i

Final Report for Sea-level Rise Response 

Modeling for San Francisco Bay Estuary Tidal

Marshes

Open-File Report 2013–1081

U.S. Department of the Interior

Prepared in cooperation with U.S. Fish and Wildlife Service
ii

U.S. Geological Survey

Photo credit: Karen Thorne (author)

Description: Using a RTK GPS a USGS technician (Kyle Spragens) takes elevation surveys on San Pablo 

Bay National Wildlife Refuge in northern San Francisco Bay, California.
iii

Final Report for Sea-level Rise Response Modeling for 

San Francisco Bay Estuary Tidal Marshes

 

By John Y. Takekawa, Karen M. Thorne, Kevin J. Buffington, Kyle A. Spragens, Kathleen M. Swanson, 

Judith Z. Drexler, David H. Schoellhamer, Cory T. Overton, and Michael L. Casazza

Prepared in cooperation with U.S. Fish and Wildlife Service

Open-File Report 2013–1081

U.S. Department of the Interior

U.S. Geological Survey
iv

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

SUZETTE M. KIMBALL, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

For product and ordering information:

World Wide Web: http://www.usgs.gov/pubprod

Telephone: 1-888-ASK-USGS

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment:

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS

Suggested Citation:

Takekawa, J.Y., Thorne, K.M., Buffington, K.J., Spragens, K.A., Swanson, K.M., Drexler J.Z., 

Schoellhamer, D.H., Overton, C.T., Casazza M.L. 2013. Final report for sea-level rise response 

modeling for San Francisco Bay estuary tidal marshes. U.S. Geological Survey Open File Report 2012-

1081, 161 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
v

TABLE OF CONTENTS

Executive Summary.......................................................................................................................................................ix

Abstract ......................................................................................................................................................................... 1

Introduction.................................................................................................................................................................... 1

Study Area..................................................................................................................................................................... 2

Methods......................................................................................................................................................................... 5

Elevation surveys....................................................................................................................................................... 5

Elevation modeling..................................................................................................................................................... 5

Vegetation surveys .................................................................................................................................................... 5

Water-level monitoring ............................................................................................................................................... 6

The Wetland Accretion Rate Model for Ecosystem Resilience (WARMER)................................................................... 7

Sea-level rise scenario model inputs.......................................................................................................................... 8

Inorganic matter model inputs.................................................................................................................................... 9

Organic matter model inputs ...................................................................................................................................... 9

Compaction and decomposition model inputs...........................................................................................................10

Results..........................................................................................................................................................................10

Elevation ...................................................................................................................................................................10

Vegetation.................................................................................................................................................................15

Water monitoring.......................................................................................................................................................17

Marsh response modeling.........................................................................................................................................19

WARMER sensitivity analysis ...................................................................................................................................22

Discussion ....................................................................................................................................................................23

Next steps.....................................................................................................................................................................25

Acknowledgments ........................................................................................................................................................25

Appendix A. Arrowhead Marsh ..................................................................................................................................30

Introduction ...............................................................................................................................................................30

Results......................................................................................................................................................................30

Elevation surveys ..................................................................................................................................................30

Vegetation surveys................................................................................................................................................34

Water-level monitoring ..........................................................................................................................................36

Marsh elevation modeling .....................................................................................................................................36

Appendix B. Black John Marsh ..................................................................................................................................42

Introduction ...............................................................................................................................................................42

Results......................................................................................................................................................................42

Elevation surveys ..................................................................................................................................................42

Vegetation surveys................................................................................................................................................44

Water-level monitoring ..........................................................................................................................................46

Marsh elevation modeling .....................................................................................................................................47

Appendix C. China Camp Marsh................................................................................................................................53

Introduction ...............................................................................................................................................................53

Results......................................................................................................................................................................53

Elevation surveys ..................................................................................................................................................53

Vegetation surveys................................................................................................................................................56

Water-level monitoring ..........................................................................................................................................58

Marsh elevation modeling .....................................................................................................................................59

References Cited...........................................................................................................................................................25
vi

Introduction ...............................................................................................................................................................64

Results......................................................................................................................................................................64

Elevation surveys ..................................................................................................................................................64

Vegetation surveys................................................................................................................................................66

Water-level monitoring ..........................................................................................................................................68

Marsh elevation modeling .....................................................................................................................................69

Appendix E. Colma Marsh .........................................................................................................................................74

Introduction ...............................................................................................................................................................74

Results......................................................................................................................................................................74

Elevation surveys ..................................................................................................................................................74

Water-level monitoring ..........................................................................................................................................77

Marsh elevation modeling .....................................................................................................................................78

Appendix F. Coon Island Marsh.................................................................................................................................83

Introduction ...............................................................................................................................................................83

Results......................................................................................................................................................................83

Elevation surveys ..................................................................................................................................................83

Vegetation surveys................................................................................................................................................86

Water-level monitoring ..........................................................................................................................................88

Marsh elevation modeling .....................................................................................................................................89

Appendix G. Corte Madera Marsh..............................................................................................................................94

Introduction ...............................................................................................................................................................94

Results......................................................................................................................................................................94

Elevation surveys ..................................................................................................................................................94

Vegetation surveys ................................................................................................................................................96

Water-level monitoring ..........................................................................................................................................98

Marsh elevation modeling .....................................................................................................................................99

Appendix H. Fagan Slough Marsh ...........................................................................................................................104

Introduction .............................................................................................................................................................104

Results....................................................................................................................................................................104

Elevation surveys ................................................................................................................................................104

Vegetation surveys..............................................................................................................................................106

Water-level monitoring ........................................................................................................................................108

Marsh elevation modeling ...................................................................................................................................109

Appendix I. Gambinini Marsh ...................................................................................................................................114

Introduction .............................................................................................................................................................114

Results....................................................................................................................................................................114

Elevation surveys ................................................................................................................................................114

Vegetation surveys..............................................................................................................................................117

Water-level monitoring ........................................................................................................................................119

Marsh-elevation modeling ...................................................................................................................................120

Appendix J. Laumeister Marsh.................................................................................................................................125

Introduction .............................................................................................................................................................125

Results....................................................................................................................................................................125

Elevation surveys ................................................................................................................................................125

Vegetation surveys ..............................................................................................................................................128

Water-level monitoring ........................................................................................................................................131

Marsh elevation modeling ...................................................................................................................................131

Appendix D. Cogswell Marsh .....................................................................................................................................64
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Appendix K. Petaluma Marsh...................................................................................................................................136

Introduction .............................................................................................................................................................136

Results....................................................................................................................................................................136

Elevation surveys ................................................................................................................................................136

Vegetation surveys..............................................................................................................................................138

Water-level monitoring ........................................................................................................................................140

Marsh-elevation modeling ...................................................................................................................................141

Appendix L. San Pablo Bay Marsh...........................................................................................................................146

Introduction .............................................................................................................................................................146

Results....................................................................................................................................................................146

Elevation .............................................................................................................................................................146

Vegetation ...........................................................................................................................................................150

Water-level monitoring ........................................................................................................................................154

Marsh elevation modeling ...................................................................................................................................155
viii

LIST OF FIGURES

1. Diagram showing climate change downscaling conceptual model.......................................................... 2

2. Diagram showing program conceptual model ......................................................................................... 3

3. Map showing San Francisco Bay estuary marsh study sites................................................................... 4

4. Photo showing vegetation data collection ............................................................................................... 5

5. Photo showing water-level data collection............................................................................................... 6

6. Diagram showing WARMER conceptual model....................................................................................... 7

7. Graph showing sea-level rise curve ........................................................................................................ 8

8. Elevation models for all sites................................................................................................................. 13

9. Graph showing distribution of marsh elevation relative to mean high water for all sites........................ 14

10. Graph showing marsh elevation relative to mean high water by site..................................................... 15

11. Graph showing dominant vegetation species composition in relation to elevation across study sites... 16

12. Graph showing salinity during 2010 at a subset of study sites.............................................................. 19

13. Graph showing calculated sediment accumulation for representative sites .......................................... 21

14. Graph showing calculated organic matter accumulation for representative sites .................................. 22

15. Graph showing sensitivity analysis of WARMER .................................................................................. 23

LIST OF TABLES

1. Summary of the amount of elevation and vegetation data collected for each site................................. 11

2. ArcGIS elevation model root-mean-square error (RMS) and standard error (SE) by site...................... 12

3. Plant communities were categorized on the basis of observed elevation distributions 

to develop marsh zones to model with sea-level rise......................................................................... 16

4. Summary of the most common plant species found across all sites. .................................................... 17

5. Water elevations (NAVD88), in meters (m), for each marsh site in 2010. ............................................. 18

6. Model inputs used for primary WARMER model sites........................................................................... 20

7. WARMER model inputs used for extrapolation to the remaining sites. ................................................. 20
ix

Executive Summary

 San Francisco Bay estuary contains the largest remaining expanse of tidal salt marsh on the 

Pacific coast of the United States. We collected baseline elevation, tidal inundation, and vegetation 

data at 12 marsh sites to examine potential effects of climate change. We used Real-Time 

Kinematic Global Positioning System (RTK GPS; measured ±2 centimeter vertical accuracy) to 

survey elevation. Vegetation was surveyed within 0.25 square meter quadrats for species 

composition, percent cover, and height. We established water-level monitoring stations at all sites 

to capture local annual variation in tidal inundation and to relate elevations to site-specific tidal

datums.

 Study sites included San Pablo Bay National Wildlife Refuge, China Camp State Park, Corte 

Madera Ecological Reserve, Fagan Ecological Reserve, Cogswell Marsh, Arrowhead Marsh, 

Colma Creek Marsh, Laumeister Marsh, Coon Island Marsh, Black John Marsh, Petaluma Marsh, 

and Gambinini Marsh. Across all sites, 19.94 square kilometers of marsh was surveyed, which is

roughly 12 percent of the remaining tidal marshes in the San Francisco Bay estuary. 

 Ground-elevation data were interpolated in ArcGIS 9.3 (kriging method) into continuous 3 x 3 meter

grid cell raster. Mean root-mean-square error for all interpolations was less than 0.10 meters. 

 Sediment cores collected along elevation transects at four representative sites (China Camp, Coon 

Island, Petaluma, and Laumeister) were used to determine historic accretion rates with horizon 

marker dating techniques (210Pb, 137Cs; Callaway and others, 2012). Percentage organic matter, 

porosity, compaction, and decomposition were incorporated with accretion rates, elevation, and 

tidal range data as inputs into a Wetland Accretion Response Model for Ecosystem Resilience 

(WARMER). Results at the representative sites were extrapolated to the remaining eight sites.

 Across all sites, 7,437 elevation points were surveyed, and 88 percent of the data were between 

1.5 and 2.1 meters (North American Vertical Datum of 1988). Sites were located at different 

elevations within the San Francisco Bay tidal range. Sites ordered by decreasing mean elevation

were Fagan (highest mean elevation), Laumeister, Gambinini, Coon Island, Petaluma, San Pablo, 

Black John, China Camp, Cogswell, Arrowhead, and Colma (lowest mean elevation).

 A total of 3,302 vegetation plots were surveyed across all sites. Low plant species richness was 

recorded, with 21 plant species identified within plots across all sites. The plant community 

composition also reflected variation in tidal inundation and water salinity. For example, the highest 

species diversity was found in marshes along the Napa River, presumably as a result of lower 

water salinity levels.

 Pickleweed (Sarcocornia pacifica) was the most common species recorded and was found in 91

percent of the plots, followed by Schoenoplectus spp. (12 percent), Distichlis spicata (9 percent), 

Spartina spp. (9 percent), Jaumea carnosa (7 percent), Grindelia stricta (7 percent), and Frankenia 

salina (4 percent). 

 Plant communities were categorized on the basis of observed elevation relative to local tidal datum 

to develop marsh zones for sea-level rise response model interpretation. Upland transition (greater

than 1.0 meter relative to mean sea level) was characterized by coyote bush (Baccharis pilularis). 
x

High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and Jaumea carnosa, 

whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia pacifica. Low marsh (0.2–

0.45 m MSL) was characterized by Spartina spp. or, in brackish areas, by Schoenoplectus spp. 

Mudflat habitat (less than 0.2 m MSL) was unvegetated or sparsely covered with Spartina spp.

 Results from the WARMER modeling showed that 96 percent (1,942 hectares) of the study area, or 

nine of the sites, would become mudflats by 2100 with a 1.24 meter sea-level rise. The three sites 

with the remaining 4 percent (85 ha) of the study area were projected to be low-marsh habitat 

dominated by Spartina spp. by 2100. All upland transition, high-, and mid-marsh habitats were 

projected to be lost by 2100.

 Accretion rates used in the WARMER model were relatively high in south San Francisco Bay

(Callaway and others, 2012), and those marshes withstood sea-level rise longer, with areas 

transitioning from high to low marsh vegetation by 2100 (for example, Cogswell, Laumeister, and 

Colma).

 Napa River sites were parameterized with higher sediment accretion rates and higher starting 

elevations with WARMER and showed the maintenance of high marsh until 2030 (0.24 meter sealevel rise) for Coon Island and 2040 (0.32 meter sea-level rise) for Fagan marsh. Between 2040 

(0.32 meter sea-level rise) and 2060 (0.57 meter sea-level rise), mid-marsh vegetation was 

maintained. Low marsh was dominate to 2090 (1.05 meter sea-level rise), at which time both 

marshes transitioned to mudflats.

 All other marsh sites had relatively low accretion rates relative to sea-level rise. WARMER 

projected all of these marshes to transition to mudflat by 2100. Corte Madera, China Camp, and 

San Pablo Bay National Wildlife Refuge marshes lost all high marsh by 2030 (0.24 meter sea-level 

rise), briefly transitioned to mid- and low-marsh plant communities, but ultimately transitioned to 

areas dominated by mudflat by 2080 (0.85 meter sea-level rise). The three marshes located on the 

Petaluma River (Gambinini, Petaluma, and Black John) lost most high-marsh habitat by 2030 (0.24 

meter sea-level rise) and transitioned to mostly mudflat by 2080 (0.85 meter sea-level rise).

 Projected loss of pickleweed (Sarcocornia pacifica) habitats by 2100 could affect many tidal marsh 

wildlife species, such as the federally endangered salt marsh harvest mouse (Reithrodontomys 

raviventris) and state threatened California black rail (Laterallus jamaicensis coturniculus). Only 4

percent of the marsh area was projected to support cordgrass (Spartina spp.) habitats by 2100, 

which also would affect distribution of the federally endangered California clapper rail (Rallus 

longirostris obsoletus).

 The modification of the San Francisco Bay estuary makes it especially susceptible to sea-level rise

since there are few areas available for upslope marsh transgression. Seven of the marsh sites we 

surveyed had adjacent open space, but five marshes were surrounded by urban infrastructure 

prohibiting upslope movement.
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Abstract

The International Panel on Climate Change has identified coastal ecosystems as areas that 

will be disproportionally affected by climate change. Current sea-level rise projections range 

widely with 0.57 to 1.9 meters increase in mea sea level by 2100. The expected accelerated rate 

of sea-level rise through the 21st century will put many coastal ecosystems at risk, especially 

those in topographically low-gradient areas. 

We assessed marsh accretion and plant community state changes through 2100 at 12 tidal 

salt marshes around San Francisco Bay estuary with a sea-level rise response model. Detailed

ground elevation, vegetation, and water level data were collected at all sites between 2008 and

2011 and used as model inputs. Sediment cores (taken by Callaway and others, 2012) at four 

sites around San Francisco Bay estuary were used to estimate accretion rates. A modification of 

the Callaway and others (1996) model, the Wetland Accretion Rate Model for Ecosystem 

Resilience (WARMER), was utilized to run sea-level rise response models for all sites. With a 

mean sea level rise of 1.24 m by 2100, WARMER projected that the vast majority, 95.8 percent 

(1,942 hectares), of marsh area in our study will lose marsh plant communities by 2100 and to

transition to a relative elevation range consistent with mudflat habitat. Three marshes were 

projected to maintain marsh vegetation to 2100, but they only composed 4.2 percent (85 

hectares) of the total marsh area surveyed. 

Introduction

Climate change effects for coastal ecosystems include projected changes in mean and 

extreme ambient temperatures, precipitation patterns, ocean temperature and acidity, extreme 

storm events, and sea-level rise (Cayan and others, 2005; Hansen and others, 2006; International 

Panel on Climate Change, 2007). Projections of mean sea-level rise (SLR) to the year 2100 are 

characterized by high uncertainty because of the difficulty in modeling melting ice-sheet 

dynamics and other ocean processes. Global sea level has risen 1.8 millimeters per year 

(mm/year) between 1961 and 1993 and 3.1 mm/year between 1993 and 2007 (International Panel 

on Climate Change, 2007). While earlier SLR projections ranged from 0.19 to 0.58 meters (m)

by 2100 (International Panel on Climate Change, 2007), more recent projections have ranged 

from 0.6 to 1.6 m (Jevrejeva and others, 2010) and 0.9 to 1.3 m (Grinsted and others, 2010). 

Vermeer and Rahmstorf (2009) projected SLR up to 1.9 m by 2100, contingent upon carbon 

dioxide (CO2) emissions and ambient temperature conditions. Local rates of observed SLR in 

San Francisco Bay estuary (SFBE) have been 2.2 centimeters (cm) per decade for a total of 19.3 

cm between 1900 and 2000 (Cayan and others, 2006). SFBE projected SLR is as much as 1.24 m 

by 2100, depending on CO2 emissions (Cayan and others, 2008). 

