DRAFT EPA/600/R-ll/058a
DO NOT QUOTE OR CITE August 2011
External Review Draft
Vulnerability Assessments in Support of the Climate Ready
Estuaries Program: A Novel Approach Using Expert
Judgment
Volume I
Results for the San Francisco Estuary Partnership
NOTICE
THIS DOCUMENT IS A PRELIMINARY DRAFT. It has not been formally released by the
U.S. Environmental Protection Agency and should not at this stage be construed to represent
Agency policy. It is being circulated for comment on its technical accuracy and policy
implications.
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC
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DISCLAIMER
This document is distributed solely for the purpose of pre-dissemination peer review
under applicable information quality guidelines. It has not been formally disseminated by EPA.
It does not represent and should not be construed to represent any Agency determination or
policy. Mention of trade names or commercial products does not constitute endorsement or
recommendation for use.
ABSTRACT
The San Francisco Estuary Partnership (SFEP), the San Francisco Bay Conservation and
Development Commission, and the Environmental Protection Agency (EPA) collaborated on an
ecological vulnerability assessment, using a novel methodology based on expert judgment, to
inform adaptation planning under EPA's Climate Ready Estuaries Program. An expert
elicitation-type exercise was designed to systematically elicit judgments from experts in a
workshop setting regarding climate change effects on two key ecosystem processes: sediment
retention in salt marshes and community interactions in mudflats. Specific goals were to assess
1) the relative influences of physical and ecological variables that regulate each process, 2) their
relative sensitivities under current and future climate change scenarios, 3) the degree of
confidence about these relationships, and 4) implications for management. For each process, an
influence diagram was developed identifying key process variables and their interrelationships
(influences). Using a coding scheme, each expert characterized the type and sensitivity of each
influence under both current and future climate change scenarios. The experts also discussed the
relative impact of certain influences on the endpoints. This report shows how particular
pathways in such diagrams can be linked to management options in the context of planning
documents to identify opportunities for 'mainstreaming' adaptation.
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Vulnerability Assessments in Support of the Climate Ready Estuaries Program: A Novel
Approach Using Expert Judgment
TABLE OF CONTENTS-OVERVIEW
Volume I: Results for the San Francisco Estuary Partnership
1. INTRODUCTION
2. EXPERT ELICITATION EXERCISE
3. MAKING THE LINK TO MANAGEMENT
4. CONCLUSIONS
Volume II: Results for the Massachusetts Bays Program
5. INTRODUCTION
6. EXPERT ELICITATION EXERCISE
7. MAKING THE LINK TO MANAGEMENT
8. CONCLUSIONS
Volume III: Lessons Learned
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CONTENTS
DISCLAIMER i
ABSTRACT i
CONTENTS Hi
LIST OF TABLES vi
LIST OF FIGURES viii
AUTHORS, CONTRIBUTORS AND REVIEWERS x
EXECUTIVE SUMMARY xi
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. PURPOSE AND SCOPE 1-1
1.2.1. Purpose 1-1
1.2.2. Scope 1-2
1.3. ROADMAP FOR THE REPORT 1-3
2. EXPERT ELICITATION EXERCISE 2-1
2.1. JUSTIFICATION FOR METHOD 2-1
2.1.1. Definition and Uses 2-1
2.1.2. Novel Application 2-1
2.2. WORKSHOP DESIGN AND METHODOLOGY 2-2
2.2.1. Workshop Goal sand Objectives 2-2
2.2.2. Approach and Methodology 2-2
2.2.2.1. Influence Diagrams 2-3
2.2.2.2. Climate Scenarios 2-4
2.2.2.3. Expert Facilitation 2-5
2.2.2.4. Coding Scheme and Exercise 2-5
2.2.2.5. Typologies for Understanding Influences and Sensitivities 2-7
2.2.2.6. Understanding Relative Impacts of Influences 2-8
2.2.2.7. Key Questions 2-9
2.3. RESULTS 2-9
2.3.1. Sediment Retention 2-10
2.3.1.1. Group Influence Diagram 2-10
2.3.1.2. Influence Types and Degrees 2-11
2.3.1.3. Influence Sensitivity 2-13
2.3.1.4. Relative Impact 2-14
2.3.1.5. Confidence 2-14
2.3.1.6. Interacting Influences 2-15
2.3.2. Community Interactions 2-16
2.3.2.1. Group Influence Diagram 2-16
2.3.2.2. Influence Types and Degrees 2-17
2.3.2.3. Influence Sensitivity 2-19
2.3.2.4. Relative Impact 2-20
2.3.2.5. Confidence 2-21
2.3.2.6. Interacting Influences 2-22
2.4. DISCUSSION OF ADAPTATION STRATEGIES 2-22
2.4.1. Restoration & Conservation 2-22
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2.4.2. Sediment Management 2-23
2.4.3. Planning & Monitoring 2-24
3. MAKING THE LINK TO MANAGEMENT 3-1
3.1. USING INFORMATION ON INFLUENCE TYPE & DEGREE,
SENSITIVITY AND RELATIVE IMPACT TO IDENTIFY KEY
MANAGEMENT PATHWAYS 3-1
3.1.1. Crosswalks: Influence Type & Degree, Sensitivity and Relative Impact... 3-1
3.1.1.1. Sediment Retention Crosswalk 3-1
3.1.1.2. Community Interactions Crosswalk 3-2
3.1.1.3. Information Gaps 3-4
3.1.2. Identifying Key Pathways for Management 3-5
3.1.2.1. Sediment Retention Example 3-5
3.1.2.2. Community Interactions Example 3-6
3.2. TOP PATHWAYS AND IMPLICATIONS FOR ADAPTATION
PLANNING 3-7
3.2.1. Top Pathways and Associated Adaptation Options 3-7
3.2.1.1. Sediment Retention Top Pathways 3-8
3.2.1.2. Community Interactions Top Pathways 3-11
3.2.1.3. Top Pathway Caveats 3-14
3.2.2. Adaptation Planning 3-15
4. CONCLUSIONS 4-1
4.1. INSIGHTS FROM THE WORKSHOP EXERCISE 4-1
4.1.1. Group Influence Diagrams 4-1
4.1.2. Characterization of Influences 4-2
4.2. APPLICATION OF WORKSHOP RESULTS 4-4
4.2.1. Top Pathways for Management 4-4
4.2.2. Mainstreaming Adaptation into Planning 4-6
4.3. GENERAL CONCLUSIONS 4-7
4.3.1. Transferability of Results and Method 4-7
4.3.2. Utility of Method for Rapid Vulnerability Assessments 4-8
REFERENCES R-l
APPENDIX A. DEVELOPMENTAL PROCESS FOR CLIMATE READY
ESTUARIES VULNERABILITY ASSESSMENT A-l
A.I. SELECT KEY GOALS, ECOSYSTEMS, AND ECOSYSTEM PROCESSES A-l
A.2. CONCEPTUAL MODELS A-l
A.2.1. General Models A-2
A.2.1.1. Salt Marshes A-2
A.2.1.2. MudFlats A-4
A.2.2. Sub-models A-6
A.2.2.1. Sediment Retention A-6
A.2.2.2. Community Interactions A-7
A.3. CONCLUSIONS A-8
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APPENDIX B. EXPERT ELICIATION WORKSHOP PREPARATION
AND IMPLEMENTATION B-l
B.I. PRE-WORKSHOP B-l
B.I.I. Selecting Workshop Participants B-l
B.I.2. "Straw Man" Influence Diagrams B-2
B.I.3. Pre-workshop Briefing and Homework Assignment B-3
B.I.4. Consolidated Influence Diagrams B-3
B.2. WORKSHOP B-4
B.2.1. Group Influence Diagrams B-4
B.2.2. Introduction to Climate Scenarios and Confidence B-4
B.2.3. Coding Exercise B-4
B.2.5. Exercise Discussions and Report-outs B-5
B.2.6. Discussion of Management Implications B-6
B.3. POST-WORKSHOP B-6
B.3.1. Review of Workshop Report B-6
B.3.2. Synthesis of Results B-6
B.3.3. Review of Draft Report B-7
APPENDIX C. PARTICIPANT HANDOUT ON CLIMATE SCENARIOS C-l
APPENDIX D. PARTICIPANT HANDOUT ON CONFIDENCE D-l
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LIST OF TABLES
Table 1-1. Breakout group participants for the expert elicitation workshop (see Appendix
B for further details on selection criteria and credentials) 1-3
Table 2-1. Summary of Climate Scenario A ("Lower-Range" Scenario) and Climate
Scenario B ("Higher-Range" Scenario): averages for mid-century 2-4
Table 2-2. Coding scheme used during the workshop exercise to characterize influences 2-6
Table 2-3. Coding scheme used during the workshop exercise to characterize interactive
influences 2-6
Table 2-4. Coding scheme used during the workshop exercise to characterize confidence 2-7
Table 2-5. Sediment Retention variable definitions clarified during group discussion 2-10
Table 2-6. Sediment Retention group influence judgments 2-11
Table 2-7. Sediment Retention group confidence for influences with agreement 2-15
Table 2-8. Sediment Retention group interactive influences with agreement under current
conditions and Climate Scenarios A and B 2-15
Table 2-9. Community Interactions variable definitions clarified during group discussion .... 2-17
Table 2-10. Community Interactions group influence judgments 2-17
Table 2-11. Community Interactions group confidence for influences with agreement 2-21
Table 2-12. Adaptation strategies and associated top pathways for management 2-22
Table 3-1. Sediment Retention group crosswalk for comparison of influence type and
degree, sensitivity and relative impact for current conditions and climate
scenarios 3-1
Table 3-2. Community Interactions group crosswalk for comparison of influence type
and degree, sensitivity and relative impact for current conditions and
climate scenarios 3-2
Table B-l. Sediment Retention breakout group participants, affiliations, and
qualifications B-2
Table B-2. Community Interactions breakout group participants, affiliations, and
qualifications B-2
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Table B-3. Example of expert elicitation handout for influences under current conditions
(Sediment Retention group) B-4
Table B-4. Example of expert elicitation handout for influences under climate scenarios
(Community Interactions group) B-5
Table B-5. Example of expert elicitation handout for interactive influences under climate
scenarios (Sediment Retention group) B-5
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LIST OF FIGURES
Figure ES-1. Selected ecosystem processes for the pilot vulnerability assessment xi
Figure ES-2. Top pathways for management of the Net Accretion/Erosion endpoint xii
Figure ES-3. Top pathways for management of the Shorebirds endpoint xii
Figure 1-1. Vulnerability assessment process 1-3
Figure 2-1. Simplified influence diagram for sediment retention 2-3
Figure 2-2. Sediment Retention group influence diagram 2-10
Figure 2-3. Sediment Retention group summary influence diagram of sensitivities under
current conditions 2-13
Figure 2-4. Sediment Retention group summary influence diagrams of sensitivities:
variance across current conditions and two climate scenarios 2-13
Figure 2-5. Sediment Retention influences indicated as having high relative impact
under current conditions 2-14
Figure 2-6. Sediment Retention group influences indicated as having high relative
impact under climate scenarios 2-14
Figure 2-7. Sediment Retention group confidence results for all influences 2-15
Figure 2-8. Community Interactions group influence diagram 2-17
Figure 2-9. Community Interactions group summary influence diagram of sensitivities
under current conditions 2-19
Figure 2-10. Community Interactions group summary influence diagrams of sensitivities:
variance across current conditions and two climate scenarios 2-20
Figure 2-11. Community Interactions influences indicated as having high relative impact
under current conditions 2-20
Figure 2-12. Community Interactions group influences indicated as having high relative
impact under climate scenarios 2-20
Figure 2-13. Community Interactions group confidence results for all influences 2-21
Figure 3-1. Sediment Retention Example Pathway. Future = Climate Scenario B 3-6
Figure 3-2. Community Interactions Example Pathway. Future = Climate Scenario B 3-7
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Figure 3-3. Top pathways for management of the Net Accretion/Erosion endpoint 3-8
Figure 3-4. Top pathways for management of the Shorebirds endpoint 3-8
Figure A-l. Salt Marsh Conceptual Model A-3
Figure A-2. Mudflat Conceptual Model A-5
Figure A-3. Sediment Retention sub-model A-7
Figure A-4. Community Interactions sub-model A-8
Figure B-l. Sediment Retention "straw man" influence diagram B-2
Figure B-2. Community Interactions "straw man" influence diagram B-2
Figure B-3. Sediment Retention influence diagrams of sensitivities: variance across
participants B-5
Figure B-4. Community Interactions influence diagrams of sensitivities: variance across
participants B-5
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AUTHORS, CONTRIBUTORS AND REVIEWERS
The National Center for Environmental Assessment (NCEA) within EPA's Office of
Research and Development (ORD) was responsible for the preparation of this report. Portions of
this report were prepared by ICF Incorporated under EPA Contract Nos. GS-10F-0124J and
EP-C-09-009. Jordan West served as the EPA Work Assignment Manager, providing
overall direction and coordination of the project, as well as co-author.
AUTHORS TECHNICAL SUPPORT
U.S. EPA ICF Incorporated
Jordan West Susan Asam Elizabeth Strange Peter Scultz
Amanda Babson Katy Maher Brock Bernstein Katharine Hayhoe
CONTRIBUTORS
San Francisco Estuary Partnership Team
Judy Kelly, San Francisco Estuary Partnership
Steve Goldbeck & Sara Polgar, Bay Conservation and Development Commission
Expert Workshop Participants
Dave Cacchione, U.S. Geological Survey Michelle Orr, Philip Williams & Associates
John Callaway, UC San Francisco Dave Schoellhamer, U.S. Geological Survey
Chris Enright, CA Dept. of Water Resources Stuart Siegel, Wetlands and Water Resources
Letitia Grenier, SF Estuary Institute Mark Stacey, UC Berkeley
Bruce Jaffe, U.S. Geological Survey Diana Stralberg, PRBO Conservation Science
Jessica Lacy, U.S. Geological Survey Lynne Trulio, San Jose State University
Lester McKee, San Francisco Estuary Institute Isa Woo, U.S. Geological Survey
REVIEWERS
The following EPA reviewers provided valuable comments on an earlier draft of this document:
Ted DeWitt & Henry Lee, U.S. EPA, Office of Research and Development
Tristan Peter-Contesse & Lester Yuan, U.S. EPA, Office of Water
Luisa Valiella, U.S. EPA, Region 9
ACKNOWLEDGMENTS
We would like to acknowledge the support of A. Grambsch and M. Slimak of EPA's National
Center for Environmental Assessment in the Office of Research and Development, and
especially our colleagues in EPA's Global Change Assessment Staff for many helpful
discussions during this project. Special thanks to C. Weaver for technical contributions.
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EXECUTIVE SUMMARY
The San Francisco Bay estuary is highly vulnerable to climate-related changes including
increased water temperatures, changes in precipitation and winds, and sea level rise. Impacts
such as increased inundation of coastal wetlands, changes in water availability and quality, and
altered patterns of sedimentation and erosion are increasingly interacting with other human
stressors such as extractive water uses and land use changes. Thus it is essential that estuary
managers become 'climate-ready' by: assessing the vulnerability of natural resources to climate
change; considering strategic choices among adaptation strategies in the near term; and engaging
in longer term planning based on a range of plausible scenarios of future change. In an era of
shrinking budgets coupled with increasingly complex decision-making needs - often taking
place in a context of uncertainty and incomplete information - managing natural resources in the
face of climate change will be challenging. There is a need for assessment methods that take
advantage of existing scientific expertise to help identify robust adaptation strategies, weigh
difficult trade-offs, and justify strong action, all in a timely and efficient manner.
The purpose of this project was to carry out a pilot vulnerability assessment for the San
Francisco Estuary Partnership's (SFEP) natural resources using expert judgment, the results of
which could be linked to adaptation planning. To this aim, EPA's Office of Research and
Development collaborated with SFEP and the San Francisco Bay Conservation and Development
Commission on a novel expert elicitation exercise for 'rapid' vulnerability assessment. A trial
exercise was carried out during a two-day workshop in which two groups of seven experts each
focused on two key ecosystem processes: sediment retention in salt marshes and community
interactions of shorebirds (Figure ES-1). The exercise, which was based on formal expert
elicitation techniques but tailored specifically for qualitative analysis of ecosystem processes,
was designed to glean expert information on the sensitivities of ecosystem process components
under future climate scenarios. This was followed by group discussions of the implications of
the results for management in light of climate change, as well as feedback on the exercise itself.
Figure ES-1. Selected ecosystem processes for the pilot vulnerability
assessment.
Sensitivities and Potential Adaptation Responses
Using the experts'judgments on the sensitivities of key ecosystem process components
to future climate conditions, it is possible to identify 'top pathways'for which there are
available adaptation options. After creating influence diagrams showing the relationships
among key process variables (Figures ES-2 and ES-3), the experts generated information on
which relationships may show, under future climate change: 1) increasing relative impact on the
overall process; 2) increasing sensitivity; and 3) abrupt threshold changes. Based on the amount
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of expert agreement on each relationship, it is possible to identify 'top pathways' of interest for
management. Three top pathways for each process are described below, with accompanying
discussion of adaptation options for management.
Figure ES-2. Top pathways for management of the Net Accretion/Erosion
endpoint. Colors are used to distinguish different pathways. Red symbols
highlight potential changes under future climate conditions.
Figure ES-3. Top pathways for management of the Shorebirds endpoint.
Colors are used to distinguish different pathways. Red symbols highlight
potential changes under future climate conditions.
Sediment Retention Green pathway: Two relationships in this pathway (Figure ES-2)
were indicated by the experts as having increasing relative impact on net accretion and erosion
under climate change. The direct effect of organic accumulation through below-ground biomass
production already has a high relative impact on the overall process, and this relative impact is
expected to increase under sea level rise associated with climate change. Likewise, the effect on
freshwater inflow of reservoir management is expected to be of increasingly high relative impact
under climate change as freshwater supplies become increasingly variable and human demand
continues to increase. Management options under this pathway include:
• Managing reservoirs for steady, lower-volume releases to regulate salinity and favor
native marsh vegetative productivity
• Investigating optimal timing of releases relative to the growing season
• Prioritizing releases designed for salinity maintenance compared to high volume pulses to
support mineral sediment transport
• 'Stepping up' Spartina (invasive cordgrass) eradication programs since increased salinity
regimes favor this invasive species.
Sediment Retention Purple pathway: The climate-related shift in this pathway (Figure
ES-2) involves an increase in the sensitivity of net mineral accumulation to changes in sediment
size. This is a direct relationship, with larger grain sizes favoring net mineral accumulation since
larger grains deposit more readily, are harder to re-suspend and provide larger building blocks
for accretion. Increasing sensitivity of net mineral accumulation to sediment size relates to the
fact that sediment flux, the other determinant of net mineral accumulation, is expected to
continue to decrease because of continuing processes responsible for historical declines from
peak sediment inputs in the past and because of potential changes in wave-driven erosive
processes. Management options under this pathway include:
• Investigating how changes in land cover (including changes from impervious to
permeable pavement systems) may affect sediment size
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• Managing reservoirs for high volume pulses to increase transport of larger grain
sediments to marshes
• Adjusting policies that prevent coarse sediment from entering the Bay, such as changing
Total Maximum Daily Load requirements to allow an increase in sediment loads for
streams that do not support salmonids
• Engaging with flood control districts to re-couple stream sediments to wetlands.
Sediment Retention Blue pathway: In this pathway (Figure ES-2), the experts identified
the potential for an abrupt threshold change in the effect of wind-generated waves on sediment
flux, from a direct to an inverse relationship of increasing relative impact on the overall process.
Under current conditions, wind-driven wave action has a net positive effect on sediment flux
onto salt marshes, as greater wave energy can mobilize and increase rates of sediment transport
from bays and adjacent mudflats deep into marsh systems. However, under future climate
conditions a threshold may be crossed because of a change in wave character as water depth
increases due to sea level rise. In deeper water, waves behave differently, with less wave energy
available for re-suspension of bottom sediments and more energy delivered to the marsh edge,
leading to increased erosion. Management options under this pathway include:
• Monitoring wind, waves and sediment fluxes to detect the threshold shift when it occurs,
and in the meantime preparing a response plan for after the shift
• Building berms or restoring oyster reefs as protective barriers against wave energy
• Locating sites to deposit dredge materials with a goal of enhancing sediment
concentrations on mudflats adjacent to marshes
• Prioritizing development of new tools for reducing wave action on the front of marshes.
Community Interactions Green pathway: Both relationships in this pathway (Figure ES-
3) were indicated as having increasing relative impact on the shorebirds endpoint under climate
change. A strong direct effect of landscape mosaic (defined as a mixture of habitats for
secondary foraging, roosting, and cover from predators that support efficient use of mudflat
feeding habitat) already has a high relative impact on shorebirds; and this may increase even
further under climate change as mudflat habitats become scarcer and smaller in extent.
Likewise, the effect on landscape mosaic of restoration is expected to be of increasingly high
relative impact under climate change as individual habitats within the mosaic are differentially
impacted by temperature increases, altered precipitation patterns, and water diversions in a
context of continuing land use change. Management options under this pathway include:
• Assessing and mapping landscape mosaics to detect changes and support management at
the landscape scale
• Managing landscape mosaics through spatial planning designed to prioritize where and
how to restore which habitats, in order to ensure a continuum of wetland and upland
ecosystems which could migrate inland as sea level rises
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• Including 'threshold landscapes' (those about to change from one set of dominant
processes to another, or from one state to another) in consideration for restoration
• Supporting legislation or incentives that encourage moving back or blocking of
development on lands where there is restoration potential now or in the future.
Community Interactions Purple pathway: This pathway (Figure ES-3) shows a high
relative impact of mudflat prey populations on shorebirds. The abundance of prey per unit area
will become increasingly important (of increasing relative impact) under climate change as
spatial extent of mudflats shrink with sea level rise. Also, there is an abrupt threshold response
of the prey community itself to water quality (specifically, dissolved oxygen), from a direct to a
very strong direct effect under climate change. As decreases in dissolved oxygen occur with
climate change due to increased temperatures and/or eutrophication, prey communities may flag.
A critical threshold may occur in the future if dissolved oxygen reaches low enough levels to
cause prey populations to crash. Management options under this pathway include:
• Protecting water quality through integrated water resources management, including
stormwater management and rainwater-harvesting (which also benefits water
conservation)
• Using permeable rather than impervious surfaces to reduce runoff
• Restoring riparian zones to act as natural filters.
Community Interactions Blue pathway: This pathway (Figure ES-3) contains two
relationships that the experts identified as sensitive to climate change. The extent of mudflat
available for foraging (i.e., the number of hours per acre that mudflats are exposed and therefore
accessible) has a direct effect on shorebird populations, and this may become increasingly strong
as a threshold effect under climate change. This is because extent of mudflat may become
limiting as sea level rises, with available foraging habitat becoming too limited to support
shorebird populations. At the top of the pathway, there is a relationship of increasing sensitivity
of freshwater inflow to water management practices (specifically, reservoir management and
upstream operations). This effect will become increasingly strong as freshwater flows from
alternate sources such as precipitation and tributaries become more variable and/or scarce under
climate change. This relationship connects back down to the shorebirds endpoint through a
series of linked variables having to do with sediment supply, transport and effects on bathymetry
(which helps determine extent of mudflat). Management options under this pathway include:
• Managing reservoir water releases to mobilize and transport sediments (e.g., through the
use of sediment maintenance flushing flows)
• Improving upstream operations to ensure greater availability of water (for more frequent
and/or intense pulse releases)
• Employing integrated water resources management, with an emphasis on shifting from
storage more toward conservation uses
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• Developing methods for moving coarse sediments into the bay (e.g., by strategically
locating dredge spoil sites to enhance sediment supplies to mudflats).
Based on the nature and timing of the sensitivity, some actions can be taken
immediately while others require monitoring and planning for multiple potential futures. In
the case of relationships that are well understood and for which there are management options
available, the nature of the expected climate-related shift has implications for when managers
may want to take action. In the case of relationships for which the expected climate-related shift
is toward increasing relative impact (and especially where the relationship is already of high
relative impact under current conditions), action can be taken immediately to put management
options into place for positive effects on those pathways. In the case of relationships for which a
change in sensitivity is possible under future climate scenarios, the expectation of increasing
sensitivity could be considered a 'notification' to managers to further study the relationship in
order to anticipate the degree and timing of the impending sensitivity and prepare best
management responses. Finally, thresholds are a particular challenge, as it is often impossible to
predict exactly when a threshold response will occur. In these cases it will be important to
monitor threshold variables to identify the shift when it occurs; in the meantime a manager might
act to keep the system 'below' the threshold as long as possible, while preparing a plan for what
to do if an unavoidable shift occurs. After a shift occurs, a manager could decide to manage the
system differently in its new state, or take no action and instead shift priorities to other goals.
Adaptation Planning
Relating top pathways and associated adaptation options to existing management
activities is a path forward for action. The top pathways described above were used to identify
adaptation options that could be applied to sensitive ecosystem process components. A variety
of additional pathways and associated adaptation options can be further explored using the
detailed tables of judgments and lists of strategies provided in this report. The next step toward
adaptation planning is to connect the top pathways and adaptation options to existing
management activities and plans. Under its current goals, SFEP is already undertaking a variety
of activities that can be related to these adaptation options, as described in its annual, mid-term
and long-term planning documents. These include specific restoration, sediment management,
monitoring and research projects and strategies. The climate change sensitivities and potential
adaptation strategies identified in this report can be cross-referenced to these activities, goals and
objectives to identify where existing work can be adjusted to better support adaptation. Some
examples of such cross-referencing are provided as a starting point for more comprehensive
adaptation planning during future planning cycles. The intent is that the results of this
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assessment can be used to prioritize increased investment in projects that take into account
specific, known climate sensitivities and make use of particular adaptation options that will be
most effective. Assessment results can also assist in priority-setting for long term research and
monitoring investment. Besides identifying well-understood relationships, the exercise also
revealed gaps in understanding of the system that indicate a need for further investigation of
some sensitivities as well as tailored projects to develop new management tools in response.
