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RECOMMENDATIONS FOR 4

ANTICIPATING SEA-LEVEL RISE IMPACTS ON 5

LOUISIANA COASTAL RESOURCES DURING 6

PROJECT PLANNING AND DESIGN 7

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TECHNICAL REPORT 9 10 11 12 13 14

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Louisiana Applied Coastal Engineering & Science (LACES) Division 23 24

24 January 2012 25

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AUTHOR INFORMATION 27 28

Kristin DeMarco, Coastal Resources Scientist 3 29 Jennifer Mouton, Coastal Resources Scientist Senior, DCL-B 30 James W. Pahl, Ph.D., Coastal Resources Scientist Manager 31 32 Office of Coastal Protection & Restoration 33 Louisiana Applied Coastal Engineering & Science Division 34 Applied Research & Development Section 35 36

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TABLE OF CONTENTS 58 59 AUTHOR INFORMATION ............................................................................................................ i 60 LIST OF TABLES ......................................................................................................................... iv 61 LIST OF FIGURES ........................................................................................................................ v 62 LIST OF ACRONYMS ................................................................................................................. ix 63 1. INTRODUCTION AND OBJECTIVES OF THIS REPORT ................................................ 1 64 2. STATE OF THE SCIENCE.................................................................................................... 2 65

2.1. Techniques for Measuring Components of Sea Level .................................................. 3 66 2.1.1. Tide Gauges .............................................................................................................. 4 67 2.1.2. In-Situ Measurement ................................................................................................. 5 68 2.1.3. Satellite Altimetry Measurement .............................................................................. 7 69

2.2. Estimates of Historical Sea-Level Rise ............................................................................ 8 70 2.2.1. Global Historical Sea-Level Rise.............................................................................. 8 71 2.2.2. Historical Regional Sea-Level Rise in the Gulf of Mexico .................................... 13 72

2.3. Projections of Future Sea-Level Rise ............................................................................. 18 73 2.3.1. Global Projected Sea-Level Rise ............................................................................ 18 74 2.3.2. Projected Regional Sea-Level Rise in the Gulf of Mexico ..................................... 25 75 2.3.3. Gulf of Mexico Regional Sea-Level Rise Rate Recommended for CPRA Use ..... 25 76

2.4. Relative Sea-Level Rise in Coastal Louisiana ............................................................... 27 77 2.4.1. Estimates of Historical Relative Sea-Level Rise in Coastal Louisiana .................. 27 78 2.4.1. Subsidence .............................................................................................................. 31 79 2.4.2. Marsh Vertical Accretion ........................................................................................ 34 80

3. SUMMARY AND RECOMMENDATIONS....................................................................... 35 81 4. REFERENCES ..................................................................................................................... 38 82 APPENDICES .............................................................................................................................. 42 83

Appendix A: Reviewer Comments and Responses ................................................................. 42 84 Appendix B: CPRA-LACES Technical Issues with the US Army Corps of Engineer’s 85 Engineering Circular No. 1165-2-211 ...................................................................................... 49 86 Appendix C: Draft Southwest Coastal Feasibility Study, Wetland Accretion Summary ........ 53 87 Appendix D: Detailed Procedure for Incorporating Sea-Level Rise into Louisiana Coastal 88 Project Planning and Design ..................................................................................................... 60 89

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LIST OF TABLES 93 94 Table and Legend Page Number Table 1. Comparison of derived acceleration constants in NRC (1987) and USACE (2009) for the generalized predictive GSLR equation E(t) = a*t + b*t2.

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Table 2. A summary of the current NOAA tide gauge stations shows that only two stations relevant to coastal Louisiana have a sufficient period of record to establish an RSLR trend.

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Table C1. Long-term accretion estimates (137Cs) from different marsh types in the Chenier and Delta Plains from different studies with large numbers of samples. (DP=Delta Plain; CP=Chenier Plain).

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Table C2. Long-term accretion estimates (137Cs) from different marsh types and habitats (interior vs. streamside). Compiled by Jarvis (2010).

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Table C3. A comparison of ranges of long-term accretion estimates (137Cs) from impounded and un-impounded brackish marsh sites in the Chenier Plain (summarized in Steyer 2008).

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Table C4. Summary statistics of recent elevation change data from the Coast-wide Reference Monitoring Stations (CRMS) in freshwater (F), intermediate (I), brackish (B) and salt (S) marshes in the Chenier Plain.\

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Table D1. Acceleration constants to be applied for sea level rise rates of 0.5 m, 1.0 m, and 1.5 m.

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LIST OF FIGURES 96 97 Figure and Legend Page Number Figure 1. This sea-level curve for Surinam and French Guiana illustrates the 18.6-year lunar nodal cycle that must be accounted for in any SLR estimation.

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Figure 2. The confidence interval around sea-level rise projections increases rapidly as tide gauge periods of record decrease below sixty (60) years.

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Figure 3. A map of the Global Sea Level Observing System Long-Term Trends (GLOSS-LTT) network of tide gauges used for calculating global sea-level rise rates, illustrates the bias in station distribution in the northern hemisphere.

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Figure 4. The map of the transects used by NOAA Ships of Opportunity deploying eXtendable BathyThermographs (XBTs) for the Low Density and Frequently Repeated transects run by ships of opportunity under the NOAA Atlantic Oceanographic and Meteorological Laboratory to measure temperature across the global ocean.

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Figure 5. The spatial distribution of Argo Array floats for the 30-day period preceding 10 March 2011, illustrates the widespread spatial coverage of the network in the world’s oceans.

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Figure 6. Global sea-level estimates calculated from satellite altimetry (red line) diverged from MSL based on tide gauge records (blue line) in for about 10 years beginning in 1999 (Church & White 2011), for reasons that are uncertain.

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Figure 7. Satellite altimetry data from NOAA, accessed on 10 March 2011 (http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/slr/slr_sla_gbl_free_txj1j2_90.pdf), illustrate an overall global SLR rate (hTOT) higher than the 20th century average of 1.7 ± 0.3 mm/yr. reported by Church and White (2006).

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Figure 8. Sea-level rise data from Church & White (2011) for the 1860-2009 time period was used to calculate an 1880-2009 linear trend of 1.5 mm/yr. (0.059 inches/yr.) and the accelerating quadratic trend discussed in the text of this report.

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Figure 9. Fitting a quadratic function to the 1880-2009 Church and White in press dataset available from CSIRO (http://www.cmar.csiro.au/sealevel/sl_data_cmar.html) results in a slightly better fit of the data than a simple linear regression.

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LIST OF FIGURES (cont.) 99 100 Figure and Legend Page Number Figure 10. The relative contributions of thermal expansion and bulk eustatic water volume to GSLR, as determined by subtraction of thermal expansion from GSLR to determine the eustatic contribution, changed between the 1993 and 2005.

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Figure 11. Satellite altimetry data from NOAA, accessed on 11 March 2011 (http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/slr/slr_sla_gom_free_txj1j2_90.pdf), illustrate an overall SLR rate for the Gulf of Mexico (hGULF) lower than the GSLR trend calculated for the same time period (2.9 ± 0.4 mm/yr.) as shown in Figure 8.

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Figure 12. A spatial depiction of the data from the TOPEX/Poseidon and Jason satellite altimeters clearly shows that regional rates of SLR can be very different at both the global scale (a) and within the Gulf of Mexico (b), covering the grey inset box).

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Figure 13. A graph of mean SLR trends for NOAA tide gauges in the Gulf of Mexico (here oriented with the eastern-most station on the left) show that both SLR and the variance around the trend are greater in the western Gulf of Mexico than in the east.

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Figure 14. Long-term stations in the western Gulf of Mexico in NOAA’s National Water Level Observation Network (NWLON) vary significantly in the period of record.

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Figure 15. This graph from Zervas (2009) describes how the 95% confidence interval of the SLR trend determined from NOAA NWLON tide gauges is highly dependent on the age of the station.

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Figure 16. SLR rates for discrete points offshore of southern Louisiana show significant east-west variation, with SLR values highest offshore of the Balize Delta and trending lower moving west across the front of the Chenier Plain.

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Figure 17. Mitrovica et al. (2009) predicted the possible distribution of sea-level change (meters) in response to a collapse of the West Antarctic ice sheet accounting for the rotation of the Earth.

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Figure 18. A SLR budget figure from Bindoff et al. (2007) highlights the range of uncertainty surrounding relative contributions to observed global sea level rise.

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LIST OF FIGURES (cont.) 102 103 Figure and Legend Page Number Figure 19. Extrapolation of the linear (black) regression for the Church & White (2011) data (as described in Figure 8) to predict global mean sea level in the 2109 results in a lower estimated sea level than if the quadratic (red) non-linear regression is used.

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Figure 20. The rate of global sea-level rise during the 21st Century is modeled to increase, with the extent of acceleration dependent on the predicted temperature increases associated with the IPCC global climate change scenarios.

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Figure 21. Observed GSLR for the period 1993-2007 from satellite altimeters exceeded IPCC best estimate predictions of GSLR made in 1995 and 2001.

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Figure 22. NRC (1987) Curves as modified in USACE (2009) to account for different assumptions of the historical linear rate of global sea-level rise.

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Figure 23. This map predicts local sea level change relative to the global average for the 21st century, as calculated from the results of 16 global climate change models running the IPCC A1B climate scenario.

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Figure 24. CPRA-LACES recommendations for future global sea-level rise by 2100, compared to a range of values from recent published scientific literature.

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Figure 25. NOAA’s tide gauge network in Louisiana does provide coverage of multiple geomorphic settings within the State’s coastal zone.

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Figure 26. RSLR trend lines for the Sabine Pass North (a) and Grand Isle (b) NOAA tide gauges illustrate the significance of geological stability on RSLR, and the difference between the more stable Chenier Plan (Sabine) and the less stable Deltaic Plain (Grand Isle).

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Figure 27. A map of subsidence developed by Britsch in 2007 illustrates the spatial variability in predicted subsidence rates in southern Louisiana.

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Figure 28. Map of projected subsidence ranges for south Louisiana generated by the Subsidence Advisory Panel for the Louisiana CPRA Master Plan 2012 Update.

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Figure D1. Data were derived by USGS from satellite altimetry data for center points of the analysis grid shown in Figure 12.

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LIST OF FIGURES (cont.) 105 106 Figure and Legend Page Number Figure D2. Map of projected subsidence ranges for south Louisiana generated by the Subsidence Advisory Panel for the Louisiana CPRA Master Plan 2012 Update, following a meeting on 14 October 2010.

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Figure D3. Screen capture of the spreadsheet that LACES has drafted for calculating RSLR curves consistent with the four-step process recommended in the Technical Report.

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LIST OF ACRONYMS 108 109 Acronym Description First Reference on

Page # CPRA Coastal Protection and Restoration Authority (of Louisiana)

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CRMS

Coast-wide Reference Monitoring System 31

EC Engineering Circular 37

GIA

Glacial Isostatic Adjustment 2

GOOS

Global Ocean Observation System 5

GLOSS

Global Sea Level Observing System 4

GRACE

Gravity Recovery and Climate Experiment 8

GSLR

Global Sea-Level Rise 2

HET Habitat Evaluation Team 34

MSL

Mean Sea Level 2

NOAA

National Oceanographic and Atmospheric Administration 4

NRC National Research Council 22

NWLON

National Water Level Observation Network 4

PDT Project Delivery Team

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PSMSL Permanent Service for Mean Sea Level

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RSLR

Relative Sea-Level Rise 2

SLR

Sea-Level Rise 1

USACE US Army Corps of Engineers

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XBT Extendable BathyThermograph

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111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130

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1. INTRODUCTION AND OBJECTIVES 133 134 Land changes in the coastal zone and high sea-level rise rates are exposing lowland areas to 135 more frequent events of saltwater intrusion, flooding and rapid shoreline erosion, magnifying the 136 negative effects of coastal storms and storm surge. Louisiana is particularly sensitive to sea-137 level rise (SLR) due to the unique geology and inherent nature of the Mississippi River Delta and 138 Chenier Plains. There is a pressing need to integrate up-to-date SLR estimates into planning 139 activities to anticipate coastal land loss patterns, protect coastal communities and adequately 140 design restoration projects. 141 142 Projections for future SLR and concurrent coastal vulnerability estimates are numerous and 143 variable. State and regional planning efforts aimed at mitigating the impacts of SLR are iterating 144 between policy development and implementation as projections are refined and confidence in 145 future estimates increases. Adaptability and flexibility are key components to ensure the 146 incorporation of the most current and accurate SLR projections into project planning and policy 147 development for the Louisiana coast. In this document we synthesize historical SLR research, 148 review the state of the current science and identify key principles and recommendations in 149 determining and incorporating SLR into coastal restoration strategies, modeling efforts and 150 project design. 151 152 The objective of this document is to make technical recommendations for incorporating SLR into 153 Coastal Protection and Restoration Authority of Louisiana (CPRA) planning and engineering of 154 habitat restoration and storm protection projects. The document is structured to: 155 156

• Deductively summarize the state of the science on the patterns of increase in the surface 157 of the global ocean, regional Gulf of Mexico and local coastal waters, in order to 158 recommend the rate(s) of anticipated SLR most appropriate for incorporating into project 159 design, planning and analysis, and 160 161

• Describe how that recommended rate(s) of local sea-level rise should be combined with 162 the present understanding of the highly variable spatial patterns in coastal landform 163 subsidence and wetland vertical accretion to predict relative SLR at specific points in the 164 Louisiana coastal zone. 165 166

It should be noted that SLR research released after August 2011 were not included in this version 167 of the report. Because global, regional and local estimates of SLR are constantly changing as 168 new data become available, this report will by necessity be revised via an iterative process. 169 CPRA plans to update this report at a minimum every five years, or as major improvements in 170 SLR understanding or changes in documented rates occur. 171 172

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2. STATE OF THE SCIENCE 173 174 Sea-level rise is caused by a variety of dynamic anthropogenic and natural factors. Estimates of 175 past and projections of future SLR are dependent on the interplay between these factors. 176 Challenges arise when attempts are made to identify changes in sea level that occur over varying 177 time scales; it is important to isolate the long term historical trends from the background of 178 regular natural cycles to make confident predictions about future trends. In any coastal zone, the 179 actual rate of SLR is a combination of global sea-level rise (GSLR) and local coastal processes 180 including natural cycles, glacial isostatic adjustment (GIA), subsidence, accretion and erosion of 181 shorelines and coastal marshes. These influences result in a local rate of SLR, expressed as 182 relative sea-level rise (RSLR) that may be very different from GSLR. 183 184 While RSLR is more relevant for management purposes, it is necessary to first evaluate GSLR 185 trends and then focus on local conditions in the Gulf of Mexico offshore of southern Louisiana to 186 inform recommendations on estimating local RSLR. In discussing the current understanding of 187 GSLR, this section will also detail the methodologies for measuring GSLR components, because 188 this information has implications for local understanding of Gulf-specific SLR and RSLR. 189 190 GSLR is the mean increase in sea surface elevation across all of the world oceans and is caused 191 primarily by two factors: thermal expansion and freshwater influx. Thermal expansion, also 192 called the steric component of GSLR, refers to the increase in total ocean volume resulting from 193 increasing ocean temperatures. Freshwater influx into oceans, technically defined as the eustatic 194 component of GSLR, causes a change in the mass of water in the ocean and is the result of 195 melting glaciers, ice sheets and other land ice, and terrestrial runoff. While the term eustatic has 196 been used in some technical literature and the popular press to describe sea level rise due to a 197 total change in the global ocean, throughout this report we will only use that term to specifically 198 refer to the change in water mass. 199 200 To accurately determine the long-term historical trend of GSLR and begin making predictions of 201 future patterns, researchers must first identify and remove the effects of natural forcings on the 202 sea-level observation data to evaluate the steric and eustatic components only. Natural forcings 203 are patterns or processes that can influence mean sea level (MSL); are typically cyclical in 204 nature; and can be atmospheric, meteorological, or caused by alterations in the rotation of the 205 Earth and moon. For example, changes in the orbital pattern of the Earth and the moon can 206 impact sea level regionally. Natural cycles can change sea surface elevation over large oceans 207 which will manifest as a short term trend in sea-level monitoring data. Therefore it is preferable 208 to have a sea-level record long enough to encompass several periods of any natural cycles to 209 properly account for and remove confounding patterns. 210 211 Probably the most prominent natural forcing influencing coastal processes on a human timescale 212 is the 18.6-year lunar nodal cycle and impacts coastal water levels and sedimentation rates. The 213 18.6 year cyclic process stems from interactions between the moon’s orbital path around the 214 Earth and the orbital plane of the Earth around the sun; specifically, the migration of the 215 intersection points between the two due to precession in the moon’s orbit. The peaks and valleys 216 in this cycle can be observed astronomically as the major lunar standstills, which are the most 217 northerly and southerly rising and setting of the moon. This cycle is predictable and observable 218

