

• Saltwater intrusion is not projected to impact groundwater wells in UMD by the 2040 time frame.

With respect to our approach to analysis, we found the following:

• Integrating separate groundwater models developed at different times and with different grid structures, representations of land use, and salinity levels substantially complicated the analysis.

• As a first-order approximation, the economic analysis indicates a high value of assets in the region in futures that are vulnerable to SLR and changing patterns of rainfall. How-ever, because this initial analysis accounts only for private building assets, important driv-ers of economic risk related to economic activity generated by commercial districts and public infrastructure are not incorporated. Future enhancements should include these important drivers of economic risk.

• Treating future land use as an uncertainty and patterns of asset growth as uncertainties is essential in any analysis of either vulnerabilities or adaptation strategies, given the many possible pathways further development might take.

• Stakeholder engagement and even more extensive interactions with decisionmakers are essential ingredients to any successful long-term planning effort.

The approach to analysis demonstrated in this project shows promise and should be con-tinued and refined. Specifically, improvements in the UMD model would enhance analysis of Miami-Dade’s vulnerabilities and understanding of the potential effectiveness of adaptation measures. The region’s vulnerability to both SLR and increased precipitation is cause for con-cern, but targeted actions could reduce further exposure of assets and mitigate the effects of saltwater intrusion on drinking water supplies.

xvii

Acknowledgments

The authors gratefully acknowledge the support of the Craig Howard of the John T. and Catherine T. MacArthur Foundation, which enabled us to engage with our partners in Miami-Dade and Broward counties in conducting this research. We would especially like to thank Broward County’s Chief Resilience Officer and Director of the Environmental Planning and Community Resilience Division, Jennifer Jurado, and Miami-Dade County’s Deputy Sustain-ability Director, Katie Hagemann, for their vision, wisdom, and support throughout this proj-ect. We are also indebted to Jayantha Obeysekera, South Florida Water Management District; Dorothy Sifuentes, U.S. Geological Survey; Maribel Feliciano and Javier Acevedo from Bro-ward County’s Planning and Development Management Division; and Samantha Danchuk, Katie Lelis, and Michael Zygnerski from Broward County’s Environmental Planning and Community Resilience Division. The authors also thank David Welter, Tibebe Dessalegne, Sashi Nair, and Jenifer Barnes of the South Florida Water Management District for their tech-nical support and collegiality.

Special thanks are due to reviewers Kyle Siler-Evans, RAND, and Michael Sukop, Pro-fessor, Department of Earth and Environment, Florida International University, for their cri-tiques and suggestions for improving the report.

We are grateful for the strong support we received along the way from Dr. Pedro José Greer, Jr.—Professor of Medicine, Founding Chair of the Department of Humanities, Health, and Society, and Associate Dean for Community Engagement at Florida International Univer-sity Herbert Wertheim College of Medicine—who also serves as a member of RAND’s Board of Trustees and Chair of the Pardee RAND Graduate School’s Board of Governors. Finally, we were saddened by the passing of Charles Zwick, a stalwart friend and supporter of RAND, before he could see the publication of this report. Charlie helped us steer our way in the region from our very first meetings in Miami and remained engaged and supportive throughout the study process. We dedicate this report to him and his vision of public policy driven by evidence and analysis.

1

CHAPTER ONE

Introduction

A changing climate and increasing rates of sea-level rise (SLR) will have consequences for Southeast Florida, and adaptive measures will be needed to reduce the most serious of these impacts to manageable levels. With a flat topography, low-lying land, and an extensive coast-line, the region will have an increasingly difficult time managing drainage and flood-control systems to achieve desired service levels. As a consequence, regional leaders in Southeast Flor-ida face decisions related to the structure and timing of investment and management strategies for water, land use, and transportation. These decisions will influence their ability to manage near- and longer-term risks from rising seas, changing patterns of precipitation, and other climate impacts. These decisions also will largely shape their ability to maintain the region’s vibrant and growing economy.

Water Management Challenges Faced in Southeast Florida

Southeast Florida is particularly vulnerable to a changing climate for several reasons. First, the region relies on a complex network of interconnected canals and stormwater management sys-tems to manage flooding for both inland and coastal communities. Second, these systems are heavily dependent on gravity operations to maintain positive flows and discharges to coastal waters for flood relief. Rising sea levels will reduce if not entirely eliminate discharge capac-ity at major control structures and stormwater outfalls. As a result, systemwide flooding will become increasingly frequent and problematic in association with high tides, storm surge, and even moderate rainfall. Third, rising groundwater levels will exacerbate flooding as rising seas saturate the limestone aquifer that underlies Southeast Florida and thereby reduce soil storage capacity. If not well planned and coordinated, land use and transportation decisions at the local and regional levels could exacerbate these impacts.

Southeast Florida communities already experience tidal waters spilling over seawalls, pushing up through stormwater systems, and bubbling from the ground. Groundwater levels can rise more than a foot at some locations in response to a single high-intensity precipita-tion event, such that drainage systems, whether shallow “French drains” or drainage wells, no longer operate as designed, and inundation appears to be more frequent and extensive. Approximately 18 salinity control structures on major canals are operating within 6 inches of their original design capacity, and at some locations, extreme high tides resulted in the reten-tion of floodwaters behind closed gates. Severe rainfall events during the historically drier winter months have also produced intense flooding, delivering as much as 22 inches of rainfall in less than 24 hours.

2 Adapting Land Use and Water Management Plans to a Changing Climate

Under the auspices of the Southeast Florida Regional Climate Change Compact, com-munities in the region are working in a constructive and organized manner to bring available resources to bear on the challenges of SLR and climate change (Southeast Florida Regional Climate Change, 2012). As one example, partner counties and cities in this effort have formally adopted a unified SLR projection to support planning activities, have integrated climate change policies in their comprehensive plans, and have undertaken a coordinated planning effort to more effectively address the challenges of climate change (Southeast Florida Regional Climate Change Compact, 2015). To help identify vulnerabilities and inform planning efforts, the counties developed inundation models, conducted preliminary risk assessments, and invested in advanced hydrologic models that integrate SLR and downscaled climate conditions.

Analytical Needs in the Region

The counties and cities in the region have been seeking analyses to support their planning activities and the integration of planning and investments across land use, water, and trans-portation infrastructure. Consistent with this commitment, partners in the compact engaged with RAND Corporation researchers to support enhanced, participatory decisionmaking and solution-oriented strategies for regional adaptation to rising seas and other climate effects.

Specifically, the collaboration with RAND sought to address the following analytical needs:

• Decision support: Decision-analysis tools could help shape and guide coordinated plan-ning and active investments. The region currently lacks the ability to consider trade-offs arising from local land use decisions and transportation- and water-related public invest-ment choices using a consistent set of analytical tools. The collaboration with RAND was aimed at contributing to development of regional-scale integrated financial- and water-management models within the framework of Robust Decision Making (RDM).

• Comparative economic analysis: The region could benefit from a systemwide evalua-tion of the potential magnitude of recurring economic losses under various climate and economic futures. Economic analyses have been limited to date, reflecting current taxable real estate values but failing to account for future economic activity or the value of public infrastructure. A decision-support process would allow comparison of alternative policy and engineering solutions intended to mitigate the impacts of rising seas and insufficient drainage.

• Analysis of externalities: Impacts of many single-jurisdiction decisions may be affecting neighboring jurisdictions. Current planning methods in Southeast Florida are challenged by the need to include externalities associated with new infrastructure, policy changes, and permitting and development decisions for individual projects. Further, uncoordi-nated local land use and water management plans and permitting in one jurisdiction could have unintended consequences of constraining choices for neighboring jurisdic-tions. In the absence of mathematical simulation tools of suitable spatial and temporal resolution, these potentially large externalities from disjointed decisionmaking need to be quantified.

• Strategies for increasing resilience of existing infrastructure: New strategies are needed to integrate resilient infrastructure planning and standards within the spatial

Introduction 3

constraints of the existing built environment and additional constraints on compatibility with adjacent infrastructure.

As determined in workshops and related conversations, RAND’s role as a neutral party was to facilitate and inform discussion among the region’s leaders about near- and longer-term public investment planning and decisionmaking related to water and land use. Within the constraints of funding and schedule, this report describes methods and results addressing the first two of these needs and speaks to the relevance of the methods to the remaining two needs.

Objectives

As a complement to the strong leadership and technical base already established in the region, RAND’s overarching goal was to help improve the region’s capacity to adapt to a changing climate and development conditions with minimal economic and social disruption. RAND’s engagement necessitated the development of an integrated model for the region that could provide a transparent, interactive tool and a level analytical playing field to assess potential interactions among water management, transportation, and land use decisions under a range of climate futures. Our aspiration was that decisionmakers and stakeholders in the region would gain a better understanding of the costs of both action and inaction across a wide range of futures.

Our analysis addressed four questions:

• How vulnerable is current and planned future urban development in Broward County and the urban portion of Miami-Dade County (referred to as UMD) to groundwater flooding?

• What are the key drivers of future risks, including SLR, precipitation, and change in eco-nomic assets?

• What areas in Broward and UMD are most at risk?• How much risk could be reduced under different adaptations?

Within the boundaries of the South Florida Water Management District (SFWMD), we have focused our analysis on selected issues and decisions affecting UMD and Broward counties to demonstrate the utility of the analytical approach. For example, Broward shares use of the Biscayne Aquifer with Palm Beach, Miami-Dade, and Monroe counties, as shown in Figure 1.1. Transportation, land use, and water management decisions in one county can have collateral impacts on drainage, flooding, groundwater levels, and saltwater intrusion in another. Ultimately, the effectiveness of changes in land use policy in Miami-Dade or Broward may depend on the extent of coordination among jurisdictions within each of the counties. We are interested in demonstrating an analytical framework that enables cities and counties to better manage these kinds of externalities when making their own development decisions and approving permits for individual projects.


Approach

We began with the existing land use and economic development plans in the region. We then evaluated how a changing climate and distribution of expected population growth could affect the performance of these plans. We considered alternative futures consistent with the land use plans and adaptive strategies that could reduce the identified vulnerabilities under a range of SLR and precipitation scenarios. For this process, we have drawn on the expertise within the counties’ planning departments to ensure development of realistic alternative land use scenar-ios. We also collaborated with the U.S. Geological Survey (USGS), SFWMD, and other local partners to both gather and interpret local information.

We worked with local partners to develop and support workshops with key stakeholders and decisionmakers as part of a facilitated participatory process. Our first meeting at SFWMD on February 24, 2015, with representatives of the Southeast Florida Regional Climate Change Compact and technical experts in the region, produced a five-task analytical process:

1. Conduct participatory scoping process and initial outreach, including identification of planning goals and associated metrics.

2. Identify simulation models, strategies to consider, and supporting data.

Figure 1.1Areal Extent of the Biscayne Aquifer

SOURCE: Broward County, Broward Water Resources Task Force, 2010.RAND RR1932-1.1

Introduction 5

3. Conduct a vulnerability analysis and evaluate current strategies using criteria associated with the planning goals.

4. Conduct an RDM analysis to identify alternative or improved strategies.5. Produce a written report and other accessible documentation and conduct outreach.

A scoping meeting on June 24, 2015, at Florida International University addressed the first task and was followed by several phone meetings and email exchanges with stakeholders and modelers to deepen our understanding of the salient planning goals, associated metrics, available simulation models, supporting data, and other issues relevant to the conduct of the pilot. Several more visits to Miami-Dade and Broward County offices and SFWMD in 2015 (in October and December) explored available data and models.

We then developed SLR and precipitation projections for use with two USGS groundwa-ter models for Broward County and UMD. These models were used to develop a preliminary analysis of groundwater flooding vulnerabilities. The results were compiled into a decision sup-port tool (DST), which was presented to stakeholders in April 2016. After the April meeting, the project team expanded the analysis to include economic implications of future flooding using newly developed economic projections of assets. The final analyses were then presented at Miami-Dade County offices in December 2016 and with an updated webinar presentation in May 2017. Over the course of this research, other webinars discussed metrics and thresholds to analyze adaptation strategies and visualize our results.

How This Report Is Organized

Chapter Two describes the analytic approach and use of RDM. It also summarizes the scope of the analysis in terms of the key goals and performance metrics, uncertainties, adaptation options and strategies, and used integrated modeling system. Chapter Three documents the integrated modeling system, data, and methods we used to develop futures for evaluating Southeast Florida’s vulnerabilities to climate and population growth. Chapter Four describes the development of plausible future SLR and precipitation conditions for use in a vulnerability analysis. Results are presented in Chapters Five and Six. Chapter Five describes and visualizes the vulnerabilities of the area under study to flooding from rising groundwater levels. Chap-ter Six examines the impacts of both SLR and more intense precipitation events on saltwater intrusion and the vulnerability of drinking water wells to higher levels of salinity. Finally, Chapter Seven synthesizes our findings and conclusions. Three appendixes provide additional technical detail for the analysis.

7

CHAPTER TWO

Approach to Analysis and Decision Support

This chapter describes the analytic approach taken to identify vulnerabilities in Southeast Flor-ida to specific climate drivers, define strategies to mitigate these vulnerabilities, and support deliberations over the different strategies.

Climate Adaptation Planning Using Deliberation with Analysis

Quantitative analysis is often indispensable for making sound policy choices when dealing with complex physical and engineered systems. Typically, these methods use a “predict-then-act” approach: Analysts assemble available evidence into best-estimate assumptions or predic-tions and then use simulation models and other mathematical tools to suggest the best strategy given these predictions. Quantitative analyses are useful in answering the question of which policy options best meet public goals given decisionmakers’ beliefs about the future. Quantitative methods, which include probabilistic risk analysis, work well when the predictions are accurate and noncontroversial (Lempert, Popper, and Bankes, 2003; Kalra et al., 2014; Lempert and Kalra, 2011).

Many aspects of infrastructure planning do not lend themselves to credible prediction-making. The short- and long-term outcomes of different public investments will depend on the evolution of many deeply uncertain factors, defined as conditions in which parties to a decision do not know or agree on the model(s) that relate their actions to consequences, the prior prob-ability distributions for key parameters to the models, and the importance of various objectives (Lempert, Norling, et al., 2003; Walker, Haasnoot, and Kwakkel, 2013). Traditional methods often prove brittle in the face of the deep uncertainties. Disagreements about future predic-tions can lead to gridlock among stakeholders. Worse, decisions tailored to one set of assump-tions about a deeply uncertain future often prove inadequate or even harmful if another future comes to pass.

For decision environments with deep uncertainty and changing conditions, stakeholders with diverse interests, and goals that may emerge from collaboration, National Research Coun-cil (2009) suggested a “deliberation with analysis” approach in which analysis is used not to identify a best or optimal strategy but rather to inform a conversation about trade-offs among robust alternatives (National Research Council, 2009). Deliberation with analysis stands in contrast to more-traditional engineering approaches to complex infrastructure planning, in which the analysts make assumptions, not always explicitly, about which public goals should drive the analysis and how competing goals should be weighted relative to one another. Such


approaches can often engender public skepticism about the integrity of the technical analysis and lead to gridlock in decisionmaking.

Many methods have been developed to help policymakers manage deep uncertainties and make choices that are robust in the face of an unpredictable future. RDM is designed to help manage deep uncertainty by helping develop policies that are robust—that is, those that satisfy decisionmakers’ objectives in many plausible futures rather than being optimal in any single best estimate of the future (Lempert, Popper, et al., 2013).

Overview of Robust Decision Making

RDM rests on a simple concept. Rather than using models and data to assess policies under a single set of assumptions, RDM runs models over hundreds or thousands of different sets of assumptions to describe how plans perform in many plausible conditions. Unlike, for example, Monte Carlo analysis, which attaches probabilities to the assumptions to estimate expected outcomes, RDM uses model evaluations to stress-test strategies. RDM draws from both sce-nario and probabilistic risk analyses to ask which policies reduce risk over which range of assumptions, inquiring, for example, what assumptions we would need to believe were true for us to reject option A and instead choose option B.

By embracing many plausible sets of assumptions or futures, RDM can help reduce overconfidence and the deleterious impacts of surprise, can systematically include imprecise information in the analysis, and can help decisionmakers and stakeholders who have differ-ing expectations about the future nonetheless reach consensus on action. In essence, RDM helps plan for the future without first predicting it. RDM has been applied to water resource management (Groves, Lempert, et al., 2008; Groves et al., 2013; Kalra et al., 2015), flood risk management (Fischbach et al., 2017), terrorism risk insurance (Dixon et al., 2007), and energy investments (Popper et al., 2009), among other things. Figure 2.1 and the following subsec-tions summarize the RDM process.