'Mainstreaming' climate change adaptation into ongoing, iterative planning processes
will increase the ability of managers to identify win-win options, weigh multiple trade-offs,
and prepare for long-term changes. For SFEP as well as other National Estuary Programs and
organizations with well established planning processes, there are benefits to 'mainstreaming'
(continuously integrating) adaptation into ongoing planning, rather than developing a stand-alone
adaptation plan. The objective is to start with actions that have multiple benefits, i.e., that
contribute to current management goals while also responding to climate change. For example,
starting with the separate pre-existing plans in support of Baylands Ecosystem Habitat Goals,
Subtidal Habitat Goals and Upland Habitat Goals, projects could be designed to coordinate
across goals and restore landscape mosaics that will support valued species such as shorebirds
not only today, but also under projected climate change (see Green pathway, Figure ES-2).
Since climate change has the potential to intensify and even create new trade-offs,
mainstreaming climate change into planning is also important for identifying and weighing
conflicts among adaptation options within the context of existing (and emerging) goals. One
example identified in this study is the simultaneous need to reduce sediments in salmon stream
habitats (under current SFEP goals) and increase coarse sediment transport to the Bay (as
indicated by the Purple pathway in Figure ES-2). Another example based on comparing
adaptation needs for two of the top pathways above is the trade-off between high volume pulses
to enable large grain sediment transport (Purple pathway, Figure ES-2) and water availability for
steady, lower-volume releases to favor vegetative productivity (Green pathway, Figure ES-2).
Given the long-term nature of the climate change challenge, mainstreaming has an
additional advantage over a stand-alone plan in that it helps counteract the tendency to postpone
adaptation actions in the face of more immediate challenges. It often may be possible to adjust
current practices in ways that achieve adaptation while still fulfilling original goals.
Furthermore, thinking ahead as part of planning is essential for anticipating which of today's best
practices may become ineffective and even 'maladaptive' as sensitivities change and threshold
shifts occur under climate change. Once thresholds have been crossed or other unavoidable
changes of significance have occurred, some management goals may have to be revised.
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Evaluation of Expert Judgment Approach
A novel methodology based on expert elicitation was developed and piloted as a tool for
'rapid assessment' of ecological sensitivities to climate change. The aim was to explore
whether it is possible to synthesize useful information from experts on key climate sensitivities
in the short time frame of a two-day workshop, using expert elicitation techniques. Expert
elicitation is a multi-disciplinary process for using expert judgment to inform decision-making
when data are incomplete, uncertainties are large, and more than one model can explain available
data. The novel methodology introduced in this study is a modification of formal (usually
quantitative) expert elicitation that uses qualitative judgments in accordance with complex
ecological questions. Influence diagrams (showing the structure of causal relationships among
variables) were used successfully to capture the experts' collective understanding of the selected
ecosystem processes, under current conditions and under two scenarios of future climate change
for a mid-century time frame. A coding scheme was used by the experts to record their
judgments, with observational notes and group discussions used to gather additional information.
The result was three categories of information based on the influence diagrams: 1) the
direction and strength of the relationships among variables, 2) the changing sensitivities of some
relationships to climate change (including potential threshold responses), and 3) the relationships
of highest relative impact on the process as a whole. When this wealth of information is
combined into a 'crosswalk' of all three categories, it is possible to identify top pathways (see
above) comprised of relatively well-understood relationships that are sensitive to climate change
and for which management are options available. Managers are encouraged to further 'mine' the
tables for other key pathways applicable to their specific sites and to identify potential research
priorities based on information gaps.
The expert elicitation exercise developed for this assessment has the potential to be
useful for other sites, processes and ecosystems. While an example North Bay site was used as
a means to focus the exercise, the variables that ended up in the final influence diagrams are
common enough that most of the results may transfer to the entire Bay for these particular
ecosystem processes. It is likely that the influence diagrams also could be transferred for use
with like ecosystem processes in other estuaries, with minor revisions for place-specific stressors
or other process variables; however the characterizations of variable relationships, sensitivity and
relative impact would have to be revised, particular to the location. Where information on
completely different processes is needed, the general methodology should be transferable to
other processes and ecosystems. The strengths of this method include the ability to capture more
recent knowledge than would be available from a literature review and more knowledge of the
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type that is closely related to management. It is also effective at integrating across disciplines
and scales, which is particularly important for ecosystem and climate change assessments.
As a proof of concept for a new type of assessment exercise, this method and its results
come with a number of caveats. This was not a comprehensive vulnerability assessment for the
whole estuary, so prioritization based on these results should be considered in the broader
context of other vulnerable processes, ecosystems and goals. Given the complexity of these
systems and instances of uneven agreement among experts, actions based on the top pathways
should be taken with care, with each manager considering the applicability of the information to
his or her own specific system. Confidence estimates for individual judgments turned out to be
challenging, so improvements have been suggested for strengthening this aspect in future
assessments. There is also the potential to simplify the coding scheme based on what was
learned in this trial run, to improve efficiency and allow experts more time to fill in data gaps.
Regardless, the expert elicitation method developed for this study was well suited for achieving
the goals of this assessment, and in a time frame much shorter than would be required for more
traditional, detailed quantitative modeling. Having a well-supported and timely study to
substantiate new and existing ideas can position managers to justify the most appropriate
management options and priorities. It also can validate research priorities by highlighting known
research gaps. Overall, the method offers opportunities to capture and integrate the existing
collective knowledge of local experts, while pushing the boundaries to develop a new
understanding of the system and identify robust adaptation options in the face of climate change.
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1 1. INTRODUCTION
2
3 1.1. BACKGROUND
4 The San Francisco Bay estuary is highly vulnerable to the impacts of climate change.
5 Sea level rise, increased air and water temperatures, changes in precipitation, and changes in
6 storm climatology and winds are already causing increased inundation of coastal wetlands and
7 marshes, changes in water availability and quality, and altered patterns of sedimentation and
8 erosion. These impacts are interacting with other anthropogenic stressors such as extractive
9 water uses and land use changes to make management of estuarine ecosystems more challenging
10 than ever. While there are many uncertainties regarding the nature of future climate changes and
11 the response of ecosystems to those changes, estuary managers can 'ready' themselves by
12 assessing the vulnerability of natural resources to climate change, making strategic choices about
13 how to implement adaptation strategies1 in the near term, and planning for longer term
14 management under a range of plausible scenarios of future change. It is the aim of EPA's
15 Climate Ready Estuaries (CRE) Program to assist National Estuary Programs (NEPs) in meeting
16 such information and planning needs.
17 As part of the CRE Program, the San Francisco Estuary Partnership (SFEP), the San
18 Francisco Bay Conservation and Development Commission (BCDC), and EPA's Office of
19 Research and Development (EPA ORD) collaborated on the design and trial of a novel
20 methodology for conducting vulnerability assessments for sensitive ecosystems of the San
21 Francisco Bay estuary. The aim was to develop assessment capabilities using expert judgment to
22 synthesize place-based information on the potential implications of climate change for key
23 ecosystem processes, in a form that would enable managers to link the resulting information to
24 adaptation planning.
25
26 1.2. PURPOSE AND SCOPE
27 1.2.1. Purpose
28 The purpose of this project was twofold: to conduct a vulnerability assessment using a
29 novel, expert judgment approach based on expert elicitation methods, and to analyze the
30 implications for adaptation planning. This was not a comprehensive vulnerability assessment for
31 the whole estuary but rather a proof of concept for a new type of assessment exercise, using two
32 key ecosystem processes of salt marsh and mudflat ecosystems as demonstration studies. This
33 was accomplished through a series of steps to: 1) identify key management goals and ecosystem
34 processes essential to meeting those goals; 2) create conceptual models of selected ecosystem
1 Throughout this report, "adaptation" refers to management adaptation rather than evolutionary adaptation.
Management adaptation refers to strategies for the management of ecosystems in the context of climate variability
and change (CCSP, 2008a).
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1 processes; 3) assess ecosystem process sensitivities to climate change; 4) consider resulting
2 vulnerabilities with respect to management goals; and 5) explore implications for adaptation
3 planning. Steps 1-2 were used to define the scope of the assessment, while steps 3-5 comprise
4 the vulnerability assessment itself.
5
6 1.2.2. Scope
7 The scoping process began with a review of the SFEP Comprehensive Conservation and
8 Management Plan in order to select key management goals upon which to focus the assessment.
9 The key ecosystem-related goals selected by SFEP in consultation with BCDC and EPA ORD
10 were:
11
12 • Restore healthy estuarine habitat to the Bay-Delta, taking into consideration all
13 beneficial uses of Bay-Delta resources
14 • Protect and manage existing wetlands
15 • Restore and enhance the ecological productivity and habitat values of wetlands
16 • Stem and reverse the decline of estuarine plants, fish and wildlife and the habitats on
17 which they depend; and
18 • Ensure the survival and recovery of listed and candidate threatened and endangered
19 species, as well as special status species.
20
21 After an information-sharing meeting with local experts to discuss the project and learn
22 about climate change impacts and adaptation work in the region, salt marshes and mudflats were
23 selected as focal ecosystems for the study. These systems were identified as highly relevant to
24 SFEP's management goals due to their ecological productivity, their habitat values for threatened
25 and endangered species, and their sensitivity to changes in climate-related variables such as sea
26 level rise and altered hydrology. For more detailed information on goal and ecosystem selection
27 processes, please see Appendix A.
28 The second step in the scoping process was the development of conceptual models to
29 understand the primary drivers and processes of salt marshes and mudflats. The conceptual
30 models were used to explore the linkages among key ecosystem processes within each
31 ecosystem, major stressors of concern, and climate drivers causing altered or new stressor
32 interactions. The models were refined to a set of five or six key ecosystem processes that are
33 essential to the maintenance of salt marsh and mudflat systems. Based on these general
34 conceptual models, two specific processes of concern were selected for further analysis. The
35 purpose was to select good processes for piloting the method, but the choice does not imply that
36 these are necessarily the only important, or the most vulnerable, processes. The processes were
37 selected based on the criteria of being identified by local experts as integral to ecosystem
38 function, increasingly sensitive to climate change, and sufficiently well-studied by the scientific
39 community to provide the basis for a more in-depth assessment.
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1 The two processes selected for further analysis were sediment retention in salt marshes
2 and community interactions in mudflats (see Executive Summary Figure ES-1). Sediment
3 retention refers to the balance between the processes of removal and deposition of sediment onto
4 a salt marsh. The topic of community interactions in mudflats was narrowed to a tractable
5 "storyline" involving several interdependent species, which was selected based on interviews
6 with local experts who were asked to identify climate-sensitive interactions of interest. The
7 storyline selected was the relationship of two species of mudflat wading birds, the Marbled
8 Godwit and the Western Sandpiper, to their predators and prey. Expanded sub-models were
9 developed for each of the two processes and served as the basis for designing the sensitivity
10 analyses of the subsequent assessment. For more detailed information on process selection and
11 conceptual model development, please see Appendix A.
12 The remaining steps of the assessment - the sensitivity analysis, vulnerability assessment,
13 and analysis of management implications - were accomplished through an expert elicitation-
14 style workshop, the results of which make up the core of this report. Expert elicitation is a multi-
15 disciplinary process using expert judgment to inform decision-making when empirical data are
16 incomplete, uncertainties are large, more than one conceptual model can explain available data,
17 and technical judgments are required to assess assumptions. During a two day workshop, a
18 novel application of the expert elicitation method was tested using two groups of seven expert
19 participants each. A list of the expert participants for each breakout group is provided in Table
20 1-1 (for additional information on selection criteria and participant credentials, please see
21 Appendix B). The participants assessed the sensitivities of salt marsh sediment retention and
22 mudflat community interactions to climate- and non-climate stressor interactions, with an eye
23 toward informing adaptation. The methodology and results of this expert elicitation exercise are
24 described in the sections that follow.
25
26 Table 1-1. Breakout group participants for the expert elicitation workshop
27 (see Appendix B for further details on selection criteria and credentials)
28
29 1.3. ROADMAP FOR THE REPORT
30 This report presents a summary of the entire project, including goal selection and
31 conceptual modeling, the expert elicitation methodology, the results of the workshop, and
32 implications for management. Figure 1-1 provides a flow chart of the assessment process and
33 report structure.
34
35 Figure 1-1. Vulnerability assessment process.
36
37 Section 2 describes the expert elicitation exercise, including the approach, the exercise,
38 and the results. Section 3 provides an analysis of the results with respect to how they may be
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1 used by estuary managers to understand ecosystem responses to climate change and engage in
2 adaptation planning. Section 4 provides key conclusions of the assessment. The appendices
3 provide additional detailed information on the activities conducted prior to and following the
4 workshop. Appendix A summarizes the goal selection and conceptual modeling processes used
5 for scoping the vulnerability assessment. Appendix B provides details on the expert elicitation
6 pre-workshop preparations and post-workshop follow-up, including expert selection criteria, pre-
7 workshop preparations by participants, and expert feedback. Appendix C and Appendix D
8 contain detailed information that was provided to the participants on the development of climate
9 scenarios and the methodology for estimating confidence.
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1 2. EXPERT ELICITATION EXERCISE
2
3 2.1. JUSTIFICATION FOR METHOD
4 2.1.1. Definition and Uses
5 Expert elicitation is a multi-disciplinary process for obtaining the judgments of experts to
6 characterize uncertainty and fill data gaps where traditional scientific research is not feasible or
7 adequate data are not yet available. The goal of expert elicitation is to characterize each expert's
8 beliefs about relationships, quantities, events, or parameters of interest. The expert elicitation
9 process uses expert knowledge, synthesized with experiences and judgments, to produce
10 conclusions about the nature of, and confidence in, that knowledge. Experts derive judgments
11 from the available body of evidence, including a wide range of data and information ranging
12 from direct empirical evidence to theoretical insights.
13 Because EPA and other federal regulatory agencies are often required to make important
14 national decisions in the presence of uncertainty, EPA's Science Policy Council formed an
15 Expert Elicitation Task Force in April of 2005 to investigate how to conduct and use this method
16 to support EPA regulatory and non-regulatory analyses and decision-making. The result was an
17 Expert Elicitation Task Force White Paper that affirms the utility of using expert elicitation and
18 provides recommendations for expert elicitation "best practices" based on a review of the
19 literature and actual experience within EPA. The draft paper (see
20 http://www.epa.gov/spc/expertelicitation/index.htm) is currently under external peer review
21 through EPA's Science Advisory Board. The best practices outlined in the draft White Paper
22 formed the basis for the design of this project's expert elicitation-style workshop.
23
24 2.1.2. Novel Application
25 The specific elicitation exercise used in this assessment was custom-designed by Dr. Max
26 Henri on of Lumina Decision Systems, Inc. Dr. Henri on is a nationally-recognized authority on
27 decision analysis methods and tools, dealing with uncertainty in environmental risk assessment,
28 and expert elicitation. As a member of EPA's Expert Elicitation Task Force, he was uniquely
29 qualified to assist in designing a novel application of expert elicitation methods for use in a two-
30 day workshop format. Specifically, Dr. Henri on developed a qualitative coding scheme for
31 expert judgments about the sensitivity of ecosystem processes to physical and ecological
32 variables, using "influence diagrams" to depict the relationships among ecosystem process
33 variables and external drivers such as climate change. This new methodology, described in
34 detail below, explores the utility of expert elicitation for conducting "rapid vulnerability
35 assessments" for ecological systems.
36
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1 2.2. WORKSHOP DESIGN AND METHODOLOGY
2 2.2.1. Workshop Goals and Objectives
3 The overarching goals of the workshop were to: 1) improve the understanding of the
4 sensitivity of selected salt marsh sediment retention and mudflat community interactions
5 processes to the projected impacts of climate change; 2) improve the ability to identify
6 adaptation strategies that mitigate those impacts, given the uncertainties; and 3) demonstrate the
7 applicability of an expert elicitation approach to this type of analysis.
8 The workshop was held March 16-17, 2010, in San Francisco, California, at the BCDC
9 offices. During the workshop, experts were divided into two breakout groups to consider each
10 ecosystem process separately. The seven participants in each breakout group (see Table 1-1)
11 were asked to provide judgments about the ecosystem process under consideration by their
12 group. For each ecosystem process, the specific objectives were to: 1) characterize the relative
13 influences of physical and ecological variables that regulate the process; 2) assess the relative
14 sensitivity of the ecosystem process to key stressors under current conditions and future climate
15 scenarios; 3) assess the degree of confidence in judgments about these relationships; and 4) relate
16 the results of the exercise to adaptation planning through group discussions. Given the range of
17 habitats and issues in the entire San Francisco Bay area, the participants were asked to consider
18 the North Bay (San Pablo Bay) when a more specific spatial scope would be useful during the
19 workshop exercise. In addition, an example site in the North Bay, China Camp, was presented as
20 a particular place upon which to focus when considering management implications; however,
21 issues and options that were not specific to China Camp were also considered during group
22 discussions.
23 For further details on workshop preparation and implementation, including selection
24 criteria for participants, please see Appendix B.
25
26 2.2.2. Approach and Methodology
27 According to protocols put forth in EPA's Expert Elicitation Task Force White Paper,
28 there are a variety of options for gathering and processing expert judgments. The specific
29 elicitation approach used in this workshop was one that asked experts to give their individual
30 judgments independently. This was done to reduce the tendency towards "group-think," i.e., the
31 tendency for many people to go along with the most vocal participant, even if s/he is not the
32 most knowledgeable. Since participants varied in their expertise about different aspects of the
33 system, they were encouraged to make adjustments to their judgments at any time based on any
34 deeper understanding gained during or after group discussions; however, consensus was not the
35 goal of the exercise. Rather, the aim was to look at the expert judgments in aggregate, while also
36 retaining information on variance in judgments. This approach is well-suited to the type of
37 qualitative judgments participants were asked to make at the workshop.
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1
2 2.2.2.1. Influence Diagrams
3 Each breakout group participated in the development of an influence diagram of the
4 ecosystem process under consideration by their group. Decision analysts use influence diagrams
5 as a way to define the qualitative structure of causal relationships among variables that experts
6 believe are of greatest importance for understanding the problem being evaluated. Influence
7 diagrams typically represent a subset of a larger, more detailed model such as the conceptual
8 models developed previously (see Appendix A).
9 A simplified influence diagram for sediment retention is provided in Figure 2-1. By
10 convention, the variables in an influence diagram are represented by rectangles (labeled boxes)
11 while arrows between the variables represent causal relationships, or "influences". Sequences of
12 arrows form pathways, all of which ultimately lead to the final variable, or endpoint, of concern.
13 In Figure 2-1, the endpoint that is being evaluated is sediment retention. Interactive effects of
14 multiple variables on each other, or on the endpoint, can occur where two "causal" variables both
15 influence (have arrows into) a common "response" variable. In Figure 2-1, an example
16 interaction is indicated by arrows B and C, where reservoir management and impervious cover
17 together could have an interactive effect on freshwater inflow.
18 In the case of community interactions, the diagram was constrained to a tractable number
19 of species of interest. It focused on the relationship of two species of mudflat wading birds, the
20 medium-bodied Marbled Godwit and the small-bodied Western Sandpiper, to their predators and
21 prey. Please see Appendix A for a more detailed explanation of this storyline.
22
23 Figure 2-1. Simplified influence diagram for sediment retention.
24
25 While influence diagrams are widely used and relatively well-understood, our proposed
26 use of qualitative degrees of influence is an innovation in expert elicitation. Typically, an expert
27 elicitation seeks to obtain expert judgments about uncertain quantities in the form of numerical
28 probability distributions. For the ecosystem processes considered during this workshop, there
29 were information, data and time limitations that made quantifying the influences as probability
30 distributions unrealistic. Instead, judgments were based on qualitative types (is the relationship
31 direct, or inverse?) and degrees (is the response small, or large?) of influences. The use of
32 qualitative degrees of influence provides much more detail than simply specifying causal
33 influences with arrows alone, but less specificity than required for quantified probabilities.
34 Participants were provided with "straw man" diagrams (see Appendix B) prior to the
35 workshop. They were asked to review these diagrams and submit their own revised versions the
36 week before the workshop. Diagram submissions were combined into one consolidated draft
37 diagram for each group that served as the starting point for discussion at the workshop. The
38 workshop itself began with each group working together to refine their diagram into a "group
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1 diagram". The group influence diagram was meant to distill the system to a tractable set of key
2 variables and influences, and as such it was not comprehensive. The groups were given
3 complete freedom to alter any part of the diagram, with the exception of the ecosystem process
4 endpoint, as long as they constrained the diagram to a total of no more than 15 boxes. At the
5 same time, participants were reminded to keep some of the top row stressor or management
6 boxes, since these would serve as key linkages back to management options. Participants were
7 also encouraged to minimize the total number of arrows in the diagram to include only the most
8 key influences. The purpose was to capture the key components and relationships of each
9 ecosystem process in a concise form that could be rapidly assessed in a workshop setting. Once
10 the group diagrams were finalized, all of the participants made their judgments using the same
11 diagram throughout the remainder of the workshop.
12
13 2.2.2.2. C/imateScenarios
14 Dr. Katharine Hayhoe of Texas Tech University, an experienced climate scientist with an
15 extensive background in regional climate assessments, developed two climate change scenarios
16 for use in the expert elicitation exercise. The scenarios represented two distinct but scientifically
17 credible climate futures for a mid-century (2035-2064) time period. (The mid-century time
18 frame was selected by the SFEP partners because of its suitability for adaptation planning.) The
19 projections were based on six leading climate models, using a lower emissions scenario (Climate
20 Scenario A) and a mid-high emissions scenario (Climate Scenario B) to generate values for
21 climate variables for use by the experts in making their judgments (see Table 2-1).
22
23 Table 2-1. Summary of Climate Scenario A ("Lower-Range" Scenario) and
24 Climate Scenario B ("Higher-Range" Scenario): averages for mid-century
25
26 Under both climate change scenarios, California will retain its Mediterranean climate
27 (cool/wet winters and hot/dry summers) and continue to experience a high degree of variability
28 in precipitation with rising sea levels. By mid-century, the "higher-range" Climate Scenario B
29 (which includes higher emissions and a more sensitive climate) is projected to experience a
30 warmer and somewhat drier climate compared to the "lower-range" Climate Scenario A (with
31 lower emissions and a lesser impact on California's climate).
32 At the workshop, Dr. Hayhoe provided the participants with an overview of major
33 climate drivers and regional trends for California. She discussed five main sources of
34 uncertainty with climate projections, including: (1) the amount of future emissions; (2) the
35 degree to which the influence of global climate change on local climate is modified by local
36 factors; (3) the sensitivity of the climate system (as feedbacks are not well understood); (4) the
37 ability of climate models to simulate climate both globally and locally; and (5) the natural
38 variability of the climate system. Because of these factors, exact predictions of climate change
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1 are not possible. However, uncertainty can be dealt with by using multiple scenarios to bracket a
2 range of plausible climate futures and identify key vulnerabilities in the system. In order to
3 consistently "bound" the consideration of future climate changes in the workshop exercise, the
4 participants were instructed to use the values provided under Climate Scenarios A and B (Table
5 2-1) to contextualize their judgments about future effects on the ecosystem processes under
6 consideration. For additional details on the climate scenarios, including data sources, please see
7 Appendix C.
8
9 2.2.2.3. ExpertFaci/itation
10 Due to the highly technical nature of the exercise, the complexity of the novel
11 methodology that was being used, and the ambitious time line for accomplishing multiple
12 outputs, it was essential that the workshop be run by skilled expert facilitators. These were
13 chosen based on a number of criteria including: proven expertise in facilitating science-based
14 workshops; general knowledge of science behind estuary management (particularly wetlands
15 ecology); and experience working on national coastal issues and/or issues in the San Francisco
16 Bay region. The expert facilitators selected were Dr. Peter Schultz, Principal at ICF
17 International, and Dr. Brock Bernstein, independent consultant and President of the National
18 Fisheries Conservation Center. Dr. Schultz (who served as facilitator for the Sediment Retention
19 group) has served as the Director and Associate Director of the U.S. Global Change Research
20 Program Office, and has two decades of experience in climate and global change research,
21 management, decision support, and communication. Dr. Bernstein (who served as facilitator for
22 the Community Interactions group) is a marine ecologist with research experience in a range of
23 coastal and oceanic environments, including San Francisco Bay, and has worked on a wide
24 variety of management and policy issues.
25 Prior to the workshop, both facilitators attended training calls in which they were fully
26 briefed on the project background and conceptual models, the workshop goals and objectives,
27 and the expert elicitation exercise. Working together and with the SFEP/BCDC/EPA team, the
28 facilitators contributed to the refinement of the workshop agenda and improvements to the
29 workshop process.