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as a periodic function in sea surface elevation, all other factors being equal (Figure 1; Gratiot et 219 al. 2008; Morris & Sundberg 2008). Relevant to this discussion is that at the peak or valley in 220 the lunar nodal cycle, the rate of sea-level rise is flattest within the periodic SLR curve. As 221 shown in Figure 1, the last major lunar standstill was evident in the low point of a de-trended 222 sea-level curve in 2006 (http://www.umass.edu/sunwheel/pages/moonteaching.html), which 223 means that the rate of SLR should be expected to increase through 2015. This cycle alters the 224 rate and degree of sedimentation and accretion in tidally influenced wetlands, significantly 225 influencing the morphology of estuaries and tidal basins (Jeuken et al. 2003) as well as adding to 226 the interannual variability in the sea level surface. To make predictions about sea-level it is 227 important to account for the 18.6-year lunar tidal cycle, and similar natural cycles, into any 228 estimates of local SLR. 229 230 231

232 Figure 1. This sea-level curve for Surinam and French Guiana illustrates the 18.6-year lunar nodal cycle 233 that must be accounted for in any SLR estimation. Figure from Gratiot et al. (2008). 234 235 236 Once the background effect of natural forcings is removed, the sea level budget (Willis et al. 237 2008; Leuliette & Miller 2009) can be expressed as 238 239

hTOT = h STERIC + hMASS (Eqn. 1) 240 241

where hTOT is the global sea level rise rate, 242 hSTERIC is the thermal expansion contribution and 243 hMASS is the eustatic contribution due to freshwater influx. 244

245 Many efforts to quantify the relative contributions of eustatic (hMASS) and steric (hSTERIC) 246 components to the total sea-level budget have been made to try and determine which contribution 247 dominates GSLR (Jevrejeva et al., 2008). As researchers attempt to close the budget, 248 independent measurements of steric, eustatic and total sea level are checked against one another. 249 This is not a trivial task and illuminating the relative contributions of thermal expansion and 250 freshwater influx are critical for making projections of sea-level change into the future. 251 252 253 2.1. Techniques for Measuring Components of Sea Level 254 255 When attempting to close the sea level budget it is important to consider the measurement 256 technique used to acquire the data. Currently sea-level data are gathered from three primary 257 sources to determine the GSLR rate: tide gauges, in situ measuring devices and satellite altimetry 258 readings. 259

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2.1.1. Tide Gauges 260

Globally, tide gauge data used for measurement of GSLR are typically acquired from the 261 Permanent Service for Mean Sea level (PSMSL), established in 1933 and run by the UK Natural 262 Environment Research Council’s National Oceanography Centre 263 (http://www.psmsl.org/about_us/). In the US, the National Oceanographic and Atmospheric 264 Administration (NOAA) operate 128 long-term National Water Level Observation Network 265 (NWLON) stations that monitor monthly mean sea-level data. Data are used to determine linear 266 trends, average seasonal cycles, and interannual variability, including estimated errors, by 267 measuring the height of sea surface relative to certain coastal benchmarks. In some areas these 268 data go back hundreds of years. Tide gauge records have definite value due to the length of time 269 evaluated, although regional and local corrections must be made to obtain a global mean for 270 these trends. Tide gauges measure the RSLR at discrete locations and estimates of GSLR from 271 tide gauge data are an average of all the tide gauge readings over a certain period of time (e.g. 272 Church & White 2004). 273 274 The length of tide gauge station record has to be sufficient to obtain a robust estimate of the 275 historic relative mean sea-level change, especially if that rate will be used to predict future SLR. 276 If the length of the record is too short it may be difficult to fully account for the impacts of 277 interannual and decadal variations in sea level, such as the fore-mentioned 18.6-year lunar tidal 278 cycle, resulting in misleading or erroneous sea level trends. The Intergovernmental 279 Oceanographic Commission (2006) suggests that the duration of a tidal record should be at least 280 two lunar nodal cycles (about 40 years) before being used to estimate a local relative sea-level 281 trend, while Douglas et al. (2001) claims that the length of record should be at least three lunar 282 nodal cycles (approximately 60 years) and have 85% coverage during that time period. At 283 minimum, record lengths shorter than 40 years in duration could have significant uncertainty that 284 can quickly outweigh any SLR projections of a few millimeters per year as the period of record 285 decreases (Figure 2). USACE (2009) describes that if only long-term estimates (≥ 40 years) are 286 available from the local tide gauge, local trends should be evaluated in a regional context, using 287 nearby station records with adequate record lengths for the same time period to compare with the 288 local data to determine validity. However, as we will discuss later in this report, there are 289 significant limitations to that approach in coastal Louisiana. 290 291 Tide gauges have traditionally been used for navigational purposes and consequently were 292 historically placed in areas of heavy water traffic restricted to coastlines and the open ocean. 293 This has led to an uneven spatial distribution of gauges, possibly hindering accuracy when 294 determining long-term global sea-level trends. At present GSLR rate and acceleration statistics 295 used by NOAA are calculated from approximately 190 stations known as the Global Sea Level 296 Observing System (GLOSS) Long-Term Trends network (Figure 3). There are some significant 297 limitations in that data, as NOAA recognizes the present station distribution is biased towards the 298 northern hemisphere (http://www.gloss-sealevel.org/). Additionally, management of the data can 299 become cumbersome and quality assurance /quality control of original long-term datasets can be 300 problematic. While these and other limitations led some researchers (Gröger and Plag 1993) to 301 conclude tide-gauge data should not be used to determine GSLR trends, such data are very useful 302 as “checks” to newer data collection methods as well as being the most appropriate, and often the 303 only, method for obtaining estimates of historical RSLR prior to 1992. 304

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305 Figure 2.The confidence interval around sea-level rise projections increases rapidly as tide gauge periods 306 of record decrease below sixty (60) years. Dots indicate the approximate graph positions for the period of 307 record of the NOAA tide gauges shown in Table 2 and Figure 18; see discussion in Section 3.3.1. Figure 308 from Zervas (2009). 309 310 311 2.1.2. In-Situ Measurement 312

In the US, NOAA’s Global Ocean Observation System (GOOS) manages a fleet of volunteer 313 observatory ships which use extendable probes, termed eXtendable BathyThermographs (XBTs), 314 to measure ocean temperature (http://www.aoml.noaa.gov/goos/uot/xbt-what-is.php). The ships 315 travel along set transects and take XBT readings; certain areas are sampled 18 times a year to 316 gain insight on interannual and seasonal variability (Figure 4). XBTs are designed to fall 317 through the water column at a known rate; the depth of the probe is not measured but inferred 318 from the time launched by a fall–rate equation provided by the manufacturer. Recently, 319 researchers found systematic errors in the fall-rate equations which resulted in temperature 320 readings being assigned to incorrect depths (Willis et al. 2007; Willis et al. 2009). These errors 321 have since been corrected for in reports making GSLR measurements and provide measurement 322 for the thermal expansion component (hSTERIC) of the sea level budget (Lyman et al. 2010). 323 324 Argo is an international project started in 2000 as part of GOOS and is made up of over 3000 325 battery-powered floats. The floats collect temperature and salinity data from the upper 326 2000meters of the ice-free ocean. Upon reaching the ocean surface, satellites detect the floats’ 327 position and the data are transmitted to a data assembly center. Argo floats measure temperature 328

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329 330 Figure 3. A map of the Global Sea Level Observing System Long-Term Trends (GLOSS-LTT) network 331 of tide gauges used for calculating global sea-level rise rates, illustrates the bias in station distribution in 332 the northern hemisphere. Map from GLOSS (http://www.gloss-sealevel.org/, accessed 19 August 2010). 333 334 335

336 337 Figure 4. The map of the transects used by NOAA Ships of Opportunity deploying eXtendable 338 BathyThermographs (XBTs) for the Low Density and Frequently Repeated transects to measure 339 temperature across the global ocean. Note the absence of any XBT transects in the Gulf of Mexico. 340 There is also no Gulf coverage from AOML’s High-Density XBT Transects (not shown, see 341 http://www.aoml.noaa.gov/phod/hdenxbt/index.php). Figure from 342 http://www.aoml.noaa.gov/phod/goos/ldenxbt/index.php, accessed 22 September 2010. 343

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and salinity as a function of depth to describe how much the increase in MSL is steric in origin 344 (hSTERIC), and how the steric signal is distributed over depth. Argo covers a large portion of the 345 world ocean (Figure 5) and collects continuous data important for GSLR estimates. Researchers 346 recently identified an error in a small portion of the Argo floats that led to a false cold bias from 347 2003-2006, which has since been corrected in current estimates (Willis et al. 2007). Conversely, 348 there may also be a potential Argo warm bias due to the inability for floats to be placed in ice 349 covered ocean areas (Roemmich & Gilson 2009). 350 351 352

353 354 Figure 5. The spatial distribution of Argo Array floats for the 30-day period preceding 10 March 2011, 355 illustrates the widespread spatial coverage of the network in the world’s oceans. Note the limited 356 coverage of Argo floats in the Gulf of Mexico. Map from http://www.argo.ucsd.edu/index.html. 357 358 359 2.1.3. Satellite Altimetry Measurement 360

The Argo Array was named such to reflect the relationship between that network of floats and 361 the Jason-1 and -2 satellite altimetry missions; data collected by the two methods are coupled to 362 attain more accurate estimates of GSLR and to evaluate relative contributions. The Jason 363 satellites followed the 1992 French-US joint TOPEX/Poseidon satellite mission to track sea level 364 height with radar altimeters (http://topex-www.jpl.nasa.gov/technology/technology.html). The 365 satellites measure total sea level and are currently active to obtain more widespread coverage. 366 Specifically, Jason-1 measures the total sea level (hTOT) and can be used in concurrence with the 367 Argo data (hSTERIC) to estimate the eustatic contribution (hMASS) to total sea level, using Equation 368 2. 369 370

hMASS = hTOT[from Jason-1] - hSTERIC[from Argo] (Eqn. 2) 371 372 where all variables are as in Equation 1. 373 374 375

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A more recent satellite mission initiated in 2002 by NASA is the Gravity Recovery and Climate 376 Experiment (GRACE), which measures the changes in mass of the world ocean and thus more 377 directly measures hMASS (http://grace.jpl.nasa.gov/information/). Additionally, GRACE was 378 used to determine the ice mass-loss for the Greenland and Antarctic Ice sheets (Khan et al. 2010; 379 Velicogna, 2009). Theoretically, GRACE eustatic sea-level measurements (hMASS) and the 380 Argo-based steric measurements (hSTERIC) should equal the total sea level measurement obtained 381 from Jason-1(hTOT), i.e. 382 383

hTOT[from Jason-1]= hMASS[from GRACE]+ hSTERIC[from Argo] (Eqn. 3) 384 385 On global scales, Argo and Jason together with satellite gravity measurements from GRACE, 386 partition global sea-level rise into its steric and mass-related components (Willis et al., 2008; 387 Cazenave et al, 2009; Leuliette and Miller 2009; Wunsch et al. 2007). Although satellite 388 altimetry-based GSLR rates were in good agreement with those shown by tide gauge records 389 (Figure 6; Ablain et al., 2009; Prandi et al. 2009) for the first six to seven years of the altimetry 390 record, values between the two data sources deviated between 1999 and 2008 for reasons that 391 were unclear as of the time that this report was drafted (Domingues et al., 2008; Church & White 392 2011). However, it is important to recognize that satellite altimetry data, only available since the 393 end of 1992, only recently represent a full 18.6-year tidal period, let alone two or three periods as 394 recommended above for defining a trend. 395 396 Although the MSL budget has been balanced occasionally (Leuliette & Miller 2009), 397 discrepancies have been identified by many regarding the relative contributions of the eustatic 398 and steric components (Bindoff et al. 2007; Lombard et al. 2007; Willis et al. 2008). Differences 399 in data processing and potential biases of collection methods could be the causes for this 400 variance. Altimetry-derived measurements and GSLR estimations need to be processed to 401 account for factors such as barometric pressure, glacial isostatic adjustment (GIA) and 402 seasonality. It is also possible that some integral component to the driving processes of MSL 403 change may not be understood. It is essential to understand these components to accurately 404 make predictions for future rates of SLR. For instance, if we are confident that the eustatic 405 contribution will be the dominant component to increases in MSL, we can expect the rate of 406 increase to be nonlinear (Cazenave & Llovel 2010) and thus neither uniform across the globe nor 407 steady to rise (Gomez et al. 2010). 408 409 410 2.2. Estimates of Historical Sea-Level Rise 411 412 2.2.1. Global Historical Sea-Level Rise 413

It cannot be emphasized enough that estimating a rate for historical GSLR is highly dependent 414 on the specific time period selected for measurement due to the changing contributions of the 415 steric and eustatic components to total MSL (Jevrejeva et al. 2006). Much of the debate about 416 the mathematical nature of historical SLR trends and defined accelerations or decelerations in the 417 rate of SLR over time is due to differential statistical trends in data within specific time frames. 418 For example, Church & White (2006) analyzed a large set of tide gauge data spanning 1870-419 1935 and calculated a historical linear GSLR rate of 0.7 ± 0.4 mm/yr. (0.028 ± 0.016 inches/yr.); 420 however, a revised analysis of the same data in Church & White (2011) that limited the range 421