Step 1: Decision Framing

The first step of RDM is to work with stakeholders and decisionmakers to structure an analy-sis of alternative strategies or plans. Generally, this consists of first establishing the goals of the plan and specific performance metrics to quantify outcomes relative to the goals. Through elicitation, uncertain factors that could affect the plan’s ability to meet goals are defined. Then, a starting set of options or decisions that could be made is defined. In some cases, the initial decision is to continue the status quo. After an analysis of the vulnerabilities of this strategy in Steps 2 and 3, described later, new decisions or options are identified. These alternative strat-egies are typically identified by revising the first step. Finally, the relationships that connect the decisions or action to the outcomes under different assumptions of the uncertainties are defined. Usually these relationships are represented by an existing integrated computational model or one that is developed to support the analysis. These four factors—metrics (Ms); uncertainties (Xs); levers, decisions, options, or strategies (Ls); and relationships (Rs)—are rep-resented in a concise XLRM matrix described later (Lempert, Popper, et al., 2013).

Approach to Analysis and Decision Support 9

Step 2: Evaluate Options Across Futures

The base strategy is next evaluated by the integrated models under as many different combina-tions of key uncertainties as time and resources permit. Alternative strategies, defined during the initial or subsequent decision-structuring step, are run through the same suite of integrated models for the same range of possible futures to assess the same performance metrics. The results of the model evaluations are stored in a single database for analysis in Step 3.

Step 3: Vulnerability Analysis

An analysis of the model results enables researchers and stakeholders to identify the combi-nations of uncertain factors that lead to the greatest vulnerabilities in the system at selected points in the future. Depending on the number of cases generated, this step may be done either through case-by-case analysis or through an automated means of identifying the most signifi-cant vulnerabilities. The outcome of the scenario discovery step is generally several scenarios that will be used to identify and stress other candidate strategies relative to the base strategy.

Step 4: New Options and Futures

In response to analysis in Steps 2 and 3, additional options and futures may be defined. For example, the initial case-generation and vulnerability analysis in Steps 2 and 3 may reveal futures of particular interest to the choice of strategy that are then explored in greater detail through new futures. As another example, identifying the vulnerabilities of several initial strat-egies and comparing their performance in Steps 3 and 5 may suggest new, better-hedged strate-gies for evaluation.

Figure 2.1Steps to Robust Decision Making

SOURCE: Adapted from Lempert et al., 2013.RAND RR1932-2.1

1. Decision framing

2. Evaluate optionsacross futures

3. Vulnerability analysis

4. New optionsand futures5. Trade-off analysis

Scenarios thatilluminate

vulnerabilities

Robuststrategies


Step 5: Trade-Off Analysis

The last step of the RDM process is conducted interactively with partners and stakeholders. The purpose is to illuminate the performance across the key planning goals of the various candidate strategies relative to the base strategy. No strategy is likely to be optimal across all performance metrics. More likely, the choice of strategy will involve making trade-offs against one or more goals relative to the other strategies. The goal of the analysis is to identify robust, adaptive strategies that are most likely to perform well across a range of futures and, thus, inform the preferences and processes that decisionmakers can follow. In this step, the hypoth-eses posed earlier are tested and evaluated.

Central to the application of RDM is the coordinated exploration of the future by ana-lysts, decisionmakers, and stakeholders. The decision-framing step sets the stage by providing an opportunity for all interested parties to convey concerns about different plausible future conditions, outcomes of interest, and possible solutions. After computer models simulate policy outcomes, the exploration of results generated in Step 2 and vulnerability analyses in Step 3 can be highly participatory, supported through interactive visualization tools. Finally, the itera-tive nature of the process emphasizes the purpose of analysis that is designed to support delib-erations, rather than prediction and the subsequent prescriptive ranking of decision options (National Research Council, 2009).

Scope of the Analysis

The decision-framing step of RDM (Step 1) formally commenced on June 24, 2015, at a scop-ing workshop held at Florida International University.1 The description of tasks in the follow-ing subsections was shaped by the discussions at the June 24 workshop. At that time, the group attempted to develop a notional XLRM matrix, summarizing the likely uncertainties, metrics, strategies, and models that would be relevant for this project. We subsequently reworked the XLRM matrix for purposes of further discussion and refinement with our partners. The fol-lowing subsections discuss the XLRM matrix used in this analysis (Table 2.1).

Planning Goals and Metrics

One of the first orders of business in the deliberative analysis was to establish a consensus on the key planning goals. In our first workshop, stakeholders identified a large list of potential goals and metrics, summarized in Table 2.2. The availability of models and resources and the project timeline required that we pare this list to a manageable number, as shown in Table 2.1.

For purposes of tractability, we winnowed the list of potential metrics in Table 2.2 in consultation with officials in Miami-Dade and Broward counties to the following key metrics used in the remainder of this report:

• Average and 90th percentile wet-season (May–October) groundwater depth: A dis-tance of 1 foot or less between the groundwater level and ground surface was deemed at risk for groundwater flooding.

1 Participants included representatives of the planning and engineering offices of Broward and Miami-Dade counties, SFWMD, cities in the region, the USGS, and nongovernmental organizations involved in land use and environmental conservation.


• Average and 90th percentile dry-season (November–April) groundwater depth: A distance of 1 foot or less between the groundwater level and ground surface was deemed at risk for inland flooding.

• Salinity at the lower boundary of the Biscayne Aquifer: Chloride concentrations exceeding 250 milligrams per liter (mg/L) are in violation of the U.S. Environmental Protection Agency’s drinking water standard (U.S. Environmental Protection Agency, 2017). Another common high-salinity threshold used in the region is 1,000 mg/L.

• Asset values at risk to inland groundwater flooding: This is the sum of grid cell–based valuations that experience distances between groundwater levels and the surface of 1 foot or less.

• Replacement costs of groundwater wells rendered unusable from saltwater intru-sion: This cost is obtained by multiplying $5.8 million by the average daily pumping rate in million gallons per day for a given well. The $5.8 million is the estimated develop-ment or replacement cost per million gallons per day drawn from the 2015 Annual Water

Table 2.1XLRM Matrix

Uncertainties (X) Levers (L)

SLRRainfallDistribution of future population growth

Land use plans and policiesLocation of water structures

Relationships (R) Metrics (M)

Urban Miami-Dade modelBroward SEAWAT modelEconomic model

Groundwater elevationsAssets at riskLocation of saltwater-freshwater interfaceOpportunity costs of water supply

Table 2.2Potential Goals and Metrics

Goals Metrics

Increase economic development Measures related to capacity for growthEconomic losses from service interruptions

Reduce storm and flood risk Expected annual damage reduction

Financial sustainability of public infrastructure and services

Preservation of property valuesNet change in property tax revenuesAnnual operating costs of water-, wastewater-, and transportation-related public services

Capital costs of new infrastructure (water, drainage, wastewater, transportation)

Cost-effectiveness of new infrastructure relative to achievement of other goals

Improve water quality Net change in location of saltwater-freshwater interface relative to current base (and/or to business-as-usual projections)

Net change in nutrient loadings and concentrations

Expand habitat for critical species Area protected


Planning Report of the Florida State Department of Environmental Protection (Florida 2015).2

Note that tidal inundation is an important metric that this analysis did not include. Results should be cross-referenced with other assessments of inundation under different SLR projections.

We focused on current conditions (based on 2015) and a future state reflecting plausible SLR conditions in the 2040–2054 time frame, coupled with future assets based on 2035 pop-ulation projections. To reconcile the lack of consistency of these different data sets, we used 2040 as “the future.”

Uncertainties

The original workshops identified a large number of potential uncertainties to consider in this analysis. As a first step, we identified three key uncertainties driving future climate vulner-abilities in Southeast Florida: SLR, rainfall patterns, and distribution of future population growth. Chapter Four provides details about how these uncertainties were quantified for use in the analysis.

In brief, we developed three SLR scenarios based on Southeast Florida Regional Climate Change Compact (2015), all relative to 1992:

• The low scenario comes from the Intergovernmental Panel on Climate Change’s (IPCC’s) Fifth Assessment Report (AR5) median curve representative concentration pathway (RCP) 8.5 (RCP 8.5) (Stocker et al., 2013): – 6 inches by 2030, 14 inches by 2060, and 31 inches by 2100.

• The middle scenario comes from the U.S. Army Corps of Engineers high curve (Engineer Regulation 1100-2-8162, 2013; U.S. Army Corps of Engineers, 2013):

– 10 inches by 2030, 26 inches by 2060, and 61 inches by 2100.• The high scenario comes from the National Oceanic and Atmospheric Administration

high curve (Parris et al., 2012): – 12 inches by 2030, 34 inches by 2060, and 81 inches by 2100.

We developed five rainfall scenarios by using downscaled future projections to apply monthly scaling factors to the current rainfall inputs in the groundwater models. Scenarios are derived from the following percentiles of future rainfall patterns across 119 projections devel-oped and provided to us by SFWMD (Dessalegne et al. 2016):

• 5th percentile = driest future (63 percent of current annual rainfall)• 25th percentile = dry future (87 percent of current annual rainfall)• 50th percentile = average future (109 percent of current annual rainfall)• 75th percentile = wet future (139 percent of current annual rainfall)• 95th percentile = wettest future (193 percent of current annual rainfall).

2 Florida State Department of Environmental Protection (2015) estimated that alternative supply projects producing 807 million gallons per day would cost about $4.5 billion.


Finally, we developed three projections of future (i.e., 2035) economic assets based on three different methods for the distribution of future population and asset growth across the region:

1. Random distribution method: Future assets are distributed randomly on cells zoned for urban development.

2. Elevation method: Future assets are preferentially distributed to high elevation cells zoned for urban development.

3. Traffic analysis zone (TAZ): Future assets are preferentially distributed to traffic cor-ridors.

These projections are generally consistent with the most recent land use planning docu-ments and population projections across the counties but differ in how future assets are dis-tributed within the plans. Alternative realizations consistent with planning documents are also possible.

Relationships

To evaluate the performance metrics in Table 2.1, we ran simulation models under a consistent set of assumptions about the future. This project developed an integrated modeling approach to

• simulate rainfall, drainage, infiltration, and recharge across the region, tracking flows and water levels at key locations (e.g., all key drainage and discharge points)

• represent the ability of retrofitted and new facilities to control salinity, reduce drainage volumes and flooding, and allow exploration of alternative portfolios of such strategies

• allow evaluation of climate-modified precipitation and temperature scenarios, preferably running the model in a “batch” mode to simulate many plausible futures

• modify land cover and land use assumptions for alternative scenarios.

Figure 2.2 summarizes the relationships among the physical and economic elements of an integrated system of models to support the research objectives described earlier. This approach projects incremental changes in water supply, water quality, and financial metrics over time. Note that the key metrics in the red box correspond to the metrics shown in Table 2.1. In the integrated framework, multiple hydrological models simulate groundwater elevations and the freshwater-saltwater interface under different assumptions about future climate conditions. The dollar value of economic assets exposed to flooding and saltwater intrusion risks are quan-tified using the groundwater elevations and freshwater-saltwater interface outputs from the hydrological models.

Each aspect of the integrated modeling framework is discussed in greater detail in the following chapters. Simulation results were used to define the climate risk by comparing the geophysical outcomes to projections of built assets (RDM Step 2). We next characterized the baseline current assets at risk to groundwater flooding and salinity intrusion under current con-ditions and across the uncertain futures (RDM Step 3—Chapter Five). This initial report does not include a full analysis of potential strategies; consequently, we did not remodel risk under different conditions reflecting adaptation strategies. Instead, we describe the types of adapta-tion approaches that would be appropriate for the vulnerabilities identified (Chapter Six).


Strategies and Levers

Guided by future land use plans developed by the counties, we explored how different growth and development patterns could reduce the assets exposed to risks of groundwater flooding and saltwater intrusion. However, this research only scratched the surface of possible strategies for reducing Southeast Florida vulnerabilities. While outside the scope of this analysis, many other options could be usefully explored within the modeling and decision-analytic approach described here.

Decision Support Tool

We developed a DST to synthesize the significant amounts of data the integrated model pro-duced and to show the key findings from the analysis. The DST enabled us, our Broward and Miami-Dade partners, and key stakeholders to interact with the analysis throughout the research. The DST was developed using the Tableau business analytics software (Tableau, 2017) and is available for use on the Tableau Public web-based platform.3

3 See Tableau, 2018, for more information about the software. A public version of the DST used in the project is available on Tableau Public (Groves and Knopman, 2017).

Figure 2.2Integrated Modeling Framework

RAND RR1932-2.2

Climatescenarios

Update inputs tohydrology models:

• Rainfall• Sea level

Hydrology models

Key metrics to analyze:• Groundwater levels• Freshwater-saltwater

interface

Quantify economic risk to flooding and saltwater intrusion

Reallocatepopulation and

assets

Land use plansand policy levers

and strategies


How This Approach Differs from Other Efforts in the Region

Our research builds on decades of observational and model-based studies to examine how groundwater in Southeast Florida may respond to a changing climate. Notably, our work fol-lows two previous studies that used the UMD and Broward models to explore how changes in SLR may impact groundwater elevations and salinity. These studies used sea-level projections based on Engineer Regulation 1100-2-8162 (2013). Specifically, Hughes and White (2014) simulated UMD groundwater from a baseline period of 1996–2010 compared with a scenario with 30 years of SLR. Hughes, Sifuentes, and White (2016) used the Broward model to exam-ine Broward groundwater changes with SLR out to 2062. While SLR was the only climate change impact considered in these two studies, the authors did explore the effects of other nonclimate future uncertainties, such as modified groundwater pumping rates based on popu-lation growth. A host of other studies have similarly examined the impact of SLR on ground-water elevations and salinities in Southeast Florida, including Sukop et al. (2018), Czajkowksi et al. (2018), Saha et al. (2011), Guha and Panday (2012), and Langevin and Zygnerski (2012).

The modeling framework summarized in Figure 2.2 advances the methods of the previ-ous studies in three key aspects: (1) simulating additional climate uncertainties, (2) including future land use as an adaptation option, and (3) connecting hydrological output with economic data for a novel hydroeconomic vulnerability analysis. Whereas most prior work focused only on SLR, we jointly examine the effects of future SLR and rainfall changes to groundwater elevations and salinities. To our knowledge, this research represents the first time that the UMD and Broward models have been simulated with future rainfall conditions. Moreover, we advanced the work of Hughes and White (2014) and Hughes, Sifuentes, and White (2016) by incorporating updated SLR projections into the groundwater models, which are based on Southeast Florida Regional Climate Change Compact (2015). We also integrated future land use plans into the hydrological modeling framework and data visualizations. This allowed us to explore how different projections of future assets, based on land use plans, could affect future flood risk and how strategically reoriented development in Southeast Florida could mitigate inland flood risks. Finally, we developed novel techniques to merge economic asset valuation data with groundwater variables for a new hydrological-economic perspective on inland flood and saltwater intrusion risk in the region.

The integrated modeling framework developed for this research is intended for the con-tinued and extended use of our partners in Southeast Florida. Additional climate uncertainties, such as changes in storm intensity and related storm surge, although not included here, could be simulated. Moreover, while we considered only land use change as the primary strategy to mitigate future flood risk, future studies using this modeling framework could extend our results by exploring different groundwater withdrawals (such as those proposed in Hughes and White, 2014, and Hughes, Sifuentes, and White, 2016) or surface-water and canal manage-ment techniques. The latter may be best accomplished by coupling the UMD and Broward models with a regional water management model, such as SFWMD’s Regional Simulation Model (SFWMD, 2005). Finally, alternative economic futures might be considered.

17

CHAPTER THREE

Integrated Modeling Framework

We coupled two USGS groundwater flow and transport models covering key areas of Miami-Dade and Broward counties to simulate groundwater in these counties under a range of future SLR and rainfall futures alongside projected land use changes (Hughes and White, 2014; Hughes, Sifuentes, and White, 2016). The simulations yielded estimates of asset values at risk to inland flooding through elevated groundwater levels and water supply constraints imposed by saltwater intrusion. These models do not account for direct inundation flooding from tides. Although the two groundwater models are distinct in terms of their underlying source code and characteristics (e.g., different grid cell resolutions), we simulate them under a common set of futures and display outputs together for a more integrated and spatially comprehensive framework than in previous studies in the region.