30
31 2.2.2.4. Coding Scheme and Exercise
32 Participants were asked to characterize each influence in their influence diagram
33 according to the coding scheme presented in Table 2-2, and to indicate their confidence in their
34 judgments using the confidence rankings described below (see next section). Influences were
35 characterized first under current conditions, and then under Climate Scenario A and Climate
36 Scenario B. The extent to which participants agreed in their judgments was variable across the
37 different influences. The rule that was adopted for determining agreement for each influence
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1 was that a majority (4 or more participants) had to have selected the same code. Majority
2 agreement among four or more participants was considered to indicate substantial agreement
3 across the group.
4
5 Table 2-2. Coding scheme used during the workshop exercise to characterize
6 influences. "Small" and "large" changes in variables are defined relative to
7 the current range of variation for each variable, with "small" indicating that
8 the variable is within its current range of variation and "large" indicating
9 that the variable has moved outside its current range of variation
10
11 Participants were also asked to characterize interactive influences of their choosing (i.e.,
12 those they deemed important), under current conditions and under the climate change scenarios,
13 according to the coding scheme presented in Table 2-3. Since participants were given the option
14 to choose which interactive influences they considered significant and to provide judgments only
15 for those influences, and were limited by time, there were often interactions where only one or
16 two participants provided judgments. Only interactions scored by three or more participants
17 were examined in order to focus on interactions judged by several participants to be significant.
18 Three or more corresponding judgments were used to define agreement for interactive
19 influences.
20
21 Table 2-3. Coding scheme used during the workshop exercise to characterize
22 interactive influences
23
24 Finally, the participants were asked to assess their current level of scientific confidence in
25 their judgments for each influence or interactive influence using the confidence coding scheme
26 presented in Table 2-4. For each influence, each participant was asked to rate his/her confidence
27 in their judgment based on: (1) the amount of scientific evidence that is available in the expert
28 community to support the judgment; and (2) the level of agreement/consensus in the expert
29 community regarding the different lines of evidence that would support the judgment. The
30 coding options for "amount of evidence" were high (H) or low (L), based on whether available
31 information is abundant and well-studied and understood, versus sparse and mostly
32 experimental/theoretical. The coding options for "level of agreement" were high (H) or low (L),
33 based on whether data, reports, and experience across the scientific community reflect a high or
34 low level of agreement about the influence. Thus it was possible to have four combinations of
35 evidence and agreement when assessing confidence: FtH, HL, LH, and LL. The rule for
36 determining agreement in confidence was the same as described above for influences: agreement
37 was defined as a majority (four or more) of the same categorization of confidence level.
38 Similarly using the same rule as above for interactive influences, agreement on confidence for
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1 interactive influences was defined as three or more of the same categorization of confidence. For
2 additional details on the method used to assess confidence, please see Appendix D.
3
4 Table 2-4. Coding scheme used during the workshop exercise to characterize
5 confidence
6
7 2.2.2.5. ryj?0fogiesf0r Understanding Influences and Sensitivities
8 Type and degree of influence
9 The group's level of understanding of the different influences (arrows) in the influence
10 diagram can be gauged by the amount of agreement in participants' selection of influence codes.
11 Sometimes participants agreed on the type of influence, but not necessarily the degree (strength)
12 of the influence. Codes 2-13 (Table 2-2) represent different combinations of types and degrees
13 of influences that can be grouped according to the following typology:
14
15 Types:
16 Direct relationship = Codes 2, 3, 6, 8, 11, 13
17 Inverse relationship = Codes 4, 5, 7, 9, 10, 12
18 Degrees:
19 Proportional response of Y to X = Codes 2- 5
20 Disproportional response of Y to X = Codes 6-13
21
22 Codes can also be paired according to the same type and degree of influence, with the
23 only distinction being whether one is considering "X" to be increasing or decreasing. For
24 example 2/3 is a direct proportional influence, with 2 indicating when "X" increases, and 3
25 indicating when "X" decreases, but in both cases "Y" is responding in a directly proportional
26 way. Six combinations of pairings are possible:
27
28 Pairings by type and degree of influence (where "X" can go up or down):
29 Direct proportional = 2/3
30 Inverse proportional = 4/5
31 Direct disproportional, strong response (xY) = 6/11
32 Direct disproportional, weak response (Xy) = 8/13
33 Inverse disproportional, strong response (xY) = 7/10
34 Inverse disproportional, weak response (Xy) = 9/12
35
36 In some cases, participants selected the same exact code, indicating that they had the
37 same understanding of the influence in terms of both type and degree. Or, sometimes
38 participants chose pairings such as 2/3 while their colleagues may only have noted a 2 or a 3; we
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1 consider these cases to also indicate a correspondence in understanding of type and degree of
2 influence, since the only distinction was whether a participant was thinking of "X" as going up
3 or down (or both).
4 In another group of cases, there was agreement on the type of influence (i.e., whether X
5 affects Y directly or inversely), although there was lack of agreement on the degree of that
6 influence. These latter cases amount to an understanding of how X affects Y, just not the
7 magnitude. It may still be useful for management to know for which influences we at least have
8 some understanding of the type of response, even if we are not sure of the magnitude.
9 Finally, there were cases in which there was such a mixture of codes selected as to
10 indicate no agreement in either type or degree of influence. This indicated that, among this
11 group of experts, the influence was poorly understood or poorly defined.
12 Sensitivity
13 It is also possible to establish a typology for assessing the sensitivity of each influence
14 (i.e., how sensitive variable Y is to changes in X), especially with regard to how those may
15 change under the climate scenarios. Several codes can indicate the same level of sensitivity, so
16 the following groupings were used to indicate three levels of sensitivity:
17
18 Low sensitivity = Codes 8-9 & 12-13
19 Intermediate sensitivity = Codes 2-5
20 High sensitivity = Codes 6-7 & 10-11
21
22 This typology was used to document all judgments, along with the following additional
23 categories of judgments:
24
25 No Influence = Code 0
26 Unknown influence = Code 1
27 None given = No judgment provided
28 Other = Response provided that does not fit into the coding scheme
29
30 2.2.2.6. UnderstandingJte/afiveImpacts0/"Influences
31 While the coding scheme described above captures the nature of individual influences, it
32 is also of interest to identify which influences and interactions the participants perceived to have
33 the greatest relative impact on the ecosystem process endpoint. Here we define relative impact
34 as the combination of not only sensitivity but also how greatly the variable is changing relative to
35 other variables. There was no coding for this in the workshop exercise; rather, this concept was
36 an emergent property of group discussions that looked at the influence diagram as a whole and
37 identified influences of greatest relative impact in the context of the entire web of influences.
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1 During group discussions that spanned both days of the workshop, information was gleaned as to
2 which influences participants perceived to have comparatively greater effects on the ecosystem
3 process endpoints, and whether this varied under the climate scenarios. These discussions were
4 captured in the workshop notes as well as in the influence diagrams, in which the participants
5 identified influences and interactions of highest relative impact (see sections 2.3.1.4 and 2.3.2.4).
6
7 2.2.2.7. Key Questions
8 As described above, there are three categories of information that together comprise the
9 collective understanding of each ecosystem process as represented by its influence diagram: 1)
10 the type and degree of influence between variables, 2) the sensitivity of "response" variables to
11 changes in "affecting" variables, and 3) the relative impact of each variable on the ecosystem
12 process endpoint. For each of the three categories of information, the following key questions
13 are addressed.
14
15 Types and Degrees of Influences:
16 • For which influences and interactions was there agreement in participants' judgments
17 (codes), and what were those codes?
18 • How did agreement on influences and interactions vary from current conditions to
19 Climate Scenario A and Climate Scenario B?
20 • For influences and interactions for which there was agreement in judgments, how did
21 confidence levels across the participants vary? Did this change under the climate
22 scenarios?
23
24 Sensitivity of Influences:
25 • For which influences and interactions was there greatest sensitivity and least
26 sensitivity in the response variable to changes in the "affecting" variable?
27 • Were there any influences or interactions where agreement on sensitivity across
28 participants increased or decreased under the climate scenarios?
29
30 Relative Impact of Influences:
31 • Which influences and interactions did the participants indicate have the greatest
32 relative impact on the ecosystem process endpoints?
33 • Were there any influences or interactions for which relative impact changed under the
34 climate scenarios?
35
36 Using the data from the coding exercise as well as information that emerged during group
37 discussions, these questions are explored in the results sections that follow.
38
39 2.3. RESULTS
40 Major outputs of the expert elicitation exercise included the group influence diagrams,
41 the judgments on influences (including interactive influences) along with their confidence
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1 estimates, information on sensitivities (including thresholds), and characterizations of relative
2 impacts. For the purpose of this study, a threshold is defined (as per Groffman et al., 2006) as a
3 point at which there is an abrupt change in an ecosystem property (such as a flip in influence
4 type from direct to inverse), or where a small additional change in a driver produces a large
5 response (such as a shift from a proportionate to a disproportionately strong response of variable
6 Y to a change in variable X).
7
8 2.3.1. Sediment Retention
9 2.3.1.1. Group Influence Diagram
10 Figure 2-2 shows the group diagram developed by the Sediment Retention group.
11 Variable definitions that were clarified by the participants during the construction of the diagram
12 are found in Table 2-5. Two main variables, Net Mineral Accumulation and Net Organic
13 Accumulation, influence the endpoint of the balance between Net Accretion and Erosion.
14 Organic and inorganic sediment accumulation processes are both influenced by Inundation
15 Regime, which is influenced by Relative Sea Level and Tides. There is a feedback loop from the
16 endpoint to Inundation Regime. The middle level in the diagram includes Tides, Relative Sea
17 Level, Freshwater Inflow, Sediment Flux, Sediment Size and Wind and Waves. Freshwater
18 Inflow and Inundation Regime are key factors influencing Net Organic Accumulation through
19 plant community composition and production. Of the management and stressor variables, three
20 are related to Water Resource Management: Delta Outflow, Reservoir Management and
21 Channelization. These influence a combination of Sediment Flux and Size and Freshwater
22 Inflow.
23
24 Figure 2-2. Sediment Retention group influence diagram.
25
26 Table 2-5. Sediment Retention variable definitions clarified during group
27 discussion
28
29 The 15-box constraint meant that the freshwater and sediment supply variables were not
30 split between Delta and tributary sources, even though much of the discussion on the diagram
31 highlighted the differences in those sources. Without separate variables differentiating between
32 local tributary and Delta freshwater inflow, Delta Outflow and Freshwater Inflow could be
33 considered to effectively act as a single variable. The fourth stressor variable is a Land Use and
34 Land Cover Change variable: Impervious Cover.
35
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1 2.3.1.2. Influence Types and Degrees
2 Agreement
3 The influences upon which participants agreed with respect to type and degree help to
4 establish the nature of those relationships and indicate which are best understood. Table 2-6
5 presents these results for the Sediment Retention group.
6
7 Table 2-6. Sediment Retention group influence judgments; columns A-Z
8 represent individual influences (arrows) in the influence diagram and rows
9 represent individual respondents: dark green = agreement on influence type
10 and degree, light green = agreement on type but not degree, gray = no
11 agreement; within columns, green numbers = same (majority) grouping of
12 type (though degree may be different), pink numbers = disagreement about
13 type, red outline = threshold response
14
15 In some cases, participants gave multiple codes for an arrow. When the multiple codes
16 represented one of the pairing types described above in section 2.2.2.5 (e.g., 2/3), both codes are
17 shown, separated by a "/".
18 If multiple codes that do not fall into a pairing were given, both codes are shown,
19 separated by a symbol indicating the nature of the combination. In the first type of combination,
20 multiple codes with "X" going in the same direction (e.g., X is increasing in both codes) are
21 separated by a "A" symbol; and where these codes conflict and would make a difference in
22 determining agreement, those cells were not counted. In cases where a reason was given for
23 multiple codes (such as when boxes had "lumping" problems and participants specified different
24 codes for different variables within the box), then the code that logically corresponded best to
25 other participants' codes (based on the notes column and other inferences) was used.
26 In the second type of combination, codes with "X" going in different directions (e.g., X is
27 increasing in one code and decreasing in the other) are separated by a "|". Since the response to
28 X can indeed be different depending on whether X is increasing or decreasing, these cells do not
29 represent a conflict but rather the opportunity to consider agreement in both the "X-up" and "X-
30 down" direction. In these cases it was possible to have agreement in one direction but not the
31 other.
32 The columns in Table 2-6 represent individual influences (arrows) in the group influence
33 diagram, and rows represent individual respondents. Dark green shaded columns indicate
34 agreement on both type and degree of influence; light green shaded columns indicate agreement
35 on type but not degree; gray shaded columns indicate no agreement. Within columns, numbers
36 in green are those that fall into the same (majority) grouping in terms of type of influence (even
37 though degree is different), while codes in pink indicate disagreement about type. Columns
38 outlined in red indicate threshold influences where there was either: 1) a change in type of
39 influence in the climate scenarios compared to current conditions (e.g., from a direct to an
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1 inverse relationship), 2) a change in sensitivity (e.g., a change from a proportional to
2 disproportional response, or 3) an indication by multiple participants in their notes or in the
3 group discussions that the influence was likely a threshold relationship of type 1 or 2 above (but
4 for which they did not know in which scenario this would occur). In these cases the type and/or
5 degree of influence for the relationship would depend on a threshold, the exact location of which
6 may be uncertain.
7 Under current conditions, there were 16 influences for which there was agreement on
8 both type and degree of influence. There were seven influences for which there was agreement
9 on type but not degree. There was no agreement for three influences. Relative to the rest of the
10 diagram, the influences from the top row variables, which represent management options, have
11 less agreement.
12 Under Climate Scenario A, there were 17 influences for which there was agreement on
13 both type and degree, which includes all of the same influences as under current conditions plus
14 one additional influence, the feedback from the endpoint to Inundation Regime. There were five
15 relationships for which there was agreement on type but not degree and four relationships had no
16 agreement. The influences for which there was no agreement include two of the same ones as
17 current and two for which there previously was agreement on type but not degree.
18 Under Climate Scenario B, there were 15 influences for which there was agreement on
19 both type and degree. There were six relationships for which there was agreement on type but
20 not degree and five relationships had no agreement. Most of the changes in Climate Scenario B
21 are influences losing agreement on degree or ones that had already changed in Climate Scenario
22 A. Inundation Regime to Wind/Waves settled into agreement on direct disproportional, strong
23 response.
24
25 Thresholds
26 Relationship N (Relative Sea Level on Inundation Regime) and relationship Z (Wind/
27 Waves on Sediment Flux) were identified to be threshold relationships under the climate
28 scenarios. The threshold of relationship N is related to the marsh response to sea level rise, and
29 is tied to the rate of sea level rise. At the point in the inundation regime where the marsh is no
30 longer able to keep up with sea level rise, the marsh elevation will drop, thereafter experiencing a
31 different inundation regime. The threshold of relationship Z occurs where wind-driven waves
32 change from a source of sediment, adding to net vertical accretion, to a net negative impact
33 through erosion of the marsh edge. This occurs because, as water depth increases due to sea
34 level rise, the effect of wave energy on re-suspension of bottom sediment will decrease while its
35 effect on marsh edge erosion will increase. In both of these cases the type or sensitivity of the
36 influence did not change across the scenarios (direct influence with intermediate sensitivity for
37 both), but the influences were indicated by participants to be important threshold relationships
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1 through the discussion. One possible reason why these thresholds identified in the discussion did
2 not show up in the coding as changes in sensitivity is because participants did not know where
3 the threshold would occur, so they did not want to attach that estimate to a particular climate
4 scenario. Alternatively, it may be that there is a threshold that represents a state change that falls
5 within the range of natural variability, so this method was not sensitive enough to identify the
6 threshold. Relative sea level and wind and waves are both closely tied to climate drivers, making
7 relationships driven by them sensitive to climate change.
8
9 2.3.1.3. Influence Sensitivity
10 Figure 2-3 shows the sensitivity results using the influence diagram, indicating where
11 there is agreement under current conditions. The typology described in Section 2.2.2.5 was used
12 to code sensitivity, with an additional differentiation within the "no agreement" category. In all
13 "no agreement" cases, there was a mixture of codes for intermediate sensitivity along with low
14 and/or high sensitivity; if at least four participants provided judgments, and there were more high
15 sensitivity judgments than low sensitivity judgments, then the dashed arrow was colored orange
16 to indicate intermediate-to-high sensitivity. Under current conditions, 19 influences for which
17 there was agreement were categorized as intermediate sensitivity. Three influences were
18 categorized as low sensitivity, two of which originate from the variable Channelization. There
19 were no instances of agreement on influences with high sensitivity. There was no agreement on
20 sensitivity for four influences.
21
22 Figure 2-3. Sediment Retention group summary influence diagram of
23 sensitivities under current conditions.
24
25 Figure 2-4 compares the sensitivities as in Figure 2-3, across the three scenarios. There
26 were no influences for which the sensitivity category changed between scenarios; the only
27 changes were between no agreement and a type of sensitivity. Under Climate Scenario A, all of
28 the same influences as those under current conditions were again categorized as intermediate
29 sensitivity, with the exception of both Freshwater Inflow and Inundation Regime on Net Organic
30 Accumulation, for which there no longer was agreement. However, Inundation Regime on Net
31 Organic Accumulation showed a trend toward increasing sensitivity (orange arrow), as did
32 Inundation Regime on Wind/Waves. The same three influences as under current conditions were
33 categorized as low sensitivity for both Climate Scenario A and Climate Scenario B.
34
35 Figure 2-4. Sediment Retention group summary influence diagrams of
36 sensitivities: variance across current conditions and two climate scenarios.
37
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1 Under Climate Scenario B, one influence which previously had no agreement (but did
2 show an orange trend), Inundation Regime to Wind/Waves, increased in agreement, which
3 resulted in a categorization of high sensitivity. Three additional intermediate sensitivity
4 influences dropped below the standard of agreement: Wind/Waves on Sediment Size, Sediment
5 Size on Net Mineral Accumulation and the feedback from Net Erosion/Accretion on Inundation
6 Regime, such that the number of influences with no agreement on sensitivity increased to eight.
7 The disagreement shows a trend of some participants estimating increasing sensitivity, with
8 several of the influences characterized as a mix of intermediate and high sensitivity (orange
9 arrows) where there had once been agreement on intermediate sensitivity.
10 One reason for lack of agreement on changes in sensitivity across scenarios, as well as
11 lack of agreement within scenarios, may have been the degree of variability among participants
12 in their judgements. Overall, there was more variability among participants than across
13 scenarios for any given participant. There were no patterns across participants, such as
14 characterizing only increasing sensitivity. Further description, as well as figures depicting
15 variability in judgments across participants, can be found in Appendix B.
16
17 2.3.1.4. Jte/ative Impact
18 Figure 2-5Figures 2-5 and 2-6 present the characterizations of relative impact between
19 current and future climate scenarios (the group's discussion did not differentiate between the two
20 future climate scenarios). Six influences were identified as having high relative impact under
21 current conditions. None of these are connected to the management options level within the
22 diagram. We have assumed that these same relationships are still of high impact under the
23 climate scenarios unless otherwise noted in the group discussion on climate change impacts. The
24 influences of Net Organic Accumulation on Net Erosion/Accretion and of Wind/Waves on
25 Sediment Flux were identified as having increasing impacts under the climate scenarios. Two
26 new influences, both on Freshwater Inflow and driven by variables in the management options
27 level, were identified as having high relative impact under the climate scenarios: Reservoir
28 Management and Channelization.
29
30 Figure 2-5. Sediment Retention influences indicated as having high relative
31 impact under current conditions.
32
33 Figure 2-6. Sediment Retention group influences indicated as having high
34 relative impact under climate scenarios.
35
36 2.3.1.5. Confidence
37 The confidence results shown in Table 2-7 are provided for the Sediment Retention
38 influences for which there was agreement on type. The lack of agreement on confidence for
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1 almost half of the judgments is a significant gap, limiting our ability to prioritize around
2 confidence judgments. Eight of the 11 influences for which there was agreement on confidence
3 across all three scenarios were scored as high evidence and high agreement (HH). The
4 influences of Reservoir Management on Sediment Flux and Sediment Size, which were both
5 categorized as HH under current conditions, showed declining confidence under the climate
6 scenarios, with scores of low evidence/high agreement (LH) under Climate Scenarios A and B.
7 Relative Sea Level on Tides, scored as HH under current conditions, also showed declining
8 confidence under the climate scenarios, with a score of low evidence/low agreement (LL) under
9 Climate Scenario A and Climate Scenario B.
10
11 Table 2-7. Sediment Retention group confidence for influences with
12 agreement: NA = No agreement; HH = High evidence, High agreement; HL
13 = High evidence, Low agreement; LH = Low evidence, High agreement; LL =
14 Low evidence, Low agreement
15
16 The confidence results shown in Figure 2-7 total all judgments across all participants.
17 The total number of HH and HL judgments decreased under the climate scenarios compared to
18 current conditions, and the total number of LH and LL judgments increased under the climate
19 scenarios compared to current conditions. The decrease in the total number of HH judgments
20 from current conditions to the climate scenarios and the corresponding increase in the total
21 number of LL judgments show that influences are less well-understood, probably due to less
22 information being available about future climate conditions.
23
24 Figure 2-7. Sediment Retention group confidence results for all influences;
25 HH = High evidence, High agreement; HL = High evidence, Low agreement;
26 LH = Low evidence, High agreement; LL = Low evidence, Low agreement.
27
28 2.3.1.6. Interacting Influences
29 Table 2-8 presents the interactive influences upon which there was agreement for the
30 Sediment Retention group. The interactive influence columns indicate the type of interactive
31 influence and associated number of participants that chose that particular interactive influence
32 type. The confidence columns indicate the confidence judgment and associated number of
33 participants that chose that particular confidence score.
34
35 Table 2-8. Sediment Retention group interactive influences with agreement
36 under current conditions and Climate Scenarios A and B: NA = No
37 agreement; HH = High evidence, High agreement; HL = High evidence, Low
38 agreement; LH = Low evidence, High agreement; LL = Low evidence, Low
39 agreement; () = Number of respondents
40
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1 Under current conditions, there were five interactive influences for which there was
2 agreement among participants in the Sediment Retention group. For each of these interactive
3 influences, Synergy was the type of influence chosen. Among these, there is a cluster of
4 multiple interactions between Inundation Regime, Sediment Flux and Sediment Size on Net
5 Mineral Accumulation. The other two interacting influences identified act on Inundation
6 Regime and Sediment Flux, so they are highly interconnected. There was only agreement on the
7 confidence for two of these interactive influences, both of which were scored as high evidence
8 and high agreement (HH).
9 Under both Climate Scenario A and Climate Scenario B, there was agreement on three of
10 the previous five synergistic interactive influences, with synergy again chosen as the type of
11 interactive influence. Again the same cluster of influences on Net Mineral Accumulation was
12 identified. There was no agreement on confidence for these interactive influences under either of
13 the future climate scenarios.
14 This lack of agreement on interacting influences was primarily due to not having many
15 influences with enough participants characterizing the same interacting influences. Of the 48
16 combinations of influences with interactions characterized by participants, only 10 could be
17 considered for agreement with at least three participants making a judgment; half of those had
18 three participants in agreement.
19
20 2.3.2. Community Interactions
21 2.3.2.1. Group Influence Diagram
22 Figure 2-8 shows the group diagram developed by the Community Interactions group.
23 Variable definitions that were developed by the participants during the construction of the
24 diagram are found in Table 2-9. Figure 2-8 shows a high degree of interconnectivity between
25 variables, especially among those directly influencing the endpoint. These variables are Extent
26 of Mudflat (and, therefore, extent of feeding habitat), Predators and Disturbance, Bed Sediment
27 Characteristics and Quality, Shorebird Prey Community and Landscape Mosaic (i.e., where
28 mudflats sit relative to other foraging and roosting habitats such as salt ponds). Many of the
29 variables encompass complex processes, which combine more than one key variable. Defining a
30 metric specific to such broad variables, including whether they are increasing or decreasing,
31 proved to be challenging. The possibility of differing assumptions about definitions among
32 participants complicates interpretation of the results. The variables indirectly affecting the
33 endpoint are primarily physical ones: Mudflat Bathymetry, Tides and Hydrodynamics, Sediment
34 Resuspension and Deposition, Wind/Waves, Water Quality, Freshwater Inflow and Sediment
35 Supply. The management and stressor variables are broad categories: Water Management,
36 Restoration and Land Use Change.
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1
2 Figure 2-8. Community Interactions group influence diagram.
4 Table 2-9. Community Interactions variable definitions clarified during
5 group discussion
6
7 2.3.2.2. Influence Types and Degrees
8 Agreement
9 Table 2-10 presents the results for the Community Interactions group. As in Table 2-6,
10 the columns in Table 2-10 represent individual influences (arrows) in the group influence
1 1 diagram, and rows represent individual respondents. Dark green shaded columns indicate
12 agreement on both type and degree of influence; light green shaded columns indicate agreement
13 on type but not degree; gray shaded columns indicate no agreement. Within columns, numbers
14 in green are those that fall into the same (majority) grouping in terms of type of influence (even
15 though degree is different), while codes in pink indicate disagreement about type. For further
16 explanation of table details, see section 2.3.1.2.
17
18 Table 2-10. Community Interactions group influence judgments; columns A-
19 KK represent individual influences (arrows) in the influence diagram and
20 rows represent individual respondents: dark green = agreement on influence
21 type and degree, light green = agreement on type but not degree, gray = no
22 agreement; within columns, green numbers = same (majority) grouping of
23 type (though degree may be different), pink numbers = disagreement about
24 type, red outline = threshold response
25
26 The participants agreed on the type and degree of influence for a smaller fraction of the
27 total number of influences than the Sediment Retention group did. Under current conditions,
28 there were 18 influences for which there was agreement on both type and degree. These are
29 spread throughout the diagram, but it is of note that there was agreement on type for all of
30 influences going into the endpoint and of those, there was also agreement on degree for all but
3 1 Bed Sediment Characteristics and Quality. There were five influences where there was
32 agreement on type but not degree of influence. There was no agreement for 13 relationships.