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422 423 Figure 6. Global sea-level estimates calculated from satellite altimetry (red line) diverged from MSL 424 based on tide gauge records (blue line) in for about 10 years beginning in 1999 (Church & White 2011), 425 for reasons that are uncertain. The deviations lead to slightly different GSLR trends from 1993-2009 426 (Table 1). 427 428 429 from 1880-1935 calculated a linear GSLR trend of 1.1 + 0.7 mm/yr. (0.043 ± 0.028 inches/yr). 430 Similarly, while Ablain et al.’s (2009) analysis of satellite altimetry data for the period 1993-431 2008 calculated a linear GSLR trend of 3.1 ± 0.6 mm/yr. (0.12 ± 0.02 inches/yr), NOAA’s 432 Laboratory for Satellite Altimetry reported an altimeter-based linear GSLR trend of 2.9 ± 0.4 433 mm/yr. (0.11 ± 0.02 inches/yr) for the 1992-2011 time period when accessed on 10 March 2011. 434 435 In addition to the period of record, estimating a historical rate of global sea-level rise is also 436 greatly dependent on both the number and spatial distribution of gauging stations selected for 437 measurement. Much of the debate about calculating historical sea-level rise trends, and the 438 recent debate over both linear vs. non-linear trends in the data and accelerations vs. decelerations 439 in the rate of sea-level rise over time results from choices in which stations are chosen and over 440 which specific time period they are analyzed (Houston and Dean 2011a, b and c; Rahmstorf and 441 Vermeer 2011; Donoghue and Parkinson 2011). 442 443

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Most estimates of the 20th century long term GSLR have been less than 2 mm/yr. (0.08 444 inches/yr.; e.g. Church et al.2004; Church and White 2006). In comparison, as introduced above, 445 NOAA satellite altimetry data based on TOPEX/Poseidon, Jason-1 and Jason-2 technologies, 446 accessed on 10 March 2011, calculated an overall 1993-2010 GSLR rate (hTOT) of 2.9 ± 0.4 447 mm/yr. (0.11 ± 0.02 inches/yr.; Figure 7). Data from several other altimeters are available, and 448 when those data are incorporated into the analyses, different GSLR trend values can result. Data 449 used in this report to document Louisiana nearshore water surface changes are only from the 450 TOPEX/Poseidon, Jason-1 and Jason-2systems, and for sake of direct data comparability this 451 discussion will only focus on that restricted dataset. 452 453 454 455 456

457 458 Figure 7. Satellite altimetry data from NOAA, accessed on 10 March 2011 459 (http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/slr/slr_sla_gbl_free_txj1j2_90.pdf), illustrate an overall 460 global SLR rate (hTOT) higher than the 20th century average of 1.7 ± 0.3 mm/yr. reported by Church and 461 White (2006). 462 463

11

Any calculation of a linear rate of historical GSLR by its nature assumes that rate has been and is 464 constant. A generalized linear regression is commonly represented as 465 466

y = mx + b (Eqn. 4) 467 468 where y is the dependent variable, 469 m is the slope of the line, 470 x is the independent variable, and 471 b is the 0-intercept on the y-axis. 472

473 Relevant to SLR calculations, and ignoring the concept of the y-axis intercept (which for SLR is 474 arbitrary), the regression equation can be expressed as 475 476

E(t) = a*t (Eqn. 5) 477 478

where E is GSLR at time t, and 479 a is the rate of GSLR (slope of the line). 480

481 However, examination of the historical data and recent literature indicate that GSLR since the 482 late nineteenth century has not been linear, but instead has been accelerating since that time. 483 While Figure 8 shows the data set by which Church & White (2006) calculated the frequently-484 cited 1.7 ± 0.3 mm/yr.(0.067 ± 0.012 inches/yr.) average GSLR for the 20th Century, in that 485 paper as well as their revised analysis in Church & White (2011) they discussed that in the least 486 any linear analysis needed to recognize several visually-obvious inflection points in the data in 487 the mid-1930s, the 1960s and the 1980s.. However, Church and White (2006, 2011) also defined 488 a non-linear, second-order polynomial (quadratic) fit to the full data set. An accelerated SLR 489 scenario is simplistically modeled by the equation: 490 491

E(t) = a*t + b*t2 (Eqn. 6) 492 493

where E, t, and a are as defined in Eqn. 5, and 494 b is an acceleration factor. 495

496 The actual calculations to determine projected sea-level rise based on the non-linear approach are 497 more complex, and are described further in the Summary and Recommendations section of this 498 document as well as the separate, more concise guidelines included as Appendix D to this report. 499 500 When linear and quadratic functions are fitted to the Church & White (2011) data shown in 501 Figure 8, both functions explain greater than 97% of the variation (Figure 9), and it could be 502 argued that the linear is an acceptable representation of the data. However, the differences in 503 forecasting a linear vs. a quadratic function into the future can become significant and the use of 504 one prediction over another should be carefully considered. Visual examination of Figure 9 505 supports some concern with the linear approach, where it can be seen that the linear trend line 506 shows a departure from (specifically an underestimate of) the observed sea-level data beginning 507 around the beginning of the 1990s. Also, and perhaps most important for a discussion of 508 recommendations to CPRA staff of how to calculate RSLR, a curvilinear historical GSLR curve 509

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510 511 Figure 8.Sea-level rise data from Church & White (2011) for the 1860-2009 time period was used to 512 calculate an 1880-2009 linear trend of 1.5 mm/yr. (0.059 inches/yr.) and the accelerating quadratic trend 513 discussed in the text of this report. Also shown is the 1870-2001 data published in Church & White 514 (2006). 515 516 517 more strongly supports a deviation from the US Army Corps of Engineers (USACE) method for 518 determining RSLR (see Appendix B). Acceleration in the historical data was also documented 519 by Jevrejeva et al. (2008) and Woodworth et al. (2009). 520 521 Recent analyses also suggest that the relative contributions of eustatic and steric influences on 522 GSLR are in flux. Bindoff et al. (2007) concluded that thermal expansion (hSTERIC) accounted 523 for approximately 50% of the total observed GSLR from 1993 to 2003. In comparison, Lyman 524 et al. (2006) proposed a slowing influence of thermal expansion in the 2003-2007 data. More 525 recent data suggest that the freshwater influx from melting glaciers and ice sheets (hMASS) is the 526 primary contributor to current GSLR (Antonov et al. 2005; Cazenave & Llovel 2010; Cazenave 527 et al. 2008; Jevrejeva et al. 2008; Nicholls & Cazenave 2010). Concurrently, satellite altimetry 528 observations estimate that the eustatic component of GSLR (ice sheet, glacial ice and land ice 529 melting) may have been up to 80%, or 2.4 ± 0.35 mm/yr. (0.094 ± 0.014 inches/yr.), of the total 530 observed increase in MSL from 2003-2007, and that thermal expansion has been slowing (Figure 531

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532 533 Figure 9. Fitting a quadratic function to the 1880-2009 Church and White (2011) dataset available from 534 CSIRO (http://www.cmar.csiro.au/sealevel/sl_data_cmar.html) results in a slightly better fit of the data 535 than a simple linear regression. Blue points are the CSIRO data, the black line and statistics describe the 536 linear regression, and the red line and statistics describe the second order (quadratic) function. 537 538 539 10; Cazenave & Llovel 2010). These data could have significant implications to how predictable 540 and relevant future GSLR scenarios are to the Gulf of Mexico, given limits in the monitoring 541 networks that define the GSLR components, as shown in Figures 4 and 5. 542 543 2.2.2. Historical Regional Sea-Level Rise in the Gulf of Mexico 544

The extensive description of sea-level components and measuring technologies above was 545 necessary to help frame the deductive approach in this report and address whether GSLR is valid 546 to predict changes in the Gulf surface elevation throughout the rest of this century. This section 547 will discuss the data available for the Gulf of Mexico, the trends in those data, and the important 548 caveats on those data and data products that must be considered. 549 550 Satellite altimetry data specific to the Gulf of Mexico do show a slightly lower rate of sea surface 551 elevation change over the past twenty years (Figure 11) than the global calculations described 552 above. This may be due to the subtropical and semi-enclosed nature of the Gulf. Because of 553 these two factors, the Gulf could reasonably be assumed to be a warmer body of water than the 554 adjacent Atlantic Ocean, especially with the dominant inflow to the Gulf coming from the even 555 warmer Caribbean Sea through the Straight of Campeche, and therefore less susceptible to steric 556

y = 1.5379x - 3060.1 R² = 0.972

y = 0.005x2 - 17.804x + 15743 R² = 0.9834

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557 558 Figure 10. The relative contributions of thermal expansion and bulk eustatic water volume to GSLR, as 559 determined by subtraction of thermal expansion from GSLR to determine the eustatic contribution, 560 changed between the 1993 and 2005. Specifically, the eustatic contribution became more important. 561 Figure adapted from http://wcrp.ipsl.jussieu.fr/Workshops/SeaLevel/Orals.html. 562 563 564 influences on MSL than the colder open ocean. However, there are very few GLOSS (Figure 3) 565 or Argo (Figure 5) resources and no XBT coverage (Figure 4) devoted to the Gulf of Mexico and 566 thus efforts to separate either eustatic (hMASS) or temperature-induced (hSTERIC) components from 567 the overall observed elevation changes are less confident. 568 569 Global satellite altimetry data and recent publications (Merrifield & Merrifield 2009) 570 demonstrate that observed GSLR has not been evenly distributed across the world ocean (Figure 571 12). Satellite altimetry-based estimates of SLR in the Gulf of Mexico (hTOT) are also highly 572 variable, with the general pattern of higher rates of SLR in the center of the Gulf and lower rates 573 in both the eastern and western margins. 574 575 To complicate the issue further, there is evidence that the tidal hydrodynamics of the western 576 Gulf of Mexico are different than for the eastern Gulf (Figure 13). Quoting Zervas (2009), 577 578

“… for the same year range of data, the Pacific, western Gulf of Mexico, and 579 Bermuda stations have wider error bars than stations in the Atlantic, eastern Gulf 580 of Mexico, and the Caribbean … The western Gulf of Mexico stations appear to 581

Thermal expansion: 0.8mm/yr

Observed sea level rise: 3.3mm/yr

Residual: ocean mass change: 2.5mm/yr

15

582 583 Figure 11. Satellite altimetry data from NOAA, accessed on 11 March 2011 584 (http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/slr/slr_sla_gom_free_txj1j2_90.pdf), illustrate an overall 585 SLR rate for the Gulf of Mexico (hGULF) lower than the GSLR trend calculated for the same time period 586 (2.9 ± 0.4 mm/yr.) as shown in Figure 8. 587 588 589

alternate between periods of higher and lower rates of sea-level rise in contrast to 590 the steadier rates seen in the eastern Gulf of Mexico.” 591 592

Unfortunately there is no information regarding the exact cause of this difference in variation and 593 trends across the coast, making it difficult to discern if the trend will impact future SLR rates. 594 This issue will be pursued in future iterations of this document. Supporting tide gauge data must 595 also be approached carefully, because only 6 of the 12 tide gauges in western and central Gulf of 596 Mexico have been in operation for more than 50 years (Figure 14) leading to a high level of error 597 when attempting to make predictions for sea-level trends within the 95% range of confidence 598 (Zervas 2009; Figures 2 and 15). 599 600 The final complication for determining historical change in the water surface of the northern 601 Gulf of Mexico is also illustrated by the satellite altimetry data. The spatial grid for analyzing 602 the altimetry data can be seen in the inset map (b) of Figure 12. When a value for SLR from the 603 1992-2010 satellite altimetry data is established at the center point for each grid cell, the data can 604 be projected as shown in Figure 16. A strong east-west gradient in derived 1992-2010 Gulf of 605

16

606 (a) 607

608 (b) 609

610 Figure 12. A spatial depiction of the data from the TOPEX/Poseidon and Jason satellite altimeters 611 clearly shows that regional rates of SLR can be very different at both the global scale (a) and within the 612 Gulf of Mexico (b), covering the grey inset box). Figures from 613 http://ibis.grdl.noaa.gov/SAT/SeaLevelRise/slr/map_txj1j2_wysiwyg.pdf 614 615 616 Mexico SLR values for the near-shore coastal waters in southern Louisiana is observable, with 617 values ranging from a high southeast of the Balize Delta and consistently decreasing both 618 westward to a low south of the mouth of the Sabine River and northward into the Lake Borgne 619 and Lake Pontchartrain systems. The range of values is significant, with the highest derived 620 SLR rates being 58% higher than the lowest values. In the absence of any specific data 621 discounting this variation, assigning a single SLR value to Louisiana’s coastal waters at this time 622 is not justified. 623

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624 625 Figure 13. A graph of mean SLR trends for NOAA tide gauges in the Gulf of Mexico (here oriented with 626 the eastern-most station on the left) show that both SLR and the variance around the trend are greater in 627 the western Gulf of Mexico than in the east. Figure from Zervas (2009). 628 629 630

631 Figure 14. Long-term stations in the western Gulf of Mexico in NOAA’s National Water Level 632 Observation Network (NWLON) vary significantly in the period of record. Figure from Zervas (2009). 633

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634 635 Figure 15. This graph from Zervas (2009) describes how the 95% confidence interval of the SLR trend 636 determined from NOAA NWLON tide gauges is highly dependent on the age of the station. Some 637 separation between eastern and western Gulf of Mexico NWLON stations is evident, emphasizing the 638 issues of differential tidal hydrodynamics described in Figure 13. 639 640 641 2.3. Projections of Future Sea-Level Rise 642 643 2.3.1. Global Projected Sea-Level Rise 644

The 18.6-year lunar tidal cycle reached a low point in 2006 (Figure 1), and as of early 2011 we 645 are early in the next upswing (J. Morris, personal communication). It is therefore reasonable to 646 expect that year-to-year changes in calculated SLR for the satellite altimetry era will increase as 647 we proceed into steeper portions of the curve. If the slowing of the thermal expansion 648 contribution to global MSL persists and the eustatic component continues to dominate the bulk 649 of the rise, coastal Louisiana may experience some slowing in the rate of GSLR followed by 650 shorter periods of rapid increase due to freshwater influx. This pattern may increase the error in 651 future estimates for GSLR and will have to be evaluated carefully to determine potential impacts 652 on projections. 653 654

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655 656 Figure 16. SLR rates for discrete points offshore of southern Louisiana show significant east-west 657 variation, with SLR values highest offshore of the Balize Delta and trending lower moving west across 658 the front of the Chenier Plain. Data were derived by USGS from satellite altimetry data for center points 659 of the analysis grid shown in Figure 12, covering the 1992.96-2010.01 period of record. Figure from the 660 Project Effects Modeling Team addressing the 2012 revision of the Louisiana Integrated Master Plan. 661 662 663 The largest uncertainty in predicting SLR over the next century is how ice sheets will respond to 664 changes in temperature (Allison et al. 2009). It has been suggested that in the continued presence 665 of warming temperatures the feedback mechanisms which allow the ice sheets to persist may be 666 disrupted to the point that state of balance would shift and the ice sheet would disintegrate or 667 “collapse” (Vaughan 2008). The Western Antarctic Ice Sheet alone has the capacity to raise sea-668 level between 3 and 8 meters (10’ and 26’; Bamber et al. 2009; Bindoff et al. 2007; Gomez et al. 669 2009). Since satellite altimetry data have become available, researchers are able to more clearly 670 investigate the dynamics of ice sheets; GRACE is specifically employed to detect ice-mass loss 671 for ice sheets in some cases. Recent observations have shown that the rates of ice mass loss from 672 2002-2009 for both the Greenland and Antarctic ice sheets are increasing, implying that the ice 673 sheet contribution to SLR is increasing, which is consistent with other observations (Velicogna 674 2009). Additionally, in the event of a full or partial collapse of the Antarctic ice sheet, spatial 675 distribution of the sea-level increase would be non-uniform, and could lead to higher sea-levels 676 in the Northern Gulf of Mexico than other parts of the global ocean (Figure 17; Gomez et al. 677 2009; Mitrovica et al. 2009).The increase may be regionally concentrated along the Pacific and 678 Atlantic coasts of the United States, which may experience an MSL increase 25% greater than 679 the global mean even in the event of a full collapse (Bamber et al. 2009). 680 681 682