Significant computation resources are required to perform many simulations using these complex groundwater models. We compiled the models and executed all simulations on Amazon Web Services cloud-based servers for rapid processing times (approximately 4 to 8 hours per run). Postprocessing of the raw model outputs was also performed on the cloud before transferring the final data sets to local servers for further analyses and visualizations.

Overview of Modeling Approach

The spatial extent of the groundwater models is shown in Figure 3.1. The Broward model encompasses roughly 450 mi2 of southeastern Broward County. The UMD model spans more than 1,530 mi2 in the eastern half of Miami-Dade County. Model boundaries include the most urbanized and populated regions of each county but exclude natural lands, such as the Water Conservation Areas and the Everglades National Park. Note that there is some overlap between the UMD and Broward model extents along the Miami-Dade–Broward County border. After ensuring consistency of results in the overlapping region, we “cropped” model outputs to be confined within their respective side of the county division for our analyses and visualizations.

To compute the effects of climate change, we considered two 15-year periods: a “current” period of 1996–2010 (also referred to as the baseline) and a “future” period of 2040–2054. The 1996–2010 period represents the overlap of the years for which the UMD and Broward models both have historical conditions (we provide additional details later). By consensus among the participants, the 2040–2054 period was chosen because it is far enough into the future to cap-ture significant climate change effects on average groundwater conditions yet is still within a reasonable time horizon for planning purposes.


The USGS models produce a range of useful outputs:

• quantification of changes in groundwater pumping and sea level on canal leakage and groundwater inflows from the Everglades

• surface water stage and flows in canals• groundwater flows and levels within the Biscayne Aquifer• exchange between canals and the aquifer• groundwater seepage from the Everglades and to Biscayne Bay• position of freshwater-seawater interface• estimates of agricultural water use, irrigation, and septic tank return flows.

While these are the best available tools for analyzing these conditions, the spatial scale the two models use masks potentially important subgrid-scale heterogeneity. Thus, it is impor-tant to recognize that some existing and potential risk may not be captured by the analysis presented in this report.

Urban Miami-Dade Model

The UMD model (Hughes and White, 2014) is based on MODFLOW-NWT (Niswonger, Panday, and Ibaraki, 2011),1 with the Surface-Water Routing Process (Hughes et al., 2012) and the Seawater Intrusion Package (Bakker et al., 2013) to represent coupled surface-water and groundwater dynamics. The model has a total grid of 189 rows and 101 columns, with a grid cell resolution of 500 ´ 500 meters, although only 15,853 cells are actively simulated in each of the model’s three vertical levels that represent the near-surface aquifer. The UMD model oper-ates on a daily time step and was calibrated with surface-water and groundwater observations from 1997 through 2004, later verified from 2005–2010.

Daily average tides at the Virginia Key station are used to represent sea levels along the model’s coastal boundaries. Daily Next-Generation Radar rainfall is applied to land-based grid cells for precipitation inputs to the model. Agricultural water use, irrigation losses, septic flows, and recreational irrigation uses also serve as model inputs. Land use is not explicitly incor-porated; rather, the effects of land use on surface and subsurface hydrological conditions are represented through land use–specific evapotranspiration characteristics from SFWMD (see Appendix A). The model includes 139 municipal water pumping wells within 20 well fields, with assigned pumping rates according to data from SFWMD and Miami-Dade Water and Sewer Department.

Broward Model

The Broward Model used in this study (Hughes, Sifuentes, and White, 2016) is called SEAWAT Version 4 (Langevin et al., 2008), which is a coupling of USGS’s three-dimensional ground-water model—MODFLOW-2000 (Harbaugh et al., 2000)—and USGS’s groundwater solute transfer model—MT3DMS Version 5 (Zheng, 2006). The model has a full grid of 411 rows

1 USGS MODFLOW-NWT is a Newton-Raphson formulation for MODFLOW-2005.

Integrated Modeling Framework 19

and 501 columns with a grid cell resolution of 500 ´ 500 feet, although only a subsection of the grid has an active groundwater component. In total, 50,179 grid cells are actively simulated in each of the model’s 12 vertical layers that represent the surficial aquifer. The model operates on a monthly time step and was calibrated to observed groundwater conditions and chloride concentrations from 1970 through 2012.

Grid cells in the Atlantic Ocean incorporate historical sea levels through monthly average tidal stages based on a composite of station data across Miami Beach, Haulover Pier, and Vir-ginia Key. Historical rainfall inputs are based on the Next-Generation Radar data set and were adjusted during model calibration using monthly multipliers. Land use in the model is repre-sented through evapotranspiration characteristics from SFWMD (see Appendix A). Primary and secondary canals are explicitly modeled, while the effects of tertiary canals are included in the model through prescribed drainage elevations. Finally, 300 municipal water pumping wells in 34 well fields are included in the model, each assigned a pumping rate according to values from SFWMD.

Linkage of Economic Metrics to Hydrologic Model Output

As a coupled natural-human system, the economies of UMD and Broward counties evolve in accordance with both natural (i.e., climate) and economic conditions. Levels and locations of future economic development, driven largely by changes in population and the associated spatial distribution, are uncertain. However, future predictions and current land use plans can direct new demographic and economic activity to specific subgeographies in each region in accordance with contemporary knowledge and preferences.

We thus used currently available planning documents in each county, coupled with population forecasts, to explore the implications of several realizations of possible economic futures. In particular, Florida Department of Revenue (DOR) property tax data files were used to map fixed capital asset values (i.e., “just value” of structures) for 2015 to each hydrologic grid cell in each county to obtain total current per-cell capital values that are at risk of damage in case of flooding or inundation (Florida DOR, 2017). We then formulated several simple rules to allocate expected future growth throughout each county (consistent with their current comprehensive land use plans and valuation) to construct future economic scenarios. In our approach, these land use futures are then subjected to the changed hydrologic conditions from the groundwater models. Chapter Four summarizes the details of this approach.

Future economic scenarios are assumed to be independent of hydrologic conditions (includ-ing future flood risk) for simplicity and to remain consistent with preexisting economic and demographic plans of the county governments. In reality, decisions related to actual regional economic development would likely take future environmental conditions (and expectations) into account. As a consequence, expected damages from future environmental risks would be capitalized into fixed asset values. However, absent additional evidence or assumptions about these responses, we chose a scenario approach to signal uncertainty about these responses. As an alternative to the scenario approach, one could, in theory, estimate the economic develop-ment response to future environmental risk or damage using a structural economic model (such as the Regional Economic Models, Inc. computable general equilibrium model) and assumptions about behavioral responses to realized conditions.


Observations About the Hydrologic Modeling Approach

Advantages

Our hydrological modeling approach has two key advantages. First, by compiling the UMD and Broward models on cloud servers, we were able to remove computational and time bur-dens traditionally associated with running complex hydrological models. The cloud provides a cost-effective platform for performing long-term simulations (e.g., on the decadal scale) and enables nearly unlimited storage space to safely and cheaply archive the large amounts of model output that result from running MODFLOW-based models. Moreover, the cloud allows flex-ible sharing settings that allow external collaborators (e.g., model experts at the SFWMD) to easily access and share code and data. Second, we also emphasized the need to apply a common set of climate changes to both models for a uniform, regional-scale analytical framework. By working with sea-level and rainfall projections that were appropriate for use broadly across Southeast Florida (see Chapter Five for details of these projections), we were able to simulate regional hydrological conditions that enabled fruitful conversations with planners and officials across multiple jurisdictions.

Potential Improvements

Our experience with the UMD and Broward models suggests several improvements to enhance their usage, integration, and interpretation. First, we recommend extending boundary condi-tions and other required model inputs (e.g., pumping rates) through 2016. Currently, UMD’s inputs extend through 2010, and Broward’s inputs extend through 2012, limiting understand-ing of groundwater conditions for the present day. Second, we recommend that source code be adjusted to allow more flexible modifications of land use, for instance by developing preprocess-ing scripts that translate land use categories to hydrological characteristics required as inputs to the models. Third, we recommend that, in addition to the binary MODFLOW format, the models also allow outputs to be stored in other common data formats (e.g., netCDF soft-ware libraries) that could expand the accessibility and processing of model results. Finally, we recommend that MODFLOW source code be included for non-Windows operating systems, which would increase model usage on different platforms, particularly for easier compilations on cloud-based servers.

Lessons Learned

Working with any hydrological model, such as MODFLOW, inevitably raises technical and scientific challenges, which were exacerbated when simulating and connecting results from two of these models. We summarize here three key lessons learned from our experiences simu-lating and analyzing results from the UMD and Broward models. First, close attention needs to be paid to the measuring units native to each hydrological model. Although the UMD and Broward models were developed by similar modeling experts and border each other spatially, they have different units of measurement. UMD is based on the metric system (e.g., meters), while Broward is based on the imperial system (e.g., feet). Converting between them is trivial but is important to keep in mind when working with both models.

Second, there are challenges with interpreting and displaying outputs from models that have different spatial resolutions. As noted in the previous section, the grid used for UMD model output is approximately three times the size of that for the Broward model. Where the models overlap along the northern Miami-Dade County–southern Broward County border

Integrated Modeling Framework 21

Figure 3.1Boundaries of USGS Groundwater Models

RAND RR1932-3.1

USGSBrowardmodel

BrowardCounty

Miami-DadeCounty USGS

UMDmodel

(see Figure 3.1), the models’ spatial resolution differences can contribute to discrepancies in model output. Visualizing data on two different model grids also posed challenges. When displaying information in Tableau, we initially had to select one marker size, which was equal to the size of the coarser UMD grid, to ensure that no white spaces were displayed. How-ever, this had the negative consequence of overshadowing finer-scale output from the Broward model. Ultimately, we were able to develop shape files corresponding to each model grid, which removed the burden of only being able to select one marker size, in favor of allowing multiple pixel sizes on a single image. This removed any artificial inaccuracies in our interactive visualizations going forward.

Third, in addition to the different spatial resolutions of the models, the models run on different time steps. UMD runs on a daily time step and coarser grid, and the Broward model runs on a monthly time step and finer grid. Monthly time steps may be only marginally effec-tive at capturing short-term water-level rise associated with large rain events. These differ-ences in resolution likely resulted in differences in results near the common boundaries of the models. While we could not fix this problem, we note the challenge of trying to integrate dif-ferent models developed for different purposes.

23

CHAPTER FOUR

Plausible Future Conditions Affecting Southeast Florida’s Built Environment

The purpose of developing an integrated modeling framework was to use it to gain insight into the vulnerability of Southeast Florida’s built environment to a range of possible future climate conditions, land uses, and asset values. In this chapter, we describe the process of developing these futures, which occurred in close consultation with our partners in the region.

Climate Scenarios

Sea levels and rainfall are two key climatic influences on Southeast Florida inland flooding and saltwater intrusion. Inland flooding can occur when near-surface groundwater levels limit rainfall infiltration and yield problematic surface pooling and runoff conditions. Saltwater intrusion refers to the phenomenon of Atlantic Ocean seawater wedging beneath lower-density freshwater in the Biscayne Aquifer at their subsurface interface. Since Southeast Florida relies heavily on groundwater withdrawals for municipal water resources, pumping wells may draw water that is too saline for safe consumption.

Rising sea levels can strongly influence inland flooding and saltwater intrusion. Higher levels lead to higher pressure, forcing seawater landward into the aquifer and raising ground-water levels that can increase inland flood risk. The saltwater-freshwater interface also advances inland, with the potential to increase the number of pumping wells affected by higher salin-ity levels. A number of coastal wells have already become contaminated by saltwater in recent years.

Rainfall changes also have significant impacts on inland flooding and saltwater intrusion. And while sea levels are certain to increase along the coast of Southeast Florida, changes to rainfall amounts are more uncertain—both in terms of the sign and magnitude.1 Less rainfall across the region generally leads to less infiltration into the aquifer and, hence, lower groundwa-ter levels and reduced chance of inland flood risk. At the same time, lower groundwater levels

1 Wet-season (approximately May through October) rainfall in Southeast Florida is characterized by frequent convective outbursts that produce large precipitation amounts. However, convective rainfall at the local level is poorly simulated by generalized circulation models (GCMs) because these models usually have grid-cell resolutions larger than a convective rainfall event. These models cannot accurately resolve the dynamics of these storms, leading to overall biases in rainfall pro-jections for Southeast Florida. Consequently, downscaling GCM rainfall projections to finer resolutions requires statistical bias–correction techniques to account for these GCM-based errors. The Bureau of Reclamation and the SFWMD used a common technique, known as bias-corrected constructed analogues (Hidalgo, Dettinger, and Cayan, 2008), to downscale the rainfall projections for this research.


would allow the saltwater-freshwater interface to move inland because of the reduced hydraulic pressure from the landward side. The reverse would happen under conditions of higher rain-fall. Increased infiltration to the aquifer would elevate groundwater levels and inland flood risk and, consequently, would increase the freshwater hydraulic pressure against the seawater. This would push the saltwater-freshwater interface toward the coast. Therefore, considering a range of SLR and rainfall changes (positive and negative) is critical to understanding how inland flood risk and saltwater intrusion may shift under a changing climate.

Sea-Level Rise Projections

SLR projections are based on Southeast Florida Regional Climate Change Compact (2015). As described in Chapter Two, we considered three SLR scenarios from the report and refer to them according to their relative changes as low, middle, and high.

Annual sea-level heights for the years 2040 through 2054, relative to the 1992 mean sea level, were computed using the following equation:

( ) ( )= × − + × −Sea level (year) a year b year1992 1992 ,2

where a represents the local rate of SLR for Key West, Florida, and is equal to 0.0022 meters per year and b represents the acceleration of SLR based on a given scenario and is 4.68 ´ 10–5, 1.13 ´ 10–4, and 1.56 ´ 10–4 meters per year for the low, middle, and high scenarios, respectively.

Monthly sea levels are linearly interpolated from the 2040–2054 yearly values. Then, monthly future sea levels are added to the time series of current tides for perturbed tidal boundary conditions used as inputs to the models. For instance, the January 2040 sea level is added to the January 1996 level, the February 2040 level to the February 1996 level, and so forth, for perturbed tidal boundary conditions. For the UMD model, which operates on a daily time step, daily tides are perturbed by the same amount for a given interpolated monthly future sea level (i.e., all daily tides in January 2040 are perturbed by the January 2040 monthly sea level).

Monthly baseline and future sea levels for each scenario used in the UMD and Broward models are depicted in Figures 4.1 and 4.2, respectively. (Note that values are converted from meters in equation 1 to inches in Figures 4.1 and 4.2.) Future levels are very similar between the models. Focusing on the UMD case, average future monthly sea levels increase by 10, 18, and 23 inches for the low, middle, and high scenarios, respectively, compared to the baseline average.

Rainfall

SFWMD provided a total of 119 daily rainfall projections to us for this research, having sta-tistically downscaled Coupled Model Intercomparison Phase 5 GCM projections to a resolu-tion of 2 mi2 covering southern Florida (Dessalegne et al., 2016). The ensemble of projections is based on downscaling several GCMs (and in some cases, multiple realizations for a single GCM) evaluated using three greenhouse gas emission scenarios, or RCPs, from the IPCC Fifth Assessment Report (Stocker et al., 2013). Specifically, the ensemble includes 36 GCM realizations from RCP 2.6, 42 GCM realizations from RCP 4.5, and 41 realizations from RCP 8.5. Therefore, this sampling of rainfall projections includes uncertainty across climate models and emission scenarios.

Plausible Future Conditions Affecting Southeast Florida’s Built Environment 25

Figure 4.1Current and Future Monthly Tides as Represented in the UMD Model

Baseline/current (1996–2010)IPCC Median (”low”)

USACE High (”medium”)NOAA High (”high”)

SOURCE: NAVD 88 = North American Vertical Datum of 1988 (the benchmark land elevation standard).RAND RR1932-4.1

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20552041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054

Figure 4.2Current and Future Monthly Tides as Represented in the Broward Model

Baseline/current (1996–2010)IPCC Median (”low”)

USACE High (”medium”)NOAA High (”high”)

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As noted in Chapter Two, we developed five future rainfall scenarios by applying monthly scaling factors to the current (1996–2010) rainfall inputs in the groundwater models. The five scenarios are derived from the 5th, 25th, 50th, 75th, and 95th percentiles across the 119 pro-jected future rainfall amounts and range qualitatively from the driest to the wettest future.