33 Under Climate Scenario A, the number of influences for which there was agreement on
34 both type and degree dropped to 12; for those that changed, they split between changing to
35 agreement on type but not degree and to no agreement. The influence of Tides and
36 Hydrodynamics on Extent of Mudflat went from no agreement to agreement on inverse
37 disproportional, strong response. There were nine relationships for which there was agreement
38 on type but not degree. There were 15 relationships for which there was no agreement.
39 Under Climate Scenario B, there were 1 1 influences for which there was agreement on
40 both type and degree. There were nine relationships for which there was agreement on type but
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1 not degree. There were 16 relationships for which there was no agreement, which includes all of
2 the same influences under Climate Scenario A along with the influence of Mudflat Bathymetry
3 on Extent of Mudflat.
4 Agreement for the type and degree of influence remained consistent across the scenarios
5 for eight relationships. Agreement on type but not degree of influence remained consistent
6 across the scenarios for three relationships. There were 11 relationships for which there was no
7 agreement on type or degree of influence across the scenarios.
8 The larger number of influences for which there was no agreement under all scenarios
9 leaves a gap which makes it difficult to understand the type or degree of influence for these
10 relationships. This is partially due to a higher occurrence of no response given for the
11 Community Interactions group. It is not possible to differentiate between lack of response due to
12 insufficient time and disinclination to answer due to lack of knowledge about the influence.
13
14 Thresholds
15 Four relationships were identified as threshold relationships under the climate scenarios,
16 based on the notes and discussions. These were: Freshwater Inflow on Tides and
17 Hydrodynamics (Relationship K); Water Quality on Shorebird Prey Community (Relationship
18 S); Bed Sediment Characteristics and Quality on Shorebird Prey Community (Relationship BB);
19 and Extent of Mudflat on Shorebirds (Relationship DD). Relationship K was characterized as a
20 direct influence of uncertain degree; coding for degree was a mixture of weak, proportional and
21 strong influences with a slight trend toward increasingly strong influences through time. Some
22 participants indicated that winter increases in freshwater flow will be very important as mudflats
23 reach a threshold of becoming subtidal; this would be especially true during high tides, where
24 flows could push a system above a threshold and create a large impact on inundation height.
25 Relationship S was characterized as direct proportional across the three scenarios. The
26 water quality aspect emphasized by the participants as a threshold was dissolved oxygen (DO).
27 They noted that small decreases in DO could have a large negative effect on mudflat prey
28 populations as a threshold is reached.
29 Relationship BB was direct proportional under current conditions, but there was no
30 agreement on degree under the climate scenarios. One participant indicated a change to a
31 disproportionately strong response through coding, but other participants did not change their
32 coding or left some blank cells, such that there was no majority agreement on degree under the
33 climate scenarios. However, participants' notes indicated that as habitat becomes more limited,
34 even small areas of poor habitat will have large effects on shorebirds. For this example as well
35 as the previous two threshold influences above, the participants chose to indicate the thresholds
36 through notes (rather than through coding) because they were not sure when (i.e., under which
37 climate scenario) the threshold was most likely to be reached.
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1 Relationship DD was unique in being the only threshold influence that was identified
2 clearly through the coding exercise. Under current conditions the relationship was considered
3 direct proportional. Under Climate Scenario A there was agreement that it was still a direct
4 relationship, but there was no agreement on degree because there was a mixture of proportionate
5 and disproportionately strong codes. Under Climate Scenario B the conversion to agreement on
6 a direct disproportionately strong relationship was complete. This reflects the opinion of the
7 participants that as access to foraging habitat on mudflats becomes limiting due to sea level rise
8 and other factors, the effect on shorebird populations will become more extreme.
9
10 2.3.2.3. Influence Sensitivity
11 Figure 2-9 shows the sensitivity results using the influence diagram, indicating where
12 there is agreement under current conditions. The typology described in Section 2.2.2.5 was used
13 to code sensitivity, with an additional differentiation within the "no agreement" category. In all
14 "no agreement" cases, there was a mixture of codes for intermediate sensitivity along with low
15 and/or high sensitivity; if at least four participants provided judgments, and there were more high
16 sensitivity judgments than low sensitivity judgments, then the dashed arrow was colored orange
17 to indicate intermediate-to-high sensitivity. Under current conditions, 19 influences for which
18 there was agreement were categorized as intermediate sensitivity. Five influences were
19 categorized as high sensitivity: both the influence of Restoration and of Land Use Change on
20 Landscape Mosaic, Landscape Mosaic on the endpoint, and both the influence of Tides and
21 Hydrodynamics and of Mudflat Bathymetry on Extent of Mudflat. There was no agreement on
22 sensitivity for 12 influences. There were no instances of agreement on influences with low
23 sensitivity.
24
25 Figure 2-9. Community Interactions group summary influence diagram of
26 sensitivities under current conditions.
27
28 Figure 2-10 compares the sensitivities as in Figure 2-9, across the three scenarios. Under
29 Climate Scenario A, 10 influences for which there was agreement were categorized as
30 intermediate sensitivity. Five influences were categorized as high sensitivity: four of the same as
31 under current conditions, with a change the influence of Land Use Change on Landscape Mosaic
32 to no agreement and new agreement for the influence of Predators and Disturbance on the
33 endpoint. The number of influences with no agreement increased substantially to 21. Seven of
34 those are in disagreement because there is a combination of intermediate and high sensitivity
35 (orange arrows). This decrease in agreement reflects a trend of increasing sensitivity for some
36 participants, but not enough to shift to a new category. It could be indicative of either
37 disagreement about at what point such a shift would occur or differing assumptions about what
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1 falls outside the current range of variability, which was left up to each participant to decide based
2 on their own knowledge and intuition.
3
4 Figure 2-10. Community Interactions group summary influence diagrams of
5 sensitivities: variance across current conditions and two climate scenarios.
6
7 Under Climate Scenario B, seven influences for which there was agreement were
8 categorized as intermediate sensitivity. Six influences were categorized as high sensitivity, with
9 the addition of Extent of Mudflat on the endpoint, which had been intermediate under current
10 conditions. This is another way to identify a threshold, when there is a change in sensitivity to a
11 more sensitive category. The number of influences with no agreement increased again to 23;
12 however, for six of these the lack of agreement was due to a mixture of intermediate and high
13 sensitivity codes (orange arrows).
14 As with the Sediment Retention group, there was more variability in judgments among
15 participants than across scenarios for any given participant. The majority of changes in
16 sensitivity across the climate scenarios are of increasing sensitivity. Further description, as well
17 as figures depicting variability in judgments across participants, can be found in Appendix B.
18
19 2.3.2.4. Jte/ativeImpact
20 Figure 2-11 and Figure 2-12 present the characterization of relative impact between
21 current and future climate scenarios (the group's discussion did not differentiate between the two
22 future climate scenarios). Relative impact was distinguished among the influences by indicating
23 primary, secondary or tertiary levels of relative impact. For the Community Interactions group,
24 the relative impacts of five influences were indicated as important under current conditions,
25 based on the discussion. The influences of Landscape Mosaic and of Extent of Mudflat on the
26 endpoint were both identified as having primary impacts. The influences of Predators and
27 Disturbance and of Shorebird Prey Community on the endpoint were identified as having
28 secondary impacts, and the influence of Bed Sediment Characteristics and Quality on the
29 endpoint was identified as having tertiary impact.
30
31 Figure 2-11. Community Interactions influences indicated as having high
32 relative impact under current conditions.
33
34 Figure 2-12. Community Interactions group influences indicated as having
35 high re/ative impact under climate scenarios.
36
37 A total of 10 influences were indicated as having high relative impact under climate
38 change conditions for the Community Interactions group (Figure 2-12). Three of the influences
39 indicated as having high relative impact under current conditions increased in relative impact
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1 when considering future climate conditions: the influences of Landscape Mosaic, Predators and
2 Disturbance, and of Shorebird Prey Community, each on the endpoint. The relative impact of
3 Extent of Mudflat on the endpoint stayed equally important. Five additional influences were
4 indicated as having high relative impact under the climate change scenarios. In addition, the
5 influence of disease on the endpoint was identified as an influence of emerging impact. Disease
6 was not an original key variable in the influence diagram, as variables were included based on
7 importance under current conditions. This influence was not scored, but was considered to be
8 important by the participants. The influence of Bed Sediment Characteristics on the endpoint
9 was indicated as having high relative impact under current conditions but not under the climate
10 scenarios. It is unclear whether this was intentional or was just not covered in the discussion of
11 relative impact under future climate conditions.
12
13 2.3.2.5. Confidence
14 The confidence results shown in Table 2-11 are provided for the Community Interactions
15 influences for which there was agreement on type. The lack of agreement on confidence for
16 two-thirds of the judgments is a major gap, limiting our ability to prioritize around confidence
17 judgments. Five of the six influences that for which there was agreement on confidence across
18 all scenarios were scored as high evidence and high agreement (HH). The influence of
19 Freshwater Inflow on Net Organic Accumulation was scored as low evidence high agreement
20 (LH) across all scenarios. The HH type of confidence was the most used type of judgment. The
21 dominant pattern on confidence across the climate scenarios was a decrease in the number of
22 influences on which there was agreement.
23
24 Table 2-11. Community Interactions group confidence for influences with
25 agreement: NA = No agreement; HH = High evidence, High agreement; HL
26 = High evidence, Low agreement; LH = Low evidence, High agreement; LL =
27 Low evidence, Low agreement
28
29 The confidence results shown in Figure 2-13 total all judgments across all participants.
30 In total, confidence decreased from current conditions to the climate scenarios, with a decrease in
31 the total number of HH and an increase in LL. However, a larger increase was in the total
32 number of no answer given, and the decreases in HL and LH are difficult to explain in total.
33
34 Figure 2-13. Community Interactions group confidence results for all
35 influences; HH = High evidence, High agreement; HL = High evidence, Low
36 agreement; LH = Low evidence, High agreement; LL = Low evidence, Low
37 agreement.
38
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1 2.3.2.6. Interacting Influences
2 Under current conditions, there were no interactive influences for which there was
3 agreement among participants in the Community Interactions group. Likewise, under both
4 climate scenarios there were no interactive influences for which there was agreement on the type
5 of interactive influence. This lack of agreement was primarily due to not having many
6 influences with enough participants characterizing the same interacting influences. Of the 24
7 combinations of influences with interactions characterized by participants, only four had at least
8 three participants make any kind of judgment, which was the threshold for agreement, but those
9 were not ever in agreement on a type of interaction.
10
11 2.4. DISCUSSION OF ADAPTATION STRATEGIES
12 With background on strategic priorities provided by SFEP, the workshop participants
13 discussed the implications of the exercise results for management. Table 2-12 lists adaptation
14 strategies that emerged during the group discussions. The experts discussed a variety of general
15 adaptation strategies as well as some specific adaptation activities that would be responsive to
16 key potential climate-related changes identified through their judgments. The strategies fall into
17 several broad categories, including Restoration & Conservation, Sediment Management and
18 Planning & Monitoring.
19
20 Table 2-12. Adaptation strategies and associated top pathways for
21 management (see section 3.2 for pathways). SG=Sediment Retention Green
22 pathway; SB=Sediment Retention Blue pathway; SP=Sediment Retention
23 Purple pathway; CG=Community Interactions Green pathway;
24 CB=Community Interactions Blue pathway; CP=Community Interactions
25 Purple pathway.
26
27 2.4.1. Restoration & Conservation
28 Restoration was identified as a powerful management tool with a variety of specific
29 planning and prioritization considerations. The experts emphasized the urgency of implementing
30 restoration projects as an immediate priority, taking climate change impacts into consideration in
31 planning. For marsh restoration, the key is getting started early enough so that marshes can be
32 established before rates of sea level rise become too high. As a restored marsh matures, it is
33 better able to keep pace vertically through vegetative production. Similarly for other types of
34 restored habitats, they will be more resilient to changing climate conditions as they mature. The
35 other temporal issue for restoration planning was the need to plan for ecological succession,
36 building a dynamic landscape mosaic that includes habitats that will thrive under future climate
37 conditions.
38 Similar and related considerations apply to conservation strategies. One consideration for
39 conservation could be habitats that are well suited to future climate conditions. Though some
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1 habitats may not survive climate change (e.g., where there are not long term opportunities for
2 migration), it may still be important to preserve and restore these habitats in the short and
3 medium term as interim habitats until alternate habitats that serve similar ecosystem functions
4 have been established.
5 Available space for habitat migration is a major consideration for both restoration and
6 conservation. On the conservation side, adjacent transitional uplands should be maintained to
7 allow for local habitat migration. Policy options may include regulation or incentives to
8 encourage relocation and to discourage development on lands where there is potential for
9 upslope habitat migration or restoration. The slope of the adjacent uplands is an important factor
10 in such conservation priorities, and the need for improved vertical data as a mapping priority was
11 highlighted in the workshop discussion. Such mapping would help to identify upland areas for
12 restoration adjacent to current healthy marshes, where migration of marshes is possible. The
13 experts especially emphasized the need to identify and prioritize wetland areas for restoration
14 where the adjacent uplands currently include complementary habitats that would contribute to a
15 complete landscape mosaic that could support valued species such as shorebirds that will be
16 stressed by climate change. Another key part of developing and conserving such landscape
17 mosaics is providing for connectivity between multiple habitats. Also underscored was the need
18 to focus restoration efforts in the North Bay because the shoreline of the South Bay is so
19 developed that it precludes the ability of marshes to migrate upland. Finally, many restoration
20 efforts to date have involved fringing marshes. The focus of restoration could be expanded
21 beyond fringing marshes to larger areas where there is available space for multiple habitats.
22 A major spatial planning consideration for restoration is the need to consider the impacts
23 of each project on adjacent and downstream habitats and future restoration projects. In
24 particular, any project that broaches levees in Suisun Marsh will impact downstream sediment
25 budgets and adjacent hydrodynamics. Restoration projects could be coordinated so that projects
26 can be planned and timed to maximize success. Deciding when and where (e.g., how far
27 upstream) to focus restoration of different marsh types will also depend on changes in the
28 salinity gradient; conditions that suit freshwater and brackish marshes will be moving upstream
29 through time under climate change, unless maintaining marsh salinity becomes a priority for
30 Delta freshwater storage policies.
31
32 2.4.2. Sediment Management
33 Sediment management is already a priority within the region and will continue to be an
34 important focus for marsh management in the context of sea level rise and changing precipitation
35 patterns. There is an expectation that sediment supply from the Sacramento Valley will continue
36 to decline due to the cessation of hydraulic mining, but currently it remains elevated above levels
37 prior to the 19th century. It will become increasingly important to support movement of
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1 inorganic sediment into restoration sites in the near term, so that salt marshes can build to
2 threshold elevations for vegetation establishment and begin contributing organic sediment to
3 maintain themselves.
4 Changes in sediment supply will require local tributaries and Delta sources to be
5 managed differently. On the tributary side, there are opportunities to reconnect streams to
6 wetlands through flood control districts. An example of a specific option for increasing
7 inorganic sediment loads from local tributaries would be to for regional water boards to consider
8 adjusting sediment TMDLs (Total Maximum Daily Loads) to allow for increases in coarse
9 sediment loads in streams that do not support salmonids. On the Delta side, Integrated Water
10 Management will be increasingly important for maintaining sediment supply, and may prompt a
11 switch in priority from storage to conservation. In the Bay, dredge sediment reuse is an
12 opportunity to redistribute sediment to desired locations. Limiting factors on current use of this
13 technique were discussed by the workshop participants, including the need for best management
14 practices and funding.
15 While most of the discussion on sediment was about keeping pace vertically with sea
16 level rise, horizontal impacts through marsh edge erosion were also discussed. There is a need to
17 develop ways to reduce wave action on the front sides of marshes. Protecting adjacent mudflats,
18 such as with berms, is one specific option.
19
20 2.4.3. Planning & Monitoring
21 The final category of adaptation strategies discussed at the workshop addressed planning
22 and monitoring. Many of the above recommendations are based on planning, including
23 prioritizing. The need to develop rapid response plans for catastrophes or contingency plans for
24 when thresholds are passed was emphasized. Preparing the political and funding conditions
25 necessary to implement such plans would be essential.
26 Monitoring will become increasingly important in order to detect when thresholds are
27 being crossed. The scales at which monitoring is focused will have to be adapted to changes in
28 restoration priorities. Monitoring at the landscape scale — especially for birds and other mobile
29 species that use multiple habitat types - will be a necessity in order to track potential thresholds.
30 This will likely require coordination among multiple agencies since many habitats and habitat
31 mosaics span jurisdictional boundaries. The current condition and extent of these habitats needs
32 to be monitored and understood now, as the current data are insufficient for a baseline at the
33 landscape level. Examining habitats at a larger scale will also be important for facilitating
34 species movements. It may become necessary to ensure that birds can move among ponds, tidal
35 marshes, and mudflats as conditions change. Some species (e.g., clapper rail, salt marsh harvest
36 mouse) may not be able to migrate from degrading areas on their own and may require
37 intervention.
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1 The discussion of adaptation strategies described above was broad and free-ranging. The
2 next section will combine the analysis of the exercise results with the ideas in Table 2-12 to
3 discuss top pathways for management given climate change and identify specific adaptation
4 options in response.
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1 3. MAKING THE LINK TO MANAGEMENT
2
3 As detailed above, the workshop resulted in a large volume of information on the
4 sensitivities of the sediment retention and community interactions processes to stressor
5 interactions under current conditions and future climate scenarios. The next step lies in
6 organizing this information into a form which managers can use to identify influences of
7 particular importance upon which to focus management interventions and adaptation planning.
8
9 3.1. USING INFORMATION ON INFLUENCE TYPE & DEGREE, SENSITIVITY
10 AND RELATIVE IMPACT TO IDENTIFY KEY MANAGEMENT PATHWAYS
11 In the workshop exercise and group discussions, the experts generated three categories of
12 information about the relationships in the influence diagrams: 1) the type and degree of each
13 influence; 2) the sensitivity of each influence (including thresholds); and 3) the high relative
14 impact of certain influences on the endpoints. All three categories of information should be
15 considered in concert when interpreting management implications. This can be done by
16 performing a "crosswalk" of all three categories of information in order to identify pathways of
17 particular interest that connect each endpoint (Net Accretion/Erosion or Shorebirds) to stressors
18 or drivers that can be addressed through particular management activities. The crosswalks as
19 well as example pathways are presented below.
20
21 3.1.1. Crosswalks: Influence Type & Degree, Sensitivity and Relative Impact
22 The crosswalks for Sediment Retention and Community Interactions are presented in
23 Table 3-1 and Table 3-2. For each influence, information on type and degree, sensitivity, and
24 relative impact is listed side-by-side, first for current conditions, followed by Climate Scenarios
25 A and B. This allows for easy comparison of all three categories of information, across all three
26 scenarios. The influences have also been rank-ordered based on the amount of information
27 available for each in terms of agreement on influence type, degree, sensitivity, relative impact
28 and threshold potential.
29
30 3.1.1.1. Sediment Retention Crosswalk
31
32 Table 3-1. Sediment Retention group crosswalk for comparison of influence
33 type and degree, sensitivity and relative impact for current conditions and
34 climate scenarios. NA = No agreement; Prop = Proportional; Disprop =
35 Disproportional; L = Low sensitivity; I = Intermediate sensitivity; H = High
36 sensitivity; H-trend = No agreement but trending toward high sensitivity; X
37 = High relative impact; t = Increasing relative impact from current; () =
38 Number of respondents; Ranking column orders the influences according to
39 completeness of information
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1
2
3 For Sediment Retention (Table 3-1), there was agreement on both type and sensitivity for
4 the majority of influences. Especially when coupled with the designation of high relative impact,
5 certain influences emerge as being of special interest for management. These are influences for
6 which we have a good understanding of the nature of the relationships, their sensitivity to
7 changes now and in the future, and their high relative impact on the endpoint of Net
8 Accretion/Erosion. Therefore these are the influences for which management interventions are
9 most likely to have the intended effects. Influences ranked one through three in Table 3-1 fall
10 into this category.
11 Even when not designated as highest relative impact, influences for which there was
12 agreement on type as well as sensitivity are equally important to consider. While not necessarily
13 of highest relative impact, they are well understood and sensitive to change, and may be linked
14 with other influences for important cumulative effects on the endpoint. Influences of rank four
15 and five, and also influence G, fall into this category (Table 3-1). Meanwhile, lack of agreement
16 on one or more of the type and sensitivity categories indicates that more information is needed to
17 understand the particular influence. It does not imply that the relationship is not potentially
18 important, but rather that it is not well enough understood by this particular group of experts for
19 managers to be confident about the response to either climate change or management
20 interventions. The remaining nine influences fall into this group. Relationship E (Water
21 Resource Management: Channelization on Freshwater Inflow) and Relationship T (Inundation
22 Regime on Net Organic Accumulation) are interesting cases in that there was no agreement on
23 influence type or sensitivity, but there was agreement on high relative impact. These influences
24 were identified as having high or increasingly-high relative impact under the climate scenarios.
25 This indicates that the influences are not well understood, yet are considered by the experts to
26 have a high relative impact on the ecosystem process endpoint of Net Accretion/Erosion. In the
27 case of these influences as well as the remaining influences in this group, priorities for further
28 investigation (in the form of literature reviews to more deeply assess existing information,
29 followed by new research where understanding is confirmed to be lacking) could be based in part
30 on which of these influences are most critical to understand since they have a high relative
31 impact or have links to other influences of special importance to the endpoint.
32
33 3.1.1.2. Community Interactions Crosswalk
34
35 Table 3-2. Community Interactions group crosswalk for comparison of
36 influence type and degree, sensitivity and relative impact for current
37 conditions and climate scenarios. NA = No agreement; Prop = Proportional;
38 Disprop = Disproportional; L = Low sensitivity; I = Intermediate sensitivity;
39 H = High sensitivity; H-trend = No agreement but trending toward high
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1 sensitivity; T = Increasing relative impact from current; () = Number of
2 respondents; Ranking column orders the influences according to
3 completeness of information
4
5 The Community Interactions crosswalk (Table 3-2) also has some influences for which
6 there was agreement on both type and sensitivity. Especially when coupled with the designation
7 of high (or increasing) relative impact across the scenarios, these influences emerge as being of
8 special interest for management. These are influences for which we have a good understanding
9 of the nature of the relationships, their sensitivity to changes now and in the future, and their
10 high relative impact on the Shorebirds endpoint. Therefore these are the influences for which
11 management interventions are most likely to have the intended effects. These influences include
12 Relationships O, GG, Q, DD, EE, FF, and E.
13 Even when not designated as highest relative impact, influences for which there was
14 agreement on type as well as sensitivity are equally important to consider. While not necessarily
15 of highest relative impact, they are well understood and sensitive to change, and may be linked
16 with other influences for important cumulative effects on the endpoint. Influences for which
17 there was agreement on both type and degree across at least two of the three scenarios include
18 Relationships S, U, Y, HH, I, X and JJ.
19 Meanwhile, lack of agreement on one or more of the type and sensitivity categories
20 indicates that more information is needed on the particular influence. Again, it does not imply
21 that the relationship is not potentially important, but rather that it is not well enough understood
22 by this particular group of experts such that more information is needed. The remaining
23 influences all lacked agreement in type and/or sensitivity for at least two of the three scenarios.
24 Relationship K (Freshwater Inflow on Tides and Hydrodynamics) and Relationship AA (Extent
25 of Mudflat on Predators and Disturbance) are cases for which there was no agreement on
26 sensitivity across the climate scenarios, but there was agreement on high relative impact. These
27 influences were identified as having increasingly-high relative impact under the climate
28 scenarios (with Relationship K being designated a threshold response). This indicates that
29 although the influences are not fully understood, they are considered by the experts to have a
30 high relative impact on the Shorebirds endpoint. In the case of influences in this group, priorities
31 for further investigation (through literature reviews and further research where needed) could be
32 based in part on which of these influences are most critical to understand since they have a high
33 relative impact or have links to other influences of special importance to the endpoint.
34 It is notable that the community interactions influence diagram had a larger proportion of
35 influences that were not well understood compared to the sediment retention group. There were
36 eight influences for which there was no agreement on any of the three categories of information,
37 for any of the three scenarios. The larger number of influences in the Community Interactions
38 diagram (36 compared to 26 for Sediment Retention) reflects the complexity of modeling both
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1 physical (e.g., sediment supply) and biological (e.g., shorebirds and their prey) components of
2 the community interactions process, and may have contributed to less agreement among the
3 participants, especially across scenarios.
4
5 3.1.1.3. Information Gaps
6 Crosswalks
1 Patterns of information gaps in the crosswalk tables varied for Sediment Retention (Table
8 3-1) compared to Community Interactions (Table 3-2). Influences for Sediment Retention were
9 relatively well understood across type and degree and sensitivity categories of information.
10 However, in quite a few cases, even though there was agreement on type there was not
11 agreement on degree. Where there was agreement under current conditions, the agreement
12 tended to carry across the climate scenarios as well. For Community Interactions, there was far
13 less agreement overall about the nature of the influences, with a greater number of gaps in
14 influence type, degree and sensitivity. Also, compared to Sediment Retention there were more
15 cases where agreement that was present under current conditions was lost under the climate
16 scenarios, indicating a greater uncertainty about how the influences might behave in the future.