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683 684 Figure 17.Mitrovica et al. (2009) predicted the possible distribution of sea-level change (meters) in 685 response to a collapse of the West Antarctic ice sheet accounting for the rotation of the Earth. The above 686 values are in addition to an underlying effective eustatic rate, for example, if GSLR is 1 meter (including 687 eustatic and steric effects), a partial collapse of the Antarctic ice sheet would result in a MSL increase of 688 1.3 meters in the Northern Gulf of Mexico. 689 690 691 Although Bindoff et al. 2007 forecasted a range of 0.18-0.59 meters (0.6’-1.9’) for GSLR by 692 2100, these values have since been described as too conservative because they did not account 693 for ice sheet dynamics (Füssel 2009). While the IPCC Working Group III report did try to 694 account for ice sheet dynamics, they only did so empirically, assuming that high temperature 695 increases (in excess of 3ºC) could result in a GSLR of 2-7 m (6.6’-23’) at the century to 696 millennia time scale due to a near or total melting of the glaciers and ice sheet (Bindoff et al. 697 2007). After investigating the ice sheet contribution, Pfeffer et al. (2008) stated that a predicted 698 GSLR of greater than 2 meters (6.6’) by 2100 was the maximum possible increase based on the 699 glacial physics, and predicted a GSLR range by 2100 of 0.8 - 2 meters (2.6’-6.6’). Future GSLR 700 scenarios will be highly dependent upon the relative contribution of ice sheets and glaciers to the 701 eustatic component to the GSLR budget, which, as discussed above, contains significant 702 uncertainties. Historical rates of GSLR are difficult to replicate, and predictions will likewise be 703 uncertain (Figure 18). 704 705

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706 707 Figure 18.A SLR budget figure from Bindoff et al. (2007) highlights the range of uncertainty surrounding 708 relative contributions to observed global sea level rise. 709 710 711 Continuation of the existing historical trends in GSL from the past 130 years (as defined in 712 Figure 9) give very different results depending on whether the linear or quadratic trend is used 713 (Figure 19). The 2010-2101 increase in GSL based on the linear trend is approximately 15 cm 714 (6”), while the increase in GSL based on the aquatic trend is approximately 26 cm (10”). The 715 red line shown in Figure 19 was from a simple effort to project the Figure 8 quadratic regression; 716 LACES is aware that appropriate statistical transformations are needed to forecast polynomial 717 functions, and will pursue a more statistically proper projection in the future in order to more 718 accurately predict future MSL with this approach. However, we maintain confidence in the 719 principle that extension of a linear function defining historical data, be it the 1.5 mm/yr.(0.06 720 inches/yr.) shown in Figure 9 or 1.7 mm/yr.(0.07 inches/yr.) data as reported in Church and 721 White (2006) is most likely overly-conservative and a risky assumption to the State’s coastal 722 human and natural resources. Interestingly, extension of the linear 2.9 mm/yr.(0.11 inches/yr.) 723 reported by NOAA (Figure 9), based on the satellite altimetry data, actually exceeds the 724 extrapolation of the nonlinear trend extension (Figure 19), but may still represent an improper 725 technique for forecasting future SLR. 726 727 More current estimates for GSLR by 2100 range from 0.5 to 2meters (1.6’ – 6.6’; Grinsted et al. 728 2009; Pfeffer et al. 2008; Rahmstorf 2007; Vermeer & Rahmstorf 2009). Much weight recently 729 has been placed on the predictions of Rahmstorf (2007), who modeled GSLR in response to the 730 IPCC global climate change model scenarios (Figure 20) and predicted a range in GSLR of 0.5- 731

22

732 733 Figure 19. Extrapolation of the linear (black) regression for the Church & White (2011) data (as 734 described in Figure 8) to predict global mean sea level in the 2109 results in a lower estimated sea level 735 than if the quadratic (red) non-linear regression is used. 736 737 738 1.4 meters (1.6’ – 4.6’) by 2100, with 1 meter (3.3’) being the most likely. Comparisons of 739 empirical data from tide gauges and satellite altimeters from 1990-2006 to model predictions of 740 IPCC scenarios have found that observed GSLR mirrored the highest rates of SLR predicted 741 (Figure 21). This demonstrates that current rates of SLR tracked the highest past IPCC 742 predictions, lending credibility to the higher end of GSLR predictions. A more recent publication 743 by Vermeer & Rahmstorf (2009), supported by a temperature-based SLR model with a 98% 744 correlation with observed data from 1880-2000, increased the predicted GSLR associated with 745 the IPCC scenarios to 0.75-1.90 meters (2.5’-6.2’). 746 747 The differences in the GSLR curves shown in Figure 19 resulted from changes in the predicted 748 rate of acceleration of GSLR. The National Research Council (NRC 1987) modeled several 749 scenarios of GSLR increase with variable acceleration rates using Equation 6. Acceleration 750 constants (b from Equation 6) were back-calculated for a priori scenarios of 0.5, 1.0 and 1.5 751 meters (1.6’, 3.3’ and 4.9’ respectively) GSLR by 2100 (Figure 22) shown by 752 NRC Curves I, II and III, respectively. The 0.5-meter (1.6’) increase was a minimum value and 753 corresponded to a minimum acceleration of the rate of rise while the 1.5-meter(4.9’) increase 754 was considered to be a maximum value that would occur with a rapid acceleration. 755 756 It is important to note that the numerical values for the acceleration constants derived by NRC 757 (1987) are specific to that effort and to the assumption of 1.2 mm/yr. as the historical linear rate 758 of GSLR as defined by the variable (a) in Equations 5 and 6. If E(t) from 759

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760 Figure 20. The rate of global sea-level rise during the 21st Century is modeled to increase, with the 761 extent of acceleration dependent on the predicted temperature increases associated with the IPCC global 762 climate change scenarios. The colored dotted lines are individual scenario-specific predictions of GSLR 763 increase, while the gray dashed lines reflect uncertainty surrounding the statistical fit of the model data. 764 Figure from Rhamstorf (2007). 765 766 767

768 Figure 21. Observed GSLR for the period 1993-2007 from satellite altimeters exceeded IPCC best 769 estimate predictions of GSLR made in 1995 and 2001. Figure from Pielke (2008). 770

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771 Figure 22. NRC (1987) Curves as modified in USACE (2009) to account for different assumptions of the 772 historical linear rate of global sea-level rise. 773 774 775 Equation 6 is held steady at 0.5, 1.0 or 1.5 meters by 2100 as shown in Figure 22, but the linear 776 rate of GSLR (a) is corrected for additional information, then mathematically the acceleration 777 constant (b) must be recalculated. This was done in USACE (2009), wherein the historical linear 778 rate of GSLR was instead assumed to be 1.7 mm/yr. As shown in Table 1, this assumption 779 resulted in different acceleration constants from those calculated in NRD (1987). The 780 implication then is that any attempt to establish a predictive sea-level rise curve for future 781 scenarios that uses historical linear values different from NRC (1987) or USACE (2009) must 782 recalculate the appropriate acceleration constants. 783 784 785 Table 1. Comparison of derived acceleration constants in NRC (1987) and USACE (2009) for the 786 generalized predictive GSLR equation E(t) = a*t + b*t2. See Equation 6 for parameter definitions. 787 788 GSLR Scenario (meters by 2100)

Acceleration Constants (b) (meters/yr2)

NRC (1987) (a) = 0.0012 meters/yr.

USACE (2009) (a) = 0.0017 meters/yr.

0.5 meters

2.80 x 10-5 2.36 x 10-5

1.0 meters

6.60 x 10-5 6.20 x 10-5

1.5 meters

1.05 x 10-4 1.005 x 10-4

789

25

Compared to the multitude of research performed to derive a global linear trend in SLR, for 790 example GSLR 2.9 ± 0.4 mm/yr. (0.11 ± 0.02 inches/yr.) in Figure 7, there have been very few 791 attempts to calculate an acceleration trend from SLR data (Woodworth et al., 2008; Church & 792 White 2011). If the long term trend in acceleration (approximately 0.01mm/yr2) were to remain 793 constant then sea level would rise 28-34 cm (11”-13”) by 2100 (Church & White 2004; Jevrejeva 794 et al. 2008). Even though the majority of scientific effort to date has concluded that the rate of 795 SLR is accelerating the claim has been made Houston & Dean (2011a) that although sea-level is 796 rising, the rate is actually decelerating based on the data and time period evaluated. This 797 conclusion has been the subject of vigorous debate in the literature (Rahmstorf and Vermeer 798 2011; Houston and Dean 2011b; Donoghue and Parkinson 2011; Houston and Dean 2011c). It 799 appears that just as the rate (a) from Equation 6) of SLR is dependent upon the time period 800 evaluated the acceleration of that rate (b from Equation 6) is as well. 801 802 We infer from the literature review that the acceleration of SLR is the most difficult value to 803 determine from the historic rate and that it changes with the period of record evaluated, 804 ultimately determining the total amount of increase in MSL at any discrete point in time. While 805 we choose to support the consensus that SLR is accelerating, this issue will be closely examined 806 in the future to evaluate progress in this area of predicting GSLR. Additionally, the eustatic 807 contribution to GSLR will most likely have the greatest impact on future SLR rates and that 808 because of the great uncertainty in future ice sheet and glacier contributions, it is also the most 809 uncertain contributor. 810 811 2.3.2. Projected Regional Sea-Level Rise in the Gulf of Mexico 812

Unfortunately, there has been very little work done to specifically model the overall change in 813 the Gulf of Mexico water surface for the rest of this century. Until these regional investigations 814 are performed MSL changes must be primarily extracted from satellite altimetry data (Figures 12 815 and 16), which is less precise due to the wide coverage and the short period of record, or from 816 the average of all the tide gauges, which can be less reliable due to the period of record. In the 817 absence of location-specific sea-level budget analyses it is unclear how the relative contributions 818 of the steric and eustatic components of SLR will manifest in the Gulf. Because we are less able 819 to discriminate between hSTERIC and hMASS as components of an overall hTOT for the Gulf we must 820 assume that a shift to a eustatic-driven SLR will affect the Gulf similarly to the global oceans as 821 described above. Modeling work of global climate change scenarios included in the IPCC AR4 822 report does suggest that the Gulf of Mexico will respond similarly to the coastal ocean, and it is 823 reasonable to assume that projections of GSLR are appropriate to carry into the Gulf (Figure 23). 824 825 2.3.3. Gulf of Mexico Regional Sea-Level Rise Rate Recommended for CPRA Use 826

To calculate a predicted future sea-surface elevation offshore of coastal Louisiana, and based on 827 the literature reviewed, LACES is recommending that CPRA staff assume that Gulf SLR will be 828 1 meter (3.3’) by 2100, with a bounding range of 0.5 – 1.5 meters (1.6’ – 4.9’). However, it is 829 recommended for project specific application that the appropriate acceleration constants (b) be 830 calculated after the local Gulf historical linear trend (a) is derived, accounting for the substantial 831 east-west variation in near-shore SLR observed through satellite altimetry (Figure 16). 832

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833 834 Figure 23. This map predicts local sea level change relative to the global average for the 21st century, as 835 calculated from the results of 16 global climate change models running the IPCC A1B climate scenario. 836 Results suggest that sea level changes in the Gulf of Mexico during the next 90 years will not differ 837 substantially from the global mean, and that the northern and western Gulf may in fact respond with a 838 slightly less local sea level change than the global average. Figure from Meehl et al. (2007). 839 840 841 While this recommendation results from an independent assessment of the available data, it is 842 generally conservative when compared to recent publications predicting future GSLR (Figure 843 24). This recommendation is also consistent with similar efforts ongoing in other states. The 844 Miami-Dade County Climate Change Task Force (2010) recommended that all county agencies 845 include SLR estimates into their planning documents accounting for a 0.46-m (1.5’) rise in sea 846 level by 2050 and an SLR of 0.9-1.5 m (3’-5’) by 2100. The first of three Maryland Coastal 847 Program Technical Guidance reports for Dorchester County (Cole 2008) estimates 0.6-0.9 m (2’-848 3’) of SLR for the Chesapeake Bay region by 2100. 849 850 This recommendation is only part of the overall prediction of future relative SLR, and must be 851 combined with predictions of subsidence and marsh vertical accretion. Those two factors are 852 described next, and the overall recommendation for estimating RSLR for project planning and 853 design purposes will follow in the last section of this report. 854 855

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856 Figure 24.CPRA-LACES recommendations for future global sea-level rise by 2100, compared to a range 857 of values from recent published scientific literature. The red line indicates the primary LACES 858 recommendation of 1 meter GSLR by 2100, with a bounding range of 0.5 and 1.5 meters (green lines). 859 Figure from http://wh.er.usgs.gov/slr/sealevelrise.html. 860 861 862 2.4. Relative Sea-Level Rise in Coastal Louisiana 863 864 2.4.1. Estimates of Historical Relative Sea-Level Rise in Coastal Louisiana 865

Relative sea-level refers to the height of sea level as measured from a particular point or area on 866 the earth's surface. Change in relative sea-level usually results from the interaction of two 867 independent processes: 1) change in the absolute elevation of the earth's ocean (GSLR), and 2) 868 local change (uplift or subsidence) in the absolute elevation of the land mass. Adding to Equation 869 6, RSLR can be represented by the generalized equation: 870 871

E(t) = a*t + b*t2 + S (Eqn. 7) 872 873

where E, t1, t2,a and b are as defined by Eqn. 6, and 874 S is rate of subsidence (or uplift in areas of glacial rebound). 875

876

28

The exact mathematic calculations of predicted future RSLR are more complex than that shown 877 in Equation 7, however. Close examination of the NRC (1987) acceleration scenarios shown in 878 Figure 22, and those carried through into USACE (2009), highlight the very important caveat 879 that those scenarios assume a 1986 start to achieve the relevant 2100 MSL. As such, the 880 mathematical values of the acceleration constants are dependent on that specific starting point, 881 and the generalized equation last defined in Equation 6 needs to be altered at this point because 882 of this specificity. Specifically, the generalized equation needs to be replaced by 883 884

E (t2-1986)-E(t1-1986)=a([t2-1986]-[t1-1986])+b(([t2-1986]2-[t1-1986]2) 885 + S([t2-1986]-[t1-1986]) (Eqn. 8) 886