Although global in distribution, the extent of tidal salt marshes is limited to the lowenergy intertidal zones of temperate estuaries, with 16,000 square kilometers (km2) found in 

North America (Greenberg and others, 2006). Marshes are dominated by plant communities that 

have varying tolerance to tidal inundation and salinity, resulting in zonation along the elevation 

gradient (Mancera and others, 2005). These low-lying areas are particularly vulnerable where 

variation in tidal depth and duration plays a major role in structuring these plant communities 

(Brittain and Craft, 2012). Marshes will be affected by climate change through accelerating SLR 

(Holgate and Woodworth, 2004; Kemp and others, 2011), shifting precipitation patterns (Hamlet 

and Lettenmaier, 2007; Bengtsson and others, 2009), erosion (Leatherman and others, 2000), and 


changing frequency and intensity of storms (Emanuel, 2005; Webster and others, 2005; 

International Panel on Climate Change, 2007). Marshes can keep pace with changes in local sea 

level through accretion processes that include sediment deposition and organic matter 

accumulation (Morris and others, 2002; Geden and others, 2011) if suspended-sediment 

concentrations and organic production are high enough (Kirwan and Gutenspergen, 2010). 

However, marshes can be lost if SLR outpaces vertical accretion processes (Morris and others, 

2002; Callaway, 2007). 

We evaluated SLR effects for tidal salt marshes with a site-specific, bottom-up approach. 

We included plant community state changes that can help identify effects on resident wildlife 

species. The scope of our quantitative assessments of SLR effects on wildlife in tidal marshes 

was developed at an ecologically relevant parcel scale, which will help local management 

strategies and climate change adaptation plans (fig. 1). The coupling of a bottom-up approach 

that includes modeling of marsh physical process and plant communities with top-down SLR 

models allowed the assessment of marsh plant community state changes through 2100 (fig. 2). 

The objectives of the study were to (1) establish baseline conditions for all marsh sites, (2) 

develop a simulation model for tidal marshes, (3) synthesize results into site-specific parcel-scale 

plant community state change models, and (4) compare SLR vulnerability across the range of 

study sites. 

Figure 1. Climate change research requires the 

downscaling of global climate models to site-specific 

habitats and wildlife species. 


Study Area

The San Francisco Bay estuary

(SFBE; California, USA) has been 

heavily affected by human development, 

which has resulted in the loss of 80 

percent of the historic tidal salt marshes, 

greatly affecting the local marsh wildlife 

(Goals Project, 1999). Eleven wildlife 

species of concern are named in the multispecies tidal salt marsh recovery plan for 

northern and central California (USFWS, 

2009), and the SFBE is the single largest 

habitat block remaining (Takekawa and 

others, 2006). The SFBE marshes support 

a variety of habitats for federal and state 

protected endemic species, such as the 

California clapper rail (Rallus longirostris 

obsoletus), the California black rail 

(Laterallus jamaicensis coturniulus), and 

the salt marsh harvest mouse (Reithrodontomys raviventris) (Spautz and others, 2006; Tsao and 

others, 2009; Takekawa and others, 2011). The estuary is located on the Pacific Flyway and is a 

Western Hemisphere Shorebird Reserve Network site of hemispheric importance used by 

migrating and wintering birds (Accurso, 1992). Bird species such as sparrows (Alameda Song 

Sparrow Melospiza melodia pusillula, Suisun Song Sparrow M. m. maxillaris, and San Pablo 

Song Sparrow M. m. samuelis) and Salt Marsh Common Yellowthroat (Geothlypis trichas 

sinuosa) are found here.

Twelve marsh sites covering 19.94 km2 (about 12 percent of the remaining marsh in the 

SFBE) were sampled and modeled for SLR response (fig. 3). Four sites were located in the 

southern portion of SFBE (Arrowhead, Cogswell, Laumeister, and Colma), and three sites were 

located within San Pablo Bay (Corte Madera, China Camp, and San Pablo). Two marshes were 

sampled along the Napa River (Fagan and Coon Island), and three sites along the Petaluma River 

(Black John, Petaluma, and Gambinini). Marsh sites varied in age, position within the tidal 

range, and adjacent land-cover type and comprised a wide variety of land owners, including 

federal and state agencies, as well as NGOs and private landowners. 

Figure 2. USGS climate change research program 

conceptual model for a bottom-up local level 

approach.


Figure 3. Location of San Francisco Bay estuary marsh study sites: 1 = Gambinini; 2 =. Petaluma; 3 = 

Black John; 4 = San Pablo Bay National Wildlife Refuge; 5 = Coon Island; 6 = Fagan; 7 = China Camp 

State Park; 8 = Corte Madera; 9 = Arrowhead; 10 = Cogswell; 11 = Laumeister; 12 = Colma. 


Methods 

Elevation surveys 

We completed survey-grade elevation surveys at marshes throughout the SFBE between 

2008 and 2010 by using a Leica RX1200 Real Time Kinematic (RTK) Global Positioning 

System (GPS) rover (± 1 cm x, y, ± 2 cm z accuracy; Leica Geosystems Inc., Norcross, Georgia; 

fig. 4). The rover positions were received in real time from the Leica Smartnet system by using a 

Code Division Multiple Access (CDMA) modem (www.lecia-geosystems.com). We used the 

WGS84 ellipsoid model for vertical and horizontal positioning. Positions were referenced to a 

National Geodetic Survey (NGS) benchmark (X 552 1956 Mare Island). The average measured 

vertical error for the benchmark during the study was ± 2.5 cm, similar to the stated error of the 

RTK GPS. Elevation was surveyed along transects perpendicular to the major sediment source 

(San Francisco Bay, Napa River, Petaluma River), with a survey point taken every 25 m; 50 m 

separated transect lines. The Geoid03 model was used in calculating elevations from orthometric 

heights (North American Vertical Datum of 1988, NAVD88), and all points were projected to 

North American Datum of 1983 (NAD83) Universal Transvers Mercator (UTM) zone 10 by 

using Leica GeoOffice (Leica Geosystems Inc, Norcross, Georgia, v 7.0.1). Orthometric heights 

were calculated on the basis of NAVD88 and the NGS Geiod03 Model. 

Elevation modeling 

We synthesized the survey data to create an elevation raster in ArcGIS 9.3 Spatial 

Analyst (ESRI 2009, Redlands, Calif.) with kriging methods (3 x 3 m cell size). We used the 

exponential model for ordinary kriging and adjusted model parameters to minimize the rootmean-square error (RMS), an internal measure of model performance. Lag size and number of 

lags were optimized for the site (lag size times lag number less than half the maximum distance 

among points, ESRI); an anisotropy adjustment was applied because of a trend in the elevation 

data. Resultant models were externally cross-validated by comparing models created with 70 

percent of the data with the remaining 30 percent 

of the points. We then used the elevation models 

as the basis for subsequent analysis, such as tidal 

inundation patterns, WARMER modeling, and 

vegetation correlations. The mean and range of 

elevation was calculated by randomly sampling 

locations from the interpolated model at a density 

of 3 per hectare (ha) to produce an unbiased 

sample for each site. We tested for unequal 

variance in marsh elevations grouped by site using 

Levene’s homogeneity variance test. A significant 

result indicted the need for non-parametric 

analysis, thus we used the Kruskal-Wallis test for 

significant differences. 

Vegetation surveys 

We recorded plant species diversity and elevation concurrently at 50 percent of the 

elevation points within a 0.25 square meter (m2) quadrat (fig. 4). We measured height (mean, 

Figure 4. Vegetation and elevation data 

were collected concurrently. [Photo by K. 

Spragens] 


maximum, measured within 0.05 m) and estimated percentage cover for each species. We 

combined all recorded Spartina spp. in analysis because of the difficulty in distinguishing native, 

hybrid, and non-native species. We categorized plant species into low, mid, high-marsh, and 

upland transition by measured elevation relative to mean sea level (MSL, in meters), which 

changed relative to elevation with SLR. We used the vegetation and elevation relationships to 

predict transitions or state changes of wildlife habitats to assess vulnerability and better 

understand outcomes for habitat persistence to 2100. 

Water-level monitoring 

We deployed water-level data loggers (Model 

3001, 0.01 percent FS resolution, Solinst Canada Ltd., 

Georgetown, Ontario) at all sites during the study 

period (fig. 5). Each site had one to four loggers (n = 

28) depending on its size. Loggers were placed at the 

mouth and upper reaches of second-order channels 

(tidal creeks) to capture the local tidal cycle and 

inundation patterns. We collected continuous data every 

six minutes throughout 2009 and 2010 to develop local 

hydrographs and inundation rates. In addition, water 

conductivity was collected at a subset of sites; however, 

these data have not been analyzed. Loggers were 

surveyed with the RTK GPS at the time of deployment 

and at each data download to correct for any 

movement. Water levels were corrected for local 

barometric pressure with data from independent 

barometric loggers (Model 3001, 0.05 percent FS 

accuracy, Solinst Canada Ltd., Georgetown, Ontario). 

The local water level data were used to develop 

elevation and tidal datum relationships for all sites. 

Water-level peaks from 2010 were averaged to create 

mean high water (MHW) and mean higher high water 

(MHHW) datums relative to NAVD88 for each site. The development of local mean sea level 

(MSL) and mean low water (MLW) tidal datums was not possible because of the relatively high 

elevation of the water loggers in the marsh channels; therefore, MSL was determined by using 

VDatum v 2.3.0 (http://vdatum.noaa.gov). All results are reported relative to local MHW 

calculated from local water data. For SFBE, MHW and MHHW are the most important metrics 

for understanding marsh plant communities and wildlife habitats. 

Figure 5. Water-level data were 

collected at all sites throughout 

the study period. [Photo by H. 

Robinson]


Figure 6. WARMER 1-D conceptual model. 

The Wetland Accretion Rate Model for Ecosystem Resilience (WARMER) 

We developed the model 

WARMER, a one-dimensional cohort 

model of wetland accretion based on 

Callaway and others (1996), to 

examine SLR projections for SFBE 

marshes. WARMER calculated 

elevation changes relative to MSL on 

the basis of projected changes in 

relative sea-level, subsidence, 

inorganic sediment accumulation, 

aboveground and belowground organic 

matter productivity, compaction, and 

decay (fig. 6) for a representative 

marsh area. Each modeled cohort 

provided the mass of inorganic and 

organic matter accumulated at the 

surface in a single year plus any 

subsequent belowground organic 

matter productivity (root growth) 

minus decay. Cohort density, a 

function of mineral, organic, and water 

content, was calculated at each time 

step to account for decay of organic 

material and autocompaction of the 

soil column. The change in relative 

elevation was then calculated as the difference between the change in modeled sea level and the 

change in height of the soil column, which was estimated as the sum of the volume of all cohorts 

over the unit area model domain. 

WARMER expanded upon the Callaway and others (1996) model by including (1) 

feedback between organic matter accumulation and elevation, (2) development of a non-linear 

relationship between inorganic matter accumulation and elevation, and (3) incorporation of a 

temporally variable SLR. The elevation of the m

was estimated as follows: 

 

where 

ሻ ൌ 	ܧሺ0ሻ െ 	ܵܮܴሺݐሻ ൅ 	∑

arsh surface, E, at time, t, relative to local MSL 

௧

௜ୀ଴ ܸ௜ሺݐሻ (1)

E(0) is the initial elevation relative to MSL; 

SLR(t) is the sea-level at time, t, relative to the initial sea level; and 

Vi(t) is the volume per unit area, or height, at time t, of the cohort formed during 

year, i. 

The total volume of an individual cohort was estimated as the sum of the mass of water, 

calculated from the porosity of the cohort, sediment, and organic matter divided by the cohort 

bulk density. 

ݐሺܧ


We developed WARMER models from the elevation, vegetation, and water-level data 

collected at each site (see Methods). For this study, accretion rates for four marshes (Coon 

Island, a portion of Petaluma Marsh, China Camp Marsh, and Whale’s Tail Marsh) were 

determined from 210Pb and 137Cs dating of sediment cores (Callaway and others, 2012). 

Accretion data from the most comparable of these four sites were used to calibrate the 

accumulation and sediment properties at the remaining sites for WARMER modeling (see

appendix). All results are presented as elevation relative to MHW.

Sea-level rise scenario model inputs

In WARMER, we incorporated a recent forecast for San Francisco Bay SLR (Cayan and 

others, 2009) based on a moderate emissions scenario (A2 in International Panel on Climate 

Change Fourth Assessment Report, Bindoff and others, 2007), which forecasts 1.24 m of SLR by 

2100 (fig. 7). The average annual SLR curve was used as the input function for the WARMER 

model. We assumed that the tide range remains constant through time, and only MSL changes

annually. 

Figure 7. Hourly water-level prediction from Cayan and others (2009) with a 1-year running mean of 

hourly data. 

2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

-200

-150

-100

-50

0

50

100

150

200

250

300

Year

Relative Sea-level, cm
10

Inorganic matter model inputs

Sediment flux from the water column to the marsh surface at a given elevation, z, was 

estimated as the product of suspended-solids concentration, SSC, and settling velocity summed 

over all times during which elevation z was inundated. For the case of constant SSC and settling 

velocity, the mass accumulation was directly proportional to the inundation frequency and 

calibrated without any direct measurement of concentration or settling velocity. Thus, sediment 

flux at a given elevation, z, measured in mass per unit area per year, Ms(z), was calculated as 

follows: 

Ms(z) = SSC ∗ ws ∗ f(z) (2)

where

f(z) is the inundation frequency as a function of elevation, and 

SSC * ws is the maximum potential sediment flux determined by calibration to 

sediment accumulation data. 

To calibrate the sediment input function, sediment accumulation rates were determined 

by using the Constant Rate of Supply (CRS) method for 210Pb from sediment cores from each of 

the four reference marshes (Robbins, 1978; Callaway and others, 2012). If the CRS method 

could not be applied because the sediment core did not capture the complete excess 210Pb profile, 

the sediment accumulation rate was determined by using the 1963 peak 137Cs horizon identified 

in the soil column. 

Organic matter model inputs

The base organic input function developed by Morris and others (2002) was used in 

WARMER. However, Morris and others (2002) used Spartina alterniflora as the basis of the 

functional relationship, although most SFBE marshes are dominated by native Sarcocornia 

pacifica (pickleweed). S. alterniflora and its hybrids have invaded parts of the estuary, but they 

are the target of an intensive control program; therefore, the Morris and others (2002) function 

was adapted for the vegetation in the SFBE. The shape of the curve that Morris and others (2002) 

developed for S. alterniflora marshes was retained, but the elevation range of the vegetation, the 

roots of the parabolic equation, and the magnitude of organic matter input were adjusted for 

SFBE marsh vegetation, primarily represented by S. pacifica. This was accomplished by fixing 

the roots of the parabolic function at MSL and maximum astronomical tide (MAT) for each site 

and then calibrating the magnitude of the predicted organic matter accumulation to measured 

organic matter input rates from the sediment cores. The parabolic equation describing the annual 

mass of organic matter accumulated per unit area, M0, was then as follows: 

M0(z) = (a + b)(z − MSL)(z − MAT) (3)

In this equation, a and b are constants with units of M

[L4T]

 [M, mass, L, length, T, Time] for above, 

and belowground production, respectively, fit to the measured organic matter accumulation rates 

in the surface layer of each sediment core for each marsh at specified elevations. The organic 

matter accumulation rate was determined from the sediment accumulation rate and the ratio of 

sediment to organic matter in the surface layer of each sediment core. Organic matter input was 

divided between above- and belowground input with a shoot to root ratio (a/b) of 0.57 based on 
1

the work of Scarton and others (2002) and Curcó and others (2002) on Sarcocornia spp. The 

mass of organic material generated belowground each year was distributed exponentially with 

depth, and the coefficient of exponential decay, kdist, was set equal to 1.0 (Deverel and others, 

2008).

Compaction and decomposition model inputs

Compaction and decomposition functions of WARMER followed Callaway and others 

(1996). Compaction of highly porous marsh sediment was determined by the rate of decrease in 

porosity from the average measured porosity of the top 5 cm of each sediment core to a lower 

limit of porosity measured at the bottom 5 cm of each sediment core. The rate of decrease, r, in 

porosity of a given cohort was estimated as a function of the density of all of the material above 

that cohort: 

r = 1 − pb

k1−pb

(4)

where 

pb is the density of the material above a cohort, and 

k1 is a calibration constant. 

Decomposition was modeled as a three-stage process where the youngest organic 

material, less than 1-year old, decomposed at the fastest rate; organic matter 1- to 2-years old 

decayed at a moderate rate; and organic matter greater than 2-years old decayed at the slowest 

rate. Decomposition also decreased exponentially with depth. The percentage of refractory 

organic material was determined from the organic content in the sediment cores. The constants 

used to parameterize the compaction and decomposition functions follow those used by Deverel 

and others (2008). 

 WARMER was run for 243 simulations with a range of values for sediment input, 

organic matter input, SLR, and initial marsh elevation in order to evaluate model sensitivity. The 

values were chosen to reflect the expected and extreme values of sediment and organic matter 

accumulation, porosity, SLR, and the initial measured or assumed marsh elevation for the SFBE

(Callaway and others, 2012). Low and high estimates of the magnitude of sea-level rise over the 

next century (50 and 150 cm; Cayan and others, 2009) were included in the modeling. 