Monthly scaling factors were developed by computing the average for each percentile across the set of 119 projected future (2040–2054) monthly rainfall patterns over the UMD and Broward model domains. For example, the January scaling factor is computed by first determining, for each projection, the 5th, 25th, 50th, 75th, and 95th percentiles of January rainfall totals for each year between 2040 and 2054. This yields 15 January values (i.e., one per each future year) for each percentile. Averaging these 15 values for each percentile produces the final January scaling factor for each scenario, as shown in Table 4.1. Figure 4.3 shows the resulting time series of current and future rainfall scenarios. We found that, when averaged across all months, these rainfall scenarios represent 63 percent (driest future, 5th percentile) up to 193 percent (wettest future, 95th percentile) of the current rainfall average (bottom row of Table 4.1).

Land Use and Asset Scenarios

As part of overall scenario development, we estimated several economic futures, out of infinitely many possible, for Miami-Dade and Broward counties. These economic futures are essentially alternative levels and spatial distributions of current and future property values at risk from inland flooding and SLR (although additional environmental or other random threats could be modeled). We used the tax records from the DOR (estimates of “just value”) to provide base-line data and used a geographic information system (GIS) to map these estimates of just value to the hydrological model grid cells for the models in each county and to map current land use categories. Future allowable land use for a grid cell was mapped using the most current version of the county-level land use plans provided to us by each county.

Several future scenarios were developed using relatively ad hoc assumptions and future land use categories for each county. These scenarios should not be viewed as forecasts of the future but rather as notional examples of how the change in the level and distribution of assets could interact with environmental scenarios to form different profiles of future risk. How-ever, at the request of Broward County, we also developed several scenarios based on forecasts contained in the county’s TAZ planning and forecast documents. These forecasts are broadly consistent with the spatial distributions and asset levels contained in the TAZ forecasts. Future users of the decision-support tool could easily experiment with alternative economic scenarios in line with their own expectations.

The following discussion details the methodology used to map the current distribution of economic assets to the model grids and explains how future land use scenarios were developed.

Asset Valuation

DOR (2015) maintains property tax data files for each Florida county, including Broward and Miami-Dade counties. Tax files include parcel-level estimates of just (market) value, as well as geographical information about each parcel suitable for use in a GIS environment. The 2015 just value data and parcel file were used as the baselines for each county. Just value was mapped to the appropriate grid for each county’s hydrological model using GIS techniques


Table 4.1Monthly Scaling Factors for the Five Future Rainfall Scenarios

Driest Future(5th percentile)

Dry Future(25th percentile)

Average Future(50th percentile)

Wet Future(75th percentile)

Wettest Future(95th percentile)

January 0.76 1.20 1.65 2.26 3.37

February 0.42 0.66 0.91 1.28 1.93

March 0.41 0.67 0.91 1.23 1.85

April 0.55 0.81 1.08 1.49 2.27

May 0.62 0.87 1.11 1.47 2.02

June 0.56 0.73 0.88 1.04 1.36

July 0.75 0.89 1.00 1.11 1.31

August 0.73 0.86 0.96 1.09 1.27

September 0.73 0.89 1.04 1.18 1.45

October 0.84 1.13 1.36 1.68 2.16

November 0.69 0.99 1.28 1.67 2.42

December 0.49 0.71 0.92 1.22 1.79

Annual average 0.63 0.87 1.09 1.39 1.93

Figure 4.3Current and Future Monthly Rainfall Averaged over the UMD and Broward Model Domains

Driest futureDry futureAverage futureWet futureWettest futureCurrent (1996–2010)

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and accounting for the fact that the parcels and the hydrological model grids did not match.2 The center point elevation of each cell was calculated using topographic data compiled and provided by the USGS.

Each county also provided us with the latest (2016) version of its comprehensive land use plans, which we used to mark potential areas of future development. The GIS shapefiles were used to map future land use categories, as defined in the comprehensive land use plans, to hydrological model grid cells using the predominant land use in any given cell (by area).3 We developed ad hoc rules of thumb, as documented in the following subsections and based on these future land use categories and grid cell elevations, to develop the level and distribution of just value for several future economic scenarios.4

Land Use

The most current (2015) estimate of land use across Miami-Dade and Broward counties is based on the “Florida Parcel Data Statewide—2015” data set from DOR and County Prop-erty Appraisers (2015). This data set is an aggregation of information from individual county property appraisers’ offices and was standardized and normalized for public utilization. Future parcel land use was based on the Miami-Dade County 2030 Comprehensive Development Master Plan (CDMP) (Miami-Dade County, 2018) and Broward County 2040 Future Land Use Plan (Broward County, 2017).

Reconciliation with Hydrologic Model Grids

GIS techniques were used to convert parcel-based land use and value information to the spa-tial scale of the groundwater model grids. We first computed the fractional extent of all par-cels contained within a given UMD and Broward grid cell. Based on each parcel’s fractional coverage, the most dominant land use category (in terms of area) and the sum of each parcel’s fractional land value were then recorded for the grid cell.5 Figure 4.4 presents a simple illustra-tion of this exercise. In this example, the grid cell entirely contains Parcels A (urban land use category, valued at $4 million, with an area of 1 mi2) and C (urban land use category, valued at $5 million, with an area of 2 mi2) and contains half of Parcel B (natural land use category, valued at $2 million, with an area of 2 mi2). Therefore, this example grid cell is dominantly urban (75 percent urban versus 25 percent natural), with a total value of $10 million ($4 mil-lion for Parcel A, plus $1 million for Parcel B, plus $5 million for Parcel C).

Standardizing Land Use Data Sets

The 2015 DOR data set for Miami-Dade and Broward Counties includes 87 current land use descriptions. Miami-Dade’s 2030 CDMP (Miami-Dade County, 2017) uses an additional 25 distinct land use descriptions, and 33 Broward’s 2040 Future Land Use Plan (Broward County

2 In particular, we used the areal interpolation method to assign just values to grid cells from incompatible parcel data (Gotway and Young, 2002).3 These categories were not necessarily the same as those in the DOR data and differed across counties.4 Scenarios were coded in Stata SE 12.5 An exception to the dominant land use rule is applied in the case of water categories. Grid cells are classified as water only if more than 95 percent of the parcels in that cell are in water-based categories. Our analysis excludes water grid cells, so this stringent rule for water classification is used to retain grid cells that represent a mix of water and valuable assets (e.g., housing along a lake).


2017) has an additional 33. To analyze results consistently across land use, we condensed all land use descriptions within these data sets into standardized categories, as described in Table 4.2.

For the current period, the 87 DOR descriptions were condensed into five categories—urban, natural areas, agriculture, water, and other—according to the details in Appendix A. Only a single urban category is used here because density classifications are not attached to urban-residential DOR descriptions and because there is no clear method for objectively deter-mining densities from the descriptions.

For future periods, the Miami-Dade CDMP (Miami-Dade County, 2017) and Broward Future Land Use Plan (Broward County, 2017) categories were condensed into nine categories (right column in Table 4.2) according to the details in Appendix A. Because the future county plans include density classifications for urban areas, we were able to create three levels of urban

Figure 4.4Simple Example of Converting Parcel-Based Land Use and Value Information to Model Grid Cells

RAND RR1932-4.4

Grid cell Grid cell

Dominant land use: Urban

Total land value: $10 million

Parcel A

Parcel C

Parc

el B

Parcel A: Urban, $4 million, 1 mi2

Parcel B: Natural, $2 million, 2 mi2

Parcel C: Urban, $5 million, 2 mi2

Table 4.2Standardized Land Use Categories Used in the Current and Future Periods

Standardized Current Land Use Categories Standardized Future Land Use Categories

Urban Urban high density

Urban medium density

Urban low density

No comparable category Rural development

Natural areas Natural areas

Agriculture Agriculture

Water Water

Other Other

Missing category Missing category

NOTES: “Other” category includes primarily vacant lands.


Figure 4.5Maps of Standardized Current and Future Land Use Across the Groundwater Models

RAND RR1932-4.5

Land use (future)Land use (current)Urban

Agriculture

Natural areas

Other

Urban high density

Urban medium density

Urban low density

Rural development

Agriculture

Natural areas

Other

Urban

Agriculture

Natural areas

Other

density in the future standardized set: urban high density, urban medium density, and urban low density. Moreover, rural development is a new category based on projected growth within the Broward categories of rural estates and rural ranches. Figure 4.5 presents the resulting maps of standardized current and future land use across the two counties.

To sharpen understanding of changes in land use between the present and future, as envi-sioned in each county’s plan, we focus specifically on the spatial distribution of change from nonurban to urban land uses (see Figure 4.6).

Spatial Divisions

To help summarize results spatially, we subdivided the model domains into distinct regions (see Figure 4.7). The Broward divisions were based on major roadways through the county and were approved by our Broward County partners. The Miami-Dade boundaries were provided to the project team by our Miami-Dade County partners.


Figure 4.6Land Use Change from the Present to 2030 for Miami-Dade and 2040 for Broward

RAND RR1932-4.6

Land use (change)

Land use changeNonurban to urbanUrban to urbanOther

Current Land Use and Distribution of Assets

In this section, we briefly describe the 2015 just values used in the analysis tool for Broward and Miami-Dade counties. Note that all elevations are relative to the NAVD 88 datum.

Broward County

Within the Broward County hydrological grid, there are 49,685 cells with nonmissing just value estimates. The average just value per cell is approximately $4.1 million (2015 dollars), with a minimum of zero and a high of $1.6 billion. Average cell elevation is 6.2 feet above sea level, with a minimum of –2 feet and a maximum of 20 feet.


Figure 4.7Regions Used for Reporting of Results

RAND RR1932-4.7

Miami-DadeBroward

RegionNorthwest BrowardNorth-central BrowardNorthest BrowardFar southwest BrowardSouthwest BrowardSouth-central BrowardSoutheast Broward

RegionIslandsLower UMDOutside UMDUpper UMP

Miami Beach

Miami-Dade County

Within the Miami-Dade County hydrological grid, there are 12,806 cells with nonmissing just value estimates. The average just value per cell is approximately $20.9 million (2015 dol-lars), with a minimum of zero and a high of $1.0 billion. Note that the grid for Miami-Dade County is considerably coarser than that for Broward County. Average cell elevation is 4.9 feet above sea level, with a minimum of –17 feet and a maximum of 16.3 feet.

Figure 4.8 summarizes the just value estimates (current) by land use type and the regions shown in Figure 4.7. The asset values range from about $10 billion for the small south-central Broward region to more than $100 billion for the large and heavily populated lower UMD region. For all regions, urban assets account for the vast majority of the value.

Future Land Use and Distribution of Assets

We used several different methods to distribute future growth (or contraction) of economic value (from 2015 to 2035) spatially across the regions based on the 2030 Miami-Dade and 2040 Broward land use plans. These allocation methods do not imply and are not intended to be forecasts of the future; rather, we used these different approaches to illustrate several of an infinite number of development scenarios that are broadly consistent with the comprehensive land use plans given the set of allocation rules described in this report. Future users of the


Figure 4.8Current Land Use Values, 2015

RAND RR1932-4.8

Value summary—current

Model Region

0 20 40 60 80 100 120

Value ($B)

Broward Northwest Broward

UMD

North-central Broward

Northeast Broward

Far southwest Broward

Southwest Broward

South-central Broward

Southeast Broward

Islands

Lower UMD

Outside UMD

Upper UMD

Land useUrbanAgricultureNatural areasOther

$36.3 billion

$16.2 billion

$40.7 billion

$21.8 billion

$30.7 billion

$9.9 billion

$11.2 billion

$32.0 billion

$117.6 billion

$10.2 billion

$78.6 billion

DST could develop alternative future economic scenarios more in keeping with their visions of future economic growth.

We developed two methods of allocating future assets each for Broward and Miami-Dade counties.6 The first, the random distribution method, randomly allocates residential growth to cells that are not currently residential but with allowable residential land use in the future according to the comprehensive land use plan. In Miami-Dade, this assumption was insuf-ficient to accommodate forecast growth, so we assumed that a particular portion of growth was from density intensification. The second, the elevation method, generally follows the same

6 For Broward county, we used an additional external spatial forecast to create additional scenarios. See “Traffic Analysis Zone (TAZ) Forecast for Broward County,” later in this chapter.


procedure as the random distribution method but prioritizes development on cells with the highest elevation.

The general allocation process was as follows. First, estimated populations for each county for 2015 and 2035 were obtained from the University of Florida’s Bureau of Economic and Business Research (Rayer and Wang, 2016) medium forecast, and average densities were devel-oped for each county on the basis of the available data (including the land use plan, current population and housing data from the American Community Survey [U.S. Census Bureau, undated], and current just values). These densities were used to cap development within any particular cell. See Appendix C for details.

Second, grid cells available for residential development were identified on the basis of cur-rent land use from DOR’s current land use codes and future land use codes from the compre-hensive land use plans. In particular, a cell was deemed “available” for residential if it fell into a designated future residential category and was currently classified as agriculture, natural area, vacant, water, or government.7

Third, average just values for future residential, commercial, and industrial land use cat-egories currently being used as residential, commercial, and industrial, respectively, were calcu-lated to get a sense of the current average values associated with each future land use category. In addition, each cell that was predominantly zoned commercial or industrial in the future was identified.

Fourth, for the random distribution method, a simulation was run that randomly allo-cated population and additional just value to each available grid cell. To accomplish this, the available population that could be accommodated by all cells in a county was calculated by multiplying the number of cells available in each class by the assumed density. A multiplier was then defined as the ratio of overall population change to the maximum population that could be accommodated, and this figure was multiplied by available residential cells in each residen-tial class to provide a limit on the number of cells that could take on additional development in each residential class. Each available cell by class was then randomly sorted and marked as eligible to accommodate new population and just value if the running sum of eligible cells was less than the limit. For all those so marked, the new (future) just value of the cell was calculated as the 2015 just value plus the estimated average value for the future land class. Future values of commercial and industrial cells were assumed to grow at the rate of population and to be distributed uniformly across each commercial and industrial cell in the model.

For the elevation method, we began with the same residential cell availability as with the random distribution method but sorted available cells by elevation (rather than randomly) and used the calculated density figures to allocate to the highest cells until all population growth were accommodated. Commercial and industrial values were allocated as in the random dis-tribution method.

Traffic Analysis Zone Forecast for Broward County

An additional land use scenario was prepared for Broward County on the basis of the 2035 forecasts contained in Acevedo and Leonard (2014). This document provides forecast informa-tion for accommodating new population growth and housing units in a spatial manner. The

7 Recall that current and future land use codes are based on the predominant land use category by area. So, for example, residential development in a cell currently classified as water and some future residential category may include up to 49 per-cent land available for development.


forecasts distribute estimates from the University of Florida’s Bureau of Economic and Busi-ness Research to the 2010 TAZs.

Each cell in the Broward hydrologic model was mapped to the 953 TAZs using GIS soft-ware such that the plurality of land area in each cell determined the TAZ to which that cell was assigned. It most cases, each TAZ contained more than one model grid cell, meaning that the model grid is finer than the TAZ grid. For nonzero and nonmissing dwelling unit values for each grid cell, the growth rates in the number of dwelling units were calculated from the TAZ forecast data from 2015 to 2035. Model cells consistent with residential, commercial, and industrial land use were identified using the available future land use maps. Using the number of dwelling units from the 2015 TAZ forecast and the just value information from the property files, per-dwelling-unit values were calculated for each TAZ.

The incremental growth in value for each model cell was then calculated by multiplying the per-dwelling-unit values by the difference in dwelling units between 2015 and 2035 and converted to a percentage of current just value. This percentage (adjusted for missing values) was used to augment current just values for the future. In a small number of cases, this incre-ment was negative. For cells with zero forecast 2015 populations or for which the incremental growth was estimated to be greater than 100 percent, the increment was calculated based on the per-capita just values by future land use category. This procedure prevents extreme outliers and retains the preexisting values in the current property tax files, while assuming proportional growth in all property values based on estimated TAZ-level growth.8 We assumed all value estimates are in real 2015 dollars. We also assumed that value estimates are based on physical growth, such that any cell that is assumed not to change the number of dwelling units and that population will be valued identically for 2015 and 2035. We term this the TAZ land use scenario.