17 Such information gaps - especially involving influences in otherwise well-understood
18 pathways that link to rich opportunities for management - could be used to prioritize targeted
19 literature reviews and/or scientific research that focuses on key process components of interest.
20 Another method for sorting through and prioritizing "non-agreement" influences for further
21 study might be to start from the perspective of management opportunities. Managers could look
22 at their most tractable and effective management levers currently available, and trace pathways
23 from those down to the endpoint of interest, as a means of identifying and selecting priority
24 influences for research. Examples of promising pathways are presented below.
25
26 Confidence
27 Confidence estimates were not included in the crosswalk tables because of extensive
28 information gaps in the form of missing estimates. It is possible that this was partly due to time
29 limitations as participants prioritized characterizing the influences before marking confidence.
30 However, another problem that may have led to gaps was that the confidence exercise did
31 not take into account specialty areas of participant knowledge. Due to the complex and
32 interdisciplinary nature of the influence diagrams and the individual specialties of the
33 participants, some participants may have been asked to make judgments on influences for which
34 they felt they had insufficient expertise. In some cases they may have elected to leave those cells
35 blank.
36 Even where confidence estimates were entered, there is cause for caution in interpreting
37 the information. Discussions during and after the exercise revealed some confusion about the
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1 definitions of evidence and agreement (as per the confidence handout in Appendix D). In
2 particular, there may have been a misunderstanding related to equating agreement alone (even
3 where there was minimal evidence) with full confidence; that is, a large amount of agreement,
4 even where little information (data) was available, may have been misconstrued as highest (HH)
5 confidence in some cases.
6 Thus, the large number of missing cells for confidence could have been due to one or
7 more of the following: 1) lack of time; 2) inability to judge confidence in certain influences due
8 to lack of expertise; and 3) confusion about the confidence definitions and coding scheme.
9 These problems could be corrected in subsequent workshops through pre-workshop trainings to
10 clarify the coding scheme, provision of a code to allow participants to indicate lack of expertise
11 as a reason for leaving a cell blank, and additional time to complete the exercise.
12
13 3.1.2. Identifying Key Pathways for Management
14 Using the crosswalk tables (Tables 3-1 and 3-2), it is possible to identify influences that
15 are well understood, become more sensitive, and have a greater relative impact under future
16 climate scenarios. By combining a series of such influences into a pathway to the endpoint, we
17 can begin to identify key responses and changes in variables of interest to management. A
18 "pathway" is defined as a series of connected variables and their influences, beginning with a
19 driver or stressor variable and ending at the endpoint. The purpose is to be able to apply
20 management interventions in order to impact the endpoint. "Management levers" are those
21 variables for which it is possible to intervene with management options; the clearest connections
22 to management options are for the top level variables that are drivers or stressors. When
23 multiple management levers are available for a pathway, the one that was more completely
24 characterized or that had potential changes under the climate scenarios was selected. Two
25 example pathways are discussed below, one for Sediment Retention and one for Community
26 Interactions, to show the process by which these types of pathways can be identified. These will
27 be followed in the next chapter by summary diagrams showing the top three pathways for each
28 process, along with discussion of specific management options.
29
30 3.1.2.1. Sediment^Retention Example
31 The pathway of Reservoir Management to Freshwater Inflow (Relationship B) to Net
32 Organic Accumulation (Relationship O) to the endpoint (Relationship U) is a relatively direct
33 route to the endpoint of Net Accretion/Erosion (Figure 3-1). For type and degree of influence,
34 Relationship B was characterized as being an inverse proportional influence under all scenarios.
35 For sensitivity, Relationship B was characterized as having intermediate sensitivity under all
36 scenarios. In terms of relative impact, Relationship B was indicated as an influence with
37 increasing impact under the climate change scenarios.
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1
2 Figure 3-1. Sediment Retention Example Pathway. Future = Climate
3 Scenario B.
4
5 Relationship O had less agreement. For type and degree of influence, Relationship O was
6 characterized as being a direct influence under Climate Scenario A (not shown in Figure 3-1),
7 but there was no agreement on type and degree under current conditions or Climate Scenario B.
8 For sensitivity, Relationship O was characterized as having intermediate sensitivity under current
9 conditions, but there was no agreement under the climate scenarios. In terms of relative impact,
10 Relationship O was indicated as having high impact under both current and future scenarios. An
11 area for further investigation would be the source of disagreement on the influence type for
12 current conditions versus Climate Scenario B. Is there a potential for a threshold since multiple
13 participants changed their characterization of type between each scenario? Or was the
14 disagreement based on differences in definitions between participants, as the influence could act
15 differently based on considering a change in timing or volume of flow? In order to use Reservoir
16 Management to impact Net Accretion, it will be necessary to understand the nature of the
17 influence of Freshwater Inflow.
18 Relationship U was characterized as a direct proportional influence under all scenarios. It
19 has intermediate sensitivity under all scenarios. In terms of relative impact, it was identified as
20 having high impact under current conditions and increasing impact under the climate scenarios.
21 When examined using all three categories of information, this is a relatively well
22 understood pathway for which there was agreement on type for two of the three influences.
23 Where there is agreement on sensitivity along this pathway, the influences are characterized as
24 intermediate sensitivity, which indicates that they would be responsive to management actions.
25 Each influence has high relative impact, with both Relationships B and U having increased
26 impact under the climate change scenarios. To manage along this pathway, an increased
27 understanding of Relationship O would be important. Understanding the specifics of how the
28 timing or volume of freshwater inflow influences the aboveground and belowground organic
29 processes that lead to net accumulation would complete the pathway so managers can utilize
30 reservoir management to increase net accretion.
31
32 3.1.2.2. Community Interactions Example
33 The pathway of Restoration on Landscape Mosaic (Relationship E) to the Shorebirds
34 endpoint (Relationship O) is well understood and highly sensitive, and its relative impact will
35 increase in the future (Figure 3-2). The management lever of Restoration is a broad category that
36 constitutes a number of management options. Relationship E was characterized as a direct
37 influence under all scenarios, but there was no agreement on degree of influence. Relationship E
38 was characterized as having high sensitivity under all scenarios. In terms of relative impact,
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1 Relationship E was indicated as an influence with increasing relative impact under the climate
2 change scenarios.
3
4 Figure 3-2. Community Interactions Example Pathway. Future = Climate
5 Scenario B.
6
7 Relationship O was characterized as a direct, disproportional strong influence under all
8 scenarios. For sensitivity, Relationship O was characterized as having high sensitivity under all
9 scenarios. The relative impact of Relationship O was indicated as primary under current
10 conditions, with increasing relative impact under the climate change scenarios.
11 When examined using all three categories of information, the Restoring the Landscape
12 Mosaic pathway stands out as well understood and likely to be responsive to management
13 options when planning for climate change. Restoration and Landscape Mosaic are broad
14 variables, so a wide variety of accompanying management options fall within this pathway. One
15 management option could be to restore salt marshes adjacent to a mudflat of concern, which
16 would provide a sediment source for the mudflat as well as secondary habitat for the shorebirds.
17
18 3.2. TOP PATHWAYS AND IMPLICATIONS FOR ADAPTATION PLANNING
19 Sections 3.1.2.1 and 3.1.2.2 above have used examples to demonstrate how the results of
20 the expert elicitation exercise can be used to help identify key pathways for management. This
21 method of identifying well-understood pathways that can be traced from endpoints of concern to
22 management levers is a useful way to explore the implications of the workshop results for
23 adaptation planning. In some cases it may be possible to identify management actions for
24 immediate implementation, i.e., where there is sufficient understanding of the relationships
25 among the variables as well as their sensitivities to act with relative confidence in the effects of
26 management interventions. Additional pathways of interest can be identified through further
27 examination of the crosswalk tables (Tables 3-1 and 3-2), using amount of information for which
28 there was agreement (to identify current best-understood influences) as well instances of climate
29 thresholds (indicating potential climate-induced shifts) to identify "top pathways" of interest for
30 management. This section describes three top pathways for the Sediment Retention and
31 Community Interactions processes, as well potential adaptation responses. This is followed by a
32 brief review of SFEP planning documents and discussion of where adaptation activities relating
33 to the top pathways could be linked into these existing plans and strategies.
34
35 3.2.1. Top Pathways and Associated Adaptation Options
36 Three top pathways for each process are presented in Figure 3-3 (Sediment Retention)
37 and Figure 3-4 (Community Interactions). For ease of viewing, each pathway is highlighted by a
38 color (green, purple or blue), and influences that undergo changes under the climate scenarios are
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1 highlighted with red boxes indicating the nature of the change. Dashed lines indicate
2 inconsistent agreement among participants under at least one scenario. The order in which the
3 pathways are presented below is not an indication of order of importance. These are all
4 management pathways with notable potential for addressing the climate sensitivities identified.
5
6 Figure 3-3. Top pathways for management of the Net Accretion/Erosion
7 endpoint. Blue, green and purple colors are used to distinguish different
8 pathways. Red boxes highlight changes under future climate conditions. *
9 indicates high relative impact under current conditions. A indicates
10 increasing relative impact under future conditions. A direct to inverse
11 threshold occurs where there is a direct effect under current conditions that
12 may shift to an inverse effect under future climate conditions. Dashed lines
13 indicate inconsistent agreement across scenarios.
14
15 Figure 3-4. Top pathways for management of the Shorebirds endpoint.
16 Blue, green and purple colors are used to distinguish different pathways.
17 Red boxes highlight changes under future climate conditions. 1° and 2°
18 indicate primary and secondary relative impact under current conditions,
19 respectively. A indicates increasing relative impact under future conditions.
20 A direct to strong direct threshold occurs where there is a direct effect under
21 current conditions that may shift to a very strong direct effect under future
22 climate conditions. Dashed lines indicate inconsistent agreement across
23 scenarios.
24
25 3.2.1.1. SedimentItetention Tap Pathways
26 Green pathway
27 The Sediment Retention example pathway described in section 3.1.2.1 above is
28 elaborated upon here as the Green top pathway (Figure 3-3). Starting with the Net
29 Accretion/Erosion endpoint and working "up" the diagram, a major determinant of the net
30 balance between accretion and erosion is the contribution of organic accumulation by way of
31 below ground biomass production. Organic accumulation has a higher relative impact on the
32 endpoint than mineral accumulation, and this relative impact is expected to increase under
33 climate change. This is because as marshes are challenged to adjust to sea level rise, vegetative
34 processes can respond by increasing below ground biomass productivity, and this may become
35 increasingly important in the context of historical (and continuing) decreases in mineral sediment
36 supplies.
37 At the next level up the pathway, net organic accumulation is directly affected by
38 freshwater inflow due to salinity effects. As freshwater flows decrease, salinity increases,
39 favoring more salt tolerant, but less productive plants in the community. Agreement on the
40 nature of this relationship was not consistent across the climate scenarios, with some participants
41 indicating the potential for an inverse relationship depending on whether or not they were
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1 considering changes in species composition. If invasive Spartina alterniflora, which is favored
2 and has higher productivity than native species under higher salinity regimes, is allowed to
3 become established, then the influence of freshwater inflow could be an inverse relationship in
4 which increased salinity favored vegetative production. However, for the purposes of this
5 discussion we are assuming that the goal is to preserve the native salt marsh habitat; hence we
6 are considering management of the direct relationship.
7 Finally, freshwater inflow is acutely affected by reservoir management (see top level of
8 pathway). Here, reservoir management refers to the practice of storing and diverting freshwater
9 supplies for flood control, agriculture and other human uses. Thus, an increase in reservoir
10 management typically results in an overall decrease in availability of freshwater flows to salt
11 marshes. This inverse effect on the volume, speed and seasonal availability of freshwater flows
12 is expected to be of increasingly high relative impact under climate change as freshwater
13 supplies become increasingly variable in a context of increasing human demand.
14 The management implications for adaptation given the relationships within this pathway
15 are that managing reservoirs for downstream salinity regulation in favor of native marsh
16 vegetative productivity will require more steady, lower-volume releases. The strategy for such
17 releases relative to the growing season requires further investigation of the effects of timing
18 versus volume of freshwater inflow on net organic accumulation, on a site-specific basis. Given
19 the high relative impact of net organic accumulation compared to net mineral accumulation, a
20 priority could be placed on releases designed for salinity maintenance compared to high volume
21 pulses to support mineral sediment transport (discussed in the Purple pathway below). In
22 addition, since increased salinity regimes favor invasive Spartina, if the goal is to preserve native
23 salt marsh, another important management consideration will be the need to continue or even
24 "step up" invasive species eradication programs.
25
26 Purple pathway
27 Starting with the Net Accretion/Erosion endpoint and working up the Purple pathway
28 (Figure 3-3), a second major determinant of the balance between accretion and erosion is the
29 contribution of mineral accumulation. Net mineral accumulation has a direct effect - in
30 conjunction with organic accumulation - on net accretion.
31 The next level up the Purple pathway represents the effect on mineral accumulation of
32 sediment size. This is another direct relationship, with larger grain sizes favoring mineral
33 accumulation, since larger grains deposit more readily, are harder to re-suspend and provide
34 larger building blocks for accretion. An important effect of future climate change may be an
35 increasing sensitivity of net mineral accumulation to sediment size. This increase is partly
36 because sediment flux, the other determinant of net mineral accumulation, is expected to
37 continue to decrease, not only because of continuing processes responsible for historical declines
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1 from peak sediment inputs in the past, but also because of potential changes in wave driven
2 erosive processes (see Blue pathway description below).
3 At the top level of the diagram, the Purple pathway links sediment size to two
4 management levers: impervious cover and reservoir management. It is likely that changes in
5 impervious cover will have important effects on sediment size, but our understanding of these
6 effects is incomplete. The influence of impervious surfaces on runoff, and resulting impacts on
7 sediment size, is highly dependent on the location of impervious surface relative to other land
8 cover, topography and proximity to water bodies. Workshop participants noted that the effect of
9 impervious surfaces on runoff is much greater than the expected effect of climate driven changes
10 in precipitation and resulting runoff patterns. Meanwhile, the effect of reservoir management on
11 sediment size is better understood. As flow volumes and speeds are reduced, there is a negative
12 impact on transport of large grains.
13 Management implications for adaptation based on this pathway vary. In the case of the
14 impervious cover management lever, further investigation of the effects of impervious cover on
15 sediment size is greatly needed in order to identify appropriate management strategies. Basic
16 information on how changes in land cover (including changes from impervious to permeable
17 pavement systems) may affect sediment size will be critical as land use change and development
18 continue to increase into the future.
19 With regard to reservoir management, high volume releases will increase transport of
20 larger grain sediments to marshes. However, there is a trade-off to consider between high
21 volume pulses to enable large grain sediment transport and water availability for steady, lower-
22 volume releases to favor vegetative production as discussed for the Green pathway (see above).
23 Other options for addressing sediment size through management could include adjusting
24 policies that prevent coarse sediment from entering the Bay. This could include a change in
25 TMDL (Total Maximum Daily Load) requirements for streams that do not support salmonids, to
26 allow an increase in sediment loads. Engaging with flood control districts is another possible
27 avenue for re-coupling stream sediments to wetlands. Current flood control priorities may not
28 take into account the future benefits of downstream wetlands as climate change and associated
29 sea level rise begin to increase the occurrence of flooding due to changes in upstream tidal
30 pulses. Also there is an opportunity to take advantage of strategies that maximize sediment
31 transport to wetlands, with the additional benefit to flood control districts of decreasing their
32 dredging needs.
33
3 4 Blue pathway
35 Like the Purple pathway, the Blue pathway (Figure 3-3) starts with the direct effect on
36 net accretion of net mineral accumulation, but from there it diverges. The next step up this
37 pathway concerns the direct relationship of net mineral accumulation to sediment flux, which is
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1 an expression of the rate of sediment supply. All else being equal, increases in sediment flux
2 result in increased net mineral accumulation.
3 An important driver of sediment flux is wind driven wave action, and the nature of this
4 relationship will potentially change under future climate conditions. Under current conditions,
5 wave action has a net positive effect on sediment flux onto salt marshes, as greater wave energy
6 can mobilize and increase rates of sediment transport from bays and adjacent mudflats deep into
7 marsh systems. However, under future climate conditions a threshold may be crossed whereby
8 the role of waves as a sediment source will decrease and the erosive effect of waves will
9 increase, leading to a shift from a direct to an inverse relationship. This threshold is caused by a
10 potential change in wave character as water depth increases due to sea level rise. In deeper water
11 waves behave differently, with less wave energy available for re-suspension of bottom sediments
12 and more energy delivered to the marsh edge, leading to increased erosion.
13 While managing wind driven waves may not immediately appear straightforward as a
14 management lever, given the importance of this potential threshold it is necessary to think
15 broadly about adaptation options for this pathway. It would be valuable to monitor wind, waves
16 and sediment fluxes in marshes in order to detect the threshold shift when it occurs. Ideally a
17 response plan would be prepared in advance of such a shift, with the necessary public and
18 management backing and resources in place to implement the plan when needed. Current tools
19 for reducing wave action on the front sides of marshes are limited; such tools need to be further
20 developed and tested in areas where waves are currently having an inverse influence on sediment
21 flux. Existing tools include building berms or restoring oyster reefs as protective barriers against
22 wave energy. Depending on the depth of adjacent water, such barriers could either be designed
23 to reduce wave energy on mudflats or on the marshes themselves. Another adaptation option
24 might be to strategically locate sites to deposit dredge materials with a goal of enhancing
25 sediment concentrations on mudflats adjacent to marshes. This could serve the dual purpose of
26 both increasing sediment fluxes and compensating for changes in water depth above mudflats.
27
28 3.2.1.2. Community Interactions Tap Pathways
29 Green pathway
30 The Community Interactions example pathway described in section 3.1.2.2 above is
31 elaborated upon here as the Green top pathway (Figure 3-4). Starting with the Shorebirds
32 endpoint and working "up" the diagram, shorebirds are best able to effectively use mudflat
33 foraging habitats only if landscape mosaics - which are defined as a mixture of habitats for
34 secondary foraging, roosting, and cover from predators — are available in close proximity.
35 Therefore, the presence of such mosaics, including ponds, diked wetlands, seasonal wetlands,
36 and muted tidal wetlands, has a strong positive effect and a high relative impact on shorebird
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1 populations. The high relative impact of this influence is likely to increase even further under
2 climate change as mudflat habitats become scarcer and smaller in extent.
3 At the next level up the pathway, landscape mosaic is directly affected by restoration.
4 Workshop participants noted that small changes in restoration can have the potential for large
5 positive effects on landscape mosaic. The relative impact of restoration on landscape mosaic is
6 likely to increase under climate change as temperatures increase, precipitation patterns change,
7 and water diversions continue to escalate. These effects also will be exacerbated by ongoing
8 and increasing land use changes (especially development), further raising the importance of
9 restoration as a mitigating force.
10 Management options for adaptation given the relationships in this pathway center on
11 restoration as a key and increasingly critical management activity. Workshop participants noted
12 that there is little monitoring of landscape mosaics currently underway, so there is almost no
13 information on the current status of, or rates of change in, mosaic habitats. Assessment and
14 mapping is needed to detect changes and manage at the landscape scale for shorebirds and other
15 mobile species that use multiple habitats.
16 Given the implications under the climate change scenarios, managers might place a
17 priority on "stepping up" management of landscape mosaics through spatial planning designed to
18 prioritize where and how to restore which habitats. The goal would be to create a continuum of
19 wetland and upland ecosystems, across a range of salinity regimes, which could migrate inland
20 as sea level rises. Workshop participants noted that managers might also include "threshold
21 landscapes" (those that are about to change from one set of dominant processes to another, or
22 from one state to another) in consideration of good landscapes for restoration. As part of spatial
23 planning, restoration should be focused on where there are good opportunities for restoration,
24 where there can be flexibility in management, and where migration inland is possible. Decision
25 makers could even be urged to consider legislation or incentives that encourage moving back or
26 blocking of development on lands where there is restoration potential now or in the future.
27
28 Blue pathway
29 Starting with the Shorebirds endpoint and working up the Blue pathway (Figure 3-4),
30 another key factor affecting shorebird populations is the extent of mudflat available for foraging
31 (i.e., the acre-hours that mudflats are exposed and therefore accessible). Extent of mudlflat has
32 a direct effect of high relative impact on shorebird populations. This direct effect will become
33 increasingly strong, and is considered a threshold effect, under climate change as extent of
34 mudflat may become limiting as sea level rises, with available foraging habitat becoming too
35 limited to support shorebird populations.
36 Continuing to work up the pathway, a major determinant of extent of mudflat is mudflat
37 bathymetry, which has a strong direct effect on the extent of mudflat exposed at low tide.
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1 Mudflat bathymetry is itself directly affected by sediment resuspension and deposition processes,
2 which are turn directly affected by sediment supply. A key source of sediment supply is the
3 direct effect of freshwater inflows.
4 An important management lever appears in the form of the sensitivity of freshwater
5 inflow to water management. Workshop participants noted that water management practices
6 (specifically, reservoir management and upstream operations) have an important direct effect on
7 freshwater inflows. This effect will become increasingly strong under climate change. This is
8 because the sensitivity of freshwater inflow to water management may increase in the future as
9 freshwater flows from alternate sources such as precipitation and tributaries become more
10 variable and/or scarce, and as the entire watershed becomes even more highly managed.
11 Implications for adaptation based on this pathway currently center around management of
12 water releases and sediment supply. Reservoir management of water releases in order to
13 mobilize and transport sediments (e.g., through the use of sediment maintenance flushing flows)
14 could become an increasingly important option; in light of this, continuing to improve upstream
15 operations in order to ensure greater availability of water (to allow more frequent and/or more
16 intense pulse releases) is warranted. Participants noted that an increasingly important technique
17 will be integrated water resources management, with an emphasis on shifting from storage more
18 toward conservation uses.
19 Attention to management options for directly enhancing sediment supply is also needed.
20 This is especially the case since sediment supply will become increasingly sensitive to
21 restoration as well as water management, with participants noting in their discussions a potential
22 synergistic interaction between the two. Methods for moving sediment (especially coarse
23 sediments) into the bay need to be developed. Adaptation options might include strategically
24 locating dredge spoil sites to enhance sediment supplies to mudflats.
25
26 Purple pathway
27 Starting with the Shorebirds endpoint, the Purple pathway (Figure 3-4) begins with a link
28 to the shorebird prey community. Shorebird communities rely on abundant mudflat prey
29 populations, which therefore have a positive effect - of high relative impact - on shorebird
30 populations. The abundance of prey per unit area will become increasingly important (of
31 increasing relative impact) under climate change as spatial extent of mudflats shrink with sea
32 level rise. This effect will be magnified if secondary feeding habitats in the landscape mosaic
33 (see Green pathway) continue to be lost due to development and other pressures.
34 Working up the next step of the pathway, shorebird communities are strongly affected by
35 water quality. Specifically, sufficient levels of dissolved oxygen (DO) have a direct positive
36 effect on prey communities. Since this is a direct relationship, this means that as decreases in
37 DO occur with climate change due to increased temperatures and/or eutrophication, prey
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1 communities may flag. A threshold may occur in the future if DO reaches low enough levels to
2 cause prey populations to crash.
3 With regard to management implications, the Purple pathway provides further
4 justification for continued prioritization of water quality management. Water quality is already a
5 major concern in the watershed; and for shorebirds, a water quality aspect of special concern
6 may be DO. Oxygen depletion usually results from high rates of microbial and/or algal
7 respiration that exceed the capacity of the water body to replenish oxygen through phytoplankton
8 photosynthesis and diffusion from the air. Excessive inputs of organic material and nutrients, for
9 example from poorly treated sewage discharges or from agricultural activities, can accelerate
10 respiration rates and trigger localized and regional oxygen depletion. Protection and
11 improvement of water quality will be critical through integrated water resources management,
12 which could include stormwater management and rainwater-harvesting (which benefits water
13 conservation as well).
14 Other improvements to water quality could occur through land use decisions (e.g., use of
15 permeable surfaces to reduce runoff) and restoration decisions (e.g., restoration of riparian zones
16 as natural filters). The importance of land use decisions was emphasized by the participants,
17 who discussed a synergistic effect of freshwater inflow on water quality that is dependent on
18 land use change. In summary, water quality is an existing concern for which there are already a
19 variety of management options available; and because the affects of water quality on mudflat
20 prey populations are known and expected to increase with climate change, this is further
21 confirmation of the importance of implementing such options, in conjunction with public
22 education and outreach to explain and justify support for their use.
23
24 3.2.1.3. Tap Pathway Caveats
25 Above we have described three pathways that scored as especially promising for
26 successful management application in light of the information provided by the particular group
27 of experts at this workshop. Given the complexity of these systems and instances of uneven
28 agreement among participants, actions based on these pathways should be considered with care.
29 A different set of pathways could be chosen based on additional meaningful criteria that are site-
30 specific and specific to individual managers' expertise. Based on their own knowledge of their
31 sites and/or input from different experts, managers are encouraged to examine the potential for
32 additional top pathways for their own particular systems by examining the crosswalk tables and
33 applying their unique knowledge.
34 While top pathways based on the expert knowledge from this workshop are useful, it is
35 also important to look at gaps in the crosswalk tables where some influences did not show
36 agreement in type, degree and/or sensitivity. Lack of agreement does not necessarily mean there
37 is no information available; often the experts did not agree based on competing evidence, or as a
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1 result of limitations of the expert elicitation process. Where there are gaps in otherwise-strong
2 pathways for management, further research - in the form of literature searches, data mining, or
3 original research if needed - could be highly valuable.