887 where all parameters are as defined in Equation 7. 888 889 All other factors held equal, new acceleration constants will have to be calculated if a more 890 contemporaneous date is assumed as t0. 891 892 Tide gauges directly measure RSLR for the water bottom on which the gauge is located. NOAA 893 presently maintains 19 tide gauges in Texas, Louisiana and Mississippi relevant to the 894 hydrodynamic environment of coastal Louisiana (Figure 25, Table 2). Of these only two have 895 the long-term period of record discussed earlier to justify calculating an RSLR trend line: Grand 896 Isle and Sabine Pass North (Figure 26a and b). The linear RSLR trend lines as calculated in 897 Zervas (2009) are substantially different for the two stations, and illustrate the importance of 898 geology on the physical stability of those stations. Sabine Pass North, in the more stable Chenier 899 Plain of southwestern Louisiana, has a much lower mean RSLR linear trend than Grand Isle, 900 which is located within the Mississippi River Deltaic Plan. Moreover, the RSLR trend for 901 Sabine Pass North is more than triple the 1.79 mm/yr.(0.070 inches/yr.) average GSLR for the 902 1958-2006 period of record (data from Church and White in press, as shown in Figure 8), and the 903 RSLR trend for Grand Isle is more than five times the 1.73 mm/yr. (0.068 inches/yr.) GSLR 904 linear trend for the 1947-2006 period of record. This comparatively high rate of RSLR is caused 905 primarily by land subsidence, unequivocally the most variable and significant contributor to 906 relative sea-level rise in coastal Louisiana. 907 908 There has been some discussion of the utility of the RSLR trend from the Eugene Island Station 909 (Figure 26c). The period of record for that station is only 1939-1974, so it is neither possible for 910 that data to inform a present understanding of RSLR for the central Louisiana coast nor serve as 911 the basis for estimates of future RSLR. This is especially the case given that the Eugene Island 912 period of record coincides with some of the highest documented rates of coastal land loss in 913 coastal Louisiana (Barras 2009). It is, however, possible to compare the Eugene Island data to 914 that from Sabine Pass North and Grand Isle where the periods of record overlap and thus allow 915 for some inference of the historical comparative stability of the central coast versus the western 916 and eastern margins of the coast. 917 918

29

919 920 Figure 25. NOAA’s tide gauge network in Louisiana covers multiple geomorphic settings within the 921 State’s coastal zone. Note that three stations are not shown on this map: Carrollton, Crescent City Air 922 Gap, and Huey Long Bridge Air Gap. Note also that the Stouts Pass and Mesquite Point stations have 923 been decommissioned. Figure from http://egisws01.nos.noaa.gov/website/co-ops/stations/viewer.htm. 924 925 926 Table 2. A summary of the current NOAA tide gauge stations shows that only two stations relevant to 927 coastal Louisiana have a sufficient period of record to establish an RSLR trend. RSLR trends are not 928 shown if not given by NOAA. NOAA has only calculated linear trends for t he Grand Isle and Sabine 929 Pass North gauges. 930 931 Station NOAA Station ID Period of Record RSLR Trend Bay Waveland Yacht Club 8747437 1978-present n/a Calcasieu Pass 8768094 2008- present n/a Carrollton 8761955 1996-present n/a Crescent City Air Gap 8761847 1984-present n/a Cypremort 8765251 2005- present n/a East Bank 8762372 2003- present n/a Freshwater Canal 8766072 2005- present n/a Grand Isle 8761724 1947-present 9.24 ± 0.59 mm/yr.* Gulfport 8745557 1979-present n/a Huey Long Bridge Air Gap 8762002 2009-present n/a Lake Charles 8767816 2002- present n/a LAWMA, Amerada Pass 8764227 2005- present n/a Pilots Station East 8760922 2004- present n/a Port Fourchon 8762075 2003- present n/a Rainbow Bridge, TX 8770520 1996-present n/a Sabine Pass North 8770570 1958-present 5.66 ± 1.07 mm/yr.* Tesoro Marine Terminal 8764044 2003- present n/a USCG New Canal 8761927 2005- present n/a West Bank 8762482 2003- present n/a 932 * RSLR trend calculated by Zervas (2009) for Grand Isle was for the time period 1947-2006, and the trend 933 calculated for Sabine Pass North was for the time period 1958-2006. 934

30

935 (a) 936

937 (b) 938

939 (c) 940

Figure 26. RSLR trend lines for the Sabine Pass North (a) and Grand Isle (b) NOAA tide gauges 941 illustrate the significance of geological stability on RSLR, and the difference between the more stable 942 Chenier Plan (Sabine) and the less stable Deltaic Plain (Grand Isle). The RSLR trend for Sabine Pass 943 North, 1958-2006, was 5.66 ± 1.07 mm/yr. (0.22 ± 0.042 inches/yr.). The RSLR trend for Grand Isle, 944 1947-2006, was 9.24 ± 0.59 mm/yr. (0.36 ± 0.023 inches/yr.).The RSLR trend line for the Eugene Island 945 tide gauge (c) for the period of record 1939-1974 demonstrates a high rate of RSLR, 9.65 ± 1.24 mm/yr. 946 (0.38 ± 0.049 inches/yr.) at that station in the central Louisiana coast. The station has been removed, so 947 only comparisons with historically contemporary stations are possible. Figures from Zervas (2009). 948 949

31

The LCA Science & Technology Program is likewise finalizing a report that calculates linear 950 RSLR trend lines for nineteen USACE-operated tide gauges in south Louisiana (Ayres in 951 preparation). That information will be included in this report as an amendment to Table 2 and 952 Figure 25. Once CPRA-LACES, most likely with NOAA assistance, has calculated linear trends 953 for all of the tide gauges shown in Figure 25, we will establish an appendix for the total pool of 954 NOAA and USACE tide gauges as a concise reference for CPRA project delivery teams. 955

956 2.4.1. Subsidence 957

The tide gauge data emphasize the importance of being able to document the contributions of 958 subsidence and accretion to the overall RSLR at discrete points in coastal Louisiana. Any effort 959 to confidently incorporate potential SLR impacts on coastal wetlands into planning must account 960 for the sum of factors influencing RSLR: 1) the change in the surface elevation of the Gulf of 961 Mexico, which is the primary topic of this document; 2) local land surface elevation change, 962 which in Louisiana is exclusively represented as subsidence; and 3) marsh vertical accretion, 963 which can offset some SLR impacts. This report does not attempt to exhaustively review the last 964 two topics, but will summarize relevant products from the state of the science that can inform 965 CPRA activities. 966 967 Subsidence is widely recognized as a significant driver of relative sea-level rise in southern 968 Louisiana, and probably the principal driver in southeast Louisiana for the near-term. There are 969 a number of independent factors that influence the rate of subsidence (Reed and Yuill 2009). At 970 the local scale the dominant factor may vary and thus we also recognize that rates of subsidence 971 are highly variable across the Louisiana coastal zone. However, our understanding of the exact 972 rates of subsidence at the local level is very limited. 973 974 Recent attempts have been made to acknowledge that spatial variability in subsidence rates and 975 factor that variability into program and project planning. The hydraulic and hydrodynamic 976 (H&H) model for the proposed Donaldsonville, Louisiana to the Gulf of Mexico Flood Control 977 Project utilized a digitized version of a coarse-scale map developed by Del Britsch of the 978 USACE New Orleans District Office (Figure 27). More recently, as part of the modeling effort 979 informing the 2012 revision of the State’s Master Plan, a Subsidence Advisory Group met on 14 980 September 2010 to assemble a draft map of a range of subsidence values that the State’s coastal 981 zone can expect through 2060 (Figure 28). The ranges in that map are significant, both within 982 and across polygons. For the project-effects modeling being undertaken for the Master Plan 983 revision, the two subsidence scenarios being analyzed are using values of 20% and 50% of the 984 range from the minimum value of the individual polygons shown in Figure 28. 985 986 Ongoing work by the Louisiana Geological Survey, commissioned by CPRA, will summarize 987 our understanding of the geological framework underlying south Louisiana as well as provide an 988 overview of historical rates of subsidence across the landscape. This work will further build on 989 the summary report Understanding Subsidence in Coastal Louisiana (Reed &Yuill 2009), 990 prepared for the State and the US Army Corps of Engineers by the LCA Science and Technology 991 Program Office. It is hoped that this information, as well as continued monitoring data from the 992 CWPPRA Program’s Coastal Reference Monitoring System (CRMS)-Wetlands stations, will 993 help to tighten the predicted ranges of subsidence shown in Figure 28. 994

32

995 (a) 996

997 (b) 998

999 Figure 27. A map of subsidence developed by Britsch in 2007 illustrates the spatial variability in 1000 predicted subsidence rates in southern Louisiana. A portion of this map was digitized (b) for use in the 1001 hydrodynamic modeling for the Donaldsonville to the Gulf project. Figure (b) from CHT 2010. 1002 1003

33

1004 1005 Figure 28. Map of projected subsidence ranges for south Louisiana generated by the Subsidence Advisory Panel for the Louisiana CPRA Master 1006 Plan 2012 Update, following a meeting on 14 October 2010.1007

1008

34

2.4.2. Marsh Vertical Accretion 1009

Our understanding of marsh vertical accretion is likewise evolving. The ability of marshes to 1010 keep up with moderate levels of RSLR via accretion of both mineral and organic soil material 1011 has long been understood (see summary in Mitsch and Gosselink 2000). Typically coastal 1012 marshes have a range of “optimum depth” and within this range they will respond positively to a 1013 rise in sea-level until it reaches a certain, heretofore undetermined, rate of rise wherein marshes 1014 will no longer be able to accrete and will drown. Organic matter production in coastal marshes 1015 is directly related to the rate of RSLR, and that up to a critical collapse threshold marshes in 1016 coastal Louisiana have the potential to organically accrete and match substantial levels of RSLR. 1017 Consistent with the calculations of RSLR above, we can thus define marsh vertical accretion as 1018 1019

MVA = f(E(t)) (Eqn. 9) 1020 1021 where MVA is the rate of marsh vertical accretion, and 1022 E(t) is as defined in Equations 5-8. 1023 1024

The optimum depth and marsh collapse threshold is likely unique to each marsh type and will 1025 depend upon the ability to maintain elevation via vegetative growth. Note this differs 1026 significantly from macrotidal coastlines where marsh accretion is sediment driven. This 1027 information will be more difficult than the subsidence predictions to incorporate in program and 1028 project planning because accretion in marshes is influenced by natural cycles, spatially 1029 dependent on the species mix of the plant communities of interest and is largely dependent on 1030 initial elevation relative to the water surface. It would be desirable to generate a map that 1031 spatially describes potential accretion in wetlands and identify by wetland type the critical 1032 threshold point of MSL increase beyond which marsh elevation collapses. It is necessary to 1033 incorporate the 18.6-year lunar nodal cycles into any accretion models in order to reflect the 1034 potential for collapse under an accelerating RSLR scenario. The failure to account for dynamic 1035 rates of GSLR under future scenarios risks underestimating the inundation stress that marsh 1036 vegetation will see, possibly leading to overly-optimistic predictions of vertical accretion and 1037 marsh persistence. While this science is nascent at present, it promises to be a significant 1038 contribution to predicting local net RSLR in Louisiana’s coastal wetlands. 1039 1040 A good example of a methodology for predicting marsh vertical accretion can be seen in the 1041 activities of the Project Delivery Team (PDT) for the Southwest Coastal Feasibility Study. The 1042 methodology adopted by that PDT and its partner Habitat Evaluation Team (HET) established a 1043 7 mm/yr. (0.28 inches/yr.) threshold under which wetland vegetation will continue accreting 1044 organic matter (see Appendix C). Beyond that threshold the wetland is assumed to convert to 1045 open water. In the case of the analysis predicting future landscapes for that feasibility study, 1046 these assumptions were applied to cells in the geospatial grid (i.e. persistence as a wetland or 1047 conversion to open water of a specific grid cell in the model). 1048 1049

35

3. SUMMARY AND RECOMMENDATIONS 1050 1051 Louisiana is experiencing a higher rate of RSLR than other parts of the world because of 1052 naturally occurring regional land subsidence. In a recent USGS Coastal Vulnerability 1053 Assessment of the Northern Gulf of Mexico (Pendleton et al. 2010) virtually the entire Louisiana 1054 coastline is identified as having a coastal vulnerability index (CVI) risk ranking as “very high”. 1055 This assessment developed CVI rankings for virtually the entire US shoreline by focusing on six 1056 variables which strongly influenced coastal response to SLR: 1) geomorphology, 2) historical 1057 shoreline change rate, 3) regional coastal slope, 4) relative sea-level change, 5) mean significant 1058 wave height and 6) mean tidal range. Although these variables are difficult to separate the end 1059 result is clear: the Louisiana coastline is one of the most at risk shorelines in the Northern Gulf of 1060 Mexico to the impacts of GSLR, the effects of which are clearly manifest in the rate of RSLR. 1061 1062 The scientific literature indicates that the rate of global SLR (GSLR) has been increasing steadily 1063 over the past several centuries. This may be seen in an increase from a 20th Century linear 1064 average based on tide gauge data of 1.7 ± 0.4 mm/yr.(0.070 ± 0.016 inches/yr.) to a linear 1065 estimate for the past eighteen years of 2.9 ± 0.4 mm/yr.(0.11 ± 0.016 inches/yr.) in the Gulf of 1066 Mexico based on satellite altimetry. Although direct comparison of the two techniques supports 1067 the validity of the altimeter readings, there is some concern regarding the short period of record 1068 for the altimetry data. However, evidence suggests that SLR for the available period of record is 1069 best represented as a single, non-linear function, which has important implications for relating 1070 RSLR and GSLR estimates, and especially for assumptions of the differential representing local 1071 land surface change. 1072 1073 More important for CPRA planning purposes is the projection of future GSLR. Based on the 1074 available data, LACES recommends that any SLR modeling scenarios models for state 1075 restoration projects assume a 1-meter (3.3’) MSL rise by 2100 compared to the late 1980sand 1076 should be bracketed by GSLR ranges of 0.5-1.5 meters (1.4’-4.9’) by 2100. The specific 1077 recommendation for factoring in the range of GSLR into local calculations of RSLR is given 1078 below. 1079 1080 While GSLR is an important factor to consider, data has revealed that variations in sea levels 1081 exist in the many regions of the earth’s oceans and water bodies. This regional variation 1082 associated with the Gulf of Mexico is much more relevant to coastal Louisiana. This paper 1083 examines regional SLR variation and, based on available data, concludes that while it does seem 1084 appropriate to bring future global SLR scenarios into the Gulf of Mexico, there is a definable 1085 east-west gradient in recent historical SLR across the Louisiana coast that needs to be considered 1086 in project planning. 1087 1088 However, GSLR presents only one piece of the puzzle when anticipating future sea level rise 1089 when planning and designing coastal restoration and protection projects. Subsidence and marsh 1090 vertical accretion represent two other factors which must be included and, in fact, may dominate 1091 land change dynamics and amplify the effects of SLR. They are critical for predicting the RSLR 1092 that the coastal wetland plant communities perceive, and both are subject to extremely high 1093 spatial and temporal variation across the Louisiana coastal zone. Although work on subsidence 1094 and marsh vertical accretion is continuing, this report gives instruction on how to determine 1095

36

these values and how to include them in project design. 1096 1097 CPRA has identified a number of technical uncertainties surrounding these recommendations 1098 that it plans to address through future research and development activities. It is CPRA’s goal 1099 that these activities will help constrain the uncertainties associated with predicting the impacts of 1100 future increases in sea level and increase our confidence in planning and implementing projects 1101 to achieve sustainable coastal Louisiana. 1102 1103 Recommendations for Calculating RSLR in Coastal Louisiana 1104 1105 Based on the information presented to this point, it is our recommendation that when 1106 participating in project planning and design activities, local RSLR be calculated using the 1107 following procedure to populate the variables of the generalized RSLR equation 1108 1109

E(t) = a*t + b*t2 + S. (Eqn. 7) 1110 1111

1. Use local observations of historical sea-level rise from contemporary satellite altimetry 1112 (Figure 16) just offshore of coastal Louisiana, in order to account for the substantial east-1113 west gradient in documented rates. Specifically, we recommend using an average of the 1114 three most proximate points shown in Figure 16. This is the rate of SLR (mm/yr.) and 1115 variable (a) from the generalized equation. 1116 1117