Methods of Distribution of Future Broward Assets

In the Broward model runs, the available cells were sufficient to accommodate the assumed population growth, which was assumed to be 215,275 persons (pushing the future popula-tion to just over 2.1 million). Total average cell value for the random distribution and equal-value elevation methods increased to $4.43 million, or about 18.5 percent. In the Elevation method, average cell values were slightly lower at $4.40 million, or an increase of approxi-mately 17.6 percent. The minimum and maximum cell values were unchanged. Figure 4.9 shows the spatial pattern of the change in value for Broward County. The method distributes population and asset growth throughout the county with some concentration along main transportation routes.

For the TAZ method, the average (nonmissing) cell value increases from just under $3.74 million in 2015 to just over $4.23 million in 2035, an increase of approximately 13.1 per-cent. This is slightly less than the random distribution and equal-value elevation methods discussed previously. However, it is consistent with the lower overall population growth rate (10.8 percent in this method, as compared to 11.4 percent using the assumptions from the previous subsection). The difference between value and population growth rates is due, in part, to a changing composition of households assumed in the TAZ forecasts and the spatial distribution of value across the model cells. Figure 4.10 shows the spatial distribution of the

8 So, for these methods, industrial and commercial property values are assumed to grow at the same rate as residential values. This assumption can be relaxed.


Figure 4.9Projected Change in Valuation Under the Equal-Value Elevation Method for Broward

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Valuation change

$0M $20M

value change for Broward using the TAZ methods and shows how this method concentrates the value change more in the specific TAZs.

Methods of Distribution of Future Miami-Dade Assets

In the Miami-Dade model simulations, the available cells were not sufficient to accommodate the assumed population growth—601,583 persons (pushing the future population to nearly 3.3 million). This meant we needed an additional mechanism to accommodate future growth. We therefore assumed that 30 percent of development would come from intensification of


Figure 4.10Projected Valuation Change for Broward Using the TAZ Method

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Valuation change

$0M $20M

existing residential development and that, for each cell selected for such intensification, 25 per-cent of the average value for a converted cell would be added to the existing residential values. These assumptions were entirely arbitrary and could be modified for other asset scenarios.

The resulting values increased average future just values for the random distribution and equal-value elevation methods to $26.7 million, an increase of 23.6 percent over the current valuation. In the elevation methods, the increase was slightly greater. Again, there was no noticeable change in the minimum or maximum cell values. Figure 4.11 shows the spatial


Figure 4.11Projected Change in Valuation Under the Equal-Value Elevation Method

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Valuation change

$0M $50M Aventura

Miami Beach


pattern of the projected change for UMD. The largest changes are clearly seen for the higher elevation regions along the “ridges” in the northwest and central portions of the county.

Summary of Change in Assets

To summarize the asset distribution methods, Figures 4.12 and 4.13 show the total change in asset valuation by region between 2015 and 2035 for the different valuation approaches. Given the differences between the two approaches, the patterns in the change in asset values are as

Figure 4.12Projected Change in Asset Values for Broward County, 2015–2035

NOTES: The current asset values by region range between $9.8 billion (South/Central Broward) and $40.0 billion(North/East Broward).RAND RR1932-4.12

Change in asset value ($B)

0 1 2 3 4 5 6 7 8 9 10

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NorthwestBroward

Change in value(elevation)

Change in value(random)

Change in value(TAZ)

North-centralBroward




NortheastBroward




FarsouthwestBroward




SouthwestBroward




South-centralBroward




SoutheastBroward




Land use (future)Urban high densityUrban medium densityUrban low densityRural development


expected. In particular, the random distribution and elevation methods are generally based on the same type of allocation mechanism (including the underlying population growth forecast), with the primary difference between the two being the prioritization of newly developed cells (e.g., randomly allocated or prioritizing higher ground). The TAZ method, however, is based on current Broward county forecasts of TAZ-level population growth (which was slightly lower, at 10.8 percent, than the 11.4 percent assumed in the other scenarios) and did not fore-cast increased industrial or commercial activity (whose values were assumed to increase at the rate of population growth in the other scenarios). To be consistent with the Broward county forecasts, we chose not to impose any additional assumptions about industrial and commercial value growth. As a result, the TAZ method included only new residential development, with a corresponding reduction in the overall increment related to the change in value in high-density land use regions.

The methods documented here are but several of an infinite number of future land use possibilities, spanning not only the total value of future fixed assets but also their spatial distri-bution across the counties. The methods rely on several key assumptions to drive results, and, in the case of the random distribution method, a simple random draw from a distribution. Incorporating additional local knowledge could significantly change the distribution of assets across both counties. However, our methods are designed to be broadly consistent with the comprehensive land use plans each county provided, and the methods allow an initial explora-tion of how differences in land use policy levers (for example, encouraging development along higher ground) could be used to partially mitigate risk. Future research could provide more-detailed forecasts and scenarios that might span the relevant decision space of policymakers, perhaps based on more sophisticated asset- and population-allocation models.

Figure 4.13Projected Change in Asset Values for Miami-Dade County, 2015–2035

NOTE: The current asset values by region range between $5.6 billion (outside UMD) and $113.3 billion(lower UMD).RAND RR1932-4.13

Change in asset value ($B)

0 5 1510 20 3025

Region

UpperUMD



LowerUMD



Islands Change in value(elevation)


OutsideUMD



Land use (future)Urban high densityUrban medium densityUrban low density


In our analysis, we wish to emphasize that land use and valuation methods are derived independently of climate conditions. Thus, there is no feedback between climate conditions (SLR and precipitation and inland flooding) and economic development. We would expect real-world systems to exhibit such a feedback. More-complete assessments should include such feedbacks.

43

CHAPTER FIVE

Vulnerability of Assets to Groundwater Flooding

This chapter presents the results of the analysis of the vulnerability of economic assets to groundwater flooding under the range of futures described in Chapter Four.

Baseline Groundwater Flooding Potential Hazards

The USGS groundwater models estimated the elevation of the groundwater table, and then we computed the distance between the groundwater table and overlying ground surface eleva-tion to yield the metric of “depth to groundwater.” The Broward model produces monthly estimates, and we average these depths over both the wet months and the dry months within a given period (i.e., current or future). In contrast, the UMD model produces daily estimates. We calculated the average over each month, and then took the average of the monthly values across the dry period and the wet period. We then reported the average depth to groundwater for the wet and dry seasons. These metrics serve as proxies for flooding caused by groundwater reaching the ground surface. Smaller depths indicate proximity to the land surface and, con-sequently, less storage capacity to handle rain events, leading to higher flood risk. To highlight regions of higher risk, we also reported the percentage of area that faces average depths to groundwater of less than 1 foot. While this approach could miss the sensitivity of some areas to higher-frequency flooding events, it does correctly identify regions that have experienced groundwater flooding as recently as June 2017.

Figure 5.1 shows the depth to groundwater in a map view (on the left) and by distribution of depth for the different Broward regions (on the right). Regions of low depths to ground-water are scattered throughout the entire county, with concentrations of such regions in the southwest corner, which has experienced recent flooding. The regional summary results show that the distribution of depths to groundwater for most of the regions peak in the 3- to 5-foot range, with additional peaks in the 0- to 1-foot range.

Figure 5.2 shows the same type of results for UMD. In contrast to Broward, the regions of low depths to groundwater are concentrated in the low-lying western regions, adjacent to Everglades National Park, and in the barrier island areas affected by SLR. The summary results show that much of UMD has an average depth to groundwater greater than 5 feet, and only a few areas have depths less than 1 foot. Note that the UMD model has a lower spatial resolution than the Broward model, which could be obscuring localized areas of low depths to ground-water within grid cells.


Figure 5.1Groundwater Depth Under Baseline Conditions for SLR and Precipitation in the Wet Season for Broward

RAND RR1932-5.1

Depth togroundwater (feet)

0.000.250.500.751.001.251.501.752.002.252.50

2.753.003.253.503.754.004.254.504.755.00

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Southeast Broward

Future Groundwater Flooding Hazards

The areas that face low depths to groundwater levels will change in the future (i.e., 2040–2054) in response to SLR and trends in precipitation. In the absence of any changes in pro-jected future rainfall relative to the present, rising sea levels will lead to groundwater levels closer to the ground surface in the coastal regions. In the absence of the effects of SLR farther inland, increases in rainfall will lead to lower depths to groundwater. Figure 5.3 shows changes in depths to groundwater in the future during the wet season for the middle SLR scenario and average precipitation conditions for Broward. The red regions on the coast show the impact that SLR will have on changes in depth to groundwater—decreasing by 2 feet or more in some areas. Inland, only very slight changes in groundwater depths are projected. That said, caution should be exercised in overinterpreting these results for inland areas because of uncertainties

Vulnerability of Assets to Groundwater Flooding 45

Figure 5.2Groundwater Depth Under Baseline Conditions for SLR and Precipitation in the Wet Season for UMD

RAND RR1932-5.2

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in the boundary conditions in each of the groundwater models, particularly on the western boundary where changes in water management are affecting depth to groundwater.

Figure 5.4 shows the change in depth to groundwater under middle SLR and average pre-cipitation for UMD, focusing only on the land area zoned for rural or urban development by 2030. Unlike for Broward, the entire region shows modest decreases in depths to groundwater (between 0.25 and 0.75 feet). The few areas where depth to groundwater declines by more than 1 foot coincide with relatively lower-lying valleys that extend westward into the coastal ridge.

The differences in the projected changes in groundwater levels between Broward and UMD are likely a consequence of differences in the structures of the models themselves. For example, the Broward and UMD groundwater models respond differently to SLR. The models are based on different versions of MODFLOW and have different grid structures that “dis-cretize” the aquifer: The UMD model has three vertical layers, while the Broward model has 12 vertical layers. Differences may also relate to variations in the hydrogeologic properties of


Figure 5.3Changes in Groundwater Depths for Broward for Middle SLR and Average Precipitation in the Wet Season, Through 2040–2054

RAND RR1932-5.3

Change in groundwater depth (feet)

–2.500 2.500

the groundwater systems underlying each county. Differences in boundary conditions could be particularly significant, especially the interactions with canals. A coarser model grid is inher-ently less sensitive to these finer features. Assumptions regarding future pumping rates could also be contributing to differences in results across the models. To keep our base case as close to the published USGS results as possible, we maintained the USGS assumptions about future pumping. We were unable to run scenarios with varying pumping rates to explore the differ-ences between the two models within the scope and time constraints of this initial research.


Figure 5.4Changes in Groundwater Depths for UMD for Middle SLR and Average Precipitation in the Wet Season, Through 2040–2054

RAND RR1932-5.4

Change in groundwater depth (feet)

–2.500 2.500


Figure 5.5Groundwater Depths Under High SLR and Wettest Precipitation in the Wet Season for Broward, 2040–2054

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RAND RR1932-5.5

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Figure 5.5 shows groundwater depths under the highest SLR and precipitation future for Broward. The increased SLR and precipitation shifts the distribution of depth to groundwater even further to the right in the histograms, with more areas projected to have depths less than 2 feet below the surface.

Figure 5.6 shows shallower depths to groundwater throughout the UMD region under wetter and high SLR conditions (compared with Figure 5.2). While depths to groundwater in most areas are greater than 5 feet, there is a noticeable shift in the distribution of depths to shallower depths (see right side of Figure 5.6).

Buildings and other assets are vulnerable to groundwater flooding across many futures when depths to groundwater are low. Figure 5.7 (left side) shows the distribution of depths to groundwater across Broward for the different SLR and precipitation futures. The right side of Figure 5.7 records the percentage of area in which depths to groundwater are lower than a user-specified threshold, which we call the groundwater threshold. Because what is considered


Figure 5.6Groundwater Depths Under High SLR and Wettest Precipitation in the Wet Season for UMD, 2040–2054

RAND RR1932-5.6

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“low” could vary by region, by the assets at risk, and by the perspective of a planner, Figure 5.7 enables the user to adjust the threshold. These results show the far southwest and southwest regions of Broward have the largest sensitivity in terms of areas with low depths to groundwa-ter. The variation across the futures is also driven largely by precipitation and not SLR. In the southeast region, the area that is vulnerable increases for middle and high SLR scenarios as well. Note that there is a significant shift in depths to groundwater in northeast Broward (as seen on left), but this only translates to minor shifts in percentage of vulnerable areas across the futures (as seen on the right).

Figure 5.8 shows the same results from the UMD model. The sensitivity of the depths to groundwater to the futures is greatest with respect to low depths to groundwater in the out-side UMD region, which corresponds to areas outside the Urban Development Boundary and


Figure 5.7Summary of Broward Vulnerabilities to Groundwater Flooding

RAND RR1932-5.7

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Figure 5.8Summary of Miami-Dade’s Vulnerabilities to Groundwater Flooding

RAND RR1932-5.8

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Figure 5.9Summary of Assets Vulnerable to Groundwater Flooding in Broward

NOTE: Bars indicates current value of assets at risk (2015). Symbols indicate future vulnerable assets(2040 time frame) across the precipitation and SLR futures. RAND RR1932-5.9

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where there are fewer assets. In the more-urbanized regions of Miami-Dade, there are signifi-cant shifts in the distribution of depths to groundwater but only modest changes in the areas that are vulnerable for a 1-foot depth to groundwater threshold.

It is important to note that these results apply to all nonwater regions of both models. To understand where assets are vulnerable, we combined the depth to groundwater with estimates of current assets and projections of future assets in the following sections.

Asset Values at Risk

We next estimated the value of assets that are vulnerable to groundwater flooding for each region in which the average depth to groundwater is less than 1 foot.

Broward

Figure 5.9 shows the currently estimated amount value at risk (bars) and future value at risk (symbols) across the SLR and precipitation futures in Broward. Most regions face significantly higher vulnerable assets in the future, although the extent of the increase depends on the SLR and precipitation futures. Northwest Broward and southwest Broward, in particular, show increasing vulnerabilities, with higher risks under the wetter futures. Northeast Broward, in contrast, shows the widest range of vulnerability across SLR scenarios.

The spatial distribution of the vulnerable assets in Broward is shown in the map on the left of Figure 5.10. The markers indicate the value of vulnerable assets for each grid point, and the bar charts on the right show the frequency of grid points containing vulnerable assets, binned by value. These results are based on the elevation method for the high precipitation and


Figure 5.10Value of Vulnerable Assets in Broward for High SLR and Wettest Precipitation Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040

RAND RR1932-5.10

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high SLR future. The map shows that vulnerable assets are distributed throughout the county, mirroring the areas of low depths to groundwater shown in Figure 5.5. The summary bar chart indicates that northwest, far southwest, and southwest Broward all have high numbers of grid points with vulnerable assets between $1 million and $5 million. The highest concentration of high-value vulnerable assets is in the northeast and north-central Broward regions, as seen in the map and bar charts.

UMD

Figure 5.11 shows the currently estimated value at risk (bars) and future value at risk (symbols) across the SLR and precipitation future in UMD. In upper UMD, the current value at risk is only $200 million, yet this increases to between $400 million and $1,600 million, depend-ing on the future. Current assets in lower UMD also have low vulnerability, yet vulnerability increases significantly depending on the SLR and precipitation future. The islands have a high amount of vulnerable assets, and these do not increase in the future—both because they are fully developed and because they are already facing high groundwater levels. Outside UMD has a modest level of vulnerable assets, which are projected to increase. For the three of four


Figure 5.11Summary of Assets Vulnerable to Groundwater Flooding in UMD

NOTE: Bars indicates current value of assets at risk (2015). Symbols indicate future vulnerable assets (2040 timeframe) across the precipitation and SLR futures. RAND RR1932-5.11

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regions with increasing vulnerable assets, the primary driver is precipitation, as shown by the symbol colors. However, SLR can also have a large effect. For example, in upper UMD, the difference between the assets at risk for the low and high SLRs (for the wettest precipitation scenario) is $400 million.