4 A final consideration is that these diagrams were developed considering the current
5 condition of each system. At this workshop there was a variable (disease for the Community
6 Interactions group) that arose as critical to the system under future conditions, even though it had
7 not been included in the diagram under current conditions. Individual managers should identify
8 - and consider management options for - any other such variables that may be specific to their
9 system or site.
10
11 3.2.2. Adaptation Planning
12 There are multiple ways to go about climate change adaptation planning, including
13 integrating adaptation into existing plans, or developing a stand-alone adaptation plan. This
14 report focuses on the planning options for SFEP, which as an NEP has several key management
15 plans. The SFEP management plans discussed here are used to demonstrate the type of
16 adaptation planning that can be done to address these particular issues. Other organizations can
17 use their particular planning documents to apply the same approach.
18 SFEP's planning documents include a Comprehensive Conservation and Management
19 Plan (CCMP), which articulates long range goals and objectives, a Strategic Plan for mid-term
20 objectives, and an annual Work Plan that lays out short-term actions to implement the goals and
21 objectives. Each of these plans addresses climate change on some level, so it makes sense to use
22 the results of this study to continue integrating climate change into each of these planning scales.
23 In this section we provide some links between SFEP's plans and the top pathways and
24 management options discussed above; this set of examples is not comprehensive, but rather is
25 meant to illustrate how the results of this study can be used to inform adaptation planning.
26 One management strategy outlined in the 1993 CCMP that pertains to the Green
27 Sediment Retention pathway (Figure 3-3) is Action AR-4.1: "Adopt water quality and flow
28 standards and operational requirements designed to halt and reverse the decline of indigenous
29 and desirable non-indigenous estuarine biota" (SFEP, 1993). The relevance of the Green
30 Sediment Retention pathway to this action is that reservoir management activities and their
31 effects on freshwater inflow are considered key to sustaining net organic accumulation through
32 this pathway. While animal species have been the primary focus of this action, it also applies to
33 managing for salt tolerance of plant species with resulting effects on plant productivity and net
34 organic accumulation.
35 Another relevant action, from the revised 2007 CCMP, is Action DW-1.1: "Conduct
36 studies, research, modeling, and analysis of sediment processes and trends to more thoroughly
37 understand sediment transport in San Francisco Bay, particularly in light of sea level rise and
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1 changing sediment inputs from the Delta and major tributaries" (SFEP, 2007). This mandate will
2 be important for informing management along both the Blue and Purple Sediment Retention
3 pathways (Figure 3-3) as well as the Blue Community Interactions pathway (Figure 3-4). For the
4 Blue Sediment Retention pathway, the priority research would be monitoring of the potential
5 threshold effect of waves on sediment flux. For the Purple Sediment Retention pathway,
6 information is needed to understand the relationship between impervious cover and sediment.
7 Such understanding could inform where and how to manage impervious cover. In addition,
8 further investigation is needed into reservoir management options that provide pulse events
9 which can increase the supply of large grain sediment. Very similar questions on how water
10 management methods affect sediment supply come up for the Blue Community Interactions
11 pathway,
12 A strategy from the revised 2007 CCMP that relates to the Green Community
13 Interactions pathway (Figure 3-4) is Action WT-4.1: "Identify, convert, or restore non-wetlands
14 to wetlands or riparian" (SFEP, 2007). While mudflats may not have been the wetlands
15 originally intended for this action, considering mudflats within the landscape mosaic is advisable
16 while implementing strategies of where to restore non-wetlands to wetlands (see also Table 2-
17 12).
18 In the 2010-2012 Strategic Plan (SFEP, 2010), one of the four key objectives for focusing
19 the implementation of the CCMP is Objective 2: "Support Estuary resilience in the face of
20 climate change". Under Sub-objective 2.3 is "Promote climate adaptation strategies and policies
21 that encourage protection and restoration of Estuary health and reduce damage to the
22 ecosystem". The workshop results of this study can be used to provide specific areas of focus for
23 that objective and for mainstreaming adaptation into applicable actions under the other
24 objectives.
25 Meanwhile, under Objective 1: "Promote integrated watershed stewardship," there is
26 Sub-objective 1.2: "Assist development of regional goals projects and management plans (i.e.,
27 Habitat Goals, Subtidal Habitat Goals, Upland Habitat Goals, regional sediment plans)". The
28 abovementioned Goals projects enumerating Baylands Ecosystem Habitat Goals (Monroe et al.,
29 1999), Upland Habitat Goals (Weiss et al., 2007; 2008; 2010), and Subtidal Habitat Goals
30 (BCDC, 2010) are highly relevant to the Green Community Interactions pathway (Figure 3-4).
31 Developing projects that coordinate across goals to connect habitat types could serve as a
32 strategy for rebuilding landscape mosaics through restoration projects. The original Baylands
33 Goals report did not take climate change into consideration, but the two newer projects consider
34 climate change as a factor.
35 Many of the current projects in the 2010-2011 Annual Work Plan are examples of
36 management options that potentially could be informed by the results of this study. For instance,
37 sediment reduction is an objective or component of 14 of the projects. For both the Blue
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1 Community Interactions pathway (Figure 3-4) and the Purple Sediment Retention pathway
2 (Figure 3-3), one management option cited in Table 2-12 is to adjust policies that keep coarse
3 sediment from reaching the Bay. Many of the current projects are based on meeting TMDL
4 restrictions for salmonid streams, which is a competing goal with developing methods to increase
5 sediment supplies into the Bay. Some current projects focus on reducing fine sediment;
6 management practices that target fine sediment only (while allowing coarse sediment) could be
7 developed or expanded to other projects where appropriate. Tools such as those in the
8 "Watershed Scale Mapping of Project Results: Linking On-the-Ground Results to Measurable
9 Regional Outcomes" designed to assist in stopping downstream sediment migration could be
10 adapted to prioritize salmonid streams while allowing increased sediment transport in other
11 streams. Such a tool would distinguish where recommended practices should target fine
12 sediment while maintaining a supply of coarse sediment, versus those streams where it is
13 important to reduce all sediment sizes. Another current project, "Innovative Wetland Adaptation
14 Techniques in Lower Corte Madera Creek Watershed", will provide results that can be used for
15 one of the recommended actions in Table 2-12: "Develop methods to reduce wind/wave action
16 on the front side of marshes". The project includes measurements for wind-wave propagation
17 and attenuation in the marsh as well as developing best practices for flood control.
18 Within each plan are a variety of additional opportunities for incorporating the workshop
19 results. The examples offered here are intended to demonstrate the links, but are not
20 comprehensive. In addition to the adaptation of current management projects and strategies, this
21 study has identified sensitivities that may require the development of entirely new management
22 options. Planning for future projects should identify opportunities to fills those needs and test
23 new methods. Conflicting goals due to trade-offs — such as the simultaneous need to reduce
24 sediments in salmonid habitats, but increase coarse sediment transport to the Bay - will become
25 increasingly problematic with climate change. Indeed, in some cases such trade-offs may
26 necessitate re-evaluation of habitat goals, and even the application of a "triage" approach to
27 prioritize certain habitats over others in the system.
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1 4. CONCLUSIONS
2
3 This report has described the results of a vulnerability assessment aimed at synthesizing
4 place-based information on the potential implications of climate change for key ecosystem
5 processes, with the intent of enabling managers to undertake adaptation planning. The
6 assessment involved identification of key management goals and ecosystem processes,
7 conceptual modeling of those processes, a climate change sensitivity analysis in a workshop
8 setting, and discussions/analysis of the potential applicability of the results for adaptation. The
9 workshop exercise - an expert elicitation sensitivity analysis combined with management
10 discussions - tested a novel approach for conducting "rapid vulnerability assessments" for
11 ecological systems. The sections that follow discuss general observations, insights, and
12 conclusions that emerged from the workshop exercise, from the analyses of management
13 implications, and from our assessment of the methodology's utility for potential use in other
14 locations/ecosystems.
15
16 4.1. INSIGHTS FROM THE WORKSHOP EXERCISE
17 4.1.1. Group Influence Diagrams
18 Figure 2-2 and Figure 2-8 were developed by the workshop participants based on edits to
19 straw man diagrams prior to the workshop, followed by group discussions and refinement of a
20 final group diagram during the workshop. While the main purpose of the group influence
21 diagrams was to establish a framework for the subsequent sensitivity analysis, these diagrams
22 represent key outputs in and of themselves. The construction of the diagrams proved an
23 interesting group exercise in building a highly constrained representation of a complex system,
24 with only the most critical elements and interrelationships included. The iterative process of
25 distillation into basic diagrams by the two interdisciplinary teams of experts resulted in some
26 interesting differences in the Sediment Retention and Community Interactions diagrams.
27 The Sediment Retention group focused on the physical components of sediment
28 processes as the highest priority factors influencing the balance of salt marsh accretion and
29 erosion in their diagram, with less focus on biological factors. There appeared to be good
30 familiarity with each piece of the diagram across all members of the group; this allowed them to
31 be specific in defining (and hence envisioning the effects of) management-related variables
32 (levers), which may have contributed to the high amount of agreement in judgments during the
33 subsequent coding exercise. The participants reported that given more time they would have
34 distinguished between delta and local tributary sediment and freshwater inputs, but were forced
35 to lump these due to the 15-variable constraint and time limitations. Nevertheless, they were
36 able to agree on an acceptable influence diagram for the exercise, with a tractable number of
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1 unidirectional influence arrows and a few large feedback loops to handle important bidirectional
2 influences.
3 The Community Interactions group was also successful in agreeing on an acceptable
4 influence diagram for the exercise. However, their diagram was more complex, with a greater
5 mixture of both physical sediment processes (which maintain mudflats) and biological processes
6 (which affect shorebirds and their prey). In fact, a set of variables similar to the Sediment
7 Retention group's diagram was nested within the Community Interactions diagram; but then the
8 Community Interactions diagram was expanded further to also include a set of variables
9 representing the three biological communities (shorebirds, prey, and predators). Due to this
10 greater complexity, the Community Interactions group was forced to use two more boxes, a
11 larger number of influence arrows, and four bidirectional arrows, resulting in nine more
12 influence arrows than the Sediment Retention group. The complexity also led to less time for
13 defining the management levers specifically, making it more difficult to judge their effects.
14 These factors - combined with varying expertise in the specialty areas of sediment processes
15 versus ecological processes in this interdisciplinary group - may have contributed to the greater
16 numbers of gaps in agreement in the subsequent exercise.
17 While the two groups had different experiences and challenges in building their influence
18 diagrams, both groups were effective in generating a useful representation of their ecosystem
19 process for the sensitivity exercise. Participants reported that the highly constrained diagram -
20 building procedure challenged them to focus on the most key elements of the system while still
21 maintaining a sufficiently realistic model for sensitivity analysis. Designing the diagrams while
22 considering current conditions, then applying climate scenarios to the same diagrams during the
23 sensitivity exercise, worked smoothly. The one exception was a disease variable in the
24 Community Interactions diagram that was not in the original diagram, but which emerged as
25 critical to the Community Interactions process under the climate scenarios. This and other
26 complications could be avoided in future workshops by providing the participants with an
27 opportunity for one more revision of the diagrams after being briefed on the climate change
28 scenarios. This would allow them to account for how future climate might raise additional
29 variables for priority consideration in the diagrams.
30
31 4.1.2. Characterization of Influences
32 One technique for ensuring the effectiveness of expert elicitation is to break down the
33 problem (i.e., what are the climate change sensitivities of the selected ecosystem processes) into
34 a set of distinct questions that clearly and explicitly define parameters and relationships of
35 interest (see EPA's white paper at http://www.epa.gov/spc/expertelicitation/index.htm). This
36 was accomplished by way of a systematized coding exercise - using the influence diagrams as a
37 framework - in which the experts made a series of judgments about individual components of
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1 the system, in order to better understand the system as a whole. For each individual influence
2 arrow in the diagram, each expert was asked to characterize the effect of variable "X" on the
3 response variable "Y", including their confidence in that judgment. Based on the results of this
4 novel methodology, some general observations of interest have emerged.
5 Participant notes and discussions revealed that for both processes, while there are many
6 intermediate (and some high) sensitivity relationships among variables that are useful to be
7 aware of for management, it was difficult to detect changes in sensitivities across the scenarios
8 based on this method. Under the climate scenarios, one influence for the Sediment Retention
9 group became highly sensitive and four others showed a trend (but no majority agreement)
10 toward greater sensitivity, but most of the sensitivities remained intermediate. For the
11 Community Interactions group, under the climate change scenarios there were seven influences
12 that trended toward increasing sensitivity, but without majority agreement, while the majority of
13 influences remained intermediate in sensitivity, or lost agreement. It was noted that natural
14 variation in most of the variables is large enough that changes generated by the climate scenarios
15 would not be enough to move the variables outside their current range of variation. In other
16 words, a response in variable "Y" would need to be outside the normal range of variation in
17 order to clearly detect a sensitivity threshold change by way of the coding scheme that was used.
18 Only one threshold (Relationship DD, Table 2-10) was indicated through coding in the
19 Community Interactions group.
20 Yet outside of the coding exercise, there were indications based on participant notes and
21 discussions that additional potential threshold relationships do exist. Identifying thresholds is
22 challenging because while there may be general recognition of the potential for certain threshold
23 effects, it can be very difficult to identify where and when a threshold may occur. Multiple
24 potential thresholds were identified in both processes, through one of two ways. In some cases,
25 participants tried to indicate thresholds with their sensitivity codes, but did so by including two
26 codes under each of the scenarios to signal uncertainty as to when the threshold might occur.
27 Others did not indicate the threshold with their codes at all because they were not sure whether
28 the climate scenarios represented a big enough change to cause threshold exceedance. In these
29 cases, the thresholds indicated in Table 2-6 and were ultimately identified through the
30 participants' notes and discussions as relationships that could change dramatically at some point
31 that is currently difficult to define.
32 Another way of identifying relationships of particular interest for management is to
33 examine the relative impact of certain influences in the context of the whole process. For both
34 processes, under current conditions the influences identified as having primary impact tended to
35 include variables closer to the endpoints (Figure 2-5 and Figure 2-11) compared to relationships
36 that emerged as high or increasingly-high impact under future climate (Figure 2-6 and Figure 2-
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1 12). This implies that variables related to management levers may become increasingly
2 important as climate changes.
3 Finally, characterization of interactions and confidence were also included in the
4 sensitivity exercise, with mixed results. Trying to consider interactive effects of multiple
5 variables moves the exercise to a much greater level of complexity. The number of possible
6 pair-wise interactions in the influence diagrams was very large, and the challenge of
7 understanding combinations of effects could become very complicated. Thus the participants
8 were not asked to attempt every possible pair-wise combination, but rather were asked to
9 indicate which interactions "leapt out" as well understood and important. Of course, even
10 looking at all pair-wise interactions would be a vast oversimplification because variables interact
11 in greater multiples than just pairs. Nevertheless, while there were only a few pair-wise
12 interactions identified by enough participants to stand out, at least these relationships are
13 sufficiently well understood to merit consideration in management planning. With regard to
14 confidence, the exercise made a good start of acknowledging the need to gauge confidence in the
15 judgments and providing a systematic way for doing so; however, the large number data gaps
16 indicate that there were difficulties with this part of the methodology. Potential reasons for these
17 difficulties, as well as potential improvements, have been discussed in section 3.1.1.3. Both
18 interactions and confidence are concepts that need further refinement and better estimation
19 methods before they can be effectively interpreted for management planning.
20
21 4.2. APPLICATION OF WORKSHOP RESULTS
22 4.2.1. Top Pathways for Management
23 When using the workshop results, it is essential to examine all three types of information
24 - influence type, sensitivity, and relative impact - when thinking about management
25 applications. For some questions, one type of information may be useful individually, but
26 because there are gaps and limitations within each type of information, a more complete
27 management picture can be built using all three types together. It is helpful to focus on
28 influences that are well understood, become more sensitive, and have a greater impact under
29 future climate scenarios. In some cases, it is possible to connect a series of influences that meet
30 these criteria to identify a path between the endpoint and a management lever. We have
31 presented what we consider to be three top pathways for management (Figures 3-3 and 3-4) for
32 each process based on the information currently available from the workshop results. These
33 delineate relatively well-understood relationships that are climate sensitive and for which there
34 are consequent implications for management adaptation.
35 The climate-related changes of interest in the top pathways are of three main types: 1)
36 changes in relative impact under climate change; 2) changes in sensitivity under climate change;
37 and 3) threshold shifts under climate change. In the case of the influences for which relative
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1 impact is likely to increase under one or both future climate scenarios, and especially where
2 relative impact is already high under current conditions, action could be taken immediately.
3 These are influences for which there is sufficient understanding and opportunity to connect to
4 management options that favor desirable outcomes, with increasing relative impact on the
5 process as a whole as climate change continues. In the case of influences for which an increase
6 in sensitivity is expected under climate change, there is still time to further study and anticipate
7 the degree and timing of the sensitivity and to prepare best management responses. An
8 expectation of increasing sensitivity could be considered a notification to monitor and plan for
9 when and how management practices can be adjusted to account for the impending change.
10 Finally, in the case of thresholds, there is often a strong expectation that a threshold shift is
11 likely, but usually a great deal of uncertainty as to exactly when the threshold will be crossed.
12 Monitoring of threshold variables is needed so that managers will be alerted immediately to the
13 shift when it occurs. In the meantime, actions can be taken to attempt to prevent the shift by
14 keeping the system "below" the threshold as long as possible, while preparing a plan for what to
15 do if an unavoidable shift occurs. After a shift occurs, managers should have a plan as to how
16 they will manage the system differently in its new state, or whether they will take no action and
17 instead shift their priorities to other goals.
18 It is important to note at this point that each pathway also sits in the context of other
19 influences with which there could be key interactions, so there may be opportunities for
20 management options beyond those most directly evident from the main pathways. Also, in the
21 case of other management pathways for which there are currently information gaps based on the
22 workshop results, it is vital to remember that lack of agreement does not mean zero
23 understanding of influences or zero degree of sensitivity. Closer inspection can show that the
24 agreement may be split between intermediate and high sensitivity, so the understanding that the
25 sensitivity of the influence is important may be obscured by the distinctions between categories.
26 It is of note that for influences for which there was agreement, the variation among participants
27 was greater than that between scenarios. This could be due to a number of reasons: a limited
28 range between the two mid-century climate scenarios; the number of assumptions each
29 participant was required to make individually for each judgment; and the interdisciplinary and
30 complex nature of the questions. This is an indication that these types of questions do not lend
31 themselves to consulting a single expert, but rather require the combined judgments of a group of
32 experts to complete the full picture. This also highlights the need for caution against relying
33 solely on combined (agreement) information: the nature of the variation across participants is
34 also important to consider. Equally important is the application of local expertise when further
35 examining the results of this study; the local manager is the best expert on his or her unique
36 system and should thus apply an appropriate filter when making final interpretations and
37 decisions based on these results.
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1
2 4.2.2. Mainstreaming Adaptation into Planning
3 The vulnerability assessment results for the two ecosystem processes presented here are a
4 big first step in the climate change adaptation planning process. We have given examples of
5 ways to tie the vulnerability assessment results to potential management options as a starting
6 point, but incorporating adaptation fully into management planning will require a more
7 systematic and comprehensive process. Planning is an iterative process, especially for climate
8 change adaptation, which is still a nascent field. Due to this iterative nature, the planning
9 recommendations presented here are based on mainstreaming (continuously integrating)
10 planning into existing planning mechanisms and documents, rather than developing a
11 comprehensive, stand-alone adaptation plan. For SFEP, nearer-term planning includes a multi-
12 year Strategic Plan and an Annual Work Plan, both of which provide ways to insert specific
13 management options into projects that are currently underway. In future plans, new projects that
14 specifically incorporate climate adaptation priorities can be added. Repeating vulnerability
15 assessments (once management options have been tested through project implementation) should
16 be part of the iterative process. Finally, this study only covered two ecosystem processes and did
17 not attempt to evaluate relative vulnerability or resilience across different ecosystem processes.
18 The vulnerabilities of additional ecosystems, processes and goals will need to be assessed, taking
19 into account what was most useful in the results of this study for adaptation.
20 Thresholds remain a major unknown, and while much can be done to improve our
21 understanding of factors affecting thresholds, some may only be revealed after they have been
22 crossed. Thus it would be advisable for monitoring plans to be put into place to track indicators
23 of state changes. Contingency plans for management actions once a system has changed states
24 could be developed, as well as contingency planning for ways to respond to catastrophic events
25 such as levee failures or earthquakes. Successful implementation of contingency responses will
26 require that the political and scientific base be put into place now for responding properly
27 following catastrophes or threshold changes. In the meantime, when prioritizing implementation
28 of adaptation actions, it is advisable to start with win-win options that contribute to current
29 management goals and efforts while also responding to current and future climate change. For
30 example, working now to proactively move highways and railroads that are barriers to marsh
31 migration where there is otherwise space for migration is not only advantageous for marshes, but
32 will also prevent damage and disruption to human transportation and infrastructure as inundation
33 from sea level rise continues. Likewise, the practice of working with authorities in flood control
34 districts to re-couple streams to wetlands will not only benefit wetlands but will also improve
35 natural flood control services.
36 Looking beyond the win-win options, many other actions will force managers to confront
37 trade-offs that will require difficult policy decisions. One example highlighted in sections 2.4.2
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1 and 3.2.2.1 is the trade-off between increasing coarse sediment supply from tributaries, which
2 comes into conflict with current sediment reduction efforts for salmonid streams. While a first
3 step is to set up different best practices for salmonid and non-salmonid streams, beyond that
4 there may come a decision point when it is no longer possible to meet both goals, so a choice
5 between the two conflicting goals will be necessary. As climate change progresses, there are
6 likely to be more trade-offs, often between short and long term goals. Mainstreaming adaptation
7 planning will provide a better chance of foreseeing conflicts between long and short term goals
8 and identifying opportunities to build support for hard decisions and creative solutions.
9
10 4.3. GENERAL CONCLUSIONS
11 4.3.1. Transferability of Results and Method
12 The results of this study were developed for two specific ecosystem processes within two
13 ecosystems. Therefore the question arises as to how transferable the results may be. The
14 sensitivities examined in this study are specific to sediment retention in salt marshes and
15 community interactions of shorebirds in mudflats, so the characterizations of influence type,
16 sensitivity and relative impact cannot be transferred directly to other ecosystems and do not
17 apply to different processes within these ecosystems. However, an example site was used as a
18 way to focus the exercise and was chosen as a representative example of intact ecosystems, thus
19 the results could be transferable to other North Bay locations in which the same ecosystem
20 processes are present. The variables that ended up in the group influence diagrams are general
21 enough that most of the results may transfer to the entire Bay, with only a few specific enough to
22 only apply to the North Bay. In addition, it is likely that the influence diagrams could also be
23 transferred for use with like ecosystems in other estuaries, with minor revisions for place-specific
24 stressors or other process variables. The characterizations of influence type, sensitivity and
25 relative impact would have to be revisited, particular to that location.
26 Where the specific results are not transferable, the methodological process is certainly
27 transferable to other processes, ecosystems and locations. The methodology used for this
28 assessment - an analysis of key ecosystem processes through expert elicitation - is a useful
29 framework for understanding the current state of knowledge and research. The experts in this
30 study were able to share their combined understanding of key processes and how they are
31 expected to respond to climate change. The expert elicitation process also helped to identify
32 where key gaps in understanding exist, what type of research is necessary, and how management
33 should proceed. This methodology is transferrable in that the process used to compile, distill,
34 and assess key information can be replicated. Expert elicitation is used in many fields of study
35 and has been demonstrated here to be useful in understanding localized climate change impacts.
36 Experts can think integratively across studies and disciplines and often have access to more
37 current research and data than is currently available or published. As the climate change
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1 research is consistently evolving, this type of process is useful for synthesizing the most current
2 information available. However, as climate change research is consistently changing, new
3 information and research will need to be integrated concurrent with management decisions.
4
5 4.3.2. Utility of Method for Rapid Vulnerability Assessments
6 Given that the method is transferable, the question of utility arises: in what cases is this
7 method advantageous? This method could be repeated elsewhere as a "rapid" vulnerability
8 assessment, with opportunity for some of the improvements that have been suggested for some
9 of the limitations. By rapid, we mean assessments that can be carried out within six months to a
10 year, as opposed to assessments based on detailed quantitative modeling that can take many
11 years. Another advantage is that this method is able to capture more recent knowledge than
12 would be available from a literature review. It is also better able to capture more knowledge of
13 the type that is closely related to management, which is less frequently published than scientific
14 studies. Finally, the information is more integrated across disciplines and scales and is designed
15 to better match the scale of adaptation decisions. In some cases new insights about management
16 effectiveness may arise while in other cases existing understanding may be validated. Having a
17 well supported study to substantiate new and existing ideas can position managers to justify the
18 most appropriate management options and priorities. It also can validate research priorities by
19 highlighting known research gaps.
20 The disadvantages are that this method is designed to focus only on a specific piece of the
21 system, compared to initial assessments that often rely on surveying the system more
22 comprehensively (though less deeply), often through literature reviews. The amount of caution
23 required to properly interpret the results is another disadvantage, given multiple limitations and
24 caveats. The method is not intended as a consensus exercise, and the large number of influences
25 without agreement present challenges to either fill those research gaps to improve agreement or
26 to manage around limited information. In addition, this is only one group of experts, and another
27 group could reach different conclusions. Group selection is critical to making sure appropriate
28 areas of expertise and conflicting views on the system are represented. This is another reason
29 why in addition to looking for areas of agreement, the results of individual judgments should also
30 be examined. At the same time, since no participant can have complete expertise in every facet
31 of a system, it is also important that participants have the opportunity to confer amongst
32 themselves and adjust their judgments based on what they learn from each other.