Results

Elevation

Low slope elevation gradients characteristic of marshes were observed at the SFBE sites 

(fig. 8). A total of 7,437 elevation points were measured from 2008 to 2010 (table 1). Overall, 

elevation fell within a small range, with 88 percent of surveyed points between 1.5 and 2.1 m 

(NAVD 88; fig. 9). Most of the marshes surveyed were located above MHW. Across all sites, 

mean elevation was 0.03 m (SD = 0.13) above MHW. Mean elevation relative to MHW varied 

significantly across sites (Levene’s homogeneity variance test, p < 0.0001; Kruskal-Wallis χ2= 

808.241, df = 11, p < 0.0001, comparisons shown in fig. 10). Marshes located along rivers 

(Gambinini, Petaluma, Black John, Coon Island, and Fagan) were at higher elevations than those 

located along the bay edge (fig. 10). Grouped by their location within the bay, marsh elevation 
1

(relative to MHW) was lowest in the south SFBE sites, higher in San Pablo Bay sites, even 

higher in Petaluma River sites, and highest in Napa River sites (ANOVA: F3 = 179.28; p < 

0.0001). The interpolated elevation models for all sites had a root-mean-square error (RMS) of 

0.1 m (table 2). 

Table 1. Summary of the amount of elevation and vegetation data collected for each site. 

[SPB refers to San Pablo Bay, SSFB refers to south San Francisco Bay, and Petaluma and Napa refers to 

the rivers draining from the north into San Pablo Bay; ha, hectare, m, meter; n, sample number; -, not 

sampled] 

Region Site

Area

(ha)

Elevation 

(n)

Mean 

Elevation (m)

Elevation 

Range (m)

Vegetation 

(n)

Petaluma Gambinini 25 217 1.9 0.35 110

Petaluma Petaluma 81 655 1.8 0.63 356

Petaluma Black John 31 213 1.8 0.45 108

SPB San Pablo East 963 434 1.9 0.72 271

SPB San Pablo West 450 962 2.0 1.49 691

Napa Coon Island 99 799 1.8 1.24 364

Napa Fagan 68 609 1.9 0.61 308

SPB China Camp 97 753 1.8 0.55 422

SPB Corte Madera 77 744 1.6 0.59 361

SSFB Arrowhead 17 274 1.6 0.37 50

SSFB Cogswell 60 523 1.8 0.55 228

SSFB Laumeister 37 717 2.1 0.47 72

SSFB Colma 25 537 1.4 1.58 -

Total 2,027 7437 1.9 1.60 3,302

Table 2. ArcGIS elevation model root-mean-square error (RMS) and standard error (SE) by site.

Study Site Model RMS Model Mean SE

Gambinini 0.09 0.09

Petaluma 0.09 0.09

Black John 0.05 0.04

San Pablo 

East 0.10 0.11

San Pablo 

West 0.15 0.15

Coon Island 0.07 0.05

Fagan 0.08 0.08

China Camp 0.09 0.09

Corte Madera 0.09 0.08

Arrowhead 0.07 0.06

Cogswell 0.09 0.09

Laumeister 0.08 0.08

Colma 0.21 0.21

Mean 0.10 0.09
13

Figure 8. Elevation models for all marsh sites (NAVD88). Model parameters were optimized to reduce 

root-mean-square error. Scale bar = 200 meters (m). 1 = Gambinini, 2 = Petaluma, 3 = Black John, 4a

= east San Pablo Bay NWR, 4b = west San Pablo Bay NWR, 5 = Coon Island, 6 = Fagan, 7 = China 

Camp State Park, 8 = Corte Madera, 9 = Arrowhead, 10 = Cogswell, 11 = Laumeister, 12 = Colma.
14

Figure 9. Distribution of marsh elevations relative to mean high water (MHW) in meters (m), for all sites. 

Random points were sampled from the elevation models at a density of 3 per hectare to produce an 

unbiased distribution. 

 
15

 a b bc bc bc cd cd d e e e f 

Figure 10. Vegetation relative to marsh mean high water (MHW), in meters (m), by site. Median (solid line), 

25 and 75 percentiles (box), and 1.5 interquartile range (whiskers). Circles indicate outliers. Letters 

denote significant differences from a Kruskal-Wallis test (alpha = 0.05). 

Vegetation

Vegetation was sampled at 3,302 locations across all marsh sites (table 1). Distinct 

zonation in plant communities was observed in relation to MHW because plants are typically 

restricted by their inundation tolerance and soil salinities. Plant species were categorized into 

low, mid, and high marsh, and upland transition communities by measured elevation relative to 

mean sea level (MSL; table 3). Categories were determined by comparing the elevation mean 

and standard deviations of each species and grouping overlapping distributions (fig. 11). These 

categories were then used to infer changes in plant community associated with changes in 

elevation due to SLR. Most of the surveyed marshes were typified by high marsh vegetation 

(table 4).
16

Figure 11. Dominant marsh plant species by elevation relative to mean high water (MHW), in meters (m), 

across study sites. GRST = Grindelia stricta; SPFO = Spartina spp.; SAPA = Sarcocornia pacifica;

JACA = Jaumea carnosa; DISP = Distichlis spicata; FRSA = Frankenia salina. Grey line represents 

sampling distribution. Plant canopy cover, which often overlaps, was estimated for each species,

thus greater than 100 percent in a given plot was possible. 

Table 3. Plant communities were categorized on the basis of observed elevation distributions to develop 

marsh zones to model with sea-level rise. 

[Species are ordered by abundance from high to low. MSL, mean sea level; m, meter; >, greater than; <, less than] 

Upland Transition High Marsh Mid Marsh Low Marsh Mudflat

(> 1.0 m MSL) (0.7 - 1.0 m MSL) (0.45 - 0.7 m MSL) (0.2 - 0.45 m MSL) (< 0.2 m MSL) 

Baccharis 

pilularis

Frankenia salina

Jaumea carnosa 

Distichlis spicata 

Sarcocornia 

pacifica 

Distichlis spicata 

Spartina spp. Sparse Spartina 

spp.

Frankenia salina Frankenia salina 

Sarcocornia Spartina spp. 

pacifica 
17

Table 4. Summary of the most common plant species found across all sites.

[Colma marsh was not surveyed for vegetation because of Spartina removal efforts, which left most of the marsh 

unvegetated during our study. SAPA, Sarcocornia pacifica; FRSA, Frankenia salina; GRST, Grindelia stricta; 

JACA, Jaumea carnosa; DISP, Distichlis spicata; n, number]

Percentage Present

Study Site SAPA

Schoenoplectus

spp.

Spartina

spp. FRSA GRST JACA DISP n

Gambinini 99.1 3.6 0.0 6.4 4.5 0.9 8.2 110

Petaluma 97.2 6.5 0.0 12.9 6.5 17.4 9.6 356

Black John 98.4 46.3 0.0 0.0 14.8 0.0 8.3 108

San Pablo East 85.7 0.9 7.4 0.5 2.8 0.0 0.0 217

San Pablo West 86.8 1.7 9.1 11.1 10.3 4.8 7.7 691

Coon Island 87.9 38.5 0.8 0.5 7.4 7.7 1.4 364

Fagan 87.3 51.0 0.3 0.3 0.3 0.3 1.9 308

China Camp 96.9 1.7 4.3 1.4 8.3 8.1 12.8 422

Corte Madera 97.2 0.0 27.4 0.6 5.0 10.2 23.9 361

Arrowhead 76.0 0.0 98.0 0.0 0.0 64.0 26.0 50

Cogswell 92.5 0.0 9.2 0.0 0.0 0.0 0.0 228

Laumeister 86.1 0.0 37.5 1.4 27.8 4.2 40.3 72

Sarcocornia pacifica was the most common species surveyed across sites, occurring at 91 

percent of 3,287 YHJHWDWLRQVXUYH\SRLQWV. Schoenoplectus spp. was the second most common species 

(12.8 percent),followed by Distichlis spicata (10.3 percent), Spartina spp. (8.9 percent), Jaumea 

carnosa (7percent), and Grindelia stricta (6.8 percent). The sites located along more brackish river 

waterhad higher species richness—Coon Island and Fagan, both on the Napa River, had the highest 

overall species richness.

All of the surveyed marshes had high S. pacifica abundance, the characteristic species of 

SFBE tidal marshes; however, several species of the marsh community differed by region (table 

4). Schoenoplectus spp. commonly associated with brackish conditions were found in the sites 

along the Petaluma and Napa Rivers but not in the south San Francisco Bay sites. Spartina spp. 

were prevalent at south San Francisco Bay, not present on the Petaluma River, very sparse on the 

Napa River, and in low abundance at San Pablo Bay sites. These patterns in Spartina spp. and 

Schoenoplectus spp. distribution were likely in response to differences in water and soil salinity 

across the SFBE (see fig. 12).

Water monitoring

Water loggers recorded tide levels from January 2010 to May 2011 for use in our 

analyses, but we continued monitoring at a majority of the sites for future analyses. The loggers 

did not capture the bottom portion of the tidal curve because they were located relatively high in 

the marsh channels. Peak tide levels for 1 year (2010–2011) were averaged for each site, 

producing sites-specific tidal datums for MHW and MHHW (table 5). 

Conductivity measurements at a subset of the sites showed the influence of freshwater 

runoff on water salinity (fig. 12). The most prominent pattern was seasonality, with a peak in 

October (except for Laumeister) corresponding to the end of the dry season in the SFBE. Coon 

Island, along the Napa River, had the lowest mean salinity in 2010, while San Pablo, located on 

the edge of the bay, had the highest. In contrast, Laumeister did not show a seasonal salinity 
18

pattern. Laumeister was the only site in south San Francisco Bay where we measured 

conductivity; thus, it is difficult to know whether it represented a different salinity regime from 

the San Pablo Bay and northern river sites or if there were problems with the conductivity 

sensor. 

Table 5. Water elevations (NAVD88), in meters (m), for each marsh site in 2010. 

[Mean sea-level (MSL) was derived from VDatum (National Oceanic and Atmospheric Administration). Mean high 

water (MHW) and mean higher high water (MHHW) were calculated from in situ data loggers. NWR, National 

Wildlife Refuge]

Site MSL MHW MHHW

Arrowhead 1.03 1.71 1.91

Black John 1.06 1.73 1.91

China Camp 1.02 1.77 1.95

Cogswell 1.14 1.86 1.99

Colma 1.03 1.77 1.98

Coon Island 1.03 1.78 1.94

Corte Madera 0.99 1.7 1.87

Fagan 1.07 1.75 1.9

Gambinini 1.07 1.8 1.96

Laumeister 1.21 1.92 2.09

Petaluma 1.04 1.76 1.92

San Pablo Bay NWR 1.04 1.69 1.85

Figure 12. Salinity, in Practical Salinity Scale (PSU) during 2010 across a subset of study sites calculated 

by taking the monthly mean of salinity from daily high tides. Seawater has a PSU of 35. 
19

Marsh response modeling 

SFBE spatial variability in tidal range, accretion rate, and initial marsh elevation resulted 

in differences in SLR risk (see appendix for site details). Sediment core data at reference sites 

(table 6) were used to inform accretion rates (sediment and organic accumulation) for all sites 

(table 7). Measured sediment accumulation used in WARMER showed higher sediment 

accumulation rates at Laumeister and Coon Island compared to China Camp and Petaluma (table 

5). Sediment accumulation and organic matter accumulation curves were then developed for the 

reference sites (figs. 13 and 14). 

Generally, sites with lower initial elevations and located lower in the tidal range (for 

example, Corte Madera, Colma, and Arrowhead) became inundated more frequently and with 

longer duration than sites with higher initial elevation and located higher in the tidal range (for 

example, Laumeister, Gambinini, and Fagan). Accretion rates used in the WARMER model were 

relatively higher in south San Francisco Bay; thus, those marshes withstood SLR to 2100, with 

areas of transition from high to low marsh vegetation by 2100 (for example, Cogswell, 

Laumeister, and Colma). Arrowhead, located in central San Francisco Bay, showed a large 

transition from mid to low marsh around 2040 (0.32 m SLR) and to mudflat by 2080 (0.85 m 

SLR). 

Table 6. Model inputs used for primary WARMER model sites. [Z represents a standardizing metric. Ms, mass of sediment; MSL, mean sea level; cm, centimeter; g cm-2 yr -1, grams 

per square centimeter per year; mg cm -2 yr -1, milligrams per square centimeter per year]

Site Sediment Organic matter Porosity Decay Elevation

(g cm -2 yr -1) (mg cm -2 yr -1) (percent) (percent) (cm above MSL)

Ms(MSL) a b Surface Depth Refractory 

C

Z-2s Z Z+2s

China 0.32 -0.00257 -0.00452 82 77 12 64 77 90

Camp

Petaluma 0.10 -0.00113 -0.00199 83 80 14 65 77 89

Coon 0.59 -0.00142 -0.00250 83 78 55 49 75 101

Island

Laumeister 1.34 -0.00443 -0.00777 76 72 49 69 83 96
20

Table 7. WARMER model inputs used for extrapolation to the remaining sites. 

[The reference site was chosen based on proximity to marsh. CC, China Camp; PRM, Petaluma; Lau, Laumeister; 

CI, Coon Island; SPB NWR, San Pablo Bay National Wildlife Refuge; Ms, mass of sediment ;MSL, mean sea 

level; cm, centimeter; OM, maximum organic matter; g cm -2 yr -1, grams per square centimeter per year] 

Arrowhead Black 

John Cogswell Colma Corte Madera Fagan Gambinini SPBNWR

Reference site CC PRM Lau Lau CC CI PRM CC

OM (g cm -2 yr -1) 0.019 0.005 0.029 0.029 0.019 0.005 0.005 0.019

Sediment Ms(MSL) 

(g cm -2 yr -1) 0.320 0.097 1.338 1.338 0.320 0.588 0.097 0.320 

Surface Porosity 

(percent) 82 83 78 78 82 83 83 82

Depth Porosity 

(percent) 77 80 77 77 77 78 80 77

Refractory Carbon 

(percent) 12 14 55 55 12 12 14 12

Elevation (cm MSL)

Z-2s 51 58 55 -11 47 65 73 34

Z 63 70 71 31 64 83 81 64

Z+2s 74 81 86 73 81 101 90 93
21

Figure 13. Calculated sediment accumulation curves (lines) and measured accumulation rates (points) for 

representative marsh sites relative to mean high water (MHW). Curves are calculated from inundation 

frequency and calibrated to accumulation rates measured by Callaway and others (2012). These rates 

were extrapolated to nearby marshes in the study. [g cm -2 yr -1, grams per square centimeter per year; 

cm, centimeters]

The remaining sites had accretion rates that were relatively low; the WARMER 

simulations projected that these marshes would transition to mudflat by the end of the century. 

For example, Corte Madera, China Camp, and San Pablo lost all high marsh by 2030 (0.24 m 

SLR), briefly transitioned to mid- and low-marsh vegetation, and ultimately transitioned to areas 

dominated by mudflats by 2080 (0.85 m SLR). The three marshes located on the Petaluma River 

(Gambinini, Petaluma, and Black John) also lost most high marsh by 2030 (0.24 m SLR) and 

transitioned to mostly mudflat by 2080 (0.85 m SLR). Because the WARMER model for the 

Napa River sites was parameterized with higher sediment accretion rates, Coon Island

maintained high marsh until 2030 (0.24 m SLR) and Fagan until 2040 (0.32 m SLR). Mid-marsh 

vegetation was maintained between 2030 and 2040 for Coon Island (0.32 m SLR) and between 

2040 and 2060 for Fagan (0.57 m SLR). Low-marsh vegetation was dominant until 2090 (1.05 m 

SLR), at which time both marshes transitioned to predominantly mudflat. Both of these sites had 

higher initial elevations than other marshes. 
22

Figure 14. Calculated organic matter accumulation (lines) and measured accumulation rates (points) for 

representative marsh sites relative to mean high water (MHW). These rates were extrapolated to 

nearby marshes in the study. [g cm -2 yr -1, grams per square centimeter per year; cm, centimeters]

Nine of the marshes transitioned to mudflat by 2100 (1.23 m SLR), whereas only three 

marshes maintained vegetation to 2100. All marshes lost upland transition and high marsh by 

2100, and mid-marsh vegetation was lost at nine marshes by 2100. These results illustrate the 

spatial variability of SLR effects across the SFBE. The three marshes that maintained marsh 

vegetation to 2100 composed only 4 percent (85 ha) of the total marsh area surveyed, whereas a 

total of 96 percent (1,942 ha) of the marsh area in our study transitioned to mudflat by 2100 

(site-specific results are described in appendices A-L).

WARMER sensitivity analysis

Callaway and others (1996) and Deverel and others (2008) noted that their respective 

models were sensitive to both initial elevation and porosity. The sensitivity analysis of 

WARMER showed the greatest differences in final elevation when SLR and sediment 

accumulation were varied (fig. 15). On average, the scenarios with the largest sediment 

accumulation were able to keep pace with SLR. Scenarios with only 50 cm of SLR by the end of 

the century had an average increase in elevation of 7 percent. The small variations in porosity 

that were observed in the marsh sediment cores did not lead to large changes in final elevation 

despite the strong non-linearity between porosity and volume. The influence of organic matter 

accumulation was also small, despite varying the observed range of accumulation rates by 

approximately ±50 percent in the sensitivity analysis. The scenarios with the lowest sediment 

accumulation rates had a greater dependence on initial elevation compared to other accumulation 

rate scenarios. The negative feedback between elevation and sediment accumulation was strong 

enough to mitigate this influence in the mean and high sediment input scenarios. 
23

Figure 15. Results of the sensitivity analysis of WARMER. Each bar is the mean final elevation for 81 runs 

with the indicated parameter. Error bars indicate ± 1 standard deviation from the mean. [SLR, sea-level 

rise; %, percentage] 

The current WARMER model could be expanded and improved for site-specific results. 

For example, a better understanding of local suspended-sediment supply (across the SFBE and 

marsh sites), settling velocity of suspended sediment and how that relates to vegetation, and 

temporal variability in deposition rates would improve the accretion function. In addition, the 

model currently does not address marsh erosion, which also would have negative feedbacks for 

marsh elevation. Any changes in hydrological conditions beside SLR and sediment availability 

over time were not incorporated into the models. 