Figures 5.12 and 5.13 show the spatial distribution of the vulnerable assets in UMD under two bounding futures—low SLR, driest precipitation and high SLR, wettest precipita-tion. Figure 5.12 shows that relatively few areas of the model domain are projected to have vul-nerable assets under this most benign future, even though the value of vulnerable assets is quite high, with the value of some vulnerable grid points exceeding $100 million. The island region includes the highest number of vulnerable grid points with values greater than $100 million. Figure 5.13 shows the regions that are vulnerable under the high SLR, wettest precipitation future. In the upper UMD region, four times as many grid points are vulnerable to flood-ing between $50 million and $100 million of assets than in the low SLR, driest precipitation future. More grid points are also vulnerable in the other asset value categories. The vulnerabil-ity of lower UMD also significantly increases from only two grid points under the low SLR, driest precipitation future to 23.

Figure 5.14 summarizes the vulnerability of asset values across the precipitation and SLR futures for UMD.1 The results are organized so that precipitation and SLR increase from left to right. The horizontal lines cutting across the bars indicate the estimate of asset values currently vulnerable for each region. For all regions except the island region, the value of assets vulner-

1 Similar results were obtained for Broward but are not shown here because precipitation changes dominate the uncer-tainty about future vulnerability.


Figure 5.12Value of Vulnerable Assets in UMD for Low SLR and Driest Precipitation Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040

RAND RR1932-5.12



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able to groundwater flooding increases as precipitation increases—seen by the overall increase in bar height from left to right—and SLR increases—seen by the increase in bar height within each precipitation category. In lower UMD, sensitivity to the futures is low except under the wet and wettest precipitation conditions, in which the value of vulnerable assets more than doubles from under $400 million to more than $900 million.

Drivers of Vulnerability to Groundwater Flooding

The preceding analysis shows that the value of assets vulnerable to groundwater flooding in both Broward and UMD will increase in the future across the range of SLR and precipita-tion futures. We next explore how much of the increased vulnerability is due to changes in the depth to groundwater (the hazard) and how much to changes in economic asset values. Figure 5.15 shows the percentage change in vulnerable assets for Broward (top) and UMD


Figure 5.13Value of Vulnerable Assets in UMD for High SLR and Wettest Precipitation Conditions in the Wet Season Under the Elevation Asset Distribution Method, 2040

RAND RR1932-5.13



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(bottom) for each SLR and precipitation future combination, disaggregated by the key driver of vulnerability. These results use the elevation method for distributing future assets for both Broward and UMD. The red bars indicate the percentage change in vulnerable assets due to changes in depths to groundwater only. For these results, assets are held constant at current levels. The yellow bars show the percentage change in vulnerable assets due to changes in asset value between now and the 2040 time frame. For these results as well, depths to groundwater are held constant at current levels. The gray bars show the residual term, which reflects remain-ing changes that are not due to the hazard or asset changes alone. All three terms sum to the total percentage change in vulnerable assets, as indicated by the vertical gray bars. The value of the total change is indicated for the bookend cases for Broward and UMD.

The results in Figure 5.15 show that changes in assets are the largest drivers of increasing vulnerability to groundwater flooding in Broward. This reflects both the continued develop-ment projected throughout Broward and the relatively high level of baseline hazards through-


Figure 5.14Total Value Vulnerable Across SLR and Precipitation Scenarios in UMD Using the Elevation Method, 2040

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RAND RR1932-5.14

out Broward. The hazard-driven vulnerability (red bars) is largely due to increases in precipita-tion; in comparison, there are only small differences across the SLR scenarios, as noted earlier.

For UMD, current vulnerabilities are markedly lower than in Broward—$2.1 billion versus $11.9 billion. The changes in vulnerabilities are a larger percentage of current values than they are in Broward but are lower in total monetary terms. The drivers of change are dif-ferent as well. While changes in assets (yellow bars) are still important, the hazard changes (red bars) exceed the asset changes under the wettest precipitation scenario for all SLR scenarios and under the wet and wettest precipitation conditions for the high SLR scenario.

The overall drivers of vulnerability also depend on the spatial location and method of esti-mating the future distribution of assets. For Broward, this is most evident by looking at the far southwest and south-central regions. Figure 5.16 shows the asset vulnerability disaggregation for these two regions for the elevation (top) and TAZ (bottom) asset distribution methods. For these regions, the change in assets is the dominant driver under the elevation asset distribution method. This suggests that, under this projection of future assets, even though the assets are


preferentially located in higher elevation regions, the general low elevation and widespread low depths to groundwater lead to higher asset vulnerability. However, under the TAZ asset distri-bution method (bottom), the increases in asset-dominated vulnerability are largely eliminated and do not increase in other regions. On a regionwide basis, the vulnerable assets under the TAZ method are $200 million lower than under the elevation method.

To further explore the spatial dimension of the drivers of vulnerability, Figure 5.17 shows which factor (hazard or asset) is dominant for Broward (for the TAZ asset method) under the low SLR, driest precipitation future (left) and the high SLR, wettest precipitation future (right). Under the former, increases in vulnerability are driven mostly by changes in asset values inland, except for the East Fort Lauderdale region. In the coastal areas, the SLR (even at its low rate) is the dominant driver of increased vulnerability. Under the high SLR, wettest precipita-tion futures (right), however, vulnerability in many more areas is dominated by the climate

Figure 5.15Disaggregated Groundwater Flooding Vulnerability for the Elevation Method

Model SLR Precipitation

0 20 40 60 80 100 120 140

Change in value of vulnerable assets (%)

Broward Low DriestDryAvgWetWettest

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UMD Low DriestDryAvgWetWettest

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Total change = $3,001 million

Total change = $379 million



NOTE: Groundwater risk summaries.RAND RR1932-5.15

Broward (TAZ asset method/UMD: Elevation asset method)


hazards. Inland areas are driven by higher precipitation, and coastal areas are driven by the combination of higher precipitation and high SLR. Th e results for the elevation method (not shown) indicate more-dispersed asset-driven vulnerability for both futures.

Figure 5.16Disaggregated Groundwater Flooding Vulnerability for Two Broward Regions and Two Asset Methods

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NOTE: The vertical orange bars indicate the current levels of vulnerability for the two Broward regions to put thechanges in context.RAND RR1932-5.16

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Figure 5.17Drivers of Vulnerability to Groundwater Flooding for Two Futures for Broward

RAND RR1932-5.17

Low SLR/driest precipitation High SLR/wettest precipitation

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Figure 5.18 shows the same type of results as Figure 5.17, but for UMD. Vulnerability of inland areas is dominated by growth in assets for the low SLR, driest precipitation future, and hazard-driven vulnerability is more widespread under the high SLR, wettest precipitation future.

Potential Strategies to Mitigate Vulnerability to Groundwater Flooding

We presented our vulnerability analysis to the Southeast Florida stakeholders and used it to motivate a discussion of possible adaptation options. Several high-level flood adaptation approaches were discussed for reducing vulnerabilities to the flood hazard impacts: elevate existing assets, improve drainage, and flood-proof or harden assets. Other high-level adapta-tion strategies were identified for reducing asset exposure, including moving existing assets to areas with lower hazards and reorienting future development to lower-hazard areas.

We then developed more-specific adaptation strategies based on these approaches, as shown in Table 5.1. The first three are intended to reduce asset growth in high-hazard areas, while the last two are intended to reduce vulnerability of assets in high-hazard areas. The elevation method is a rough approximation of the first strategy—increase density at higher elevations—and the TAZ asset method for Broward is a rough approximation of the second strategy—increase density along future transportation corridors.

To consider where these strategies might be best deployed and roughly estimate their adaptation value, we looked first at locations in Broward where growth in assets is the primary driver of risk for the TAZ asset method. This asset method roughly reflects the first option


Figure 5.18Drivers of Vulnerability to Groundwater Flooding for Two Futures for UMD

RAND RR1932-5.18

RAND RR1932-5.18

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Table 5.1Strategies to Mitigate Groundwater Flooding for Broward and Miami-Dade

Type Strategy Implementation

Status quo Broward: Implement 2040 Future Land Use PlanMiami-Dade: Implement 2030 Comprehensive Development Plan

Reduce asset growth in high-hazard areas

1. Increase density at higherelevations

Broward: Above 5 feetMiami-Dade: Above 8 feet (along ridge)

2. Increase density alongfuture transportation corridors

Broward: Along 2035 long-range transportation projectsMiami-Dade: Along SMART plan

3. Send and receive Reorient development away from high-vulnerability areas

Reduce vulnerability of assets in high-hazard areas

4. Cut and fi ll Fill is used to raise existing development or increase elevation of new development

5. Increase pumping anddrainage


Figure 5.19Asset-Dominated Vulnerabilities in Broward for Average Precipitation and Mid-SLR Future, 2040

RAND RR1932-5.19

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for reducing asset growth in high-hazard areas—increase density along future transportation corridors. As shown in Figure 5.19, there are four areas with concentrated asset-driven vul-nerabilities—east Fort Lauderdale, south-central Broward (just east of Cooper City), west- central Broward (Sawgrass region), and north Broward (Coconut Creek region). The stacked bar chart (right) totals the value of all projected assets vulnerable due to asset growth. Two grid points in the Coconut Creek region account for about 10 percent of the increased vulnerability— $157 million of the $1.5 billion total. Increased assets in the Sawgrass region account for just over $600 million of the total. The four regions together account for just over $1 billion of the $1.5 billion total, suggesting that future development in these regions could be affected by these low depths to groundwater conditions.

Under the elevation method (not shown), the total increased vulnerability from growth in assets is more than twice that for the TAZ method ($3.6 billion). The change in assets for the four regions identified under the TAZ method also accounts for only $213 million of the $3.6 billion. This shows that, while development that follows the TAZ asset method would reduce asset-driven vulnerabilities by 60 percent, it leads to increased concentrated risk in the four regions identified earlier. This provides opportunities to mitigate this new vulnerability through the use of pumping and/or drainage, per adaptation Strategy 5 in Table 5.1.


Figure 5.20Hazard-Dominated Vulnerabilities in Broward for All but the Wettest Precipitation Scenario, 2040

RAND RR1932-5.20

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Next, we examined where hazards dominate future vulnerabilities in Broward. The total value of vulnerable assets under any of the futures is $2.2 billion. About $900 billion of assets (42 percent of the total) are only vulnerable to the highest precipitation scenario. Figure 5.20 shows the remaining assets that are vulnerable under at least one of the remaining 12 futures (four precipitation scenarios times three SLR scenarios). Assets worth $867 million are located on the east side of Florida’s North-South Turnpike and are grouped in discrete high-vulnerabil-ity areas, such as East Fort Lauderdale, the Hollywood area, and South Pompano Beach. These coastal areas will also face risks from flooding caused by rising tides, which is not addressed in this research. These areas are strong candidates for Strategies 4 (cut and fill) and 5 (increase pumping and drainage).

In UMD, the future vulnerabilities are driven more by hazard changes than by asset changes—$3.1 billion versus $497 million. Current vulnerabilities are low, and future vulner-ability will be due to increasing hazards in areas with current assets and areas where future assets might go. Figure 5.21 shows the locations and summation of vulnerabilities due to asset changes (left) for the elevation asset change method and due to hazard changes (for all futures except for the highest precipitation scenario) (right).

For UMD, simply focusing new development on elevated areas (as shown in the figure)—Strategy 1—eliminates 19 percent of the vulnerability due to asset changes that would occur


Figure 5.21Regions of Vulnerability Increases for Miami-Dade Based on All but the Wettest Precipitation Scenario, 2040

RAND RR1932-5.21

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Ch

ang

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$1M $200M$1M $200M$1M $200M

under the random method. The right side of the figure shows that about one-half of the total hazard-driven vulnerability is due to the highest precipitation scenario. For the remaining hazard-driven vulnerability, about one-third ($517 million) of the assets are in downtown Miami. The cluster of vulnerable regions in western UMD (Doral region) accounts for $600 million of the remaining. As with Broward, Strategies 4 and 5 will be required to address the hazard-driven vulnerability.

65

CHAPTER SIX

Vulnerability of Wells to Saltwater Intrusion

Along with risks to inland flooding due to near-surface groundwater levels, Miami-Dade and Broward counties must also prepare for shifts in the saltwater-freshwater interface under vary-ing rainfall and SLR futures. The position of this interface determines the number of ground-water pumping wells that are intruded by saltwater, which may lead to unsafe drinking water quality and may force the region to turn to alternative water sources. In general, SLR acts to push the interface inland because it increases the horizontal water pressure difference between the ocean and groundwater in the Biscayne Aquifer. Increased rainfall on land and infiltration into the aquifer act to freshen groundwater, raise the water table, and push the interface back toward the ocean. Conversely, reduced rainfall and infiltration may enable the interface to move even further inland. The exact position and extent of the interface is also modulated by the region’s geologic characteristics and water management infrastructure (e.g., salinity control pumps and canal stages).

Broward Wells and Saltwater Intrusion

As modeled, Broward County would experience significant shifts in the saltwater-freshwater interface under different SLR and rainfall futures. Figure 6.1 shows the locations, pumping rates, and current replacement costs of groundwater wells in Broward County; darker blues indicate higher pumping rates, and larger sizes indicate higher replacement costs, as described in Chapter Two. In total, 46 wells have a replacement cost of more than $10 million each, indicating how costly and critical groundwater supplies are to the drinking water system in Broward County and in Southeast Florida generally.

Figure 6.2 shows the position of the wells superimposed on three maps of the geographic extent of saltwater intrusion at the base of the Biscayne Aquifer under baseline conditions and a middle and high SLR and rainfall future. The figure represents three salinity thresh-olds: less than 250 mg/L (shaded gray, below the EPA safe water threshold), between 250 and 1,000 mg/L (shaded yellow, moderate exceedance of safe water threshold), and greater than or equal to 1,000 mg/L (shaded red, extreme exceedance of safe water levels).

Figure 6.2 also shows that SLR acts to generally push the saltwater interface further inland by the 2040 time frame, a condition that could be mitigated by higher land-based rainfall or exacerbated by drier futures. Under baseline conditions, only six out of 291 wells are impacted by chloride levels greater than 250 mg/L. Under the middle SLR scenario, with average pre-cipitation, many more wells are exposed to elevated chloride levels—33 above 1,000 mg/L and seven between 250 mg/L and 1,000 mg/L. For the high SLR and dry future, 43 wells are


Figure 6.1Well Locations, Replacement Costs, and Pumping Capacities for Broward County

RAND RR1932-6.1

$0.9M

$10.0M

$20.0M

$29.6M

Replacement cost ($2015)

Well pumping (gallons per day)

0 5,000,000

Vulnerability of Wells to Saltwater Intrusion 67

Figure 6.2Well Locations and Level of Saltwater Intrusion Across Broward County, 2040

RAND RR1932-6.2

$0M$10M$20M

Replacement cost ($2015) ≥ 1,000 mg/L

≤ 1,000 mg/L

≤ 250 mg/L

Zero

Biscayne chloride levels

Baseline, baseline Mid, average

SLR/precipitation

High, dry

exposed to chloride levels above 1,000 mg/L. For these two futures, chloride levels in nearly all wells in the Hollywood, Dania, and Dixie well fields exceed safe drinking water standards (e.g., >250 mg/L).

To summarize effects across the region, Figure 6.3 depicts the replacement costs associ-ated with intruded wells for all futures (each column is an SLR scenario, and each row is a precipitation scenario). The region currently experiences potential saltwater intrusion and asso-ciated replacement costs of $16.8 million (length of gray bar for “baseline” future for salinities exceeding 1,000 mg/L). These costs grow significantly under all SLR scenarios, while holding precipitation at its historical levels (average precipitation scenario). For instance, the middle SLR scenario produces replacement costs of $23.9 million and $221.4 million for salinity thresholds of 250–1,000 mg/L and greater than 1,000 mg/L, respectively. However, it is plau-sible that the region may experience wetter or drier rainfall conditions in the future, poten-tially reinforcing or counteracting the effects of SLR. Comparing the middle SLR scenario with historical rainfall to one with a wet scenario, replacement costs of wells with salinities greater than 1,000 mg/L change from $221.4 million to $140.7 million. However, under the dry rainfall scenario, replacement costs rise to $242.7 million. As these results indicate, it is critically important for the region to consider both SLR and rainfall changes when examining the vulnerability of its well fields.


Miami-Dade Wells and Saltwater Intrusion

Our model simulations revealed very minor interface shifts at the base of the Biscayne Aquifer across Miami-Dade County under all SLR and rainfall futures.1 Figure 6.4 shows the ground-water well locations and salinity for the high SLR, average precipitation future. While many wells are close to regions with projected high chloride concentrations, all are projected to have chloride levels below 1 mg/L.