2. Calculate the acceleration constant that assumes a MSL increase of 1 meter (3.3’) by 1118 2100 as the most heavily-weighted project alternative, while also testing MSL increases 1119 of 0.5 meters (1.6’) and 1.5 meters (4.9’) to account for uncertainty in the literature. 1120

1121 This provides the change in water levels over time at a project location. To localize further, 1122 1123

3. Add in local subsidence values obtained from the most proximate local source, which is 1124 variable (S)in Equation 7. 1125

1126 In order to predict the persistence the coastal wetland, and specifically the persistence of the 1127 wetland surface or conversely marsh surface collapse and drowning, a fourth step is necessary. 1128 1129

4. Use the sum of #s 1-3 above to establish an inundation function, especially the rate of 1130 inundation for the period of analysis, in order to predict local responses of marsh vertical 1131 accretion as those models and data products become available. This can be inferred from 1132 scientific literature if no reliable data exist on site, or can be estimated from vegetation 1133 productivity models if available. 1134

1135 As discussed in Section 2.4.1., predicting future RSLR must account for the acceleration 1136 constants (variable b) being specific to NRC (1987) acceleration scenarios having a starting 1137 point of 1986. Appendix D of this report shows specifically how the variables discussed feed 1138 into a refined version of Equation 8 that accounts for that specific starting point. 1139 1140

37

Note that this recommended process differs from that described by the US Army Corps of 1141 Engineers’ (USACE) Engineering Circular (EC) 1165-2-211 (USACE 2009). The EC mandates 1142 how the USACE must estimate RSLR during the planning and engineering of water resources 1143 projects in the coastal zone, and LACES staff recognize that USACE staff participating on a 1144 PDT with the State for projects cost-shared with USACE will have to run RSLR scenarios in 1145 accordance with the EC. However, because of a number of significant technical issues with the 1146 EC that are described in Appendix B, we recommend that State staff participating on those PDTs 1147 also require the RSLR scenarios described in this document be run in addition to the EC-defined 1148 scenarios, and that the 4-step recommendation described here supersede EC mandates on any 1149 projects that the State is pursuing without the Corps as a cost-share partner. 1150 1151

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APPENDICES 1332 1333 Appendix A: Reviewer Comments and Responses 1334 1335 CPRA-LACES released a draft document for internal LACES comment on 27 September 1336 2010. 1337 1338 Comments on 20100927CPRA LACES SLR Document FINAL DRAFT, 8 October 2010, from 1339 Honora Buras, Louisiana Office of Coastal Protection and Restoration 1340 1341 I only have one technical comment, based on some information I ran across a while back that 1342 might be relevant. I was asked several years ago by Karim’s Ecological Review group to look 1343 into subsidence rates to use that were more updated than those based on Shea Penland’s work 1344 (1988, 1990) using tide gauge data. There were some issues that I ran across related to the use of 1345 these data that I never saw any resolution of. This may be relevant or not to your paper, but I 1346 just wanted to bring it to your attention, since you reference these tide gauge data in numerous 1347 places. Shea’s analysis was done before we were aware of the extent of coast-wide sinking of 1348 benchmarks that these tide gauges were tied to. It was never clear whether any of the records 1349 were later adjusted for this. Fortunately, the tide gauge with the longest record – Grand Isle- is 1350 one of the most stable of all and apparently subsidence in this location is much less than in most 1351 of the coast (according to Dokka’s work). However, even this gauge was actually a replacement 1352 for a different gauge in a slightly different location. This is not often mentioned when 1353 referencing the long-term record. Some said this older gauge was on the opposite side of a fault, 1354 thus differentially subsiding. Others disputed this. Only the earliest years of the record are at 1355 this other location. The datum used for all of these before NAVD88 was not very reliable. (I 1356 was once told that NGVD stood for “No Good Vertical Datum.”) Another problem with using 1357 tide gauge data for subsidence was that many of them are actually strongly influenced by 1358 localized runoff or river flow. I remember there was one near the mouth of the Atchafalaya that 1359 was obviously influenced by the heavier flows in flood years, I think this is the one at Eugene 1360 Island. Since this still is a reflection of water surface elevation, although likely somewhat 1361 localized, it may not be the issue in calculating SLR at the local level as it might be for 1362 subsidence alone. In fact, Shea’s estimates really gave more of a RSLR than subsidence alone, 1363 so they worked for the purpose at the time and we had nothing better. This was also before 1364 GSLR was so well studied. Since I have not been involved in any of the efforts on subsidence 1365 since that time, I do not know if this question has already been addressed by the subsidence team 1366 or others. I have not seen it addressed in any white papers or proceedings from any of the 1367 subsidence working groups. The issue of sinking benchmarks, and the extent of the subsidence 1368 problem was very controversial in house at the time. This was right about the time the draft 1369 NOAA technical report 50 (Shinkle & Dokka 2004) on subsidence was being debated. You 1370 describe some localized east-west trends in SLR that need to be accounted for in addition to the 1371 GSLR rate. Even though you state this was based on altimetry data, is it possible that this trend 1372 is related to the subsidence trends that may be manifest in some of the data, especially if it is 1373 based on tide gauges? In looking at the map in figure 17, it appeared to have some correlation 1374 with the subsidence trends. 1375 1376 While I recognize that the information presented in this paper is based on highly technical 1377 research and requires the use of terminology that may not be familiar to the reader, I believe the 1378

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paper, in some cases, focuses more on the details of the scientific debate than the relevance and 1379 may not be useful for the average CPRA employee attempting to understand and apply the 1380 information in project planning, design and implementation. I assume this is not meant to be a 1381 paper for publication in a climate change journal, but for practical use as applied science for our 1382 purposes. Even if meant to be published, white papers are usually much more simplified and 1383 summary documents that take the information gleaned from various sources and synthesize it 1384 into a format and language that is easy for most readers to understand, even if they have no 1385 training in the subject area. You do a good job of defining the terminology where it is more 1386 easily understood, but the language in the summaries is difficult to follow. The conclusions need 1387 to be explained or at least summarized in language that everyone from engineers, biologists, 1388 ecologists, geologists, and oceanographers to senior administrative officials can understand. 1389 1390 For example, in the executive summary, I would not use the numbered bullet format in lines 39-1391 57 to explain such a complicated concept with such long, complicated sentences. (The same 1392 applies to the similar write-up on pp 25-26.) Bullets should be reserved for simplified concepts 1393 or points. These are paragraphs. If I was merely trying to figure out what rate of GSLR to use, 1394 and didn’t care about all the background information that you used to derive it, I would have a 1395 hard time putting my finger on it easily. Provide the description in text then give an example of 1396 its use in one or more typical applications that are anticipated. Where the process is described 1397 for determining what number to use for SLR at specific locations, the format is especially 1398 awkward. It would be useful to separate the explanation of how the numbers are derived from 1399 the actual number to use and the formulas. The executive summary overall should be more of a 1400 very simplified explanation of how you determined a number or method to use and give an 1401 example of its use. 1402 1403 The summary should also, in plain language, tell of the caveats and describe uncertainties and 1404 what type of future work is necessary or planned to resolve them. Additionally, I would have 1405 liked to see at least some discussion of potential implications of this rate of SLR and any 1406 associated uncertainty in how projects are designed and implemented. For example, under the 1407 scenario given, are there potential implications to using rocks on shorelines? Should we be 1408 abandoning some areas as unsustainable along the outer fringes of the coast? Should we spend so 1409 much time worrying about sculpting marsh to the exact intertidal elevation, or should we build in 1410 some additional height or heterogeneity of heights into our projects to account for it? You give 1411 an explanation of how to incorporate this information into determining marsh elevation, but also 1412 should give some examples of using this information for structures or other types of projects, not 1413 just marsh restoration. Remember we are in the realm of applied science, so show us how to 1414 apply this information in our work. 1415 1416 One additional thing I would change throughout the text, for clarity, is how the rate of change is 1417 given. You present it as an overall increase in sea level by 2100, but the starting reference year 1418 is not necessarily given in each case (i.e. what is the time period of this 1m rise?). Therefore, it 1419 is hard to determine from this what annual rate is presented, and if it is the same for each 1420 reference. For example, if you state that we (or some other state) will use an increase of 1m by 1421 2100, is that from this year or the year of the particular publication? Is it a linear rate? I think 1422 presenting annual rates in each case would be more useful, especially since this document is 1423 meant to be a living one and will be referenced in future years as well as this one. I also would 1424

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have liked to see the reasonable range of the uncertainty (sort of a margin of error) expressed 1425 somewhere in the summary, in addition to the number recommended for now. 1426 Response: We agree that the format and organization of the document could be improved. In 1427 order to make the document easier to use, we have simplified the process for determining the rate 1428 of GLSR into a step-by-step process that engineers and scientists can easily calculate. We have 1429 moved this information to the front of the document so it is easy to locate. We do feel, however, 1430 that it is important to retain the scientific background information for reference. Therefore, we 1431 have adjusted the organization of the document, corrected any formatting or typographical errors 1432 and moved the technical discussion to the back. 1433 1434 Satellite altimetry works by orbiting satellites emitting signals that travel to the earth and are 1435 then reflected back to the satellite. The time it takes for the signal to travel that distance is then 1436 used to calculate the distance between the satellite and the surface. My continually emitting and 1437 receiving signals, the satellites can record the surface of the ocean. This allows us to monitor the 1438 sea surface height or sea level. Because this method of measurement does not require the use of 1439 a benchmark anchored to a land surface for reference, such as is used for tide gauge 1440 measurement, subsidence is not an issue. Therefore, the satellite altimetry is not influenced by 1441 subsidence so the east-west trend that is identified in the white paper is not related to the 1442 subsidence trend the commenter mentions. Subsidence can be a factor in tide gauge data; 1443 however, the east west GLSR trend depicted in the white paper is based on satellite altimetry 1444 data. 1445 1446 While those questions are important to answer, this white paper was prepared to inform policy on 1447 sea level rise and, therefore, is limited to that discussion. We approached this paper by 1448 researching and synthesizing the current science related to GSLR, using that information to 1449 estimate the most likely GLSR over the next century, and how to best incorporate GSLR into 1450 CPRA project planning and design. There are many other factors other than just GLSR that must 1451 be factored into any specific project as the commenter points our; however, this white paper is 1452 limited to estimating a GLSR. The issues commenter mentions should be addressed during 1453 project design and not by this white paper. 1454 1455

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CPRA-LACES released a draft document for internal CPRA comment on 23 November 2010. 1456 1457 Comments on 20101123 CPRA LACES SLR Document FINAL DRAFT, 12December 2010, from 1458 Summer Martin, Louisiana Office of Coastal Protection and Restoration 1459 1460 Comment on Line 189 through 191: Why would you want to remove [the influence of natural 1461 cycles on sea level rise estimates]? It’s still a change in SL whatever the cause? 1462 1463 Response: GLSR generally refers to the increase in MSL that is not natural and/or cyclical and is 1464 long term. We are primarily interested in this trend because it is assumed that natural systems, 1465 marshes in particular, are quite capable of persisting in the event of natural cycles. It is the long 1466 term change in sea-level against this background of natural forcings that will ultimately affect the 1467 ability for coastal areas to maintain elevation. Moreover, a change in MSL long term will 1468 significantly alter the degree to which natural cycles will affect an area. For example, if the 18.6 1469 year lunar cycle tends to increase sea-level in an area by 2 cm over 10 years and the GSLR rate 1470 in that area is 1mm/yr. then the overall effect of the natural cycle in those 10 years will be 3 cm, 1471 and when the 18.6 year cycle ebbs, it will ebb by only 2 cm, leaving 1cm of overall increase in 1472 MSL. 1473 We believe that this was unclearly described in the section you referenced and have altered that 1474 section to clarify. 1475 1476 Comment on Line 583 through 586: I don’t understand this sentence; what is it you are 1477 recommending? 1478 1479 Response: We are recommending that in order to account for the change in water level (not 1480 accounting for land change and RSLR at this point) at a specific project, that management teams 1481 assume that by 2100 sea-level in that area will increase by 1 meter and use the associated 1482 acceleration constant from the curve NRC II, 6.2 x 10-5and apply this to the yearly trend found 1483 from satellite altimetry or local tide gauges to determine the change in MSL for any other date, 1484 see equations 5 and 6 for clarification. 1485 We believe this was unclearly described in the section you referenced and have altered the text to 1486 clarify. 1487 1488 1489

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CPRA-LACES released a draft document for comment by select State agencies on 17 May 1490 2011. Written comments were received from the Department of Natural Resources and the 1491 Department of Wildlife & Fisheries. Informal comments were also received from the 1492 Department of Environmental Quality and the Department of Transportation & Development, 1493 and the Governor’s Office of Homeland Security and Emergency Preparedness. 1494 1495 Comments on 20110517 CPRA LACES SLR Document, 31May 2011, from 1496 Louis E. Buatt, Assistant Secretary, Office of Coastal Management, Louisiana Department of 1497 Natural Resources 1498 1499 The Office of Coastal Management appreciates the opportunity to review the document 1500 referenced above. While we recognize that the document is being distributed for informational 1501 purposes to persons and groups with responsibilities and interests in the issue of sea-level effects 1502 on coastal resources, I do want to take this opportunity to provide feedback for your 1503 consideration in future iterations of the document at such time as it may be revised to reflect the 1504 progress of state of knowledge on the subject. 1505 1506 I and my staff have found the document to be an excellent review of a complex and often 1507 controversial topic and appreciate the work you and your staff have done to provide a readable 1508 and comprehensive synopsis of the issue as it pertains to the Louisiana coast. I believe the white 1509 paper will be a useful tool to those working with issues affected by relative sea-level rise and 1510 appreciate the effort to discuss the related factors that distinguish these effects seen in Louisiana 1511 in the context of observed subsidence and vertical marsh accretion. 1512 1513 I would offer only two comments for your consideration in future editions of the whitepaper. 1514 First, I believe the Executive Summary might be enhanced by a slightly expanded discussion of 1515 the issue as observed in Louisiana and the Gulf of Mexico and of the process being 1516 recommended in the paper. While readable to its intended technical audience, it will inevitably 1517 be read by many not versed in the jargon of SLR terminology, and who will read little else but 1518 the Executive Summary. 1519 1520 Second, I believe it would be appropriate to mention in the context of planning and design of 1521 structural and non-structural projects and management measures of the Master Plan, that the 1522 State's Coastal Management Program, through its regulatory authorities, does have enforceable 1523 policies related to subsidence and inundation and the risks associated with them, whether caused 1524 by SLR or other phenomena. This could be useful particularly in planning exercises undertaken 1525 to implement the Master Plan. 1526 1527 Finally, I am obliged to point out one very minor editing miscue which remains in the document 1528 as a result of the automated editing which we all now use. In line 937 of the document the word 1529 "of' appears when it seems obvious that the writer intended that the word be "or." 1530 1531 Once again, let me offer kudos to you and your staff who have prepared an excellent and useful 1532 document dealing with a very complex and constantly changing subject. 1533 1534 Response: We agree with the need for a revised discussion and have removed the Executive 1535

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Summary from the Technical Report. In its place we have created a stand-alone Summary of the 1536 Technical Report for Coastal Managers, which is more comprehensive but is written without the 1537 jargon of the earlier Executive Summary. 1538 1539

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Comments on 20110517 CPRA LACES SLR Document, 17 June 2011, from 1540 Heather Warner-Finley, Louisiana Department of Wildlife and Fisheries 1541 1542 Thanks very much for the opportunity to review this work. We think that RSLR is a critical 1543 parameter to consider and monitor relative to restoration efforts and protection and navigation 1544 planning. It’s great that you guys are taking it seriously, and we’re particularly pleased that 1545 additional research / modeling is planned. 1546 1547