Discussion 

Resource land managers responsible for the protection of wildlife species and their 

habitats were our focus in developing site-specific projections and plant-community response to 

SLR. By identifying SLR habitat thresholds, our project aimed to provide land managers with the 

science support necessary to make informed decisions and develop climate change adaptation 

strategies. Our models identified differences in risk for individual marshes from SLR in the 

SFBE, including the timing of habitat transitions. 
24

2QWKHJURXQGPDUVKHOHYDWLRQDQGSODQWFRPPXQLW\VXUYH\VDORQJZLWKWLGDOUHJLPH

PRQLWRULQJSURYLGHGWKHVLWHVSHFLILFGDWDLPSRUWDQWIRUHVWDEOLVKLQJGHWDLOHGEDVHOLQHFRQGLWLRQV

to develop SLR models. The most common method for obtaining SLR modeling elevation data 

has been aerial LiDAR (Light Detection And Ranging). However, LiDAR is generally unable to 

penetrate dense marsh-vegetation cover and produces elevation errors 10–40 cm greater than 

ground-based measurements (Foxgrover and others, 2011; Schmid and others, 2011). The error 

in LiDAR can represent nearly half of the total marsh slope and can skew SLR response 

modeling results, especially from early in the century through the year 2050. Our results showed 

that initial elevation, along with tidal range and suspended-sediment availability, were key inputs 

for effectively modeling marsh response from SLR in the SFBE. 

Sediment accretion can partially offset increased SLR through 2100. South San Francisco 

Bay marshes are projected to maintain mid-marsh vegetation throughout most of this century, 

with a transition to low-marsh vegetation in the later part of the century, largely as a result of

high sediment accretion rates. In contrast, San Pablo Bay sites are not projected to have high 

enough accretion rates to maintain elevations that keep pace with SLR, and they represent some 

of the largest remaining parcels in the estuary. San Pablo Bay is generally considered to be at a 

turbidity maximum within the estuary (Rhul and others, 2004; Stralberg and others, 2011); thus, 

the limited sediment-core data might not reflect the current rate of sediment accumulation there. 

The accuracy of marsh accretion models is largely dependent on accurate sediment accumulation 

functions and calibration data. While the accretion models might not accurately reflect the 

accretion at a specific site, the ranges of input values do represent a cross section of possible 

marsh accretion simulations for the SFBE. Our results show that three marshes would maintain

marsh vegetation to 2100, but these marshes only composed 4 percent (85 ha) of the total marsh 

area surveyed, while the remaining 96 percent (1,942 ha) of the marsh area in our study 

transition to mudflat by 2100. 

Urbanization in the SFBE estuary makes it especially susceptible to SLR because there 

are limited opportunities for upslope transition. Seven of our marsh sites have open space (nonurban) surrounding the sites. With management actions and restoration, these marshes have 

potential to respond to SLR by moving upslope. In contrast, five sites are surrounded by urban 

infrastructure and, therefore, will have no opportunity to move upslope with SLR. If these 

marshes are unable to transition upslope because of levees or development, they will become 

mudflats. 

 The added effects of climate change on marsh ecosystems could greatly increase threats 

to already vulnerable wildlife populations and species (Ohlemuller and others, 2008). Increased 

rates of inundation will be ecologically significant for obligate marsh species, especially those 

that are already limited in number (for example, California clapper rail) and those that also have 

low dispersal ability (for example, salt marsh harvest mouse). Species that rely on marsh habitat 

for feeding, reproduction, or cover from predators will be negatively affected by SLR. Our 

projections show the loss of high and mid-marsh vegetation by 2050 in most areas. These areas 

are dominated by Sarcocornia pacifica, a plant that is critical for providing habitat structure for 

the salt marsh harvest mouse, California black rail, and nesting song birds. Low-marsh 

vegetation will persist in most areas until 2070 and is dominated by Spartina spp., which 

provides habitat used by the California clapper rail. However, low marsh was projected to be lost 

at 96 percent of the surveyed areas by 2100, and, if representative, could result in the loss of a 

significant amount of habitat for the California clapper rail population. 
25

Next steps

Our program recognizes the importance of extensive and improved integration of 

physical and biological monitoring that could facilitate the discovery of important trends and 

signals for SLR. A better understanding of the spatial variability of available suspendedsediment and deposition rates for both organic matter and sediment would greatly improve these 

site-specific results and is a future goal of the program. We believe this type of baseline data 

collection can be used to identify and prioritize restoration sites and land acquisitions that can be 

good candidates for marsh perpetuation in light of SLR. In addition, the continued risk to listed 

species needs to be assessed by evaluating movements, nesting requirements, and food 

availability for species. A better understanding of wildlife response to increased inundation is 

especially needed. The Coastal Ecosystem Response to Climate Change (CERCC) program 

recently has been expanded to Humboldt Bay, Anaheim Bay, and the San Diego estuary, as well 

as to a range of coastal sites in California, Oregon, and Washington. Consistent with the goal of 

the USGS Science Strategy, the CERCC program will support models that predict ecosystem 

change and assess consequences of climate change and its effects on coastal ecosystems, and it 

will do so at a bottom-up local level appropriate for land managers developing adaptation plans.

Acknowledgments

The authors would like to thank the USGS Western Ecological Research Center and the 

USGS National Climate Change and Wildlife Science Center for funding support and recent 

program contributions from the U. S. Fish and Wildlife Service California Landscape 

Conservation Cooperative and Fish and Wildlife Service Region 8 Inventory and Monitoring and 

Science Applications Program. We also would like to thank the USGS Native American 

Internship Program and participants, B. Hoskinson, C. Hudson, and H. Vaska. The authors would 

like to thank T. Bui, D. Drolette, T. Edgarian, P. Elsen, J. Felis, T. Henner, L. Smith, and L. 

Terrazas for project field assistance. Helpful review comments were provided by G. Wylie and 

N. Athearn. 
26

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29

Appendixes:

Results by site

Site-specific data are available by request. Contact:

Dr. John Y. Takekawa

USGS Western Ecological Research Center

San Francisco Bay Estuary Field Station 

505 Azuar Dr. 

Vallejo, Calif. 94592

707-562-2000
30

Appendix A. Arrowhead Marsh

Introduction

Located in central San Francisco Bay, Arrowhead Marsh (hereafter, “Arrowhead”) is a 

tidal salt marsh that is owned by Oakland Port Authority and managed by East Bay Regional 

Parks as part of the (300 hectares) Martin Luther King Jr. Regional Park. Arrowhead is 

recognized as an important stopover on the Pacific Flyway and is part of the Western Shorebird 

Reserve Network. Arrowhead is also home to the federally-endangered California clapper rail 

(Rallus longirostris obsoletus), which occupies the low intertidal habitat dominated by cordgrass 

(Spartina spp.) 

This study focused on 17.0 hectares (ha) of Arrowhead. Elevation and vegetation surveys 

were carried out in the winter of 2009 by using an RTK GPS. To monitor tidal inundation, two 

water level loggers were deployed from 2009 to 2010. Beginning in 2006, a chemical herbicide 

(chemical mow) was applied to the western half of the marsh in an attempt to control the 

invasive Spartina densiflora and the S. densiflora x S. foliosa hybrid. As a result of the treatment, 

vegetation was not surveyed in the western portion of Arrowhead. 

Results

Elevation surveys

A total of 274 elevation measurements were taken at Arrowhead (fig. A-1). The elevation 

range was 0.87–1.84 meters (m) with a mean of 1.62 m (NAVD88). Over half (65 percent) of the 

survey points fell within 1.55–1.70 m, a 0.15 m range. Arrowhead was the second lowest marsh 

surveyed in this study, with 87 percent of the elevation points located below mean high water 

(MHW; fig A-2). Only 13 percent of survey points were located at elevations above MHW. A 3-

m resolution elevation model was developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial 

Analyst by using the kriging method (fig. A-3). This baseline elevation model was used as the 

initial state in the Wetland Accretion Response Model for Ecosystem Resilience (WARMER) 

sea-level rise (SLR) model; WARMER results were extrapolated across the elevation model. 
31

Figure A-1. Arrowhead Marsh with elevation and vegetation survey points from 2009. 
32

Figure A-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), at 

Arrowhead Marsh. 
33

Figure A-3. Elevation model (3-meter resolution) developed from ground RTK GPS 

elevation data.
34

Figure A-4. Stratification of vegetation species was observed relative to mean high water (MHW), in meters 

(m). Species codes: SAPA = Sarcocornia pacifica; SPAR = Spartina spp.; JACA = Jaumea carnosa; DISP

= Distichlis spicata. 

Vegetation surveys

Vegetation and elevation surveys were done concurrently in December of 2009. A total 

of 50 locations (fig. A-1) were measured for vegetation composition, height (centimeters), and 

percentage cover (table A-1). We did not distinguish between invasive and native Spartina spp.,

nor among Schoenoplectus spp., in the survey. Vegetation in marshes is sensitive to soil salinity, 

inundation patterns, and disturbance; therefore, a stratification of vegetation species relative to 

MHW (fig. A-4) was observed within this low-slope marsh. 
35

Table A-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species in Arrowhead Marsh.

[cm, centimeter; m, meter; n, sample number]

Species Elevation 

(MHW, m)

Elevation SD 

(MHW, m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover SD

(percent) n 

Presence

(percent)

Sarcocornia pacifica -0.15 0.07 21.63 16.40 25.97 18.88 22.18 17.05 38 76.00

Spartina spp. -0.17 0.13 41.61 26.40 53.57 33.47 44.86 32.42 49 98.00

Jaumea carnosa -0.13 0.07 11.88 7.25 14.47 8.58 31.38 25.40 32 64.00

Distichlis spicata -0.11 0.06 17.00 10.10 19.69 11.46 13.00 13.92 13 26.00
36

Water-level monitoring 

Site-specific water level was monitored at Arrowhead for 1 year between 

December 2009 and November 2011. Water level was measured by using two data 

loggers: one deployed at the mouth of a second order channel and one in the marsh 

interior. During 2010, MHW was 1.72 m and mean higher high water (MHHW) was 1.91 

m for the site (NAVD88). Water levels were recorded throughout the year to evaluate 

seasonal patterns in tides. The period when the marsh platform (defined as mean 

elevation) was inundated most often was from December 2009 through February 2010 

(fig. A-5). During those months, above average water levels were recorded because of 

several record-breaking storms that brought low air pressure and substantial rainfall, 

resulting in higher than predicted tides. The cumulative rainfall in January 2010 was 

above average throughout the San Francisco Bay estuary (SFBE), and daily rainfall 

records were broken in some locations (National Oceanic and Atmospheric 

Administration). This resulted in longer inundation periods of the marsh platform. 

Figure A-5. Percentage of time Arrowhead was inundated monthly, based on the mean elevation 

of the marsh platform. 

Marsh elevation modeling 

Arrowhead had a low starting elevation and was located low in the tidal range, 

relative to our other study sites. The WARMER model results indicated that Arrowhead 

will not keep pace with sea-level rise (SLR) through this century. WARMER results 

showed a gradual reduction in elevation relative to MHW over time, with a more 

dramatic decline after 2060 (fig. A-6). By 2080, the marsh was projected to be below 

mean sea level (MSL) and, therefore, to transition to a mudflat (fig. A-7). 
37

Figure A-6. WARMER scenarios for Arrowhead elevation change. Elevation above mean high 

water (MHW), in centimeters (cm), is plotted against model year with two standard deviations (SD). 

2000 2020 2040 2060 2080 2100

-100

-80

-60

-40

-20

0

20

Elevation above MHW, cm

Year

Arrowhead Marsh

- 2SD

Mean elevation

+ 2SD
38

Figure A-7. Spatial WARMER results for Arrowhead. WARMER accounts for changes in relative 

sea level, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters] 
39

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. A-8 and A-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, vegetation at Arrowhead is 

primarily categorized as mid marsh with some high-marsh vegetation. All high marsh 

vegetation was projected to disappear with a 0.06 m SLR. The largest transition was

around 2040 (0.32 m SLR), with a projected change from mid- to low-marsh vegetation. 

A transition to complete mudflat was projected by 2080 (0.85 m SLR). 

The WARMER model parameters for Arrowhead were extrapolated by using 

sediment core data from China Camp marsh; thus, SLR projections need to be interpreted 

with caution because local sedimentation processes could differ. To improve results, local 

site-specific sediment core data could be collected along with suspended-sediment 

concentration data to characterize sediment deposition potential. 
40

Figure A-8. Area of Arrowhead Marsh within a given tidal range for the duration of the simulation 

period. [MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater 

than; <, less than]. 

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

Arrowhead Marsh

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
41

Figure A-9. Arrowhead WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition. 
42

Appendix B. Black John Marsh 

Introduction

Black John Marsh (hereafter, “Black John”) is located in Sonoma County at the 

confluence of Black John Slough, Rush Creek, and the Petaluma River. It is owned and

managed by the California Department of Fish and Game. It is influenced by both tidal 

flow and freshwater input from the Petaluma River. Black John is part of the Petaluma 

Marsh complex, which is the largest marsh complex in California that has never been 

diked or drained. The land surrounding the marsh is used almost exclusively for 

agriculture and light grazing. In 2003, the California State Coastal Conservancy funded 

the acquisition and restoration of a 255.8 hectare (ha) parcel just west of Black John, 

increasing the potential size of the marsh. Black John is home to several endangered 

species and species of concern such as California black rail (Laterallus jamaicensis 

coturniculus).

This study focused on 30.9 ha portion of Black John. Elevation and vegetation 

surveys were completed in 2010 by using an RTK GPS. To monitor tidal inundation and 

salinity, two water-level loggers were deployed in 2009. 

Results

Elevation surveys

A total of 213 elevation measurements were taken at Black John (fig. B-1). The 

elevation range was 1.18–1.97 meters (m), with a mean of 1.75 m (NAVD88). Half (50

percent) of the survey points were within 1.70–1.80 m, a 0.1 m range (fig. B-2). Over half 

(67 percent) of the survey points were located at elevations above mean high water 

(MHW). A 3-m resolution elevation model was developed in ArcGIS 9.3 (ESRI, 

Redlands, Calif.), by using the kriging method (fig. B-3). This baseline elevation model 

was used as the initial state in the WARMER sea-level rise (SLR) model; WARMER 

results were extrapolated across the elevation model. 
43

Figure B-1. Elevation and vegetation survey points at Black John in 2010. 

Figure B-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), at 

Black John. 
44

Vegetation surveys

Vegetation and elevation surveys were carried out concurrently in June 2010. A 

total of 108 locations (fig. B-1) were measured for vegetation composition, height, and 

percentage cover (table B-1). We did not distinguish between Spartina spp., or among 

Schoenoplectus spp., in the survey. Vegetation in marshes is sensitive to soil salinity, 

inundation patterns, and disturbance; therefore, a stratification of vegetation species 

relative to MHW (fig. B-4) was observed within this low-slope marsh. 

Figure B-3. Elevation model (3-meter resolution), developed from ground RTK GPS elevation 

data. Parameters were optimized to produce minimal root-mean-square error. 
45

Table B-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Black John.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD (MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Salicornia pacifica 0.02 0.12 37.59 11.65 51.46 14.62 73.22 30.60 282 91.56

Spartina spp. -0.29 0.03 47.50 3.54 50.00 7.07 13.00 16.97 2 0.65

Schoenoplectus spp. 0.02 0.10 64.66 17.01 82.18 23.42 13.10 15.46 125 40.58

Grindelia stricta 0.09 0.13 68.09 21.71 83.26 28.38 41.05 25.88 58 18.83

Jaumea carnosa 0.13 - 15.00 - 20.00 - 25.00 - 1 0.32

Frankenia salina 0.16 0.31 27.58 8.81 35.00 11.48 45.42 38.82 12 3.90

Distichlis spicata 0.13 0.24 25.83 9.17 33.47 9.07 38.36 28.66 66 21.43

Lepidium latifolium 0.38 0.34 92.00 21.63 113.38 19.60 35.75 27.76 8 2.60

Atriplex triangularis 0.01 0.05 13.33 7.64 15.00 13.23 8.67 7.09 3 0.97

Baccharis pilurais 0.19 0.34 60.00 22.32 91.67 33.02 28.22 28.85 27 8.77
46

Water-level monitoring

Site-specific water level was measured from December 2009 to November 2010. 

Water level was measured by using two data loggers: one deployed at the mouth of a 

second order channel and one in the marsh interior. Mean high water (MHW) was 1.73 

m, and mean higher high water (MHHW) was 1.91 m for the site (NAVD88). The period 

when the salt marsh platform (defined as mean marsh elevation) was inundated most 

often was during January and February 2010 (fig. B-5). During those months, water 

levels were above average because of several record breaking storms that brought low air 

pressure and substantial rainfall, resulting in higher than predicted tides. The cumulative 

rainfall in January 2010 was above average throughout the San Francisco Bay area, and 

daily rainfall records were broken in some locations (National Oceanic and Atmospheric 

Administration). This resulted in longer inundation periods of the marsh platform. Mean 

salinity during 2010 at Black John was 15.3 (SD = 6.6) practical salinity scale (PSU). 

Figure B-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus spp.; BAPI = 

Baccharis pilurais; LELA: = Lepidium latifolium; SPAR = Spartina spp; FRSA = Frankenia salina; 

GRST = Grindelia stricta; JACA = Jaumea carnosa; DISP = Distichlis spicata; ATTR = Atriplex 

triangularis
47

Marsh elevation modeling 

The WARMER scenario indicated that Black John will not keep pace with sealevel rise (SLR) through this century. WARMER results showed a gradual reduction in 

elevation relative to MHW over time, with a more dramatic decline after 2060 (fig. B-6). 