Key Findings

Saltwater intrusion is not projected to impact groundwater wells in UMD through the 2040 time frame. However, it has the potential to impact many groundwater wells in Broward County. Under the most favorable future (low SLR, wettest precipitation), seven wells with a total replacement cost of about $40 million could be impacted by groundwater salinities

1 Hughes and White (2014) found a similar insensitivity in UMD simulations, and our results closely match those pre-viously reported. Therefore, we direct the reader to Figures 23 and 58 and the accompanying text in Hughes and White (2014) for a more in-depth discussion of saltwater intrusion in Miami-Dade County.

Figure 6.3Vulnerable Wells Across Futures in Broward County; 2040 Conditions and Current Values

Precipitation BiscayneChloride

SLRLow Mid High

50 100 150 200

Replacement cost (2015 $M)

Driest < 250 mg/L

< 1000 mg/L

≥ 1000 mg/L

Dry < 250 mg/L

< 1000 mg/L

≥ 1000 mg/L

Avg < 250 mg/L

< 1000 mg/L

≥ 1000 mg/L

Wet < 250 mg/L

< 1000 mg/L

≥ 1000 mg/L

Wettest < 250 mg/L

< 1000 mg/L

≥ 1000 mg/L

$242.2M

$34.9M

$5.6M

$221.4M

$36.1M

$20.8M

$143.5M

$46.6M

$72.4M

$96.7M

$70.7M

$75.5M

$106.7M

$55.4M

$40.0M

$245.7M

$21.5M

$15.6M

$242.7M

$34.3M

$5.7M

$221.4M

$23.9M

$21.5M

$140.7M

$44.7M

$75.8M

$151.2M

$24.3M

$60.8M

$252.9M

$24.3M

$7.4M

$252.9M

$27.1M

$2.8M

$245.5M

$29.8M

$7.3M

$199.4M

$18.5M

$43.3M

$111.3M

$71.4M

$70.3M

50 100 150 200 50 100 150 200

NOTE: In the baseline condition, wells with only $16.8 million of replacement cost are exposed to salinities greaterthan 1,000 mg/L. RAND RR1932-6.3

Vulnerability of Wells to Saltwater Intrusion 69

Figure 6.4Well Locations and Salinity for the High SLR, Average Precipitation Future in Miami-Dade County, 2040

RAND RR1932-6.4

≥ 1,000 mg/L

≤ 1 mg/L

Biscayne chloride levels


that exceed 1,000 mg/L. Under the least favorable future (high SLR, very dry precipitation), 43 wells with a total replacement cost of more than $250 million could be impacted by high-salinity groundwater.

71

CHAPTER SEVEN

Discussion and Conclusions

Southeast Florida already is facing many challenging infrastructure investment decisions along-side decisions about patterns of future growth. These decisions will be made even more difficult in the future as the manifestations of a changing climate become more apparent. While each county and city in Southeast Florida is proceeding on its own path, all also recognize the value of collaborating on a regional scale to share best practices in analysis and ensure that their indi-vidual actions reinforce, rather than conflict with, one another.

This project represents a first-stage effort to build a decision support platform that could facilitate regional analysis of vulnerabilities and evaluation of adaptation strategies on a level analytical playing field. To demonstrate the integrated approach, the RAND team and its partners chose to consider Miami-Dade and Broward counties’ future (through the 2040 time frame) vulnerability to flooding and intrusion of saltwater into drinking water wells as a con-sequence of rising sea levels, changes in precipitation patterns, and assumptions about how future asset growth is distributed across the region. The analysis linked two USGS groundwa-ter models developed separately for the two counties with a simple economic model of asset values as a function of groundwater levels and the location of the saltwater-freshwater inter-face. We systematically evaluated adaptation opportunities under plausible climate hazards and projections and found that vulnerabilities to a changing climate concentrated not only on high-value coastal development but also on some inland areas. The analysis further demon-strated the importance of looking at a wide range of possible future conditions to understand the range of possible outcomes within the region.

As is always the case with simulation models of complex physical phenomena, results should be interpreted carefully. However, without qualification, any analysis of strategies to mitigate the effects of a changing climate on Southeast Florida must consider both SLR pro-jections and varying precipitation futures in tandem because of their interrelated impacts on flooding and movement of the saltwater-freshwater interface. In this regard, the analy-ses described in this report break new ground. Further, results are highly variable across the region, depending on current and possible future land use and asset values in place, distance from the coast, and heterogeneity in the underlying hydrogeology of the region. A model of UMD with higher spatial resolution, at least to the same level as the Broward model, would likely improve the vulnerability assessment.


Summary of Key Findings

With the limitations of the analysis in mind, we found the following:

• Vulnerability to flooding in both counties exists in many inland locations and could increase in the future (through the 2040 time frame) under one or more of the rainfall scenarios considered. Coastal communities are not the only regions that are vulnerable.

• Understanding the heterogeneity across the region should help planners and other deci-sionmakers better focus their efforts and funding on the areas most vulnerable to flooding and the wells most likely to be threatened by saltwater intrusion.

Our results for Broward showed the following:

• Broward is currently vulnerable to flooding in which groundwater is lifted to the surface by high tides as a consequence of low surface elevations.

• In the future (i.e., the 2040 time frame), changes in precipitation patterns could increase or decrease depth to groundwater broadly across the county. SLR will reduce depth to groundwater in coastal areas, with the high SLR scenario leading to decreases of up to 2 feet in some areas.

• Broward has more than $12 billion of assets that are potentially vulnerable under current conditions. The value of these vulnerable assets is expected to increase between 22 per-cent and 45 percent by around 2040, both because of future development in currently impacted areas and because of increased hazards due to SLR and precipitation pattern changes.

• Orienting future development toward TAZs reduces future vulnerable assets by 45 per-cent over a development scenario that only considers elevation ($2.0 billion versus $3.7 bil-lion).

• The remaining vulnerability due to asset growth is concentrated in three to four areas of the county, providing an opportunity for adaptation to accompany future development in these areas. Vulnerability to increased groundwater flooding hazards could affect more than $2 billion of future assets; however, $900 million of these assets would be impacted only at the highest rate of SLR evaluated.

• Saltwater intrusion has the potential to impact many groundwater wells in Broward County. Under the most favorable future (low SLR with very wet precipitation patterns), wells with replacement costs of about $40 million could be impacted by groundwater salinities that exceed 1,000 mg/L. Under the least favorable future (high SLR, very dry precipitation), wells with a total replacement cost of more than $250 million could be impacted by high salinity groundwater.

Our results for Miami-Dade showed the following:

• UMD is much less vulnerable to tidally induced groundwater flooding than Broward, with only a few areas currently experiencing average depths to groundwater less than

Discussion and Conclusions 73

1 foot, and areas along UMD’s ridge not exhibiting any vulnerability within the time frame of the analysis.1

• In the future (i.e., 2040 time frame), changes in precipitation patterns could increase or decrease depth to groundwater broadly across the county, and SLR will reduce depth to groundwater in coastal areas. These changes would lead to low depths to groundwater primarily in the coastal regions and far-western areas of UMD.

• About $2.1 billion of assets in UMD are potentially vulnerable under current condi-tions—a significantly lower value than in Broward. Increases in vulnerable assets by around 2040 of between about 25 percent and 270 percent of current levels are expected, both because of future development in currently impacted areas and because of increased hazards due to SLR and precipitation pattern changes.

• Directing future growth toward areas of naturally higher ground decreases future vul-nerable assets by 19 percent over a development scenario that does not consider elevation ($497 million versus $614 million) (in the 2040 time frame).

• The remaining vulnerability due to asset growth is concentrated in two areas of UMD—downtown Miami and Doral—providing an opportunity for adaptation to accompany future development in these areas. About one-half of the vulnerability to increased groundwater flooding hazards would only occur under the highest rate of SLR evaluated through the 2040 time frame, per our assumptions.

• Saltwater intrusion is not projected to impact groundwater wells in UMD by the 2040 time frame.

With respect to our approach to analysis, we found the following:

• Integrating separate groundwater models developed at different times and with different grid structures, representations of land use, and salinity levels substantially complicated the analysis.

• As a first-order approximation, the economic analysis indicates a high value of vulner-able assets in the region in futures that are vulnerable to SLR and changing patterns of rainfall. However, because our initial analysis accounts only for private building assets, important drivers of economic risk related to economic activity generated by commercial districts and public infrastructure were not incorporated.

• Treating future land use as an uncertainty and patterns of asset growth as uncertainties is essential in any analysis of either vulnerabilities or adaptation strategies, given the many possible pathways further development might take.

• Stakeholder engagement and even more-extensive interactions with decisionmakers are essential ingredients to any successful long-term planning effort.

Conclusions

This approach to analysis shows promise and should be continued and refined. Specifically, improvements in the UMD model would enhance analysis of Miami-Dade’s vulnerabilities

1 These areas are still vulnerable to rainfall event flooding and tropical storms, and locally there are pockets of highly vul-nerable areas based on topography.


and understanding of the potential effectiveness of adaptation measures. Further analysis of the policy levers related to spatial development and feedbacks between the environmental and economic systems is also warranted. The region’s vulnerability to both SLR and increased pre-cipitation is cause for concern, but targeted actions could reduce further exposure of assets and mitigate the effects of saltwater intrusion on drinking water supplies.

75

APPENDIX A

Simulating Future Land Use in the Broward Model

This appendix first provides details on how future land use changes were simulated within the Broward model.1 It then demonstrates that groundwater hydrology in the Broward model is only slightly altered from baseline conditions when implementing the future land use pattern. As a consequence of this finding, we did not explicitly represent future land use patterns within the UMD model, although the process of doing so would follow a process similar to the one in the Broward model.

Representing Future Land Use in the Broward Model

We did not explicitly input land use categories into the Broward model but rather used an evapotranspiration characteristic associated with a given land use type as the model input. Specifically, the key variable is the extinction depth, which equals the point below a surface elevation at which evapotranspiration ceases from the water table and is analogous to the depth from the land surface to the bottom of the deep root zone (DDRZ). DDRZs for the 25 allow-able land use categories in the model are shown in Table A.1. A reference DDRZ map was generated and calibrated by the USGS during the Broward model development. This reference DDRZ map was then scaled on a decadal basis by calibrated multipliers (Table A.2) to rep-resent changes in land use over time across the Broward model extent. According to Hughes, Sifuentes, and White (2016), these multipliers generally increase in time, consistent with lower groundwater levels under more-urbanized, developed conditions.

To represent the 2040 Broward County Future Land Use Plan in the Broward model, we developed an additional land use scaling factor for 2040 that was multiplied by the most recent (2010–2012) factor, 7.70. The 2040 scaling factor was calculated by first creating a 2040 DDRZ map. 2040 DDRZ values for each grid cell were obtained by matching the Broward 2040 standardized land use categories (as described in Appendix B) to appropriate land use categories allowed within the model (left column of Table A.1). The DDRZ scaling factor was then computed as the ratio of the area-averaged 2040 DDRZ to the area-averaged reference DDRZ, which equaled 1.90. This 2040 DDRZ scaling factor was then multiplied by 7.70 for a 2040 decadal scaling factor of 14.63, which was input into the model source code.

1 This process was developed through personal communication with Joseph Hughes and Dorothy Sifuentes at USGS.


Hydrological Impacts of Future Land Use in the Broward Model

We tested the effects of future land use patterns on hydrological outputs from the Broward model compared with the system under current land use patterns. Comparing area-averaged groundwater levels across the dry season and wet season for cases where the current land use

Table A.1Allowable Land Use Categories and Corresponding Depth to the Root Zone in the Broward Model

Land Use CategoryDDRZ (feet)

Agriculture Citrus 4.0

Irrigated pasture 2.0

Row crops 3.0

Sugarcane 3.8

Forest Forested uplands 11.0

Forested wetlands 9.0

Mangroves 0.7

Melaleuca 7.0

Rangeland Shrubland 7.0

Urban High density 1.5

Low density 4.0

Medium density 2.5

Water Open water 0.0

Cattail 3.0

Wetland Freshwater marsh 1.2

Marl prairie 6.5

Mixed cattail/sawgrass 4.0

Ridge and slough I 2.8

Ridge and slough II 3.0

Ridge and slough III 1.5

Ridge and slough IV 3.0

Ridge and slough V 4.0

Sawgrass plains 4.5

Stormwater treatment area and above-ground reservoir

5.0

Wet prairie 2.0

SOURCE: Based on SFWMD data.

Simulating Future Land Use in the Broward Model 77

patterns are used and then when the 2040 land use patterns are explicitly represented in the model indicated that future land use insignificantly altered underlying hydrological condi-tions in the model (i.e., on the order of 1 percent different). This finding justified our choice to not explicitly change land use codes in the model; rather, we simply overlaid future land use patterns and future hydrological output (using historical land use codes in the model) in the analyses.

Table A.2Calibrated Extinction Depth Multipliers Used to Simulate Historical Land Use Changes in the Broward Model

Period Multiplier

1950–1959 5.30

1960–1969 4.48

1970–1979 5.44

1980–1989 6.64

1990–1999 7.62

2000–2009 7.96

2010–2012 7.70

SOURCE: Adopted from Table 1.4 in Hughes et al. (2016).

79

APPENDIX B

Standardizing Land Use Categories

This appendix presents the methodologies used to create a standard set of current and future land use categories for Miami-Dade and Broward County. Standardization was performed to condense dozens of categories into a smaller amount for spatially summarizing model outputs and data visualizations in a more meaningful fashion. For the future case, RAND held discus-sions with Miami-Dade and Broward County to ensure county-to-county consistency when converting land use types unique to each county’s future land use plan to a common set of categories.

Standardizing Current Land Use Categories

A total of 87 land use classifications within the DOR tax parcel database were found to be dominant land categories across the UMD and Broward model grid cells. RAND condensed these 87 categories into a smaller standardized set using the key in Table B.1.

Standardizing Future Land Use Categories

Miami-Dade and Broward Counties provided RAND with their respective future land use plans. Each plan contained a unique set of land use categories that were transformed to a stan-dardized set of categories using the keys in Tables B.2 and B.3.