• Contribution of freshwater from melting glaciers and ice sheets – the paper’s authors 1548 acknowledge that this phenomenon may be THE primary contributor to global sea level 1549 rise. “These data could have significant implications to how predictable and relevant 1550 future GSLR scenarios are to the Gulf of Mexico given limits in the monitoring networks 1551 that define the GSLR components…” The paper then goes on to identify the acceleration 1552 constants that will be used and recommend use of a bracketed model to estimate sea level 1553 rise. Would it be valuable to try to estimate the uncertainty that is brought into the 1554 equation by the addition of freshwater from ice melt? Is that well accounted for in the 1555 high SLR scenario of 1.5m? 1556

• Subsidence seems to be another variable that will require much more research and 1557 modeling. Nyman’s data cited in the SW Coastal study that a deteriorating marsh 1558 appeared to accrete at a higher rate is fascinating. This seems to introduce another large 1559 source of uncertainty in any attempt to predict how coastal projects will perform in the 1560 future. 1561

1562 We applaud you for beginning this work. 1563 1564 Response: We have added language highlighting the uncertainty involved in the eustatic 1565 contribution to GLSR and, to support that language, included Figure 18. 1566 1567

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Appendix B: CPRA-LACES Technical Issues with the US Army Corps of Engineer’s Engineering 1568 Circular No. 1165-2-211 1569

1570 The US Army Corps of Engineers released Engineering Circular (EC) 1165-2-211 on 1 July 1571 2009 to guide Corps staff on how to account for local relative sea-level rise in water resource 1572 project planning and engineering. The recommendations in this report have been proposed 1573 because we recognize that there are several significant technical issues, with the EC that limit its 1574 use in southern Louisiana. While the EC is not a mandate per se for non-Corps entities such as 1575 the State to use during project implementation, it does become a required component of any 1576 water resources project for which the State is the local sponsor because it is Corps internal 1577 policy. While there is a requirement in the EC for Corps staff to account for specific RSLR 1578 scenarios in project planning, there is the flexibility for the local sponsor to add other scenarios 1579 for consideration. The fours-step process of accounting for RSLR described in this document 1580 represents such an alternative and, if adopted, internal CPRA policy. 1581 1582 The EC process begins in Step 2 (page C1 of USACE 2009) by looking for local tide gauges that 1583 can serve as a data source for RSLR calculations. The EC requires that tide gauges are 1584 appropriate when the data period of record is greater than forty years, for the reasons of 1585 excessive confidence intervals with shorter data records discussed in Section 2.1.1. There are 1586 only two NOAA tide gauges that have that period of record, the Grand Isle (NOAA Station ID 1587 #8761724) and Sabine Pass North (NOAA Station ID #8770570) gauges. RSLR can be 1588 mathematically represented as 1589 1590

E(t) = a*t + b*t2 + S*t (Eqn. B1) 1591 1592

where E(t) is MSL at time t, 1593 a is the observed rate of GSLR, 1594 b is the acceleration constant, and 1595

S is rate of subsidence (or uplift in areas of glacial rebound). 1596 1597 For the Grand Isle gauge, NOAA’s calculation of RSLR for the time period 1947-2006 was 9.24 1598 ± 0.59 mm/yr. (Zervas 2009). Referencing the variables in Eqn. 6, the 9.24 mm/yr. rate of RSLR 1599 is equal to (a + S), the linear rate of GSLR plus the subsidence function, with no acceleration 1600 constant because of the linear definition and no marsh vertical accretion function because the 1601 gauge is in open water. 1602 1603 With this information it appears that we pass EC's step 2 and proceed to step 4, where it must be 1604 decided if the "... long-term gauges can be used ... [to] represent local ... conditions at [the] 1605 project site." Steps 4 and 5 essentially seek to establish if the physical gauge location is 1606 representative of the project location, which for coastal Louisiana would imply that the Sabine 1607 Pass North gauge is representative of the Chenier Plain and the Grand Isle gauge is 1608 representative of the Mississippi River Deltaic Plain. If we assume yes to both, we are then 1609 asked in Step 8 if there is a stable geologic platform within the same region as the project site. 1610 Our recommendation is that no, data from these two specific gauges in Louisiana cannot be used 1611 to characterize conditions throughout the Chenier and Delta Plains, because of the high degree of 1612 variability in subsidence rates illustrated in Figures 27 and 28 of the Technical Report. Fallback 1613 answers on the decision points of the EC are to "Consult with a tidal hydrodynamics expert." 1614

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The implication of this is that the geological complexities of coastal Louisiana render the EC 1615 approach to predicting SLR unusable. 1616 1617 If for argument’s sake we do to accept that the two gauges at Grand Isle and Sabine Pass are 1618 appropriate representatives for the Delta and Chenier Plains, respectively, the EC then instructs 1619 mathematically estimating subsidence by subtracting GSLR from the RSLR gauge record. To 1620 determine the rate of subsidence, the EC instructs an assumption that GSLR is a constant linear 1621 rate of 1.7 mm/yr., which was the overall linear rate of GSLR for the 20th century defined by 1622 Church & White (2006). Subtraction of the linear GSLR function of 1.7 mm/yr. from the linear 1623 RSLR function of 9.24 mm/yr. gives an assumed constant rate of subsidence at the gauge of 7.5 1624 mm/yr. 1625 1626 The first technical concern with the EC approach involves the determination of subsidence from 1627 the RSLR tide gauge data by subtraction of the linear rate of GSLR for the 20th century, and 1628 specifically the problem that there are two linear curves being compared even though they 1629 represent different time periods (1900-2000 for GSLR, 1947-2006 for RSLR). Examination of 1630 the Church & White (2006) dataset shows that while the overall GSLR rate for the 20th century 1631 was calculated at 1.7 ± 0.3 mm/yr., there are two evident curves of different slope embedded in 1632 the overall 1870-2001 graph, with the 1936-2001 time period having a linear GSLR rate of 1.84 1633 ± 0.19 mm/yr. This discrepancy reiterates the importance we discussed in the main report of 1634 understanding the period of record of the SLR data. Data should only be compared where there 1635 is direct temporal overlap, which in the case of the Church & White vs. Grand Isle gauge data 1636 would be the time period 1947-2001, because there is no reason to expect that the actual slope of 1637 either line for the restricted time period would be equal to the slopes for either GSLR or RSLR 1638 for their full periods of record. 1639 1640 To illustrate the difference, while the subtraction using the EC gave an assumption of 7.5 mm/yr. 1641 as described above, comparison of more comparable data for the rate of RSLR for the Grand Isle 1642 tide gauge of 9.85 ± 0.35 mm/yr. (1947-1999; Zervas 2001) and GSLR for 1936-2001 equal to 1643 1.84 ± 0.19 mm/yr. gives the differential of 8.01 mm/yr., which by the EC would be the assumed 1644 subsidence rate. The implication here is a potential miscalculation of the rate of subsidence that 1645 will be carried out into the project lifespan planning horizon because of the assumption that 1646 subsidence will remain constant. Although the differential is minor, this example was illustrative 1647 only, since a direct comparison of 1947-2001 data is needed and the results would be expected to 1648 likewise differ. 1649 1650 The next technical concern, however, is the assumption that the 20th century mean rate of GSLR 1651 is appropriate to compare with the tide gauge data. Specifically, both tide gauge and more recent 1652 satellite altimetry data indicate that the present rate of GSLR has accelerated beyond the 1653 20thcentury rates determined by Church & White (2006). As shown in Figure 7 of the main 1654 report, the present linear rate of GSLR from the satellite altimetry data is 2.9 mm/yr. for the 1655 1992-2010 period of record. The EC does not advise using data with periods of record shorter 1656 than 40 years, though it does say that 1657 1658

“If estimates based on shorter terms are the only option, then the local trends 1659 must be viewed in a regional context, considering trends from simultaneous time 1660

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periods from nearby stations to ensure regional correlation and to minimize 1661 anomalous estimates. The nearby stations should have long enough records 1662 (greater than 40 years) to determine reasonable trends, which can then be 1663 compared to the shorter, local sea-level records …” 1664

USACE 2009, Page B-4 1665 1666 This leaves pursuing projects in coastal Louisiana in a conundrum. If faith is placed in the global 1667 data showing an acceleration in the rate of GSLR during the past two decades, continuing to use 1668 the lower long-term GSLR rate will result in a difference calculation of subsidence that is 1669 probably greater than in reality. In the case of Church & White (2006) vs. satellite altimetry 1670 data, that difference would be as much as 1.2 mm/yr. The other option is to use adjacent gauges 1671 with longer-term data, but that option is of little use in southern Louisiana because of the 1672 recognition that there are very evident spatial differences in both observed SLR (Figure 16) and 1673 subsidence (Figure 27). Not using current estimates of the rate of GSLR also will underestimate 1674 future MSL in EC scenarios because the acceleration scenarios (b in Equation B1) are applied to 1675 the base rate of GSLR. 1676 1677 Admittedly, some of these differences in calculated subsidence rates and magnitudes when 1678 carried out over a typical 50-year project life span are of questionable ecological or engineering 1679 significance. The underlying philosophical question is whether we accept a process that gets to a 1680 number that’s “close enough” even though we recognize that the mathematics behind the 1681 calculations are flawed. However, the discussion to this point presupposes that continued use of 1682 a linear function for either GSLR or the tide gauge RSLR is appropriate. The remaining two 1683 technical issues have much more of a potential for significant differences in calculated depths of 1684 RSLR. 1685 1686 The fact that the rate of GSLR has consistently increased across discrete periods of time between 1687 1870 and 2010 suggests that even the historical rates of GSLR need to be assigned a non-linear 1688 function instead of a linear function. The data were not available as of the writing of this version 1689 of the report to discuss the difference in fit between a linear and exponential function for the 1690 RSLR tide gauge data at Grand Isle. Visual examination of the data, however, does suggest a 1691 more linear response than that shown for GSLR. This has extremely important implications 1692 because evidence of a more exponential GSLR rate and a more linear RSLR rate means that 1693 there is no mathematical possibility for subsidence to have been historically constant. In fact, if 1694 we retain subsidence defined as the difference between RSLR and GSLR, subsidence must have 1695 decreased during the period of record. This is in agreement with recent data from Morton et al. 1696 (2009) which suggests that reduced subsurface fluid withdrawal in the recent past compared to 1697 the 1950s-1970s may be leading to declining rates of subsidence. The assumption in the EC of a 1698 constant, subtraction-based calculation of subsidence rate carrying into the future then becomes 1699 invalid. Subsidence would decline over time, and the EC process would substantially over-1700 predict RSLR for the project’s period of analysis. 1701 1702 While on the issue of subsidence, it is also a concern that the EC says to assume the subsidence 1703 rate calculated as discussed above across the entire coastal area for which the specific tide gauge 1704 is representative. Even with its existing limitations, Figure 27 of the main report illustrates our 1705 current understanding that subsidence varies across Louisiana’s coastal zone, and that using the 1706

52

Grand Isle gauge to represent the deltaic plain and the Sabine Pass North gauge to represent the 1707 Chenier Plain is unrealistic. Any attempt to remove the subsidence calculations from the EC 1708 process and replace that data with a more spatially-explicit estimation of coastal zone subsidence 1709 pushes any SLR analysis closer to our recommendations. 1710 1711 The final technical issue is concern over the future sea-level rise scenarios that are mandated in 1712 the EC process. Specifically, the EC requires that three future scenarios be examined: 1713 1714

• A continuation of the current linear rate of RSLR at the tide gauge for the project 1715 period of study (50 years), defined as the “Low Scenario”; 1716

• An accelerated rate of SLR over the period of study, defined by the NRC (1987) 1717 Scenario I curve, which uses the acceleration constant of 2.36 x 10-5 mm/yr2 to 1718 accomplish a 0.5-meter rise in SLR by 2100, defined as the “Intermediate Scenario”; 1719 and 1720

• An accelerated rate of SLR over the period of study, defined by the NRC (1987) 1721 Scenario III curve, which uses the acceleration constant of 1.005 x 10-4 mm/yr2 to 1722 accomplish a 0.5-meter rise in SLR by 2100, defined as the “High Scenario.” 1723

1724

As discussed in the main report, there is a strong argument for a non-linear (i.e. accelerating) 1725 trend for present and predicted future SLR. Although the argument has been made that 1726 consideration of the “Low Scenario” only increases the range of potential outcomes studied, with 1727 such a low probability for this scenario any attempt to model a future linear rate is a questionable 1728 use of limited time and financial resources. For planning and design scenarios we believe the 1729 NRC I curve to be the most appropriate “low” value for the future. 1730 1731 Our recommendation in the main document was to plan for a 1-meter rise in MSL by 2100, 1732 based on the general consensus of the scientific literature. This is not one of the standard 1733 scenarios listed in the EC. Bracketing an analysis of SLR with the NRC (1987)-based 1734 acceleration constants for a 0.5- and 1.5-meter rise in MSL by 2100 does not allow us to estimate 1735 the impacts of a 1-meter GSLR unless the slope of the landscape is constant, which it is not. The 1736 EC does stipulate that “The analysis may also include additional intermediate rates, if the project 1737 team desires.” In joint state-Corps projects LACES and CPRA should require analyzing the 1-1738 meter projection of MSL increase by 2100.However, given the discussion above regarding the 1739 EC calculation and assumption of a linear subsidence rate, we recommend that CPRA only push 1740 for a 1-m SLR scenario using the 4-step process described in this report. 1741 1742

53

Appendix C: Draft Southwest Coastal Feasibility Study, Wetland Accretion Summary 1743 1744 VERTICAL SOIL ACCRETION ESTIMATES IN LOUISIANA MARSHES 1745 1746 Background 1747 1748 Coastal marshes can adjust vertically and maintain a dynamic equilibrium with Relative Sea 1749 Level Rise (RLSR) up to a certain rate (Morris et al. 2002). This active adjustment, to a great 1750 degree, is controlled by organic matter production and mineral sediment deposition (Turner et al. 1751 2001, Nyman et al. 2006). While erosion with tides can remove surface detritus from the marsh, 1752 a large proportion of the organic matter storage that helps maintain the vertical elevation of the 1753 soil can be linked to belowground root production. 1754 1755 Along the gulf coast, soil organic matter accumulation controls vertical accretion of the marsh, 1756 and mineral matter contributes less to the process of vertical adjustment. This is because organic 1757 matter occupies more than twice the volume of an equivalent mass of mineral matter (clay, silt, 1758 sand). Louisiana’s mature marshes (outside of the active deltas) rely on organic matter 1759 accumulation, through plant production, to adjust to relative sea level rise. 1760 1761 Soil organic matter can be preserved or lost by reducing or oxidizing soil conditions. Persistent 1762 saturation favors long-term organic matter storage, while drought and moisture loss can cause 1763 oxidation, resulting in soil elevation loss. Thus, for coastal wetlands, long-term soil elevation is 1764 in a dynamic equilibrium with water-level variation. Also, there is an optimum elevation where 1765 plant production, organic storage, and soil elevation gain is optimized. A marsh at a high 1766 elevation in the tidal frame may have high plant production but organic matter oxidation is high, 1767 resulting in a static soil elevation. The converse situation, where marsh elevation is low and 1768 inundation is severe, plant production and organic matter accumulation are impaired. While these 1769 general relationships are recognized, quantitative relationships of soil elevation, hydrology, and 1770 plant production have not been fully developed for Louisiana wetlands. 1771 1772 The purpose of this technical memorandum is to review the data pertaining to wetland vertical 1773 accretion in Louisiana to support the development of future landscape projections with sea-level 1774 rise. One of our objectives is to contrast geographic and coast-wide accretion patterns, but 1775 another objective is to understand the capacity of herbaceous coastal wetlands to accrete 1776 regardless of geography. Understanding the upper limit of wetland accretion is important since 1777 there is substantial sea level rise anticipated with a future coastal landscape. 1778 1779 Data synthesis and results 1780 1781 Most of the work done in Louisiana’s coastal marshes to understand longer-term (since 1963) 1782 accretion processes has been done using 137Cs. Shorter-term estimates of accretion use feldspar 1783 marker horizons placed on the soil and cored over time to measure the vertical accumulation of 1784 sediments. The marker horizon measurements can be coupled with vertical elevation 1785 measurements with a Rod Surface Elevation Table (RSET) to understand how much surface 1786 compaction occurs over time. Our goal here is to see if general patterns of accretion emerge 1787 between marsh types and provinces and provide suggestions for consideration. Here we present 1788 four tables of data summarized from various sources. 1789