By 2080, the marsh was projected to be below mean sea level and to transition to mudflat 

(fig. B-7). 

Figure B-5. Percentage of time Black John was inundated monthly, based on the mean elevation 

of the marsh platform. 
48

Figure B-6. WARMER scenarios for Black John marsh elevation change. Elevation, in centimeters 

(cm), above mean high water (MHW) is plotted against model year with two standard deviations 

(SD). 

2000 2020 2040 2060 2080 2100

-120

-100

-80

-60

-40

-20

0

20

Elevation above MHW, cm

Year

Black John Marsh

- 2SD

Mean elevation

+ 2SD
49

Figure B-7. WARMER results for Black John. WARMER accounts for changes in relative sea

level, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
50

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. B-8 and B-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, Black John is a mixture of 

mid- and high-marsh vegetation. All high-marsh vegetation was projected to disappear by 

2040 (0.24 m SLR). The largest change was projected for around 2040 (0.32 m SLR), at 

which time the majority of the marsh transitions to low marsh, a habitat zone that 

currently represents a negligible area at Black John. A transition to complete mudflat was 

projected by 2080 (0.85 m SLR). 

The WARMER model parameters for Black John were extrapolated by using 

sediment core data from Petaluma Marsh; thus, predictions need be interpreted with 

caution, as local sedimentation processes can be different between these marshes. In 

addition, quality-control issues with the Petaluma sediment cores resulted in the removal 

of data that indicated high sedimentation rates. The Petaluma River is a major source of 

sediment to San Francisco Bay; therefore, it is likely that the current inputs to WARMER 

underestimate accretion potential at Black John. To improve results, local site-specific 

sediment core data could be collected along with suspended-sediment concentration data 

to characterize sediment deposition potential. 
51

Figure B-8. Percentage of area of Black John within a given tidal range for the duration of the 

simulation period. [MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; 

>, greater than; <, less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

Black John Marsh

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
52

Figure B-9. Black John WARMER results in terms of plant communities relative to mean sea 

level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition. 
53

Appendix C. China Camp Marsh

Introduction

China Camp State Park (hereafter, “China Camp”) is a 613.9 hectare (ha) park 

owned and managed by California State Parks. China Camp is located in Marin County 

and the city of San Rafael. It contains a large expanse of undeveloped historic marsh, 

with adjacent uplands composed of oak woodlands. China Camp is part of the National 

Oceanic and Atmospheric Administration National Estuarine Research Reserve (NERR) 

network, where it is used as a reference for healthy marsh and a living laboratory for staff 

and scientists.

China Camp is located adjacent to Gallinas Creek on San Pablo Bay. It is 

influenced by tidal flow from the San Pablo Bay as well as freshwater flow from Gallinas 

Creek. China Camp provides habitat for state listed species, such as the California black 

rail (Laterallus jamaicensis), and federally endangered species, such as salt marsh harvest 

mouse (Reithrodontomys raviventris) and California clapper rail (Rallus longirostris 

obsolete).

This study focused on 96.7 ha of marsh at China Camp. Elevation and vegetation 

surveys were done in 2010 by using RTK GPS. To monitor tidal inundation, four waterlevel loggers were deployed in 2010. 

Results

Elevation surveys

A total of 753 elevation measurements were taken at China Camp (fig. C-1). The 

elevation range was 0.87–2.28 meters, (m) with a mean of 1.79 m (NAVD88). Over half 

(58 percent) of the survey points were within 1.75 m–1.85 m, a 0.1 m range (fig. C-2). 

China Camp was a relatively high marsh with the majority (67 percent) of survey points at 

elevations above mean high water (MHW). A 3-m resolution elevation model was 

developed in ArcGIS 9.3 (ESRI, Redlands, Calif.), Spatial Analyst by using the kriging

method (fig. C-3). This baseline elevation model was used as the initial state in the 

WARMER sea-level rise (SLR) model; WARMER results were extrapolated across the 

elevation model. 
54

Figure C-1. China Camp with elevation and vegetation survey points from 2010
55

Figure C-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), 

at China Camp. 
56

Vegetation surveys 

Vegetation and elevation were surveyed concurrently at China Camp during 

February–March of 2010. A total of 423 locations (fig. C-1) were measured for 

vegetation composition, height, and percentage cover (table C-1). We did not distinguish 

between invasive and native Spartina, nor among Schoenoplectus species, in the survey. 

Vegetation in marshes is sensitive to soil salinity, inundation patterns, and disturbance; 

therefore, a stratification of vegetation species relative to MHW (fig. C-4) was observed 

within this low-slope marsh. 

Figure C-3. ArcGIS elevation model (3-meter resolution) developed from ground RTK GPS 

elevation data.
57

Table C-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at China Camp.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.01 0.10 29.72 8.78 41.76 10.30 81.45 24.51 408 96.68

Spartina spp. -0.46 0.21 34.25 13.92 44.08 15.51 12.75 11.33 12 2.84

Schoenoplectus spp. -0.13 0.16 18.00 21.01 23.00 27.23 16.29 25.84 7 1.66

Grindelia stricta 0.06 0.10 62.03 22.07 66.10 24.87 26.45 20.41 29 6.87

Jaumea carnosa 0.01 0.06 12.76 3.36 16.88 4.32 19.82 26.24 34 8.06

Frankenia salina 0.09 0.04 19.50 5.36 24.33 5.85 24.33 30.64 6 1.42

Distichlis spicata 0.03 0.10 17.85 8.65 22.91 9.40 19.00 26.51 54 12.80

Lepidium latifolium 0.11 0.08 20.40 22.43 24.40 23.29 12.20 11.95 5 1.18
58

Water-level monitoring

Site specific water level was monitored at China Camp for 1 year from February 

2010 to January 2011. Water level was measured by using two data loggers deployed in 

second order channels. MHW was 1.77 m, and mean higher high water (MHHW) was 

1.95 m for the site (NAVD88). The period when the marsh platform (defined as mean 

marsh elevation) was inundated most often was February 2010 (fig. C-5). We believe 

these long periods of inundation were the result of above average cumulative rainfall in 

January 2010 throughout the San Francisco Bay (SFB) area (National Oceanic and 

Atmospheric Administration). 

Figure C-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus spp.; LELA = Lepidium 

latifolium; SPAR = Spartina spp.; FRSA = Frankenia salina; GRST = Grindelia stricta; JACA = Jaumea 

carnosa; DISP = Distichlis spicata. 
59

Marsh elevation modeling 

China Camp SLR response modeling showed that the marsh was able to maintain 

its elevations through 2030, unlike lower elevation marshes around SFB. However, 

WARMER projections showed a gradual reduction in elevation relative to MHW over 

time after 2030, with a more dramatic decline after 2060 (fig. C-6). By 2080, the marsh 

was projected to be under mean sea level (MSL) and to transition to a mudflat (fig. C-7). 

Despite its relatively high elevation, WARMER results indicated that China Camp will 

not keep pace with local SLR through this century. 

Figure C-5. Percentage of time China Camp was inundated monthly, based on the mean elevation 

of the marsh platform. 
60

Figure C-6. Modeled WARMER scenarios of marsh elevation change at China Camp. Elevation 

above mean high water (MHW), in centimeters (cm), is plotted against model year with two 

standard deviations (SD). 

2000 2020 2040 2060 2080 2100

-100

-80

-60

-40

-20

0

20

Year

Elevation above MHW, cm

China Camp Marsh

- 2SD

Mean elevation

+ 2SD
61

Figure C-7. WARMER results for China Camp. WARMER accounts for changes in relative seallevel, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
62

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. C-8 and C-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. China Camp is dominated by midand high-marsh plant communities dominated by Sarcocornia pacifica, with primarily

low-marsh vegetation adjacent to the bay. China Camp elevations are between high and 

mid-marsh plant community classifications; therefore, WARMER showed a brief 

expansion of high-marsh habitat in 2020 (0.13 m SLR), which was an artifact of the 

classification bins based on all vegetation observed in SFBE. Plant communities then 

returned to relatively the same distribution as in 2000 between 2030 (0.13 m SLR) and

2050 (0.44 m SLR). However, by 2060 (0.57 SLR), most of the marsh was projected to

transition to low marsh plant communities. By 2090 (1.05 m SLR), the marsh was 

projected to transition completely to mudflat. 

Figure C-8. Percentage of area of China Camp within a given tidal range for the duration of the 

simulation period. [MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; 

>, greater than; <, less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

China Camp Marsh

Year

Percent area

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
63

Figure C-9. China Camp WARMER results in terms of plant communities relative to mean sea 

level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
64

Appendix D. Cogswell Marsh

Introduction

Cogswell marsh (hereafter, “Cogswell”) is located in Alameda County within the 

city of Hayward along the eastern shore of south San Francisco Bay. It is located along 

the Hayward Regional Shoreline and is part of a 250-hectare (ha) tidal salt marsh 

restoration and enhancement project that was completed in 1980. It is currently managed 

by East Bay Regional Park as part of the Hayward Regional Shoreline. This marsh 

provides habitat for many migratory and marsh species, including the federally 

endangered California clapper rail (Rallus longirostris obsoletus).

This study focused on 60.3 ha of Cogswell. Elevation and vegetation surveys 

were done in 2008 and 2009 by using an RTK GPS. To monitor tidal inundation, one 

water level logger was deployed during 2009–2010. 

Results

Elevation surveys

A total of 523 elevation measurements were taken at Cogswell (fig. D-1). The 

elevation range was 1.66–2.71 meters (m), with a mean of 1.83 m (NAVD88). Half of the 

survey points were within 1.75–1.90 m, a 0.15 m range. The majority (64 percent) of 

survey points were located at elevations above mean high water (MHW; fig. D-2). A 3-m 

resolution elevation model was developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial 

Analyst by using the kriging method (fig. D-3). This baseline elevation model was used 

as the initial state in the WARMER sea-level rise (SLR) model; WARMER results were 

extrapolated across the elevation model. 

 
65

Figure D-1. Cogswell Marsh, with elevation and vegetation survey points from 2008–2009. 

Figure D-2. Distribution of elevation samples relative to local mean high water (MHW, in meters (m),) 

at Cogswell Marsh. 
66

Vegetation surveys

Vegetation and elevation surveys were conducted concurrently in April and May 

of 2008 and 2009. A total of 228 locations (fig. D-1) were measured for vegetation 

composition, height, and percentage cover (table D-1). We did not distinguish between 

invasive and native Spartina spp., nor among Schoenoplectus spp., in the survey. 

Vegetation in marshes is sensitive to soil salinity, inundation patterns, and disturbance; 

therefore, a stratification of vegetation species relative to MHW (fig. D-4) was observed 

within this low-slope marsh. 

Figure D-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data.
67

Table D-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Cogswell.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.05 0.13 30.79 7.84 37.82 10.05 90.39 22.84 211 94.20

Spartina spp. 0.03 0.08 57.19 14.45 69.50 16.78 17.94 12.59 21 9.20
68

Water-level monitoring

Site-specific water level was monitored at Cogswell from December 2009 to 

November 2010. Water level was measured by using one data logger deployed at the 

mouth of a second order channel. MHW was 1.86 m, and mean higher high water 

(MHHW) was 1.98 m for the site (NAVD88). Water levels were recorded throughout the 

year to evaluate seasonal patterns in tides. The period when the marsh platform (defined 

as mean elevation) was inundated most often was from December 2009 through February 

2010 (fig. D-5). During January and February 2010, above average water levels were 

recorded because of several record-breaking storms that brought low air pressure and 

substantial rainfall, resulting in higher than predicted tides. The cumulative rainfall in 

January 2010 was above average throughout the San Francisco Bay area, and daily 

rainfall records were broken in some locations (National Oceanic and Atmospheric 

Administration). This resulted in longer inundation periods of the marsh platform. 

Figure D-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; SPAR = Spartina spp.; and SCIR =

Schoenoplectus spp.
69

Marsh elevation modeling 

WARMER results showed a gradual reduction in elevation relative to MHW over 

the century, with a more dramatic decline after 2060 (fig. D-6). By 2100, the marsh was 

projected to be below MHW (fig. D-7). Sites that had higher starting elevations (thus 

located higher in the tidal range), such as Cogswell, also had higher accretion rates and 

were less susceptible to SLR. 

Figure D-5. Percentage of time Cogswell was inundated monthly based on the mean elevation of 

the marsh platform. 
70

Figure D-6. WARMER results for Cogswell. WARMER accounts for changes in relative sea-level, 

subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
71

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. D-8 and D-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.4 –0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, vegetation at Cogswell is 

predominantly high- and mid-marsh vegetation. High accretion rates, due, in part, to high 

suspended-sediment concentrations in South San Francisco Bay, helped maintain the 

mid-marsh habitat at Cogswell through 2070 (0.67 m SLR). After the rate of sea-level 

rise increases in the second half of the century, Cogswell was projected to begin 

decreasing in relative elevation and transition to low-marsh habitat by 2100 (1.23 m 

SLR). Unlike most marshes within this project, Cogswell did not transition to a mudflat 

by 2100. 

Figure D-7. WARMER scenarios of marsh elevation change for Cogswell. Elevation above mean 

high water (MHW), in centimeters (cm), is plotted against model year with two standard deviations 

(SD). 
72

The WARMER model parameters for Cogswell were extrapolated by using 

sediment core data near Laumeister marsh; thus, predictions need to be interpreted with 

caution because local sedimentation processes could differ between these marshes. To 

improve results, local site-specific sediment core data can be collected along with 

suspended sediment concentrations to characterize sediment deposition potential. 

Figure D-8. Percentage of area in Cogswell within a given tidal range for the duration of the 

simulation period. [MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; 

>, greater than; <, less than]
73

Figure D-9. Cogswell WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
74

Appendix E. Colma Marsh

Introduction

Colma Creek Public Shore contains Colma marsh (hereafter, “Colma”) and is 

located in south San Francisco Bay just north of San Francisco International Airport. It is 

fed by Colma Creek, which has its headwaters at San Bruno Mountain. Colma is owned 

and maintained by the Port of San Francisco in San Mateo County. Colma provides 

important habitat for migratory shorebirds and the federally-endangered California 

clapper rail (Rallus longirostris obsoletus).

 This study focused on 24.6 hectares (ha) of marsh. Beginning In 2006, a 

herbicide treatment was applied to the marsh in an attempt to control the invasive 

Spartina densiflora and the S. densiflora x S. foliosa hybrid. As a result of this treatment, 

vegetation at Colma was extremely sparse; therefore, vegetation surveys were not done. 

Elevation surveys were done in 2010 by using an RTK GPS, and a water-level logger was 

deployed in 2010 to monitor tidal inundation. 

Results

Elevation surveys

A total of 537 elevation measurements were taken at Colma (fig. E-1). The 

elevation range was 0.38–2.31 meters (m) with a mean of 1.38 m (NAVD88). Over half 

(67 percent) of the survey points fell within 1.01–1.61 m, a 0.6 m range. Colma was the 

lowest marsh surveyed in this study, with 87 percent of the elevation points taken located 

below mean high water (MHW; Fig E-2). A 3-m resolution elevation model was 

developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial Analyst by using the kriging

method (fig. E-3). This baseline elevation model was used as the initial state in the 

WARMER sea-level rise (SLR) model; WARMER results were extrapolated across the 

elevation model. 
75

Figure E-1. Colma marsh with elevation survey points from 2010. 
76

Figure E-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m),

at Colma. 
77

Water-level monitoring

Site-specific water level was monitored at Colma in 2010. Water level was 

measured by using one logger deployed in a second order channel. MHW was at 1.77 m, 

and mean higher high water (MHHW) was at 1.97 m for the site (NAVD88). Water 

levels were recorded throughout the year to evaluate seasonal patterns in tides. The 

period when the marsh platform (defined as mean marsh elevation) was inundated most 

often was from January through February 2010 (fig. E-4). The loggers recorded above 

average water levels due to several record-breaking storms that brought low air pressure 

and substantial rainfall, resulting in higher-than-predicted tides. The cumulative rainfall 

in January 2010 was above average throughout the San Francisco Bay area, and daily 

rainfall records were broken in some locations (National Oceanic and Atmospheric 

Administration). This resulted in longer than normal periods of the marsh inundation.

Figure E-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data.
78

Marsh elevation modeling 

WARMER results indicated that Colma will keep pace with local sea-level rise 

(SLR) through this century (figs. E-5 and E-6). Initial elevation was below MHW and 

was projected to increase slightly until 2060 (0.57 m SLR), when it begins to decline (fig. 

E-5). Colma was projected not to reach MHW elevations during the century. WARMER 

projected that elevation relative to MSL would peak in about 2050, at which time the 

SLR curve became exponential, and the marsh began subsiding (fig. E-6). 

Vegetation data from the other 11 sites were categorized as mudflat, low, mid, 

high marsh, or upland transition plant communities and used to interpret the WARMER 

SLR results (figs. E-7 and E-8). Upland transition (greater than 1.0 m MSL) was 

characterized by coyote bush (Baccharis pilularis). High marsh (0.7–1.0 m MSL) was 

characterized by Frankenia salina and Jaumea carnosa, whereas mid marsh (0.45–0.7 m 

MSL) was dominated by Sarcocornia pacifica. Low marsh (0.2–0.45 m MSL) was 

characterized by Spartina spp. or, in brackish areas, by Schoenoplectus spp. Mudflat 

habitat (less than 0.2 m MSL) was unvegetated or sparsely covered with Spartina spp.

Assuming high sediment deposition, Colma was projected to transition from a 

mudflat to mid-marsh plant community by 2030 (figs. E-7 and E-8). By 2100, Colma was 

projected to be primarily composed of low marsh. The high suspended-sediment 

concentrations in south San Francisco Bay could allow Colma to maintain its elevation 

relative to SLR. However, the absence of established vegetation due to the recent 

herbicide treatment could result in erosion and loss of sediment trapping ability. As a 

result of the chemical treatment, Colma currently functions as a mudflat. 