Table B.1Translating DOR Land Use Categories to Standardized Categories

DOR CategoriesStandardized

Categories

Acreage not zoned agricultural with or without extra features Natural areas

Airports (private or commercial), bus terminals, marine terminals, piers, marinas Urban

Auto Urban

Bowling alleys, skating rinks, pool halls, enclosed arenas Urban

Centrally assessed Other

Churches Urban

Clubs, lodges, union halls Urban



Categories

Colleges (nonprivate) Urban

Community shopping centers Urban

Condominiums Urban

Cooperatives Urban

Counties (other than public schools, colleges, hospitals) including nonmunicipal government Urban

Cropland soil capability class I Agriculture

Cropland soil capability class II Agriculture

Cropland soil capability class III Agriculture

Department stores Urban

Drive-in restaurants Urban

Drive-in theaters, open stadiums Urban

Enclosed theaters, enclosed auditoriums Urban

Federal, other than military, forests, parks, recreational areas, hospitals, colleges Urban

Financial institutions (banks, savings and loan companies, mortgage companies, credit services) Urban

Forest, parks, recreational areas Natural areas

Golf courses, driving ranges Natural areas

Grazing land soil capability class I Agriculture

Grazing land soil capability class II Agriculture

Grazing land soil capability class IV Agriculture

Heavy industrial, heavy equipment manufacturing, large machine shops, foundries, steel fabricating plants, auto or aircraft plants

Urban

Homes for the aged Urban

Hospitals (nonprivate) Urban

Hotels, motels Urban

Improved agricultural Agriculture

Insurance company offices Urban

Leasehold interests (government-owned property leased by a nongovernmental lessee) Urban

Light manufacturing Urban

Lumber yards, sawmills, planing mills Urban

Military Urban

Mineral processing, phosphate processing, cement plants, refineries, clay plants, rock and gravel plants

Urban

Miscellaneous residential (migrant camps, boarding homes, etc.) Urban

Mixed use—store and office or store and residential combination Urban

Table B.1—Continued

Standardizing Land Use Categories 81



Categories

Mobile homes Urban

Mortuaries, cemeteries, crematoriums Urban

Multifamily—ten units or more Urban

Multifamily—fewer than ten units Urban

Municipal, other than parks, recreational areas, colleges, hospitals Urban

Nightclubs, cocktail lounges, bars Urban

Office buildings, nonprofessional service buildings, multistory Urban

Office buildings, nonprofessional service buildings, one story Urban

Open storage, new and used building supplies, junk yards, auto wrecking, fuel storage, equipment and material storage

Urban

Orchard groves, citrus, etc. Agriculture

Ornamentals, miscellaneous agricultural Agriculture

Orphanages, other nonprofit or charitable services Urban

Other food processing, candy factories, bakeries, potato chip factories Urban

Outdoor recreational or parkland, or high-water recharge subject to classified use assessment Natural areas

Packing plants, fruit and vegetable packing plants, meat packing plants Urban

Parcels with no values Other

Parking lots (commercial or patron), mobile home parks Urban

Poultry, bees, tropical fish, rabbits, etc. Agriculture

Private schools and colleges Urban

Privately owned hospitals Urban

Professional service buildings Urban

Public county schools—including all property of Board of Public Instruction Urban

Race tracks (horse, auto, or dog) Urban

Regional shopping centers Urban

Repair service shops (excluding automotive), radio and TV repair, refrigeration service, electric repair, laundries, laundromats

Urban

Residential common elements / areas Urban

Restaurants, cafeterias Urban

Retirement homes not eligible for exemption Urban

Right-of-way, streets, roads, irrigation channel, ditch, etc. Urban

Rivers and lakes, submerged lands Water

Sanitariums, convalescent and rest homes Urban

Service stations Urban



Categories

Sewage disposal, solid waste, borrow pits, drainage reservoirs, waste land, marsh, sand dunes, swamps

Natural areas

Single family Urban

State, other than military, forests, parks, recreational areas, colleges, hospitals Other

Stores, one story Urban

Subsurface rights Other

Supermarkets Urban

Tourist attractions, permanent exhibits, other entertainment facilities, fairgrounds (privately owned)

Urban

Utility, gas and electricity, telephone and telegraph, locally assessed railroads, water and sewer service, pipelines, canals, radio/television communication

Urban

Vacant commercial Other

Vacant governmental Other

Vacant industrial Other

Vacant institutional, with or without extra features Other

Vacant residential Other

Warehousing, distribution terminals, trucking terminals, van and storage warehousing Urban

Wholesale outlets, produce houses, manufacturing outlets Urban


Table B.2Translating Miami-Dade 2030 CDMP Land Use Categories to Standardized Categories

Miami-Dade 2030 CDMP Land Use CategoriesStandardized Future Land

Use Categories


Business and office Urban high density

Environmental protection Natural areas

Environmentally protected parks Natural areas

Estate-density residential 1–2.5 DU/AC Urban low density

Estate-density residential with density increase 1 Urban low density

High-density residential 60–125 DU/AC Urban high density

Industrial and office Urban medium density

Institutions, utilities, and communication Urban medium density

Low-density residential 2.5–6 DU/AC Urban low density

Low-density residential with density increase 1 Urban medium density

Standardizing Land Use Categories 83

Miami-Dade 2030 CDMP Land Use CategoriesStandardized Future Land

Use Categories

Low-medium-density residential with density increase 1 Urban medium density

Low-medium-density residential 6–13 DU/AC Urban medium density

Medium-density residential 13–25 DU/AC Urban medium density

Medium-density residential with density increase 1 Urban medium density

Medium-high density residential 25–60 DU/AC Urban medium density

Office or residential Urban medium density

Open land Other

Parks and recreation Natural areas

Restricted industrial and office Urban medium density

Terminals Urban high density

Transportation (row, rail, metrorail, etc.) Urban high density

Water Water

Zoo Miami entertainment area Urban medium density

NOTE: DU/AC = dwelling units per acre.


Table B.3Translating Broward Future Land Use Plan Categories to Standardized Categories

Broward Future Land Use Plan Categories

Standardized Future Land Use Categories


Commercial Urban high density

Commercial facilities Urban medium density

Commercial recreation Urban medium density

Con–natural reservations Natural areas

Con–reserve water supply areas Water

Electrical generation facility Urban medium density

Employment center—high Urban medium density

Employment center—low Urban low density

Estate-1 residential Urban low density

High-50 residential Urban medium density

Industrial Urban medium density

Irregular residential Urban low density

Local activity center Urban medium density

Low-medium–10 residential Urban medium density

Low-2 residential Urban low density



Broward Future Land Use Plan Categories

Standardized Future Land Use Categories




Medium-16 residential Urban medium density

Medium-high–25 residential Urban medium density

Office park Urban medium density

Recreation and open space Other

Regional activity center Urban medium density

Residential area Urban low density

Right of way Urban low density

Rural estates Rural development

Rural ranches Rural development

Transit oriented corridor Urban medium density

Transit oriented development Urban medium density

Transportation Urban medium density

Utilities Urban medium density

Water Water

85

APPENDIX C

Grid-Cell Density Assumptions

This appendix documents the grid-cell density assumptions used, in part, to determine the development of future residential-class model cells for the Broward and Miami-Dade land use scenarios. We derived these estimates from the data and land use classifications in the data, plus dwelling information from the U.S. Census with our own adjustments in some cases, when low sample size precluded robust estimates. As noted in the body of the report, the grid of cells for Broward County is considerably finer than that for Miami-Dade County. Changes in these assumptions would change the spatial distribution of assets and the overall value of future assets in each county.

Table C.1 shows the assumptions for persons and the value per cell for each residential land class in the Broward model. Table C.2 shows the assumptions for population and value for each residential land class in the Miami-Dade model.

Table C.1Persons per Cell and Value per Cell for Broward County by Future Residential Land Use

Persons per Cell

Value per Cell (2015 $)

Estate-1 residential 11 2,831,395

High-density residential 847 8,890,114

Irregular residential 75 5,270,824

Low-density residential 53 5,270,824

Low-medium-density residential 75 5,104,231

Medium-density residential 100 6,210,320

Residential in dashed-line area 291 6,736,685

Rural estates 11 2,104,591

Rural ranches 11 1,504,159

SOURCE: Authors’ calculation based on land use and property tax data, Census Bureau data, and subjective adjustment.


Table C.2Persons per Cell and Value per Cell for Miami-Dade County by Future Residential Land Use

Persons per Cell

Value per Cell (2015 $)

Estate-density residential 1–2.5 DU/AC 215 51,205,048

Estate-density residential with density increase 1 430 67,241,768

High-density residential 60–125 DU/AC 12,888 184,800,000

Low-density residential 2.5–6 DU/AC 537 55,938,832

Low-medium-density residential with density increase 1 644 54,850,339

Low-medium-density residential 6–13 DU/AC 1,504 53,761,846

Medium-density residential 13–25 DU/AC 2,792 57,888,858

Medium-high density residential 25–60 DU/AC 5,370 73,756,465

Office or residential 537 61,026,614

SOURCE: Authors’ calculation based on land use and property tax data, Census Bureau data, and subjective adjustment.

87

Abbreviations

AR5 Fifth Assessment Report (IPCC)

CDMP Comprehensive Development Master Plan

DDRZ deep root zone

DOR Florida Department of Revenue

DU/AC dwelling units per acre

GCM general circulation model

GIS geographic information system

IPCC Intergovernmental Panel on Climate Change

mg/L milligrams per liter

MODFLOW USGS three-dimensional hydrologic groundwater model

MODFLOW-NWT a Newton-Raphson formulation of MODFLOW

NAVD 88 North American Vertical Datum of 1988 (the benchmark land elevation standard)

RCP representative concentration pathway

RDM Robust Decision Making

RSM SWFMD’s Regional Simulation Model

SEAWAT Broward County’s USGS three-dimensional variable density solute transport groundwater model

SFWMD South Florida Water Management District

SLR sea-level rise

TAZ Traffic Analysis Zone

UMD Urban Miami-Dade

USGS U.S. Geological Survey

XLRM Decision-framing step in Robust Decision Making process (X = uncertainties; L = levers; R = relationships; and M = metrics)

89

References

Acevedo, Javier, and Bill Leonard, Traffic Analysis Zones and Municipal Forecasts Update, Broward County, Florida, 2014. As of April 20, 2018: http://www.broward.org/Planning/FormsPublications/Documents/2014TAZMunicipalForecasts.pdf

Bakker, Mark, Frans Schaars, Joseph D. Hughes, Christian D. Langevin, and Alyssa M. Dausman, “Documentation of the Seawater Intrusion (SWI2) Package for MODFLOW,” U.S. Geological Survey Techniques and Methods, Bk. 6, Ch. A46, Reston, Va.: U.S. Geological Survey, 2013. As of April 2, 2018: http://pubs.usgs.gov/tm/6a46/

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———, Broward Water Resources Task Force Report, June 2010. As of April 20, 2018: http://www.broward.org/waterresources/WaterResources/Documents/bwrtfreport061010.pdf

CDMP—See Miami-Dade County.

Czajkowski, Jeffrey, Vic Engel, Chris Martinez, Ali Mirchi, David Watkins, Michael C. Sukop, and Joseph D. Hughes, “Economic Impacts of Urban Flooding in South Florida: Potential Consequences of Managing Groundwater to Prevent Salt Water Intrusion,” Science of the Total Environment, Vol. 621, April 15, 2018, pp. 465–478. As of April 3, 2018: https://www.sciencedirect.com/science/article/pii/S004896971732973X?via%3Dihub

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Groves, David G., Jordan R. Fischbach, Nidhi Kalra, Edmundo Molina-Perez, David Yates, David Purkey, Amanda Fencl, Vishal K. Mehta, Ben Wright, and Grantley Pyke, Developing Robust Strategies for Climate Change and Other Risks: A Water Utility Framework, Santa Monica, Calif.: RAND Corporation, RR-977-WRF, 2014a. As of April 2, 2018: https://www.rand.org/pubs/research_reports/RR977.html

Groves, David G., Jordan R. Fischbach, Debra Knopman, David R. Johnson, and Kate Giglio, Strengthening Coastal Planning: How Coastal Regions Could Benefit from Louisiana’s Planning and Analysis Framework, Santa Monica, Calif.: RAND Corporation, RR-437-RC, 2014b. As of April 2, 2018: https://www.rand.org/pubs/research_reports/RR437.html

Groves, David G., Robert J. Lempert, Debra Knopman, and Sandra H. Berry, Preparing for an Uncertain Future Climate in the Inland Empire: Identifying Robust Water Management Strategies, Santa Monica, Calif.: RAND Corporation, DB-550-NSF, 2008. As of April 23, 2018: http://www.rand.org/pubs/documented_briefings/DB550.html

Groves, David G., and Debra Knopman, “Framework for Adapting Land Use and Water Management Plans to a Changing Climate in Miami-Dade and Broward Counties, Florida: Decision Support Tool,” December 2017. As of April 9, 2018: https://public.tableau.com/views/RR-1932-MCF_review/Introduction?:embed=y&:display_count=yes&publish=yes

Guha, Hillol, and Sorab Pandray, “Impact of Sea Level Rise on Groundwater Salinity in a Coastal Community of South Florida,” Journal of the American Water Resources Association, Vol. 48, No. 3, June 2012, pp. 510–529. As of April 3, 2018: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1752-1688.2011.00630.x

Harbaugh, Arlen W., Edward R.Banta, Mary C. Hill, and Michael G. McDonald, MODFLOW-2000, the U.S. Geological Survey Modular Ground-Water Model—User Guide to Modularization Concepts and the Ground-Water Flow Process, Reston, Va.: U.S. Geological Survey, Open-File Report 00-92, 2000. As of April 3, 2018: https://pubs.usgs.gov/of/2000/0092/report.pdf

Hidalgo, Hugo G., Michael D. Dettinger, and Daniel R. Cayan, Downscaling with Constructed Analogues: Daily Precipitation and Temperature Fields over the United States, California Energy Commission, PIER Energy-Related Environmental Research, CEC-500-2007-123, January 2008. As of April 3, 2018: http://www.energy.ca.gov/2007publications/CEC-500-2007-123/CEC-500-2007-123.PDF

Hughes, Joseph D., and Jeremy T. White, Hydrologic Conditions in Urban Miami-Dade County, Florida, and the Effect of Groundwater Pumpage and Increased Sea Level on Canal Leakage and Regional Groundwater Flow, Reston, Va.: U.S. Geological Survey, August 2014. As of April 3, 2018: https://pubs.usgs.gov/sir/2014/5162/

Hughes, Joseph D., Christian D. Langevin, Kevin L. Chartier, and Jeremy T. White, Documentation of the Surface-Water Routing (SWR1) Process for Modeling Surface-Water Flow with the U.S. Geological Survey Modular Ground-Water Model (MODFLOW-2005), U.S. Geological Survey Techniques and Methods, Bk. 6, Ch. 40 (Version 1.0), Reston, Va.: U.S. Geological Survey, 2012. As of April 3, 2018: https://pubs.usgs.gov/tm/6a40/pdf/Hughes_TM6-A40.pdf

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Hughes, Joseph D., Dorothy F. Sifuentes, and Jeremy T. White, Potential Effects of Alterations to the Hydrologic System of the Distribution of Salinity in the Biscayne Aquifer in Broward County, Florida, Reston, Va.: U.S. Geological Survey, 2016. As of April 3, 2018: https://pubs.er.usgs.gov/publication/sir20165022

Kalra, Nidhi, Stephane Hallegatte, Robert Lempert, Casey Brown, Adrian Fozzard, Stuart Gill, and Anku Shah, Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty, Washington, D.C.: World Bank, Policy Research Working Paper 6906, 2014. As of April 20, 2018: http://documents.worldbank.org/curated/en/365031468338971343/pdf/WPS6906.pdf

Kalra, Nidhi Rajiv, David G. Groves, Laura Bonzanigo, Edmundo Molina Perez, Cayo Ramos Taipe, Carter J. Brandon, and Ivan Rodriguez Cabanillas, Robust Decision-Making in the Water Sector: A Strategy for Implementing Lima’s Long-Term Water Resources Master Plan, Washington, D.C.: World Bank, June 30, 2015. As of April 20, 2018: http://documents.worldbank.org/curated/en/2015/06/24701804/peru-robust-decision-making-water-sector-strategy-implementing-lima’s-long-term-water-resources-master-plan

Knopman, Debra, and Robert J. Lempert, Urban Responses to Climate Change: Framework for Decisionmaking and Supporting Indicators, Santa Monica, Calif.: RAND Corporation, RR-1144-MCF, 2016. As of April 2, 2018: https://www.rand.org/pubs/research_reports/RR1144.html

Langevin, Christian D., and Michael Zygnerski, “Effect of Sea-Level Rise on Salt Water Intrusion Near a Coastal Well Field in Southeastern Florida,” Groundwater, Vol. 51, No. 5, 2012, pp. 781–803, November 2012. As of April 3, 2018: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1745-6584.2012.01008.x

Langevin, Christian D., Daniel T. Thorne, Jr., Alyssa M. Dausman, Michael C. Sukop, and Weixing Guo, SEAWAT Version 4: A Computer Program for Simulation of Multi-Species Solute and Heat Transport, U.S. Geological Survey Techniques and Methods, Bk. 6, Ch. A22, Reston, Va.: U.S. Geological Survey, 2008. As of April 3, 2018: https://pubs.usgs.gov/tm/tm6a22/pdf/tm6A22.pdf

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JUSTICE, INFRASTRUCTURE, AND ENVIRONMENT

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RR-1932-MCF

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Florida’s Miami-Dade and Broward counties are vulnerable to flooding and intrusion of saltwater into drinking water wells as a consequence of sea level rise (SLR), changes in precipitation, and the distribution of future asset growth across the region. It is uncertain how these drivers will evolve in the future, so it is important to understand the risks, what areas are most at risk and why, and possible ways to mitigate the risks. Looking out to the 2040 time frame, the analysis linked two groundwater flow simulation models developed separately for the two counties with a simple economic model of asset values as a function of groundwater levels and the location of the saltwater-freshwater interface. Adaptation opportunities were evaluated against a number of climate hazards and future projections of asset growth. The results demonstrate that vulnerability to climate change is not constrained to high-value coastal development but also includes inland areas where groundwater is shallow and wetter rainfall patterns could cause flooding. The region’s vulnerability to both SLR and increased precipitation is cause for concern, but targeted actions, such as focusing development on higher ground, could reduce further exposure of assets and mitigate effects of saltwater intrusion on drinking water supplies.

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