33 Nevertheless, the expert elicitation method developed for this study was well suited for
34 achieving the purpose and goals of the assessment. In addition to achieving the workshop goals,
35 several unexpected benefits emerged from the workshop. Participants reported that the
36 combination of the development of the influence diagrams with systematic judgments facilitated
37 thinking about the system and questions of climate change vulnerability in a different way than
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1 they had previously. Several expressed an intention to explore adapting the method for use in
2 other workshop or classroom settings. Many participants found that the multidisciplinary
3 interactions with colleagues were a valuable, personal learning experience and that the group
4 together generated new insights about the system and links to management that may not have
5 been seen by individuals. In short, the method demonstrated in this project offers opportunities
6 to capture and integrate the existing collective knowledge of local experts, while pushing the
7 boundaries to develop a new understanding of the system and management options in the face of
8 insufficient data and deep uncertainty about future climate.
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1 APPENDIX A. DEVELOPMENTAL PROCESS FOR
2 CLIMATE READY ESTUARIES VULNERABILITY ASSESSMENT
O
4 A.l. SELECT KEY GOALS, ECOSYSTEMS, AND ECOSYSTEM PROCESSES
5 The SFEP partners participated in several discussions and meetings to outline
6 management priorities, key resources to consult, and other considerations for selecting key goals
7 for the assessment. As a starting point, SFEP's Comprehensive Conservation and Management
8 Plan (CCMP) (SFEP, 1993; SFEP, 2007) was examined and discussed to select four to six key
9 management goals as a focus of the assessment. These goals would help to further refine the
10 analysis to specific ecosystems, ecosystem processes, stressors of concern, and indicators for
11 measuring changes in the ecosystem. Selected management goals included:
12 • Restore healthy estuarine habitat to the Bay-Delta, taking into consideration all
13 beneficial uses of Bay-Delta resources;
14 • Stem and reverse the decline of estuarine plants, fish and wildlife and the habitats on
15 which they depend;
16 • Ensure the survival and recovery of listed and candidate threatened and endangered
17 species, as well as special status species;
18 • Protect and manage existing wetlands; and
19 • Restore and enhance the ecological productivity and habitat values of wetlands.
20
21 Following an October 2008 kickoff meeting with over 30 local experts to share
22 information on climate change impacts and adaptation work in the region, salt marshes and
23 mudflats were selected as two wetland habitats of focus for the project. These systems were
24 identified by the local experts as highly relevant to SFEP's management goals due to their
25 ecological productivity and their habitat values for threatened and endangered species, and are
26 deemed highly sensitive to changes in climate-related variables such as sea level rise and altered
27 hydrology. In order to explore the linkages among such climate-related variables, their
28 interactions with non-climate stressors of concern, and the key ecosystem processes that
29 maintain the systems, a conceptual model was developed for each ecosystem type.
30
31 A.2. CONCEPTUAL MODELS
32 The conceptual models were intended to serve as a framework for further analysis in the
33 vulnerability assessment. The models depicted likely pathways by which climate drivers may
34 directly or indirectly affect interacting stressors that impact ecosystem processes. The process is
35 intended as iterative, as we learn from exploring the first two ecosystem processes, next steps
36 can involve focusing on additional ecosystem processes, or for repeating a similar analysis for
37 additional habitats. The development of the conceptual model has also served to help with the
38 scoping process; we began with a comprehensive list of ecosystem processes and indicators and
39 then chose those more important to and best representing healthy salt marsh functioning. The
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1 total number of possible ecosystem processes was narrowed down to five or six key ones for the
2 ecosystem. The models also included a similar number of variables that may serve as indicators
3 for the status of these endpoints. Ecosystem processes and indicators were identified in
4 discussions among SFEP, BCDC, and EPA ORD, as well as through examination of the Delta
5 Regional Ecosystem Restoration Implementation Plan's conceptual models developed by the
6 CALFED Bay-Delta Program (Schoellhamer et al., 2007; Kneib et al., 2008). To ensure
7 consistency with current research, these indicators were cross-walked with indicators developed
8 for the Watershed Assessment Framework, which were being incorporated into a revision of
9 SFEP's CCMP (San Francisco Estuary Indicators Team, 2008).
10 Stressor interactions are stressors that work together to affect ecosystem functioning.
11 These included both non-climate and climate-related influences that stress salt marsh and
12 mudflat ecosystems. Pre-existing stressors and stressor interactions were identified during the
13 development of salt marsh and mudflat conceptual models, and impacts of these stressors of
14 concern were identified using the SFEP Comprehensive Conservation and Management Plan.
15 Climate drivers are climate variables that may impact ecosystem processes directly (e.g.,
16 raise water temperature) or indirectly (e.g., cause changes in nutrient inputs). The climate
17 drivers relevant to salt marshes and mudflats were identified by first examining climate drivers
18 for estuarine systems outlined in Synthesis and Assessment Product 4.4: Preliminary review of
19 adaptation options for climate-sensitive ecosystems and resources (CCSP, 2008a), followed by
20 extensive discussions among the SFEP partners. The climate drivers were then mapped to the
21 key processes of each ecosystem, either directly or through interactions with pre-existing
22 stressors. These pathways provided the basis for the development of the conceptual models. The
23 pathways included are intended as a heuristic, without distinguishing between the magnitudes
24 between them. It is not possible to include all possible system components and connections
25 between them. General models are first presented, and then additional detail for individual
26 ecosystem processes is described in the two sub-models.
27
28 A.2.1. General Models
29 A.2.1.1. Salt Marshes
30 The general model for salt marshes in presented in Figure A-l. Climate drivers in the salt
31 marsh conceptual model include: changes in air temperature, changes in precipitation, sea level
32 rise, and changes in storm climatology and wind. Changes in air temperature refers to the
33 variation from the climatological mean surface air temperature in a particular region. Changes in
34 precipitation refers to variation from the climatological mean of the amount, intensity, frequency
35 and type of rainfall, snowfall and other forms of frozen or liquid water falling from clouds in a
36 particular region, changes refer to both the form and flow of precipitation. Sea level rise is
37 defined as "relative sea-level rise," the change in sea level relative to the elevation of the
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1 adjacent land, which can also subside or rise due to natural and human induced factors. Relative
2 sea-level changes include both global sea-level rise and changes in the vertical elevation of the
3 land surface. Changes in storm climatology and wind refers to the variation from the
4 climatological mean of the frequency, intensity and duration of extreme events (such as
5 hurricanes, heavy precipitation events, drought, heat waves, etc.) and the changes in the direction
6 and timing of the dominant seasonal winds.
7
8 Figure A-l. Salt Marsh Conceptual Model.
9
10 Stressor interactions within the salt marsh conceptual model include: changes in water
11 temperature, changes in salinity, sedimentation and erosion, flooding, invasive species, other
12 human uses, land use/land cover change, contaminants, and altered flows/water demand.
13 Changes in water temperature refers to variation in the climatological mean surface water
14 temperature in a particular region. Changes in salinity are measured by changes in the location
15 along the estuary of different salinity zones (e.g., polyhaline, mesohaline, and oligohaline), or
16 changes in vertical stratification based on salinity. Sedimentation and erosion includes the
17 transport, deposition, and removal of soil and rock by weathering, mass wasting, and the action
18 of streams, waves, winds and underground water. Flooding is defined as an excess of water that
19 does not recharge ground water beyond time frames typical for watersheds due to high
20 precipitation events, storm surge, or infrastructure damage. Invasive species are alien species
21 (species not native to a particular ecosystem) whose introduction causes, or is likely to cause,
22 economic or environmental harm or harm to human health. Other human uses is a catch all
23 category based on the CCMP which includes the use of the marsh and surrounding area for
24 activities such as fishing, shipping and ports, dredging, transportation projects, sand mining,
25 recreational use, marinas, and industrial uses that may impact the marsh. Land use/land cover
26 change is defined as the current use of marsh and human-induced changes to the marsh or
27 surrounding land, including wetland alteration and expansion of the built environment.
28 Contaminants include material that creates a hazard to the ecosystem by impairing water quality,
29 poisoning or through the spread of disease (e.g., mercury, selenium, PCBs, DDT, chlordane,
30 dieldrin, dioxin, trash and debris, and acid mine drainage). Altered flows/water demand includes
31 upstream water diversions for agricultural, industrial, or urban uses that change the natural flow
32 of freshwater and sediment into the marsh, including leveeing, diking, damming, filling, or
33 channeling.
34 Ecosystem processes in the salt marsh conceptual model include: community
35 interactions, primary productivity, sediment retention, water retention, nutrient cycling, and
36 water purification. Community interactions is defined as the interrelations among species within
37 the ecosystem. Primary productivity is the production of energy by plants and phytoplankton
38 within the entire system. Sediment retention is the balance between the processes of removal
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1 and deposition of suspended sediment. Water retention is defined as the capability to buffer
2 against flooding. Nutrient cycling is the process of transfer of nutrients between organisms and
3 the water. Water purification is defined as the removal of pollutants and harmful
4 microorganisms.
5 Indicators within the salt marsh conceptual model include: species population size, water
6 quality standards, freshwater inflow, sediment quantity, extent of aquatic habitat, and
7 biodiversity. Species population size is defined as the number of similar organisms residing in a
8 defined place at a certain time, including threatened and endangered species, native species, and
9 invasive species. Water quality standards are provisions of State or Federal law which consist of
10 designated uses for waters of the United States, and water quality criteria for such waters based
11 upon such uses. Criteria address the values for water quality indicators (e.g., water temperature,
12 salinity, water contaminant exposure, biological thresholds for water contamination, nutrient
13 concentrations, water toxicity) that are required to support designated uses. Freshwater inflow is
14 the amount of freshwater inflow to the estuary from the watershed. Sediment quantity is defined
15 as suspended sediment concentration. Extent of aquatic habitat is defined as the area of all
16 contiguous, vegetated salt and brackish wetland, or mean width of marsh (may be divided into
17 low or high marsh or by dominant species). Biodiversity is the presence and abundance of
18 different species types (e.g., fish, birds, SAV).
19 The salt marsh conceptual model focuses on a limited number of ecosystem processes
20 that are key to the habitat and region. In some instances, a component of the system may fill
21 roles at multiple levels, and the model does not represent all possible roles a particular
22 component may fill. The model does not take the cumulative effects of climate stressors or
23 tipping points/critical thresholds into account. The model does not include ocean acidification as
24 a climate driver, as current understanding of salt marshes indicate it as secondary compared to
25 the other stressors.
26
27 A.2.1.2. Mud Flats
28 The general model for mudflats is presented in Figure A-2. Climate drivers in the
29 mudflat conceptual model include: changes in air temperature, changes in precipitation, sea level
30 rise, and changes in storm climatology and wind. Changes in air temperature refers to the
31 variation from the climatological mean surface air temperature in a particular region. Changes in
32 precipitation refers to variation from the climatological mean of the amount, intensity, frequency
33 and type of rainfall, snowfall and other forms of frozen or liquid water falling from clouds in a
34 particular region, changes refer to both the form and flow of precipitation. Sea level rise is
35 defined as "relative sea-level rise," the change in sea level relative to the elevation of the
36 adjacent land, which can also subside or rise due to natural and human induced factors. Relative
37 sea-level changes include both global sea-level rise and changes in the vertical elevation of the
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1 land surface. Changes in storm climatology and wind refers to the variation from the
2 climatological mean of the frequency, intensity and duration of extreme events (such as
3 hurricanes, heavy precipitation events, drought, heat waves, etc.) and the changes in the direction
4 and timing of the dominant seasonal winds.
5
6 Figure A-2. Mudflat Conceptual Model.
7
8 Stressor interactions within the mudflat conceptual model include: changes in water
9 temperature, changes in salinity, sedimentation and erosion, flooding, invasive species, other
10 human uses, and contaminants. Changes in water temperature refers to variation in the
11 climatological mean surface water temperature in a particular region. Changes in salinity are
12 measured by changes in the location along the estuary of different salinity zones (e.g.,
13 polyhaline, mesohaline, and oligohaline), or changes in vertical stratification based on salinity.
14 Sedimentation and erosion includes the transport, deposition, and removal of soil and rock by
15 weathering, mass wasting, and the action of streams, waves, winds and underground water.
16 Flooding is defined as an excess of water that does not recharge ground water beyond time
17 frames typical for watersheds due to high precipitation events, storm surge, or infrastructure
18 damage. Invasive species are alien species (species not native to a particular ecosystem) whose
19 introduction causes, or is likely to cause, economic or environmental harm or harm to human
20 health. Other human uses includes the use of the marsh and surrounding area for activities such
21 as fishing, shipping and ports, dredging, transportation projects, sand mining, recreational use,
22 marinas, and industrial uses that may impact the marsh. Contaminants include material that
23 creates a hazard to the ecosystem by impairing water quality, poisoning or through the spread of
24 disease (e.g., mercury, selenium, PCBs, DDT, chlordane, dieldrin, dioxin, trash and debris, and
25 acid mine drainage).
26 Ecosystem processes in the mudflat conceptual model include: community interactions,
27 primary productivity, biomass, nutrient cycling, and key species. Community interactions is
28 defined as the interrelations among species within the ecosystem. Primary productivity is the
29 production of energy by plants and phytoplankton, within the entire system. Biomass is the total
30 mass of biological material within the system or within a particular category or group. Nutrient
31 cycling is the process of transfer of nutrients between organisms and the water. Key species are
32 species which serve as a foundation for other species or fill a similar pivotal role for the rest of
33 the ecosystem (e.g., ecosystem engineers). Indicators within the mudflat conceptual model
34 include: species population size, water quality standards, extent of aquatic habitat, and
35 biodiversity. Species population size is defined as the number of similar organisms residing in a
36 defined place at a certain time, including threatened and endangered species, native species, and
37 invasive species. Water quality standards are provisions of State or Federal law which consist of
38 designated uses for waters of the United States, and water quality criteria for such waters based
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1 upon such uses. Criteria address the values for water quality indicators (e.g., water temperature,
2 salinity, water contaminant exposure, biological thresholds for water contamination, nutrient
3 concentrations, water toxicity) that are required to support designated uses. Extent of aquatic
4 habitat is defined as the area of all contiguous, vegetated salt and brackish wetland, or mean
5 width of marsh (may be divided into low or high marsh or by dominant species). Biodiversity is
6 the presence and abundance of different species types (e.g., fish, birds, SAV).
7 The mudflat conceptual model focuses on a limited number of ecosystem processes that
8 are key to the habitat and region. In some instances, a component of the system may fill roles at
9 multiple levels, and the model does not represent all possible roles a particular component may
10 fill. The model does not take the cumulative effects of climate stressors or tipping points/critical
11 thresholds into account. The model does not include ocean acidification as a climate driver, as
12 current understanding of salt marshes indicate it as secondary compared to the other stressors.
13
14 A.2.2. Sub-models
15 Following the development of the general ecosystem models, one ecosystem process
16 within each model was chosen to move to the specifics for an individual ecosystem process. The
17 purpose was to select good processes for piloting the method, but the choice does not imply that
18 these are necessarily the only important, or the most vulnerable, processes. Sediment retention
19 was identified as a key salt marsh process because of the importance of sediment supply to allow
20 for marsh development and growth. In the Bay, sediment supply has been declining due to
21 changes in human activities and the use of the land and waterways (Jaffe et al., 1998; Wright and
22 Schoellhamer, 2004). SFEP, BCDC, and other regional partners have done extensive work on
23 examining changes in sediment and how these changes may be influenced by changes in climate.
24 This provided the basis for the development of the sediment retention submodel.
25 Community Interactions was chosen as the second ecosystem process of focus. To select
26 a specific well-constrained "storyline" of interactions between 2-4 species for this process, ICF
27 and EPA consulted with SFEP, BCDC, and regional experts on key sensitivities for this process
28 within the Bay system. The shorebird and mudflats interaction was selected for further study
29 because of the priority status of key species and the climate sensitivities that structure the system.
30 As mudflats may be one of the habitat types most vulnerable to climate changes (especially sea
31 level rise) the interactions among wading shorebirds were identified as a key process for study.
32 This provided the basis for the development of the community interactions submodel.
33
34 A.2.2.1. Sediment Retention
35 The sediment retention submodel is presented in Figure A-3. It focuses on the balance
36 between the processes of removal and deposition of sediment onto a salt marsh and the resultant
37 ability of the marsh to persist in the face of climate change. The accumulation of sediments and
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1 marsh vertical accretion result from interactions among tidal imports, vegetation dynamics, and
2 depositional processes. Freshwater flow and coastal storms transport and deposit sediments onto
3 the marsh surface, and the roots and stems of marsh vegetation retain sediment that would
4 otherwise be carried away from the marsh by wind and waves. Over time, the accumulation of
5 dead and dying organic matter produces peat, and the combination of peat accumulation and
6 sediment deposition gradually builds up the marsh surface. Ultimately, it is the balance between
7 marsh vertical accretion and sea level rise that determines whether a tidal marsh will persist in
8 the face of rising seas or will convert to tidal flats or open water (Reed, 1995).
9
10 Figure A-3. Sediment Retention sub-model.
11
12 In the San Francisco Estuary, there is an annual cycle of sediment deposition and
13 resuspension that begins when freshwater flow from the Delta in winter carries pulses of
14 sediment to the bay. Most of this new sediment is deposited in shallow areas and where tidal
15 velocities are lower. In spring and summer suspended sediment concentrations increase again as
16 a result of wind-wave resuspension of bottom sediments (Ruhl and Schoellhamer, 2004).
17 Sediment supply will play an increasingly critical role in this process. The supply of sediments
18 is declining as the estuary completes the shift from a system with larger sediment loads as a
19 result of past hydraulic mining to one that has a reduced sediment supply due to the cessation of
20 mining and an increase in tributary dams that trap sediment upstream (Jaffe et al., 1998; Wright
21 and Schoellhamer, 2004).
22 A number of key climate variables (air temperature, precipitation, storm climatology and
23 wind, and sea level rise) and interacting human stressors (e.g., altered flows, dredging/dredge
24 disposal, land use/land cover changes) may impact this process, either directly or indirectly.
25 Increases in winter storms and in strong wind-driven waves may increase erosion of uplands,
26 increasing sediment availability. The North Bay has become somewhat erosional because of an
27 altered balance between riverine sediments and sediment transport. Storms and storm surges
28 may also carry more sediment away from marshes or promote resuspension of bottom sediments,
29 leading to increased suspended sediment concentrations. Sediment deposition and retention on
30 the marsh surface will ultimately depend on marsh geomorphology and surface vegetation (Orr
31 etal.,2003).
32
33 A.2.2.2. Community Interactions
34 The community interactions submodel is presented in Figure A-4. This submodel
35 focuses on community interactions between two species of mudflat wading birds, the Marbled
36 Godwit and the Western Sandpiper, and their predators and prey. Inundation and sediment
37 regimes influence not only mudflat extent, but also mudflat trophic dynamics (Takekawa et al.,
38 2006a). The trophic structure of North Bay mudflats includes invertebrates within mudflat
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1 sediments, shorebirds that feed on mudflat infauna (Stenzel et al., 2002), and Peregrine Falcons
2 and Merlins, which prey on shorebirds (Page and Whitacre, 1975; Ydenberg et al., 2004).
3
4 Figure A-4. Community Interactions sub-model.
5
6 A number of key climate variables and interacting human stressors (altered flows,
7 dredging, land use/land cover changes) may impact these trophic interactions, directly or
8 indirectly. Depending on sediment supply, increased inundation from sea level rise may drown
9 mudflats, while an increase in winds and wave action from more frequent and intense storms
10 may change sediment deposition patterns. Because suspended sediment concentrations are
11 sensitive to the extent and elevation of mudflats, as mudflat elevations decrease, suspended
12 sediment concentrations may decrease over time (Orr et al., 2003). If sediment deposition does
13 not keep pace with sea level rise, mudflat invertebrates will become less available for shorebirds.
14 There is limited information on shorebird diets, making it difficult to group shorebirds by
15 prey type, but shorebirds can be distinguished based on the depth at which they probe into the
16 sediment for prey. Short-legged shorebirds that are shallow probers, represented by Western
17 Sandpiper, forage in the top layer of sediments (< 3 cm) and will lose foraging habitat first. But
18 eventually mudflat invertebrates will also become inaccessible to long-legged deep probers,
19 represented by Marbled Godwit, which penetrate up to 8 cm into the substratum (Takekawa et al.
20 2006a).
21
22 A.3. CONCLUSIONS
23 The analysis of available data for potential indicators and of existing models indicated
24 that there was insufficient information available on metrics for the indicators to answer the
25 sensitivity questions of this assessment using quantitative modeling. However, it was also
26 evident that a vast amount of information local knowledge was available through consultation
27 with regional experts in the processes of interest. This led to the development of the expert
28 elicitation workshop approach described in Chapter 2 of this report. The workshop was meant to
29 serve as an opportunity to supplement current knowledge based on background research and
30 examine potential changes that may occur due to climate influences. The conceptual diagrams
31 described above provided the basis for the development of the initial influence diagrams used at
32 the workshop (as described in Chapter 2 of this report) as well as context for how these
33 ecosystem processes of focus fit with the rest of the ecosystem.
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1 APPENDIX B. EXPERT ELICIATION WORKSHOP PREPARATION
2 AND IMPLEMENTATION
O
4 B.I. PRE-WORKSHOP
5 B.I.I. Selecting Workshop Participants
6 The SFEP partners developed a list of criteria for selecting highly qualified local experts
7 who spanned the range of disciplines, science and management continuum, and empirical versus
8 theoretical research experience needed to collectively characterize the ecosystem processes under
9 consideration. Criteria for selecting participants included:
10
11 • Demonstrated understanding of the body of literature with regard to sediment
12 retention OR community interactions (depending on which breakout group), as
13 evidenced by academic training, research, and publications;
14 • Demonstrated ability to think of uncertainty in qualitative terms ;
15 • Knowledge of science behind estuary management, as evidenced by academic
16 training, research, and publications;
17 • Knowledge of estuary management issues as evidenced by academic training,
18 research, and publications;
19 • Past work in SFEP region; and
20 • Past work with salt marsh development/sediment retention processes (the balance of
21 sediment supply versus loss) OR mudflat development/community interactions
22 (interactions of shorebirds and their predators and prey), depending on the candidate's
23 proposed breakout group.
24
25 These criteria were considered in developing a list of qualified candidates for each
26 breakout group. Candidates were then contacted to determine their availability and interest in
27 testing a new method for vulnerability assessment. Workshop participants included the
28 following individuals:
29
30 Sediment Retention Breakout Group:
31 Dave Cacchione, U.S. Geological Survey
32 John Callaway, UC San Francisco
33 Chris Enright, CA Department of Water Resources
34 Bruce Jaffe, U.S. Geological Survey
35 Lester McKee, San Francisco Estuary Institute
36 Dave Schoellhamer, U.S. Geological Survey
37 Mark Stacey, UC Berkeley
38
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1 Community Interactions Breakout Group:
2 Letitia Grenier, San Francisco Estuary Institute
3 Jessica (Jessie) Lacy, U.S. Geological Survey
4 Michelle Orr, Philip Williams & Associates
5 Diana Stralberg, PRBO Conservation Science
6 Stuart Siegel, Wetlands and Water Resources
7 Lynne Trulio, San Jose State University
8 Isa Woo, U.S. Geological Survey
9
10 The expertise of each of the individual participants contributed to the interdisciplinary
11 complexity of the group. Experts were selected from the management and adaptation research
12 communities, and represented federal and state government agencies, research and consulting
13 organizations, and academia. The credentials for each of the participants, including past and
14 current work and research and areas of expertise, are summarized for the Sediment Retention
15 group in Table B-l, and for the Community Interactions group in Table B-2.
16
17 Table B-l. Sediment Retention breakout group participants, affiliations, and
18 qualifications
19
20 Table B-2. Community Interactions breakout group participants,
21 affiliations, and qualifications
22
23 B.1.2. "Straw Man" Influence Diagrams
24 An initial "straw man" influence diagram (Figure B-l and Figure B-2) for each breakout
25 group was developed by ICF, EPA ORD, SFEP, and BCDC prior to the workshop based on the
26 more detailed salt marsh and mudflat conceptual models and sediment retention and community
27 interactions submodels developed previously (see Appendix A). The "straw man" influence
28 diagrams differed from the more comprehensive conceptual models in that they focused on only
29 those elements of the model that participants believe are most critical for understanding
30 responses of the ecosystem process to the human and climate stressors under consideration. The
31 "straw man" influence diagrams were used in the pre-workshop briefing and homework
32 assignment in order to further refine the sediment retention and community interactions influence
33 diagrams.
34
35 Figure B-l. Sediment Retention "straw man" influence diagram.
36
37 Figure B-2. Community Interactions "straw man" influence diagram.
38
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1 B.1.3. Pre-workshop Briefing and Homework Assignment
2 Participants participated in a pre-workshop briefing call and a homework assignment that
3 would be used to develop consolidated influence diagrams to be used at the workshop. The pre-
4 workshop briefing call was held on March 2, 2010. This call gave participants a briefing on the
5 background of the project, work to date, the purpose of the workshop, and an overview of the
6 homework assignment. Part of the background material presented was information on an
7 example site for participants to consider when more spatial specificity would be useful during the
8 workshop exercise. China Camp, a site on the southwest shore of San Pablo Bay, was chosen
9 because it includes large wetland areas in a transitional salinity zone with intact adjacent
10 habitats.