The WARMER model parameters for Colma were extrapolated by using sediment 

core data near Laumeister marsh, thus predictions need to be interpreted with caution 

because local sedimentation processes are likely very different between these marshes. 

To improve results, local site-specific sediment core data could be collected along with 

suspended sediment concentrations to characterize sediment deposition potential. 

Figure E-4. Percentage of time Colma was inundated monthly based on the mean elevation of the 

marsh platform. 
79

Figure E-5. WARMER results for Colma. WARMER accounts for changes in relative sea-level, 

subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
80

Figure E-6. WARMER scenarios for marsh elevation change at Colma. Elevation above mean high 

water (MHW), in centimeters (cm), is plotted against model year with two standard deviations 

(SD). 
81

Figure E-7. Area of Colma within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]
82

Figure E-8. Colma WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
83

Appendix F. Coon Island Marsh

Introduction 

Coon Island marsh (hereafter, “Coon Island”) is located in Napa County along the 

Napa River and is bordered on the east by Mud Slough. Coon Island is influenced by 

tidal flow from San Pablo Bay as well as freshwater flow from the Napa River. It is 

surrounded by salt pond restoration sites in the Napa Sonoma Marshes Wildlife Area. 

The marsh is owned by California Department of Fish and Game and is home to federally 

endangered species, including the salt marsh harvest mouse (Reithrodontomys 

raviventris) and California clapper rail (Rallus longirostris obsoletus). 

This study focused on 98.7 hectares (ha) of Coon Island. Elevation and vegetation 

surveys were done in 2009 by using an RTK GPS. To monitor tidal inundation and 

salinity, two water level loggers were deployed between 2009 and 2010. 

Results

Elevation surveys

A total of 799 elevation measurements were taken at Coon Island (fig. F-1). The 

elevation range was 0.82–2.07 meters (m), with a mean of 1.83 m (NAVD88). Over half 

of the survey points fell within 1.70–1.90 m. Coon Island was one of the lower marshes 

surveyed, with the majority (71 percent) of survey points at elevations below mean high 

water (MHW; fig. F-2). 

A 3-m resolution elevation model was developed in ArcGIS 9.3 (ESRI, Redlands, 

Calif.) Spatial Analyst by using the kriging method (fig. F-3). This baseline elevation 

model was used as the initial state in the WARMER sea-level rise (SLR) model; 

WARMER results were extrapolated across the elevation model. 
84

Figure F-1. Coon Island Marsh with elevation and vegetation survey points from 2009. 
85

Figure F-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), 

at Coon Island. 

Figure F-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data. 
86

Vegetation surveys 

Vegetation and elevation were surveyed concurrently at Coon Island in 

November and December of 2009. A total of 364 locations (fig. F-1) were measured for 

vegetation composition, height, and percentage cover (table F-1). We did not distinguish 

between invasive and native Spartina spp., nor among Schoenoplectus spp., in the survey. 

Vegetation in marshes is sensitive to soil salinity, inundation patterns, and disturbance; 

therefore, a stratification of vegetation species relative to MHW (fig. F-4) was observed 

within this low-slope marsh. 
87

Table F-1.

Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), and 

presence by species at Coon Island.

[cm, centimeter; m, meter; n, sample number]

Species Elevation 

(MHW, m)

Elevation 

SD (MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD 

(cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.09 0.09 50.11 12.69 57.79 13.74 75.60 27.90 320 87.91

Spartina spp. -0.37 0.13 91.00 27.62 103.33 20.82 26.67 17.56 3 0.82

Schoenoplectus spp. -0.02 0.13 86.29 24.84 96.66 26.72 26.57 22.66 140 38.46

Grindelia stricta 0.11 0.06 73.25 24.98 83.75 27.22 32.25 15.60 20 5.49

Jaumea carnosa 0.12 0.10 27.18 8.67 33.46 14.74 36.96 26.56 28 7.69

Frankenia salina 0.12 0.01 35.50 20.51 39.50 21.92 55.00 21.21 2 0.55

Distichlis spicata 0.15 0.03 28.60 9.37 29.80 9.52 1.80 0.45 5 1.37

Lepidium latifolium 0.13 0.07 91.50 10.47 104.61 18.37 25.28 21.84 18 4.95

Atriplex triangularis 0.09 0.08 21.00 4.64 26.20 9.31 11.40 13.50 5 1.37

Baccharis pilularis 0.19 - 60.00 - 95.00 - 60.00 - 1 0.27

Baccharis douglasii 0.14 0.00 75.00 14.14 83.50 19.09 55.00 28.28 2 0.55

Juncus spp. 0.11 0.10 53.00 14.28 59.33 15.13 7.83 10.94 6 1.65
88

Water-level monitoring

Site-specific water level was monitored at Coon Island from December 2009 to 

November 2010. Water level was measured by using two data loggers: one deployed at 

the mouth of a second order channel and the other in the marsh interior. MHW was at 

1.78 m, and mean higher high water (MHHW) at 1.94 m for the site (NAVD88). Water 

levels were recorded throughout the year to evaluate seasonal patterns in tides. The 

period when the marsh platform (defined as mean elevation) was inundated most often 

was from January 2010 through February 2010 (fig. F-5). During those months, above 

average water levels were recorded because of several record breaking storms that 

brought low air pressure and substantial rainfall, resulting in higher than predicted tides. 

The cumulative rainfall in January 2010 was above average throughout the San Francisco 

Bay area, and daily rainfall records were broken in some locations (National Oceanic and 

Atmospheric Administration). This resulted in longer inundation periods of the marsh 

platform. Mean salinity during 2010 at Coon Island was 11.8 (SD = 6.1) practical salinity 

scale. 

Figure F-4. Stratification of vegetation species was observed relative to mean high water (MHW) in 

meters (m). Species codes: SAPA: = Sarcocornia pacifica; SCIR = Schoenoplectus spp.; BAPI = 

Baccaris pilularis; LELA = Lepidium latifolium; SPAR = Spartina spp.; FRSA = Frankenia salina; 

GRST = Grindelia stricta; JACA = Jaumea carnosa; DISP = Distichlis spicata; ATTR = Atriplex 

triangularis; BADO = BaccKaris douglasii; JUNC = Juncus spp.
89

Marsh elevation modeling 

WARMER results indicated that Coon Island will not keep pace with local SLR 

through this century. WARMER projected a gradual reduction in elevation relative to 

MHW over time, with a more dramatic decline after 2060 (Fig F-6). By 2090, the marsh 

was projected to be under MSL and would, therefore, transition to a mudflat (fig. F-7). 

Figure F-5. Percentage of time Coon Island is inundated monthly based on the mean elevation of 

the marsh platform. 
90

Figure F-6. WARMER scenarios of marsh elevation change at Coon Island. Elevation above 

mean high water (MHW), in centimeters (cm), is plotted against model year with two standard 

deviations (SD). 

2000 2020 2040 2060 2080 2100

-80

-60

-40

-20

0

20

40

Year

Elevation above MHW, cm

Coon Island Marsh

- 2SD

Mean elevation

+ 2SD
91

Figure F-7. WARMER results for Coon Island. WARMER accounts for changes in relative sealevel, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
92

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Thus, vegetation data were categorized as mudflat, low, mid, high marsh, or upland 

transition plant communities and used to interpret the WARMER SLR results (figs. F-8 

and F-9). Upland transition (greater than 1.0 m MSL) was characterized by coyote bush 

(Baccharis pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia 

salina and Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by 

Sarcocornia pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp.

or, in brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, vegetation at Coon Island 

is primarily high and mid-marsh plant communities. The amount of high-marsh 

vegetation was projected to increase between 2010 (0.06 m SLR) and 2030 (0.57 m 

SLR). By 2060 (0.57 m SLR), WARMER projected a considerable increase in low-marsh 

vegetation. After 2060 (0.57 m SLR), the elevation of the marsh rapidly decreased, and, 

by 2090 (1.05 m SLR), the marsh became primarily mudflat. The WARMER model 

parameters for Coon Island were collected by using sediment core data from the site.

Figure F-8. Area of Coon Island within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Coon Island Marsh

Year

Percent area

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
93

Figure F-9. Coon Island WARMER results in terms of plant communities relative to mean sea 

level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
94

Appendix G. Corte Madera Marsh 

Introduction

Corte Madera Marsh (hereafter, “Corte Madera”) is located in Marin County 

along San Pablo Bay and at the mouth of Corte Madera Creek. Corte Madera is part of 

the 400 hectare (ha) Corte Madera Ecological Reserve, which is owned and managed by 

the California Department of Fish and Game. It was designated as an Ecological Reserve 

in 1975 and contains federally endangered California clapper rails (Rallus longirostris 

obsoletus), which occupy the low intertidal zone dominated by cordgrass (Spartina spp.). 

It also contains San Pablo song sparrows (Melospiza melodia samuelis) and California 

black rails (Laterallus jamaicensis coturniculus), California species of concern. 

This study focused on 76.8 ha of Corte Madera. Elevation and vegetation surveys 

were done in 2010, by using an RTK GPS. To monitor tidal inundation, two water-level 

loggers were deployed during 2009–2010. 

Results

Elevation surveys

A total of 744 elevation measurements were taken at Corte Madera (fig. G-1). The 

elevation range was 1.31–2.08 meters (m), with a mean of 1.63 m and a range of 0.77 m 

(NAVD88). Over half (80 percent) of the survey points fell within 1.5–1.8 m. Corte 

Madera was among the lowest marshes surveyed in this study, with 72 percent of the 

elevation points located below mean high water (MHW; fig. G-2). A 3-m resolution 

elevation model was developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial Analyst

by using the kriging method (fig. G-3). This baseline elevation model was used as the 

initial state in the WARMER sea-level rise (SLR) model; WARMER results were 

extrapolated across the elevation model. 
95

Figure G-1. Corte Madera with elevation and vegetation survey points from 2009. 

Figure G-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), 

at Corte Madera marsh. 
96

Vegetation surveys

Vegetation and elevation were surveyed concurrently at Corte Madera in March 

2010. A total of 361 locations (fig. G-1) were measured for vegetation composition, 

height, and percentage cover (table G-1). We did not distinguish between invasive and

native Spartina spp., nor among Schoenoplectus spp., in the survey. Vegetation in 

marshes is sensitive to soil salinity, inundation patterns, and disturbance; therefore, a 

stratification of vegetation species relative to MHW (fig. G-4) was observed within this 

low-slope marsh. 

Figure G-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data.
97

Table G-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Corte Madera.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica -0.06 0.12 27.14 7.46 38.10 9.65 70.07 33.92 351 97.23

Spartina spp. -0.17 0.08 36.43 10.32 51.39 15.19 23.79 25.19 99 27.42

Grindelia stricta 0.08 0.12 41.57 20.80 49.14 21.26 15.14 10.73 14 3.88

Jaumea carnosa -0.02 0.05 12.14 3.07 15.35 3.02 24.97 30.36 37 10.25

Frankenia salina 0.21 0.19 14.00 9.90 17.50 12.02 9.00 1.41 2 0.55

Distichlis spicata 0.00 0.08 21.16 6.07 28.21 6.65 29.99 27.76 126 34.90

Lepidium latifolium -0.01 0.08 18.60 7.55 23.70 10.09 11.30 9.20 10 2.77
98

Water-level monitoring

Water levels were recorded throughout the year to evaluate seasonal patterns in 

tides. Site-specific water level was monitored from August 2010 through July 2011 (fig.

G-5). Water level was measured by using two data loggers: one deployed at the mouth of 

a second order channel and another in the marsh interior. MHW was at 1.70 m, and mean 

higher high water (MHHW) was at 1.87 m for the site (NAVD88). 

Figure G-4. Stratification of vegetation species was observed relative to mean high water (MHW) in meters 

(m). Species codes: SAPA = Sarcocornia pacifica; LELA = Lepidium latifolium; SPAR = Spartina spp.; 

FRSA = Frankenia salina; GRST: = Grindelia stricta; JACA = Jaumea carnosa; DISP = Distichlis spicata. 
99

Marsh elevation modeling 

Corte Madera was one of the lowest marshes relative to MHW in this study, 

second only to Colma marsh. Sites with lower accretion rates and starting elevations that 

were located lower in the tidal range became inundated more frequently, making them 

more susceptible to SLR. WARMER results indicated that Corte Madera will not keep 

pace with local SLR through this century. WARMER projected a reduction in elevation 

relative to MHW through 2100, with a more dramatic decline after 2060 (fig. G-6). By 

2090, the marsh was projected to be under mean sea level (MSL) and to transition to a 

mudflat (fig. G-7). 

Figure G-5. Percentage of time Corte Madera was inundated monthly based on the mean 

elevation of the marsh platform. 
100

Figure G-6. WARMER results for Corte Madera. WARMER accounts for changes in relative seallevel, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
101

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. G-8 and G-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, vegetation at Corte Madera 

is prominently mid marsh with approximately one-quarter high-marsh habitat. All highmarsh vegetation was projected to disappear by 2030 (0.24 m SLR). Nearly all midmarsh vegetation was projected to disappear by 2060 (0.57 m SLR), and, by 2080 (0.85 

SLR), Corte Madera was projected to be primarily mudflat. 

The WARMER model parameters for Corte Madera were extrapolated by using 

sediment core data from China Camp Marsh, thus predictions need to be interpreted with 

caution because local sedimentation processes could differ between these marshes. To 

improve results, local site-specific sediment core data could be collected along with 

suspended-sediment concentrations to characterize sediment deposition potential. 

Figure G-7. WARMER scenarios for Corte Madera elevation change. Elevation above mean high 

water (MHW), in centimeters (cm), is plotted against model year with two standard deviations (SD). 

2000 2020 2040 2060 2080 2100

-120

-100

-80

-60

-40

-20

0

20

Elevation above MHW, cm

Year

Corte Madera Marsh

- 2SD

Mean elevation

+ 2SD
102

Figure G-8. Area of Corte Madera within a given tidal range for the duration of the simulation 

period. [MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater 

than; <, less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

Corte Madera Marsh

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
103

Figure G-9. Corte Madera WARMER results in terms of plant communities relative to mean sea 

level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.. 
104

Appendix H. Fagan Slough Marsh

Introduction

Fagan Slough Marsh (hereafter, “Fagan”) is located in Napa County along the 

Napa River. It has been managed by the California Department of Fish and Game since 

1979 and is part of the Fagan Slough Ecological Reserve. The marsh is influenced by 

tidal flow from San Pablo Bay by way of Steamboat Slough (north) and Fagan Slough 

(south) and receives freshwater input from the Napa River. Fagan is surrounded by salt 

evaporation ponds, private property, and small upland areas with live oaks. Fagan 

provides habitat for state listed species, such as the California black rail (Laterallus 

jamaicensis) and federal endangered species, such as salt marsh harvest mouse 

(Reithrodontomys raviventris) and California clapper rail (Rallus longirostris obsoletus). 

This study focused on 67.9 hectares (ha) of Fagan. Elevation and vegetation 

surveys were completed in 2010 by using an RTK GPS. To monitor tidal inundation, two 

water level loggers were deployed and monitored during 2010–2011. 

Results

Elevation surveys

A total of 481 elevation measurements were taken at Fagan (fig. H-1). The 

elevation range was 1.16–2.25 meters (m) with a mean of 1.90 m (NAVD88). Over half 

(66 percent) of the survey points fell within 1.9–2.0 m, a 0.1 m range. Fagan is a 

relatively high elevation marsh, with 88 percent of survey points at elevations above 

mean high water (MHW; fig H-2). A 3-m resolution elevation model was developed in 

ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial Analyst by using the kriging method (fig. H3). This baseline elevation model was used as the initial state in the WARMER sea-level 

rise (SLR) model; WARMER results were extrapolated across the elevation model. 
105

Figure H-1. Fagan Marsh with elevation and vegetation survey points from 2010. 

Figure H-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), 

at Fagan Marsh. 
106

Vegetation surveys

Vegetation and elevation were surveyed at Fagan concurrently in April and July 

2010. A total of 241 locations (fig. H-1) were measured for vegetation composition, 

height, and percentage cover (table H-1). We did not distinguish between invasive and

native Spartina spp., nor among Schoenoplectus spp., in the survey. Vegetation in 

marshes is sensitive to soil salinity, inundation patterns, and disturbance; therefore, a 

stratification of vegetation species relative to MHW (fig. H-4) was observed within this 

low-slope marsh. Fagan also had relatively high marsh-species richness, with 17 species 

recorded.

Figure H-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data.
107

Table H-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Fagan.

[cm, centimeter; m, meter; n, sample number]

Species Elevation 

(MHW, m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD 

(cm)

Cover

(percent)

Cover SD

(percent) n 

Presence

(percent)

Sarcocornia pacifica 0.16 0.11 36.97 10.57 49.24 12.95 61.57 36.47 267 86.69

Spartina spp. 0.03 - - - - - 35.00 - 1 0.32

Schoenoplectus spp. 0.11 0.14 77.88 30.11 94.56 35.55 15.58 21.08 157 50.97

Grindelia stricta 0.05 0.02 81.50 14.85 81.50 14.85 47.50 60.10 2 0.65

Jaumea carnosa 0.03 - 38.00 - 40.00 - 55.00 - 1 0.32

Frankenia salina 0.16 - 40.00 - 44.00 - 77.00 - 1 0.32

Distichlis spicata 0.20 0.03 26.80 7.36 29.40 6.88 14.00 26.89 5 1.62

Lepidium latifolium 0.14 0.13 92.23 26.66 101.40 27.79 44.02 34.28 53 17.21

Atriplex triangularis 0.09 0.17 27.50 13.50 34.14 16.44 9.57 8.39 14 4.55

Triglochin concinna 0.24 0.04 41.13 8.54 52.25 11.21 27.75 26.86 8 2.60

Typha angustifolia -0.11 0.21 107.75 22.53 126.67 27.98 26.33 36.27 12 3.90

Potentilla anserina 0.18 0.07 25.87 6.89 30.73 7.50 11.72 13.26 78 25.32

Baccharis pilurais 0.23 0.03 96.50 17.79 96.50 17.79 79.75 28.05 4 1.30

Artemisia spp. 0.13 0.01 33.33 19.73 39.33 17.93 11.33 4.04 3 0.97

Ruppia maritima 

occidentalis

0.12 0.05 36.50 3.54 36.50 3.54 4.00 1.41 2 0.65

Cotula coronopifolia -0.11 - 25.00 - 29.00 - 25.00 - 1 0.32

Juncus spp. 0.19 0.06 55.36 15.13 67.00 20.60 7.14 14.77 22 7.14
108

Water-level monitoring

Site-specific water levels were monitored at Fagan from July 2010 to June 2011. 

Water level was measured by using two data loggers: one deployed at the mouth of a 

second order channel and one in the marsh interior. MHW was at 1.75 m, and mean 

higher high water (MHHW) at 1.90 m for the site (NAVD88). Inundation patterns were 

recorded throughout the year to evaluate seasonal patterns. The period when the salt 

marsh platform (defined as mean elevation) was inundated most often was June 2011 

(fig. H-5). 

Figure H-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus spp.; BAPI = 

Baccaris pilularis; LELA = Lepidium latifolium; SPAR = Spartina spp.; FRSA = Frankenia salina;

GRST = Grindelia stricta; JACA = Jaumea carnosa; DISP = Distichlis spicata; ATTR = Atriplex 

triangularis; JUNC = Juncus spp; TYAN = Typha angustifolia; RUOC = Ruppia maritima occidentalis; 

POAN = Potentilla anserina; COCO = Cotula coronopifolia; TRCO = Triglochin concinna. 
109

Marsh elevation modeling 

WARMER projected that Fagan will not keep pace with local SLR through this 

century. WARMER results showed a gradual reduction in elevation relative to MHW 

over time, with a more dramatic decline after 2060 (fig. H-6). By 2090, Fagan was 

projected to be under mean sea level (MSL) and, therefore, to transition to a mudflat (fig. 

H-7). 

Figure H-5. Percentage of time Fagan was inundated monthly based on the mean elevation of the 

marsh platform. 
110

Figure H-6. WARMER results for Fagan. WARMER accounts for changes in relative sea-level, 

subsidence, inorganic sediment accumulation, above and below ground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
111

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. H-8 and H-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, Fagan is roughly 90 

percent high-marsh habitat. The amount of high marsh was projected to steadily decline 

until 2060 (0.57 m SLR), at which point all high marsh transitions to mid and low marsh. 

A transition to mud flat was projected by 2090 (1.05 m SLR). 

The WARMER model parameters for Fagan were extrapolated by using sediment 

core data from Coon Island, thus predictions need to be interpreted with caution because

local sedimentation processes could differ between these marshes. To improve results, 

local site-specific sediment core data could be collected along with suspended-sediment 

concentrations to characterize sediment deposition potential. 

Figure H-7. WARMER scenarios for Fagan elevation change. Elevation above mean high water 

(MHW), in centimeters (cm), is plotted against model year with two standard deviations (SD). 

2000 2020 2040 2060 2080 2100

-80

-60

-40

-20

0

20

40

Elevation above MHW, cm

Year

Fagan Island Marsh

- 2SD

Mean elevation

+ 2SD
112

Figure H-8. Area of Fagan within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

Fagan Island Marsh

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
113

Figure H-9. Fagan WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
114

Appendix I. Gambinini Marsh

Introduction

Gambinini marsh (hereafter, “Gambinini”) is privately owned and is located in 

Sonoma County along the Petaluma River at the northern tip of the Petaluma marsh 

complex. The Petaluma marsh complex is the largest marsh in California that has never 

been diked or drained for agriculture. Because of its proximity to the Petaluma River, 

Gambinini is influenced by flow from the Petaluma River as well as tidal flow from San 

Pablo Bay. This marsh is surrounded by oak woodlands and light grazing. This marsh 

provides important habitat for species of concern, including the California black rail 

(Laterallus jamaicensis). 

This study focused on 24.8 hectares (ha) of Gambinini. Elevation and vegetation 

surveys were done in winter of 2009 by using an RTK GPS. To monitor tidal inundation 

and salinity, two water level loggers were deployed during 2009–2010. 

Results

Elevation surveys

A total of 217 elevation measurements were taken at Gambinini (fig. I-1). The 

elevation range was 1.27–2.10 meters (m), with a mean of 1.86 m (NAVD88). Over half 

(70 percent) of the survey points fell within 1.80–1.95 m, a 0.15 m range (fig. I-2). The 

majority (81 percent) of survey points were located at elevations above mean high water 

(MHW). A 3-m resolution elevation model was developed in ArcGIS 9.3 (ESRI, 

Redlands, Calif.) Spatial Analyst by using the kriging method (fig. I-3). This baseline 

elevation model was used as the initial state in the WARMER sea-level rise (SLR) 

model; WARMER results were extrapolated across the elevation model. 
115

Figure I-1. Gambinini marsh with elevation and vegetation survey points from 2009. 
116

Figure I-2. Distribution of elevation samples relative to local mean high water (MHW), in meters 

(m), at Gambinini marsh. 

 
117

Vegetation surveys

Vegetation and elevation were surveyed concurrently at Gambinini in October of 

2009. A total of 110 locations (fig. I-1) were measured for vegetation composition, 

height, and percentage cover (table I-1). We did not distinguish between invasive and

native Spartina spp., nor among Schoenoplectus spp., in the survey. Vegetation in 

marshes is sensitive to soil salinity, inundation patterns, and disturbance; therefore, a 

stratification of vegetation species relative to MHW (fig. I-4) was observed within this 

low-slope marsh. 

Figure I-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation data.
118

Table I-1. Mean marsh elevation relative to mean high water (MHW), average, and max height , percentage cover with standard deviations (SD), 

and presence by species at Gambinini.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.06 0.10 27.83 9.58 40.46 11.82 87.50 23.70 109 99.09

Schoenoplectus spp. -0.10 0.14 27.50 15.20 33.25 19.96 14.00 17.72 4 3.64

Grindelia stricta -0.03 0.06 45.00 20.00 60.40 27.26 26.20 37.48 5 4.55

Jaumea carnosa -0.03 - 12.00 - 17.00 - 75.00 - 1 0.91

Frankenia salina 0.11 0.09 25.00 2.08 30.57 5.97 28.29 22.60 7 6.36

Distichlis spicata 0.13 0.06 15.78 4.79 20.78 4.74 33.22 31.29 9 8.18

Lepidium latifolium 0.07 0.08 87.53 30.30 104.00 30.67 23.24 19.41 17 15.45
119

Water-level monitoring

Site-specific water level was monitored between December 2009 and December 

2010; data were unavailable for August 2010 as a result of equipment failure. Water level 

was measured by using two data loggers: one deployed at the mouth of a second order 

channel and one in the marsh interior. MHW was at 1.80 m, and mean higher high water 

(MHHW) at 1.96 m, for the site (NAVD88). Water levels were recorded throughout the 

year to evaluate seasonal patterns in tides. The period when the marsh platform (defined 

as mean elevation) was inundated most often was January 2010 (fig. I-5). Above average 

water levels were recorded in January because of several record breaking storms that 

brought low air pressure and substantial rainfall, resulting in higher than predicted tides. 

In January 2010, the cumulative rainfall was above average throughout the San Francisco 

Bay area, and daily rainfall records were broken in some locations (National Oceanic and 

Atmospheric Administration). This resulted in longer inundation periods of the marsh 

platform. Mean salinity during 2010 at Gambinini was 12.8 (SD = 8.2) practical salinity 

scale. 

Figure I-4. Stratification of vegetation species was observed relative to mean high water

(MHW), in meters (m). Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus

spp.; LELA = Lepidium latifolium; FRSA = Frankenia salina; GRST = Grindelia stricta; JACA =

Jaumea carnosa; DISP = Distichlis spicata. 
120

Marsh-elevation modeling 

WARMER results indicated that Gambinini is unlikely to keep pace with local 

SLR through this century. Initial elevation was relatively high compared to other study 

sites; however, results showed a gradual reduction in elevation relative to MHW over 

time, with a more dramatic decline after 2080 (fig. I-6). By 2080, the marsh was 

projected to be under mean sea level (MSL) and to function as a mudflat (fig. I-7). 

Figure I-5. Percentage of time Gambinini was inundated monthly based on the mean elevation of 

the marsh platform. Data were not available for August because of equipment failure. 
121

Figure I-6. WARMER results for Gambinini. WARMER accounts for changes in relative sea-level, 

subsidence, inorganic sediment accumulation, above and below ground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
122

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition 

plant communities and used to interpret the WARMER SLR results (figs. I-8 and I-9). 

Upland transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis 

pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and

Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia 

pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp. or, in 

brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, Gambinini is primarily 

composed of high- and mid-marsh plant communities. WARMER showed a steady 

decline in the amount of high marsh beginning in 2020 (0.06 m SLR). By 2040 (0.32 m 

SLR), all high marsh was projected to transition to mid marsh. By 2060 (0.57 m SLR), 

most of Gambinini was projected to be low marsh, and by 2080 (0.85 m SLR), 

WARMER projected that the entire marsh would be below MSL and transition to a 

mudflat. 

The WARMER model parameters for Gambinini were extrapolated by using 

sediment core data from Petaluma Marsh, thus predictions need to be interpreted with 

caution because local sedimentation processes could differ between these marshes. In 

addition, quality control issues with the Petaluma sediment cores resulted in the removal 

of data that indicated high sedimentation rates. The Petaluma River is a major source of 

sediment to San Francisco Bay; thus, it is likely that the inputs to WARMER are 

underestimating accretion potential. To improve results, local site-specific sediment core 

data could be collected along with suspended-sediment concentrations to characterize 

sediment deposition potential. 

Gambinini Marsh

Figure I-7. Modeled scenarios of marsh elevation change for Gambinini. Elevation above mean 

high water (MHW), in centimeters (cm), is plotted against model year. 

2000 2020 2040 2060 2080 2100

-120

-100

-80

-60

-40

-20

0

20

Elevation above MHW, cm

Year

Giambini Island Marsh

- 2SD

Mean elevation

+ 2SD
123

Figure I-8. Area of Gambinini within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

Giambini Island Marsh

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL

Gambinini Marsh
124

Figure I-9. Gambinini WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
125

Appendix J. Laumeister Marsh

Introduction

Located in south San Francisco Bay in Santa Clara county, Laumeister Marsh

(hereafter, “Laumeister”) is owned by the City of Palo Alto. It is managed by the U.S. 

Fish and Wildlife Service as part of Don Edwards National Wildlife Refuge, which is the 

largest refuge in San Francisco Bay estuary. Laumeister is recognized as an important 

stopover on the Pacific Flyway and is home for endangered species such as the California 

clapper rail (Rallus longirostris obsoletus). 

The focus of this study was on 36.8 hectares (ha) at Laumeister marsh. Elevation 

and vegetation surveys were done in 2009–2010 by using an RTK GPS. To monitor tidal 

inundation and salinity, two water level loggers were deployed from 2009 to 2010. 

Results

Elevation surveys

A total of 717 elevation measurements were taken at Laumeister (fig. J-1). The 

elevation range was 1.15–2.20 meters (m), with a mean of 1.98 m (NAVD88). Over half 

(53 percent) of the survey points fell within 1.95–2.05 m, a 0.1 m range. A majority (86

percent) of the survey points were above mean high water (MHW; fig J-2). Laumeister 

was the second highest marsh surveyed in this study. A 3-m resolution elevation model 

was developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial Analyst by using the 

kriging method (fig. J-3). This baseline elevation model was used as the initial state in the 

WARMER sea-level rise (SLR) model; WARMER results were extrapolated across the 

elevation model. 
126

Figure J-1. Laumeister Marsh with elevation and vegetation survey points from 2009 and 

2010. 
127

Figure J-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m) 

at Laumeister Marsh. 
128

Vegetation surveys

Vegetation and elevation were surveyed concurrently in 2010. A total of 72

locations (fig. J-1) were surveyed for vegetation composition, height, and percentage

cover (table J-1). We did not distinguish between invasive and native Spartina spp., nor 

among Schoenoplectus spp., in the survey. Vegetation in marshes is sensitive to soil 

salinity, inundation patterns, and disturbance; therefore, stratification of vegetation 

species relative to MHW (fig. J-4) was observed within this low-slope marsh. 

Figure J-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data.
129

Table J-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Laumeister.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.07 0.15 29.95 7.59 39.34 8.60 72.42 34.57 62 86.11

Spartina spp. 0.00 0.14 57.56 12.86 68.78 11.35 28.41 31.28 27 37.50

Grindelia stricta 0.08 0.21 64.20 12.97 72.20 14.76 27.10 31.09 20 27.78

Jaumea carnosa 0.08 0.07 14.67 4.04 21.33 2.89 55.00 37.75 3 4.17

Frankenia salina 0.25 - 30.00 - 33.00 - 30.00 - 1 1.39

Distichlis spicata 0.08 0.17 23.69 6.07 32.76 6.87 57.41 30.61 29 40.28

Lepidium latifolium -0.08 0.46 28.50 7.85 44.00 6.73 46.25 44.98 4 5.56
130

Figure J-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; LELA = Lepidium latifolium; SPAR = 

Spartina spp.; FRSA = Frankenia salina; GRST = Grindelia stricta; JACA = Jaumea carnosa; DISP = 

Distichlis spicata. 
131

Water-level monitoring 

Site-specific water level was monitored for 1 year between December 2009 and 

December 2010. Water level was measured by using two data loggers: one deployed at 

the mouth of a second order channel and one in the marsh interior. Water levels were 

recorded throughout the year to evaluate seasonal patterns in tides. MHW was at 1.92 m, 

and mean higher high water (MHHW) at 2.09 m, for the site (NAVD88). The period 

when the marsh platform (defined as mean elevation) was inundated most often was from 

December 2009 through February 2010 (fig. J-5). Above average water levels were 

recorded during those months because of several record breaking storms that brought low 

air pressure and substantial rainfall, resulting in higher than predicted tides. The 

cumulative rainfall in January 2010 was above average throughout the San Francisco Bay 

area, and daily rainfall records were broken in some locations (National Oceanic and 

Atmospheric Administration). This resulted in longer inundation periods of the marsh 

platform. Mean salinity during 2010 at Laumeister was 13.4 (SD = 7.9) practical salinity 

scale. 

Figure J-5. Percentage of time Laumeister was inundated monthly based on the mean elevation of 

the marsh platform. 

Marsh elevation modeling 

Laumeister had high initial elevation and accretion rates, which allowed it WRsustain 

marsh elevation longer than other study sites. Despite this, WARMER results indicated 

that Laumeister would not keep pace with local SLR through this century. WARMER 

results showed a gradual reduction in elevation relative to MHW over time, with a more 

dramatic decline after 2060; by 2050, the marsh was projected to be below MHW (fig. J6). By 2100, Laumeister was projected to remain approximately 0.4 m above mean sea 

level (MSL; fig. J-7). 
132

Figure J-6. WARMER scenarios of marsh elevation change for Laumeister. Elevation above mean 

high water (MHW), in centimeters (cm), is plotted against model year with two standard deviations 

(SD). 
133

Figure J-7. WARMER results for Laumeister. WARMER accounts for changes in relative sealevel, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
134

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Thus, vegetation data were categorized as mudflat, low, mid, high marsh, or upland 

transition plant communities and used to interpret the WARMER SLR results (figs. J-8 – 

J-9). Upland transition (greater than 1.0 m MSL) was characterized by coyote bush 

(Baccharis pilularis). High marsh (0.7–1.0 m MSL) was characterized by Frankenia 

salina and Jaumea carnosa, whereas mid marsh (0.45–0.7 m MSL) was dominated by 

Sarcocornia pacifica. Low marsh (0.2–0.45 m MSL) was characterized by Spartina spp.

or, in brackish areas, by Schoenoplectus spp. Mudflat habitat (less than 0.2 m MSL) was

unvegetated or sparsely covered with Spartina spp. Currently, Laumeister is primarily 

composed of high-marsh vegetation, and low marsh is dominated with Spartina. Model 

results showed that high accretion rates, due, in part, to high suspended-sediment 

concentrations in south San Francisco Bay, would sustain high-marsh habitat through 

2060 (0.57 m SLR). Once the rate of sea-level rise increased in the second half of the 

century, Laumeister would begin to lose relative elevation and transition to 

predominantly mid-marsh habitat by 2080 (0.85 m SLR). By 2100, Laumeister would 

transition predominantly to low-marsh habitat (1.23 m SLR). 

Figure J-8. Area of Laumeister within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]
135

Figure J-9. Laumeister WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition. 
136

Appendix K. Petaluma Marsh

Introduction

Petaluma Marsh (hereafter, “Petaluma”) is located in Sonoma County on the 

Petaluma River and adjacent to Petaluma Slough. It is owned and managed by the 

California Department of Fish and Game. Petaluma is influenced by tidal flow from San 

Pablo Bay as well as freshwater flow from the Petaluma River. This site is part of the 

largest remaining natural tidal salt marsh in California and is recognized as an important 

habitat for many state listed species, such as the California black rail (Laterallus 

jamaicensis), and federally endangered species, such as WKHsalt marsh harvest mouse 

(Reithrodontomys raviventris). 