11 The homework assignment asked participants to review a number of items: (1) selected
12 articles relevant to the ecosystem process breakout group to which they were assigned (for the
13 Sediment Retention breakout group: Orr et al., 2003; Ruhl and Schoellhamer, 2004; and Wright
14 and Schoellhamer, 2004; for the Community Interactions breakout group: Galbraith et al., 2005;
15 Takekawa et al., 2006; Page and Whitacre, 1975; and Stenzel et al., 2002); (2) conceptual models
16 of the ecosystem and ecosystem process to which they were assigned; and (3) the draft influence
17 diagram for the ecosystem process to which they were assigned. Participants were asked to
18 review the draft influence diagram and provide recommendations on what should be added or
19 removed. Participants were asked to add or subtract variables or relationships until the
20 preliminary influence diagram matched their understanding of the process. We asked
21 participants to include no more than 10-15 variables in the diagram in order to keep it focused on
22 the highest priority influences. We also asked participants to focus on current conditions
23 (including current climate) when reviewing and commenting on the diagram.
24 Participants were asked to provide a quantitative definition for each variable, a metric for
25 measuring the variable, and a range of values for the metric. Participants were also asked to
26 assign values to the metrics they selected. This could include actual measured values (e.g., 35
27 km3 of inflow) as well as a range of values (e.g., 5 to 50 km3 of inflow).
28
29 B.I.4. Consolidated Influence Diagrams
30 The preliminary diagram for each breakout group was revised prior the workshop based
31 on the participants' homework responses. The process involved examining the participants'
32 responses and constructing a tally of the variables used and influences (arrows) included.
33 Variables and influences that were most frequent across all responses were included in the
34 consolidated influence diagrams. For the both the Sediment Retention and Community
35 Interactions groups, all but one of the participants provided comments on the preliminary
36 influence diagram. Based on the responses from the participants, consolidated influence
37 diagrams were developed for the workshop.
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1
2 B.2. WORKSHOP
3 B.2.1. Group Influence Diagrams
4 Group influence diagrams were developed during the first day of the workshop. Within
5 their breakout groups, the participants discussed how the consolidated influence diagrams should
6 be refined for use as a final "group" influence diagram. The participants added, removed, or
7 redefined variables based on a group discussion. The group diagrams were to become the basis
8 for the expert elicitation exercise of assigning judgments about influences among variables. The
9 Sediment Retention and Community Interactions group influence diagrams are provided in
10 Chapter 2.
11
12 B.2.2. Introduction to Climate Scenarios and Confidence
13 The participants received two handouts designed to orient them to the climate scenarios
14 and to the methodology for assessing confidence. The first handout contained a summary of
15 Climate Scenarios A and B, which was used by the participants in assessing the sensitivity of salt
16 marshes and mudflats across a range of plausible scenarios of climate change. It explained the
17 development of two climate futures in a mid-century (2035-2064) time frame. Participants used
18 these scenarios on Day 2 to make new judgments compared to their judgments under "current
19 conditions" on Day 2. The full climate scenarios handout can be found in Appendix C.
20 The second handout presented explanatory information and a coding scheme for use by
21 the participants in assessing their confidence in each of their judgments under both current
22 conditions and under Climate Scenarios A and B. The full handout may be found in Appendix
23 D.
24
25 B.2.3. Coding Exercise
26 Following the development of the group influence diagrams, participants were asked to
27 make their individual judgments on the diagram using the coding scheme. As described in
28 Chapter 2, the participants used the coding scheme to make judgments on the following: (1) type
29 and degree of influence for each relationship included in the influence diagram; (2) the
30 associated confidence for each influence judgment; (3) type of interactive influences for
31 relationships of their own choosing; and (4) the associated confidence for each interactive
32 influence judgment. These judgments were done for current conditions (on the first day of the
33 workshop), and Climate Scenario and Climate Scenario (on the second day of the workshop).
34 Example handouts that participants used to make their judgments are provided in Tables B-3, B-
35 4, andB-5.
36
37 Table B-3. Example of expert elicitation handout for influences under
38 current conditions (Sediment Retention group)
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1
2 Table B-4. Example of expert elicitation handout for influences under
3 climate scenarios (Community Interactions group)
4
5 Table B-5. Example of expert elicitation handout for interactive influences
6 under climate scenarios (Sediment Retention group)
7
8 B.2.4. Variation Across Participants in Sensitivity Judgments
9 For both the Sediment Retention and Community Interactions groups, variability among
10 participants in their judgments contributed to lack of agreement on sensitivities for some
11 influences. Figure B-3 presents the full range of variation among participants of the Sediment
12 Retention group by showing the same trio of figures as shown in Figure 2-4, but broken out for
13 each individual participant. Looking across all the participants, there was more variability
14 between participants than across scenarios for any given participant. There were no patterns
15 across participants, such as characterizing only increasing sensitivity. The changes across the
16 scenarios made by Participant 3 were of only increasing sensitivity, Participant 1 only had one
17 change to decreasing sensitivity, and Participants 2, 6 and 7 had both increases and decreases,
18 sometimes across the scenarios for one influence. Partipants 4 and 5 made no changes in
19 sensitivity across the climate scenarios.
20
21 Figure B-3. Sediment Retention influence diagrams of sensitivities: variance
22 across participants.
23
24 For the Community Interactions group, Figure B-4 presents the full range of variation
25 among participants by showing the same trio of figures as those shown in Figure 2-10, but
26 broken out for each individual participant. Looking across all the participants, we see that there
27 is again more variability between participants than across scenarios for any given participant.
28 The majority of changes in sensitivity type across the climate scenarios are of increasing
29 sensitivity. The changes across the scenarios made by Participants 1, 2 and 4 are of only
30 increasing sensitivity; Participant 5 only had one change, to decreasing sensitivity; Participants 3,
31 6 and 7 had both increases and decreases, but more of the former.
32
33 Figure B-4. Community Interactions influence diagrams of sensitivities:
34 variance across participants.
35
36 B.2.5. Exercise Discussions and Report-outs
37 After participants made their individual judgments on the influence diagram using the
38 coding exercise, the participants reconvened in their breakout groups for a group discussion.
39 Participants discussed their reactions to the exercise and how it was structured, individual
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1 judgments on type and degree of influence, individual judgments on confidence, key issues and
2 gaps in understanding. This group discussion often helped to clarify issues that participants may
3 have had in understanding the coding scheme or influences that they may have lacked clarity on.
4 Based on this group discussion, the facilitator helped the participants to identify some key
5 points that emerged. These key points addressed issues such as key influences, important
6 pathways, thresholds, significant changes associated with climate change, management
7 implications, etc. One of the participants from each breakout group presented these key points to
8 the larger group to summarize the discussion. Following the discussion, participants were given
9 time to revisit their individual judgments.
10
11 B.2.6. Discussion of Management Implications
12 Following the breakout group discussions and exercise of making individual judgments,
13 participants gathered in the larger group to discuss management implications. This discussion
14 would help SFEP and BCDC to examine some of the key issues that emerged from the expert
15 elicitation exercise and translating those issues into action. The facilitator led the discussion by
16 asking participants to consider how climate stressors might impact the estuary across a range of
17 management scenarios. The discussion also explored research and data needs, suggestions for
18 water and sediment management, and fundamental shifts in management that may be necessary.
19
20 B.3. POST-WORKSHOP
21 B.3.1. Review of Workshop Report
22 A report was developed subsequent to the workshop documenting key outputs in two
23 sections: key results and workshop discussions. This report provides a documentation of all of
24 the participant materials, including: participant guidance documents, participant homework
25 responses, handouts and other materials used at the workshop, and individual participant
26 judgments. Key points that emerged during the breakout group and larger group discussions are
27 summarized, as well as the discussion on management implications. Participants were asked to
28 review this report and provide any comments. These comments were incorporated into a final
29 workshop report, which is available upon request from the authors.
30
31 B.3.2. Synthesis of Results
32 A synthesis of results was developed in order to analyze the participants' individual
33 judgments made at the workshop. The synthesis reviews the objectives of conducting the expert
34 elicitation workshop and identifies key questions that the synthesis of judgments seeks to answer.
35 It reviews the coding schemes used by participants during the workshop and summarizes a
36 coding typology that was used to group codes to characterize types and degrees of influences and
37 sensitivities. Finally, it describes the methodology for analyzing the available judgments and
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1 presents key results in the form of tables and figures. The contents of this synthesis comprise
2 much of the substance of the results sections of this report.
3
4 B.3.3. Review of Draft Report
5 The workshop report and preliminary results reports were used to develop this technical
6 report to present the synthesis results and place them in the larger context of the implications for
7 management and SFEP's capacity to respond. The draft report was revised based on an internal
8 review by EPA scientists. The report is now under public and expert peer review. Following
9 this review, a final report will be developed that responds to the public and peer-review
10 comments. An additional report that focuses on lessons learned across the two assessments for
11 SFEP and MBP will also be developed.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 APPENDIX C. PARTICIPANT HANDOUT ON CLIMATE SCENARIOS
2
3 SFEP Workshop Climate Scenarios
4
5 This handout is intended to assist participants in assessing the sensitivity of salt marshes and
6 mudflats across a range of plausible scenarios of climate change. It provides the details of two
7 distinct but scientifically credible climate futures for a mid-century (2035-2064) time period.2
8 Participants will use these scenarios in revisiting their assessments of influence completed on the
9 first day.
10
11 Two Climate Change Scenarios: "Lower-Range" and "Higher-Range"3
12
13 Under both climate change scenarios, California will retain its Mediterranean climate (cool/wet
14 winters and hot/dry summers) and continue to experience a high degree of variability in
15 precipitation with rising sea levels. By mid-century, the "higher-range" scenario (including
16 higher emissions and a more sensitive climate) is projected to experience a warmer and
17 somewhat drier climate compared to the "lower-range" scenario (with lower emissions and a
18 lesser impact on California's climate).
19
20 Development of the Climate Scenarios
21
22 The two bounding scenarios were developed from a collective group of studies in large part
23 funded by the California Energy Commission (CEC) under the mandate of the Governor's
24 Biennial Climate Change Report. A majority of the climate projections presented here were
25 developed by Cayan et al. (2009), based on projections from 6 leading climate models.4 These
26 models were selected based on their reasonable representation of historical simulation of
27 seasonal precipitation, seasonal temperature, the variability of annual precipitation, and El
28 Nino/Southern Oscillation (ENSO). All models were run with both a lower emission scenario
29 (Bl SRES) and a mid-high emission scenario (A2 SRES) to capture a range of plausible future
30 emissions trajectories. The "lower-range" and "higher-range" temperature and precipitation
31 scenarios for 2035-2064 compared to 1961-1990 baseline conditions are based on these climate
32 model simulations, for the SRES Bl (lower) and SRES A2 (higher) scenarios, respectively.
33 Regional projections were developed by statistical downscaling.
34
35 For a given U.S. coastal location, relative sea level rise may differ from global estimates due to a
36 number of factors such as changes in local ocean circulation, ocean density, vertical land motion,
2 These two futures are designed to capture a large part of the uncertainty inherent to future projections that is the
result of two key factors: (1) the amount of future emissions of greenhouse gases from human activities that are
driving global change, and (2) the ability of scientists to simulate the response of the Earth's climate system to those
emissions.
3 The usage of the terms "lower-range" and "higher-range" refers to the scenarios provided in this handout and are
not intended to reflect the lowest and highest possible futures.
4U.S. NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) CM2.1; the National Center for Atmospheric
Research (NCAR) Parallel Climate Model (PCM); the National Center for Atmospheric Research (NCAR)
Community Climate System Model (CCSM); the Max Plank Institute ECHAM5/MPI-OM; the Center for Climate
System Research of the University of Tokyo MIROC 3.2 medium-resolution model; and the French Centre National
de Recherches Meteorologiques (CNRM) models.
5 Statistical downscaling methodology includes constructed analogues, bias correction and spatial downscaling of the
results from each of the 6 climate models.
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1 erosion/sedimentation, gravitational effects, etc. Relative sea level rise in California has
2 demonstrated similar rates of rise compared to global estimates (Cayan et al. 2008). Many
3 California studies recommend using projections of global sea level rise estimates, which assumes
4 relative sea levels continue to rise at the same rate as projected global sea level rise. The "lower-
5 range" sea level rise estimate is provided as a mid-range of Rahmstorf (2007) and high-end of
6 IPCC TAR. The "higher-range" sea level rise estimate is provided as the high estimate of
7 Rahmstorf (2007).
9
10
Summary of Climate Scenarios: Averages for Mid-Century
Temperature6
Precipitation
Annual Average
Average Increase of
Winter Temperature8
Average Increase of
Summer
Temperature0
Extreme Heat Daysy
Annual Changelu
Winter change
Heavy Events
"Lower-Range"
Scenario
+2.8°F (1.6°C)
+2.5°F (1.4 °C)
+4.0°F (2.2 °C)
+10 days/year
-4.5%
"Higher-Range"
Scenario
+3.5°F (1.9 °C)
+2.7°F (1.5 °C)
+4.5°F (2.5 °C)
+16 days/year
-7%
Reduced winter precipitation
Decline in frequency of precipitation events
(exceeding 3mm/day) but not a clear signal in changes
of precipitation intensity
Since the 1920s, minimum and maximum daily temperature have been observed to have increased in California
with minimum temperature increasing at a greater rate accented by a small cooling trend in the summer (Cayan et al.
2009). These averages are for 2035-2064 projections relative to a 1961 to 1990 baseline for Bl and A2 emission
scenarios.
7 Approximate results using Bl and A2 emissions scenarios and three global climate models (PCM1, GFDL CM2.1,
HadCMS) (CEC 2006).
8 These results are for Sacramento, California. This warming is projected to be more moderate along the coastline
(50 km from the coast) rising considerably inland (Cayan et al. 2009). These averages are for 2035-2064 projections
relative to a!961 to 1990 baseline for Bl and A2 emissions scenarios.
9 Extreme heat days are defined as when the daily maximum temperature exceeds the 95th percentile of temperature
from the 1961-1990 historical averages of May-September days. 1961-1990 extreme heat days are approximately 8
days/year based on model runs. Results are provided by Cayan et al. (2009) using three climate models (CNRM
CMS, GFDL CM2.1, MICRO 3.2; with bias corrected spatial downscaling) for Bl and A2 emissions scenarios.
Mid-century projections suggest hot daytime and nighttime temperatures increase in frequency, magnitude, and
duration (Cayan et al. 2009). Extreme warm temperatures in California, historically a July and August phenomenon,
will increase in frequency and magnitude likely beginning in June and may continue into September (Hayhoe et al.
2004; Gershunov and Douville 008; Miller et al. 2008).
10Results are averaged across 6 GCMs using the grid point nearest to Sacramento (Cayan et al. 2009) for Bl and A2
emissions scenarios.
11 These results are provided by CEC (2008).
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Sea Level
Storms/Wind15
Total Increase for
205012
Hourly Sea Level
Exceedances14
"Lower-Range"
Scenario
+30 cm
1343
"Higher-Range"
Scenario
+45 cm13
1438
Tendency toward a decline in storms.16 Projections suggest an increased
tendency for heightened sea level events to persist for more hours.
ENSO is not projected to increase in frequency or intensity.
1
2
3
What else do these changes mean for our system?
Snow Pack Change
Spring Runoff
Seasonal Changes in Amount
of Freshwater Inflow to the
Bay from the Delta in 206018
For the Sacramento-San Joaquin watershed, April watershed-
total snow accumulation projected to drop by 64% by 2060. 1T
Spring runoff occurring earlier and reduced overall
October through February: inflow +20%
March through September: inflow -20%
4
5
6
7
8
9
10
11
Where can I find additional information?
California Climate Change Research Center
http://www.climatechange.ca.gov/research/index.html
Union of Concerned Scientists
www. climatechoi ces. org/ca
Sea level rise relative to 2000 levels. This study applies Rahmstorf's methodology of estimating sea level rise as a
function of rising temperatures. This study assumes sea level rise along the coast to be the same as global estimates
given the observed rate of rise along the southern California coast has been about 17 to 20 cm per century similar to
that of global sea level rise (assume no future changes in other factors that affect relative sea level rise such as
changes in regional/local ocean circulation, ocean density, etc.) (Cayan et al. 2009). DMRS also provides
recommended 2050 global sea level rise estimates relative to 1990 values: 11 cm (direct extrapolation of observed
increased during the 20th century), 20 cm (low-end value of Rahmstorf and approx mid-range of IPCC TAR), 30 cm
(approx mid-range of Rahmstorf and high-end of IPCC TAR); 41 cm (high end of Rahmstorf) (DMRS 2007).
13 The total difference between mean range and spring range of 1.7 ft (50.3 cm) is slightly larger than the higher-
range scenario rise of 45 cm, based on the Point San Pedro tide station.
http://tidesandcurrents.noaa. gov/tides 10/tab2wc 1 a.html# 128
14 The hourly sea level exceedance is defined as the maximum duration (hours) when San Francisco sea level
exceeds the 99.99th % level (140 cm above mean sea level) based on the GFDL climate change (A2) simulation
using the Rahmstorf sea level scheme averaged 2 to 4 hours increase for mid-century (Cayan et al. 2009).
15 These results are provided by Cayan et al. (2009).
16 Storm is defined as sea level pressure (SLP) equaling or falling below 1005 millibar (mb).
17 Results provided by the Bay-Delta watershed model driven by temperature projections from a parallel climate
model under a 'business-as-usual' scenario relative to 1995-2005 (precipitation is assumed to remain consistent with
today's observations) (Knowles and Cayan 2004).
18 This study does account for reservoirs, in-stream valley diversions, and in-Delta withdrawals and assumes no
future management adaptation or altered demand patterns (Knowles and Cayan 2004).
This document is a draft for review purposes only and does not constitute Agency policy.
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1 Works Cited
2
3 California Energy Commission (CEC). 2006. Our Changing Climate: Assessing the Risks to
4 California. A Summary Report from the California Climate Change Center.
5
6 California Energy Commission (CEC). 2008. The Future is Now: An Update on Climate Change
7 Science, Impacts, and Response Options for California. A report from the California Climate
8 Change Center California Energy Commission's Public Interest Energy Research Program.
9
10 Cayan, D., P. Bromirski, K. Hayhoe, M. Tyree, M. Dettinger, & R. Flick. 2008. Climate change
11 projections of sea level extremes along the California coast. Climatic Change 87(0), 57-73. DOI:
12 10.1007/sl0584-007-9376-7.
13
14 Cayan, D., M. Tyree, M. Dettinger, H. Hidalgo, T. Das, E. Maurer, P. Bromirski, N. Graham,
15 and R. Flick. 2009. Climate Change Scenarios and Sea Level Rise Estimates for the California
16 2008 Climate Change Scenarios Assessment. California Climate Change Center. CEC-500-
17 2009-014-D.
18
19 Delta Risk Management Study (DRMS) 2007. Technical Memorandum: Delta Risk
20 Management Study (DRMS) Phase 1.
21
22 Gershunov A., and H. Douville. 2008. Extensive summer hot and cold extremes under current
23 and possible future climatic conditions: Europe and North America. In: H. Diaz, and R. Murnane
24 (Eds.), Climate Extremes and Society. Cambridge University Press.
25
26 Hayhoe K., D. Cayan, C. B. Field, P. C. Frumhoff, E. P. Maurer, N. L. Miller, S. C. Moser, S. H.
27 Schneider, K. N. Cahill, E. E. Cleland, L. Dale, R. Drapek, R. M. Hanemann, L. S. Kalkstein, J.
28 Lenihan, C. K. Lunch, R. P. Neilson, S. C. Sheridan, and J. H. Verville. 2004. "Emissions
29 pathways, climate change, and impacts on California." PNAS 101(34): 12422-12427. Aug. 24;
30 Epub Aug. 16, 2004.
31
32 Knowles, N. and D. Cayan. 2004. Elevational Dependence of Projected Hydrologic changes in
33 the San Francisco Estuary and Watershed. Climate Change 62: 319-336.
34
35 Miller, N. L., K. Hayhoe, J. Jin, and M. Auffhammer. 2008. "Climate, Extreme Heat, and
36 Electricity Demand in California." Journal of Applied Meteorology and Climatology
37 47:1834-1844.
This document is a draft for review purposes only and does not constitute Agency policy.
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1 APPENDIX D. PARTICIPANT HANDOUT ON CONFIDENCE
2
3 Method for Assessing Confidence in Expert Judgments
4
5 Characterization of uncertainty is a critical component of assessment science. Thus this
6 workshop exercise includes a component in which the expert participants will assess their current
7 level of scientific confidence in each influence for which they are making a judgment. The aim is
8 to provide information on not only degrees of influence among variables, but also the degree of
9 uncertainty associated with each judgment, given the current state of knowledge in the scientific
10 community.
11
12 The design of this analysis is derived from general guidance on uncertainty from recent large
13 assessment efforts such as those of the Intergovernmental Panel on Climate Change (IPCC) and
14 the U.S. Climate Change Science Program (CCSP) [e.g., see Moss and Schneider, 2000; IPCC,
15 2004; IPCC, 2005; CCSP, 2008; CCSP, 2009]. One fundamental principle is the distinction
16 between uncertainty expressed in terms of "likelihood" of an outcome versus "level of
17 confidence" in the science underlying the finding. Likelihood is relevant when assessing the
18 chance of defined future occurrence or outcome, and involves assigning numerical probabilities
19 to qualifiers such as "probable," "possible," "likely," "unlikely" (CCSP 2009). In contrast, level
20 of confidence refers to the (qualitative) degree of belief within the scientific community that
21 knowledge, models, and analyses are accurate, based on the available evidence and the degree of
22 consensus in its interpretation. We are taking this latter approach.
23
24 Each expert is asked to rate his/her confidence in each judgment about degree of influence based
25 on: (1) the amount of scientific evidence that is available to support the judgment; and (2) the
26 level of agreement/consensus in the expert community regarding the different lines of evidence
27 that would support the judgment. These confidence attributes are further described below:
28
29 High/low amount of evidence: Is the judgment based on information that is well-studied and
30 understood, or mostly experimental or theoretical and not well-studied? Does your experience in
31 the field, your analyses of data, and your understanding of the literature indicate that there is a
32 high or low amount of information on this influence? Sources of evidence - in order of relative
33 importance - include: 1) peer-reviewed literature; 2) grey literature; 3) data sets; 4) personal
34 observations and personal communications.
35
36 High/low amount of agreement: Do the studies and reports across the scientific community, as
37 well as your own experience in the field or analyzing data, reflect a high degree of agreement
38 about the influence, or do they lead to competing interpretations?
39
40 Based on the above, levels of confidence in judgments can be sorted into four general categories:
41
42 • Well established = high evidence/high agreement (HH);
43 • Competing explanations = high evidence/low agreement (HL);
44 • Established but incomplete = low evidence/high agreement (LH);
45 • Speculative = low evidence/low agreement (LL).
This document is a draft for review purposes only and does not constitute Agency policy.
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1 References
2
3 CCSP, 2008: Preliminary review of adaptation options for climate-sensitive ecosystems and
4 resources. A Report by the U.S. Climate Change Science Program and the Subcommittee on
5 Global Change Research. [Julius, S.H., J.M. West (eds.), J.S. Baron, B. Griffith, L.A. Joyce, P.
6 Kareiva, B.D. Keller, M.A. Palmer, C.H. Peterson, and J.M. Scott (Authors)]. U.S.
7 Environmental Protection Agency, Washington, DC, USA, 873 pp.
8
9 CCSP, 2009: Best practice approaches for characterizing, communicating and incorporating
10 scientific uncertainty in climate decision making. A Report by the U.S. Climate Change Science
11 Program and the Subcommittee on Global Change Research. [Morgan, M.G., H. Dowlatabadi,
12 M. Henrion, D. Keith, R. Lempert, S. McBride, M. Small, and T. Wilbanks (Authors)].
13 Washington, DC, 156pp.
14
15 IPCC, 2004: Workshop on Describing Scientific Uncertainties in Climate Change to Support
16 Analysis of Risk and of Options. Working Group I Technical Support Unit, Boulder, Colorado
17 [Manning, M., M. Petit, D. Easterling, J. Murphy, A. Patwardhan, H.-H. Rogner, R. Swart and
18 G. Yohe (eds.)], May 11-13, 2004, National University of Ireland, Maynooth, Co. Kildare,
19 Ireland, 146 pp. Available at:
20 http://ipcc-wg 1 .ucar.edu/meeting/URW/product/URW Report v2.pdf.
21
22 IPCC, 2005: Guidance Notes for Lead Authors of the IPCC Fourth Assessment Report on
23 Addressing Uncertainties, 4 pp. Available at: www.ipcc.ch/pdf/assessment-report/ar4/wgl/ar4-
24 uncertaintyguidancenote.pdf.
25
26 Moss, R. and S.H. Schneider, 2000: Uncertainties in the IPCC TAR: Recommendations to lead
27 authors for more consistent assessment and reporting. In: Guidance Papers on the Cross Cutting
28 Issues of the Third Assessment Report of the IPCC [Pachauri, R., T. Taniguchi, K. Tanaka
29 (eds.)], World Meteorological Organisation, Geneva, Switzerland, 33-51.
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