This study focused on 80.6 hectares (ha) of Petaluma marsh. Elevation and 

vegetation surveys were done in 2009 by using an RTK GPS. To monitor tidal inundation 

and salinity, four water level loggers were deployed in 2009. 

Results

Elevation surveys

A total of 655 elevation measurements were taken at Petaluma (fig. K-1). The 

elevation range was 0.87–2.28 meters (m), with a mean of 1.82 m (NAVD88). Petaluma 

was one of the highest elevation marshes surveyed. Nearly three-fourths (74.8 percent) of 

the survey points were within 1.75–1.90 m, a 0.15 m range (fig. K-2). The majority (89

percent) of survey points were at elevations above mean high water (MHW). A 3-m 

resolution elevation model was developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial 

Analyst by using the kriging method (fig. K-3). This baseline elevation model was used 

as the initial state in the WARMER sea-level rise model; WARMER results were 

extrapolated across the elevation model. 
137

Figure K-1. Petaluma Marsh with elevation and vegetation survey points from 2009. 

Figure K-2. Distribution of elevation samples relative to local mean high water (MHW), in meters (m), 

at Petaluma Marsh.
138

Vegetation surveys

Vegetation and elevation surveys were done concurrently in October–November 

of 2009. A total of 357 locations (fig. K-1) were measured for vegetation composition, 

height, and percentage cover (table K-1). We did not distinguish between invasive and

native Spartina spp., nor among Schoenoplectus spp., in the survey. Vegetation in 

marshes is sensitive to soil salinity, inundation patterns, and disturbance; therefore, 

stratification of vegetation species relative to MHW (fig. K-4) was observed within this 

low-slope marsh. 

Figure K-3. Elevation model (3-meter resolution) developed from ground RTK GPS elevation 

data. 
139

Table K-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at Petaluma.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.08 0.07 39.82 10.98 49.22 11.30 84.53 24.69 345 96.91

Schoenoplectus spp. -0.07 0.24 63.04 26.68 71.57 30.38 15.04 14.43 23 6.46

Grindelia stricta 0.11 0.07 66.22 21.25 75.67 24.71 25.06 18.94 18 5.06

Jaumea carnosa 0.06 0.05 16.97 5.32 20.40 6.51 21.05 21.95 62 17.42

Frankenia salina 0.11 0.06 31.20 8.16 35.80 8.50 47.02 33.15 46 12.92

Distichlis spicata 0.04 0.04 21.18 5.38 24.82 6.03 20.27 22.38 33 9.27

Lepidium latifolium 0.11 0.08 96.22 18.89 106.06 18.63 24.94 16.54 36 10.11

Baccharis piluODris 0.30 0.19 71.67 38.84 84.00 35.68 55.00 39.69 3 0.84
140

Water-level monitoring

Site-specific water level was monitored for 1 year from December 2009 to November 

2010. Water level was measured by using four data loggers: two deployed at the mouth of 

second order channels and two in the marsh interior. Water levels were recorded throughout the 

year to evaluate seasonal patterns in tides. MHW was at 1.76 m, and mean higher high water 

(MHHW) at 1.92 m, for the site (NAVD88). The period when the marsh platform (defined as 

mean elevation) was inundated most often was from January 2010 through February 2010 (fig.

K-5). During those months, above average water levels were recorded because of several recordbreaking storms that brought low air pressure and substantial rainfall, resulting in higher than 

predicted tides. The cumulative rainfall in January 2010 was above average throughout the San 

Francisco Bay area, and daily rainfall records were broken in some locations (National Oceanic 

and Atmospheric Administration). This resulted in longer inundation periods of the marsh 

platform. Mean salinity during 2010 at Petaluma was 14.4 (SD = 8.3) practical salinity scale. 

Figure K-4. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m). Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus spp.; BAPI = 

Baccaris pilularis; LELA = Lepidium latifolium; FRSA = Frankenia salina; GRST = Grindelia stricta;

JACA = Jaumea carnosa; DISP = Distichlis spicata. 
141

Marsh-elevation modeling 

WARMER results indicated that Petaluma will not keep pace with local SLR through this 

century. WARMER results projected a gradual reduction in elevation relative to MHW over 

time, with a more dramatic decline after 2060 (Fig K-6). By 2080, the marsh was projected to be 

under mean sea level (MSL) and therefore, to transition to a mudflat (fig. K-7). 

Figure K-5. Percentage of time Petaluma Marsh was inundated monthly based on the mean 

elevation of the marsh platform. 
142

Figure K-7. WARMER results for Petaluma marsh. WARMER accounts for changes in relative 

sea-level, subsidence, inorganic sediment accumulation, above and below ground organic matter 

productivity , compaction, and decay. Non-linear sea-level rise projections for California were 

used (Cayan and others, 2009). [MSL, mean sea level; m, meters]
143

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition plant 

communities and used to interpret the WARMER SLR results (figs. K-8 – K-9). Upland 

transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis pilularis). 

High marsh (0.7–1.0 m MSL) was characterized by Frankenia salina and Jaumea carnosa, 

whereas mid marsh (0.45–0.7 m MSL) was dominated by Sarcocornia pacifica. Low marsh 

(0.2–0.45 m MSL) was characterized by Spartina spp. or, in brackish areas, by Schoenoplectus

spp. Mudflat habitat (less than 0.2 m MSL) was unvegetated or sparsely covered with Spartina 

spp. Currently, Petaluma is roughly 90 percent high marsh. However, by 2030 (0.24 m SLR), 

WARMER projected Petaluma to transition almost completely to mid marsh (figs. K-9 and K10). Petaluma was projected to transition to low marsh, dominated by Spartina spp. and

Schoenoplectus spp., by 2060 (0.57 m SLR). By 2080 (0.85 m SLR), the marsh would likely lose 

vegetation and become primarily mudflat. 

Quality-control issues with the Petaluma sediment cores resulted in the removal of data 

that indicated high sedimentation rates. The Petaluma River is a major source of sediment to San 

Francisco Bay; thus, it is likely that the inputs to WARMER underestimated accretion potential.

Figure K-6. WARMER scenarios for Petaluma marsh elevation change. Elevation above mean 

high water (MHW), in centimeters (cm), is plotted against model year with two standard deviations 

(SD). 

2000 2020 2040 2060 2080 2100

-120

-100

-80

-60

-40

-20

0

20

Year

Elevation above MHW, cm

Petaluma River Marsh

- 2SD

Mean elevation

+ 2SD
144

Figure K-8. Area of Petaluma within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Petaluma River Marsh

Year

Percent area

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
145

Figure K-9. Petaluma WARMER results in terms of plant communities relative to mean sea level 

(MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition. 
146

Appendix L. San Pablo Bay Marsh 

Introduction

San Pablo Bay National Wildlife Refuge marsh (hereafter, “San Pablo”) is located on the 

north shore of San Pablo Bay in Sonoma, Solano, and Napa Counties. It is owned and managed 

by the U.S. Fish and Wildlife Service, with adjacent lands owned and managed by California 

Fish and Game and Sonoma LandTrust. It is one of the largest areas of intact marsh in the San 

Francisco Bay estuary. San Pablo supports federally and state-listed species, such as the salt 

marsh harvest mouse (Reithrodontomys raviventris) and the California black rail (Laterallus 

jamaicensis). San Pablo is also an important stopover on the Pacific Flyway and provides critical 

migratory and wintering habitat for shorebirds. San Pablo is influenced both by the tides and by 

freshwater input from Petaluma River, Sonoma Creek, and Napa River. 

Because of the size of San Pablo, we split data analysis into two sections: 962.6 hectares 

(ha) on the northeast side and 449.5 ha on the northwest, separated by Sonoma Creek. Elevation 

and vegetation surveys were done in 2008–2009 by using an RTK GPS. To monitor tidal 

inundation and salinity, water-level loggers were deployed in 2009. 

Results

Elevation

A total of 1,396 elevation measurements were made at San Pablo (figs. L-1 and L-2). The 

elevation range was 0.56–3.11 meters (m), with a mean of 1.94 m (NAVD88). Of the survey 

points, 58 percent fell within 1.7–2.1 m. Over half (64 percent) of the survey points were at 

elevations above mean high water (MHW; fig. L-3). A 3-m resolution elevation model was 

developed in ArcGIS 9.3 (ESRI, Redlands, Calif.) Spatial Analyst by using the kriging method 

(figs. L-4 and L-5). This baseline elevation model was used as the initial state in the WARMER 

sea-level rise (SLR) model; WARMER results were extrapolated across the elevation model. 
147

Figure L-1. Western portion of San Pablo with elevation and vegetation survey points from 

2009. 

Figure L-2. Eastern portion of San Pablo with elevation and vegetation survey points from 

2008. 
148

Figure L-3. Distribution of elevation relative to local mean high water (MHW), in meters (m), for all San Pablo 

data. 
149

Figure L-4. Elevation model (3-meter resolution) developed from ground RTK GPS elevation data 

for west San Pablo.
150

Vegetation

Vegetation and elevation were surveyed concurrently at San Pablo in the summers of 

2008 and 2009. A total of 888 locations (fig. L-1) were measured for vegetation composition, 

height, and percentage cover (tables L-1 and L-2). We did not distinguish between invasive and

native Spartina spp., nor among Schoenoplectus spp., in the survey. Vegetation in marshes is 

sensitive to soil salinity, inundation patterns, and disturbance; therefore, stratification of 

vegetation relative to MHW (figs. L-6 – L-7) was observed within this low-slope marsh. 

Figure L-5. Elevation model (3-meter resolution) developed from ground RTK GPS elevation data 

for east San Pablo.
151

Table L-1. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at west San Pablo.

[cm, centimeter; m, meter; n, sample number]

Species

Elevation 

(MHW, 

m)

Elevation 

SD 

(MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD 

(cm)

Max 

Height 

(cm)

Max 

Height 

SD 

(cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.08 0.19 44.41 12.49 52.82 13.56 76.43 33.73 579 84.90

Spartina spp. -0.38 0.34 70.95 17.84 77.51 16.98 36.02 25.43 63 9.24

Schoenoplectus

spp.

-0.21 0.19 31.08 33.15 32.42 34.75 15.58 23.36 12 1.76

Grindelia stricta 0.03 0.18 67.38 16.17 73.49 17.62 34.90 29.49 71 10.41

Jaumea carnosa -0.06 0.21 16.45 8.26 20.00 9.86 57.24 34.31 33 4.84

Frankenia salina 0.17 0.27 20.92 10.53 25.50 11.77 40.47 32.05 76 11.14

Distichlis spicata 0.09 0.28 13.83 8.57 17.11 10.90 56.49 32.87 53 7.77

Lepidium latifolium 0.18 0.24 46.67 32.31 48.33 33.54 18.11 18.81 9 1.32

Atriplex triangularis 0.07 0.41 20.00 16.96 22.00 18.23 12.60 20.98 5 0.73

Baccharis pilurais 0.37 0.30 76.50 27.02 85.90 34.91 46.00 32.35 20 2.93

 
152

Table L-2. Mean marsh elevation relative to mean high water (MHW), average, and max height, percentage cover with standard deviations (SD), 

and presence by species at east San Pablo.

[cm, centimeter; m, meter; n, sample number]

Species Elevation 

(MHW, m)

Elevation 

SD (MHW, 

m)

Mean

Height 

(cm)

Mean

Height 

SD (cm)

Max 

Height 

(cm)

Max 

Height 

SD (cm)

Cover

(percent)

Cover 

SD

(percent)

n 

Presence

(percent)

Sarcocornia pacifica 0.05 0.16 42.64 14.26 54.40 14.75 72.07 32.27 166 80.58

Spartina spp. -0.16 0.25 66.13 17.44 74.38 16.26 21.94 17.25 16 7.77

Schoenoplectus spp. 0.13 0.14 90.00 14.14 107.50 10.61 15.00 7.07 2 0.97

Grindelia stricta 0.21 0.10 50.00 22.14 59.17 26.45 44.33 38.82 6 2.91

Frankenia salina 0.10 - 26.00 - 42.00 - 98.00 - 1 0.49

Lepidium latifolium 0.31 0.10 72.00 21.80 84.42 18.97 31.83 36.90 12 5.83

Baccharis pilurais 0.30 0.71 50.00 21.21 67.00 1.41 22.50 3.54 2 0.97
153

Figure L-6. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m), at west San Pablo. Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus

spp.; BAPI: Baccaris pilularis; LELA = Lepidium latifolium; SPAR = Spartina; FRSA = Frankenia salina;

GRST = Grindelia stricta; JACA = Jaumea carnosa; DISP = Distichlis spicata; ATTR = Atriplex 

triangularis.

 
154

Water-level monitoring

Site-specific water level was monitored for 1 year between December 2009 and 

November 2011. Water level was measured by using four data loggers: two deployed at the 

mouth of second order channels and two in the marsh interior. Mean high water (MHW) was at 

1.68 m, and mean higher high water (MHHW) at 1.85 m, for the site (NAVD88). Water levels 

were recorded throughout the year to evaluate seasonal patterns in tides. The period when the 

marsh platform (defined as mean marsh elevation) was inundated most often was n January when 

the diurnal high tides were highest (fig. L-8). Mean salinity during 2010 at San Pablo was 19.0 

(SD = 5.9) practical salinity scale. 

Figure L-7. Stratification of vegetation species was observed relative to mean high water (MHW), in 

meters (m), at east San Pablo. Species codes: SAPA = Sarcocornia pacifica; SCIR = Schoenoplectus

spp.; BAPI = Baccaris pilularis; LELA = Lepidium latifolium; SPAR = Spartina spp.; FRSA = Frankenia 

salina; GRST = Grindelia stricta.

 
155 

Marsh elevation modeling 

WARMER results indicated that San Pablo will not keep pace with local SLR through 

this century. Results showed a gradual reduction in elevation relative to MHW over time, with a 

more dramatic decline after 2060 (fig. L-9). By 2090, the marsh was projected to be under MSL 

and, therefore, to transition to a mudflat (figs. L-10 and L-11). 

Figure L-8. Percentage of time all of San Pablo was inundated monthly based on the mean 

elevation of the marsh platform. 
156

Figure L-9. WARMER results for west San Pablo. WARMER accounts for changes in relative 

sea-level, subsidence, inorganic sediment accumulation, above and belowground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
157

Figure L-10. WARMER results for east San Pablo. WARMER accounts for changes in relative 

sea-level, subsidence, inorganic sediment accumulation, above and below ground organic matter 

productivity, compaction, and decay. Non-linear sea-level rise projections for California were used 

(Cayan and others, 2009). [MSL, mean sea level; m, meters]
158

Elevation relative to the local tidal datum can be tied to vegetation observations. 

Vegetation data were categorized as mudflat, low, mid, high marsh, or upland transition plant 

communities and used to interpret the WARMER SLR results (figs. L-12 – L-14). Upland 

transition (greater than 1.0 m MSL) was characterized by coyote bush (Baccharis pilularis). 

High marsh (0.7 – 1.0 m MSL) was characterized by Frankenia salina and Jaumea carnosa, 

whereas mid marsh (0.45 – 0.7 m MSL) was dominated by Sarcocornia pacifica. Low marsh 

(0.2 – 0.45 m MSL) was characterized by Spartina spp. or, in brackish areas, by Schoenoplectus

spp. Mudflat habitat (less than 0.2 m MSL) was unvegetated or sparsely covered with Spartina 

spp. Currently, west San Pablo is primarily mid and high marsh. High marsh was projected to 

increase from 2000 through 2020 (0.13 m SLR). All high marsh vegetation was projected to be 

gone by 2040 (0.32 m SLR). Mid marsh was projected to dominate until 2060 (0.57 m SLR), at 

which time it transitions to low marsh. A transition to mudflat was projected by 2080 (0.85 m 

SLR). 

East San Pablo is also primarily mid-marsh habitat, with some high-marsh vegetation 

bordering San Pablo Bay. All high marsh was projected to be mostly gone by 2020 (0.13 m 

SLR), with mid marsh projected to be gone by 2050 (0.44 m SLR). Low marsh was projected to 

dominate until 2070 (0.67 m SLR), at which time east San Pablo would transition below MSL to 

a mudflat. All vegetation was gone by 2090 (1.05 m SLR) 

The WARMER model parameters for San Pablo Bay were extrapolated by using 

sediment core data from China Camp marsh; thus, predictions need to be interpreted with caution 

because local sedimentation processes could differ between these marshes. To improve results, 

local site-specific sediment core data could be collected along with suspended-sediment 

concentrations to characterize sediment deposition potential. 

Figure L-11. WARMER scenarios for San Pablo elevation change. Elevation above mean high 

water (MHW), in centimeters (cm), is plotted against model year with two standard deviations (SD). 

2000 2020 2040 2060 2080 2100

-120

-100

-80

-60

-40

-20

0

20

40

Elevation above MHW, cm

Year

San Pablo Bay NWR

- 2SD

Mean elevation

+ 2SD
159

Figure L-12. Area of San Pablo within a given tidal range for the duration of the simulation period.

[MHHW, mean higher high water; MHW, mean high water; MSL, mean sea level; >, greater than; <, 

less than]

2000 2020 2040 2060 2080 2100

0

20

40

60

80

100

Year

Percent area

San Pablo Bay NWR

>MHHW

MHHW-MHW

MHW-Low marsh

Low marsh-MSL

<MSL
160

Figure L-13. West San Pablo WARMER results in terms of plant communities relative to mean 

sea level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.
161

Figure L-14. East San Pablo WARMER results in terms of plant communities relative to mean 

sea level (MSL), in meters (m): mudflat, low, mid, or high marsh, or upland transition.