54

1790 Longer- term, coast-wide patterns, Cesium 137 (Table C1): This is a comparison of delta and 1791 Chenier Plain accretion estimates from Nyman et al. (2006), which comprises some of his work 1792 with colleagues R. DeLaune and W. Patrick; they present information from 68 cores. Also in 1793 Table 1, Turner et al. 2000 present research in salt marshes of the Delta Plain with data from 52 1794 cores. A study was led by USGS (Piazza et al. in press) that analyzed 48 cores from the Delta 1795 and Chenier Plain following Hurricanes Katrina and Rita. 1796 1797 1798 Table C1. Long-term accretion estimates (137Cs) from different marsh types in the Chenier and Delta 1799 Plains from different studies with large numbers of samples. (DP=Delta Plain; CP=Chenier Plain). 1800

Marsh type Province Hydrology Cores (n) Accretion rate

cm yr-1

Nyman et al. 2006

Stable

fresh DP/CP

14 0.82 (0.15) brackish DP/CP

12 0.88 (0.14)

salt DP

12 0.59 (0.14) Deteriorating

brackish DP/CP

8 0.96 (0.32) salt DP

22 0.98 (0.36)

Turner et al. 2001

salt DP

52 0.66 (0.21)

Piazza et al. in press

fresh DP natural 15 0.57 (0.13) brackish DP natural 5 0.72 (0.14) salt DP natural 10 0.64 (0.16)

fresh CP natural 5 0.57 (0.25) brackish CP natural 3 0.65 (0.23) brackish CP impounded 10 0.38 (0.04) mean

0.70

median

0.66 1801 1802

1) Mean estimates of accretion average 0.7±0.2 cm yr-1 and fall within a range of 0.38-0.98 1803 cm yr-1 regardless of marsh type or province. 1804

2) There is coherence of salt marsh accretion in the Delta Plain regardless of study: 1805 0.59±0.14 cm yr-1 (n=12, Nyman et al. 2006, from stable marshes) 1806 0.66±0.21 cm yr-1 (n=52; Turner et al.) 1807 0.64±0.16 cm yr-1 (n=10; Piazza et al. in press) 1808

55

3) In the Delta Plain, deteriorating salt marshes exhibit higher rates of accretion (0.98±0.36 1809 cm yr-1) than stable salt marshes (0.59 ±0.14 cm yr-1) (see Nyman et al. 2006). 1810

4) Mean estimates of accretion have error terms of 0.15-0.3 cm yr-1. Thus, any comparison 1811 of mean accretion among marsh type or province is not likely to be considered 1812 significantly different enough to warrant separation. 1813

5) Fresh marshes have the capacity to accrete at equal or higher rates than salt marshes, 1814 regardless of geological province (individual estimates of fresh or salt marshes accreting 1815 at 1.0 cm yr-1 are not uncommon). 1816

6) In the Chenier Plain, data from Piazza et al. in press suggest that accretion could be significantly 1817 lower for impounded marshes; this needs further investigation (see Table 3). 1818

1819 Longer-term, coast-wide patterns, Cesium 137 (Table 2): Jarvis (2010) assembled a literature 1820 review of accretion estimates of streamside and interior marshes where available. The Jarvis 1821 (2010) summary presented estimates from various radiometric and physical measurement 1822 techniques, but only the 137Cs estimates are used here. 1823 1824 1825 Table C2. Long-term accretion estimates (137Cs) from different marsh types and habitats (interior vs. 1826 streamside). Compiled by Jarvis (2010). 1827

Interior marsh Streamside marsh

Province Site Name

Marsh type

accretion rate (cm yr-1)

accretion rate (cm yr-1) Source

Delta Barataria B. F 0.65 1.06 Hatton et al. 1983

Delta Barataria B. I 0.64 1.35 Hatton et al. 1983

Delta Barataria B. B 0.59 1.40 Hatton et al. 1983

Delta Barataria B. S 0.75 1.35 Hatton et al. 1983

Delta Palmetto B. F 0.90 (0.10) 0.99 (0.17) DeLaune et al. 19891

Delta Barataria S 0.75 1.10, 1.35 DeLaune et al. 19781

Delta Lafourche P. S 0.47 (0.09) 0.68 (0.17) DeLaune et al. 19891

Delta Breton Sound F 0.65 (0.18)

DeLaune and Pezeshki 20031

Delta Fourleague B. S 0.66 1.35 Baumann et al. 1984

Delta Old Oyster B. S 0.48 (0.09)

Rybczyck and Cahoon 2002

Delta B. Chitigue S 2.26 (0.09)

Rybczyck and Cahoon 2002

Delta Delta NWR F

>1.59 Wilson and Allison 2008

Delta Barataria B. S

0.67 (0.49) Wilson and Allison 2008

Delta Breton Sound S

0.80 (0.17) Wilson and Allison 2008

Chenier Cameron P. B 0.56 (0.11) 0.57 (0.10) DeLaune et al. 19891

Chenier Calcasieu L. I 0.78

DeLaune et al. 19831

mean

0.78 1.1

median

0.65 1.1

1828 1829 1830

56

1) A consistent accretion pattern is detectable with streamside greater than interior marsh. 1831 2) For interior marshes, mean and median accretion rates were 0.78 and 0.65 cm yr-1, 1832

respectively. 1833 1834 Longer-term, Chenier Plain patterns, Cesium 137 (Table 3): Steyer (2008) summarized Chenier 1835 Plain accretion studies and made comparisons among and impounded and un-impounded (open) 1836 sites. He also examined how accretion may vary with distance inland from the Gulf of Mexico 1837 in the areas of Sabine basin and Rockefeller WMA. 1838 1839 1840 Table C3. A comparison of ranges of long-term accretion estimates (137Cs) from impounded and un-1841 impounded brackish marsh sites in the Chenier Plain (summarized in Steyer 2008). 1842

Hydrology

Impounded accretion rate

Un-impounded accretion rate Source

cm yr-1 cm yr-1

0.33-0.64 0.27-0.58 Foret 1997

0.31-0.38 0.46-0.54 Foret 2001

0.16-0.38 0.28-0.35 Phillips 2002

0.31-0.60 0.26-0.50 Steyer 2008

1843 1844

1) Impounded and un-impounded marshes have the capacity to accrete up to 0.6 cm yr-1 in 1845 the Chenier Plain. 1846

2) Sabine Basin: Steyer (2008) concluded accretion in the Sabine basin was significantly 1847 greater in un-impounded (0.51±0.05 cm yr-1) than impounded (0.36±0.02 cm yr-1) marsh. 1848 However, along a gradient inland from the coast (0-8 km) there was no discernable 1849 difference in accretion rate. 1850

3) Rockefeller WMA: The opposite was true of the Rockefeller study area, where Steyer 1851 (2008) found a significant effect of distance inland on accretion which was highest close 1852 to the gulf (0.7-0.8 cm yr-1) and decreased (0.4-0.5 cm yr-1) 8 km inland. The 1853 impoundments only occurred far inland so that impoundment effects could not be 1854 adequately evaluated. 1855 1856

Shorter-term, Chenier Plain patterns, marker horizon accretion (Table 4): This is a statistical 1857 summary of accretion (using feldspar), vertical elevation change, and subsidence rates measured 1858 through 2010 at more than 120 Coast-wide Reference Monitoring Stations in Southwest 1859 Louisiana. These CRMS data are available at www.lacoast.com/crms_viewer and were compiled 1860 by Tommy McGinnis, OCPR Lafayette Field Office. 1861 1862

1) Examining the recent CRMS data, mean vertical accretion among marsh types range 1863 from 0.68 to 1.01 cm yr-1. 1864

2) Mean elevation change among these wetlands is less than 0.65 cm yr-1. 1865 3) If we integrate all marsh types together, the mean accretion, elevation change, and 1866

57

Table C4. Summary statistics of recent elevation change data from the Coast-wide Reference Monitoring Stations (CRMS) in 1867 freshwater (F), intermediate (I), brackish (B) and salt (S) marshes in the Chenier Plain. Some stations have different record lengths, 1868 and the data are representative of conditions through the year 2010 (Data compiled from www.lacoast.com/crms_viewer by T. 1869 McGinnis, OCPR Lafayette field office). 1870 1871

Marsh Elevation (ft., NAVD88)

Vertical Accretion (cm/yr.)

Elevation Change (cm/yr.)

Subsidence (cm/yr.)

(ft.,

N

AV

D88

) A

ccre

tion

(cm

/yr.)

ch

ange

(c

m/y

r.)

Subs

iden

ce

(cm

/yr.)

Marsh type F I B S F I B S F I B S F I B S

All marsh types combined

# stations 20 53 44 9 16 39 39 7 20 49 44 9 16 38 39 7 126 101 122 100

mean 1.12 1.21 1.31 1.24 1.01 1.06 0.68 0.80 0.00 0.58 0.30 0.65 0.65 0.43 0.29 0.19 1.23 0.89 0.39 0.39

median 1.12 1.24 1.36 1.27 0.89 1.03 0.76 1.12 -0.01 0.58 0.38 0.66 0.38 0.42 0.46 0.58 1.27 0.83 0.50 0.45

std deviation 0.53 0.52 0.34 0.40 1.08 2.30 0.93 0.76 1.79 1.26 0.68 0.73 1.32 2.40 1.01 1.01 0.46 1.61 1.17 1.70

1872

58

subsidence rates are 0.89, 0.39, and 0.39 cm yr-1, respectively. 1873 4) Shallow subsidence is an important process that affects surface elevation adjustment of 1874

Chenier Plain wetlands. 1875 1876 General Conclusions 1877 1878 The 137Cs dating technique is well accepted and has been used across the coast. The method is 1879 particularly useful for understanding the upper limits of vertical accretion and estimating the 1880 relative contributions of organic and mineral accretion among different marsh types. Examining 1881 the summary data presented by different investigators, marsh types from fresh-to-salt apparently 1882 have a similar capacity for high rates of accretion, on the order of 1.0 cm yr-1 (and even higher in 1883 the streamside habitats). Deteriorating salt marshes (22 cores, 0.98 cm yr-1, Nyman et al. 2006) 1884 seem to have an upper limit threshold of 1.0 cm yr-1. That high accretion is associated with 1885 marshes in a deteriorating landscape may be explained by their topographically low position, 1886 where deposition is favored and organic matter remains saturated. Nyman et al. 2006 suggest that 1887 Louisiana marshes can compensate for a RSLR rate up to about 1.0 cm yr-1. Submergence rates 1888 in excess of this would likely result in a reversion of emergent marsh to open water (also termed 1889 ‘marsh collapse’). 1890 1891 There are limitations on what we can infer with accretion estimates. In the past, accretion 1892 estimates have been used to conclude whether the wetland is ‘keeping up’ with sea level rise. A 1893 problem with this approach is that the elevation of the wetland (with respect to the mean tidal 1894 range or landscape) is unknown or ignored. The practical consequence is that the time to a 1895 critical submergence threshold (with respect to plant health) occurs sooner for a low lying marsh 1896 than that of a higher marsh. In other words, a higher or lower rate of accretion is needed to offset 1897 submergence simply depending on the marsh elevation. Moreover, a high accretion rate may be a 1898 symptom of a relatively low marsh elevation (compensational deposition in low areas) if we 1899 assume over a long time period that peat accretion is in equilibrium with mean water or tide 1900 level. The added problem that accretion cannot predict actual elevation change can be 1901 problematic (Cahoon 2006). General considerations can be made about long-term accretion 1902 estimates and wetland processes: 1903 1904

1) It is generally accepted that vertical soil elevation maintenance (peat accretion), is in 1905 dynamic equilibrium with mean water level (or sea level). 1906

2) Theoretically, accretion rates should vary with elevation within a wetland system (this 1907 relationship is not well established for different Louisiana wetlands). Ultimately, the 1908 elevation of a wetland determines the length of time before it succumbs to a critical 1909 submergence. 1910

3) Long-term accretion may not reliably predict the actual elevation change in high 1911 subsidence (shallow or deep compaction) environments. 1912

1913 A long-term accretion estimate of 0.7 cm yr-1 captures the central tendency of all herbaceous 1914 marsh data that have been reviewed. Currently, there seems to be a lack of evidence to support 1915 applying a habitat specific accretion rate; that is, there is evidence of high accretion rates in both 1916 salt and fresh marshes. The long-term data show that Chenier Plain marshes have accreted over 1917 the last 50 years at rates of ~0.5±0.2 cm yr-1 while shorter-term data (CRMS) shows mean 1918

59

accretion rates of ~0.8 cm yr-1 (Table 4). At 120 stations, median elevation change was 0.50 cm 1919 yr-1 (Table 4). This elevation gain corresponds well with the long-term RSLR rate of 0.56 cm yr-1 1920 measured at Sabine Pass. In other words, wetland elevation gain should be approximately 1921 equivalent to contemporary RSLR rates. 1922 1923 In general, comparatively high RSLR rates in the Delta Plain have produced greater vertical 1924 elevation change and accretion than that observed in the Chenier Plain. Without considering 1925 other stresses to wetland health, Chenier Plain marshes should be stable to RLSR rates on the 1926 order of 0.7 cm yr-1, as herbaceous wetlands in the Chenier Plain should respond similarly to 1927 increasing submergence as those of the Delta Plain. 1928 1929 1930 Literature Cited 1931 1932 Cahoon, D.R. 2006. A review of major storm impacts on coastal wetland elevations. Estuaries 1933

and Coasts. 29:889-898. 1934 DeLaune, R.D., R.H. Baumann, and J.G. Gosselink. 1983. Relationships among vertical 1935

accretion, coastal submergence, and erosion in a Louisiana gulf coast marsh. Journal of 1936 Sedimentary Petrology 53:147-157. 1937

DeLaune, R.D., J.H. Whitcomb, W.H. Patrick, Jr., J.H. Pardue, and S.R. Pezeshki. 1989. 1938 Accretion and canal impacts in a rapidly subsiding wetland. 137Cs and 210Pb techniques. 1939 Estuaries 12:247-259. 1940

Foret. J.D. 1997. Accretion, sedimentation, and nutrient accumulation rates as influenced by 1941 manipulations in marsh hydrology in the Chenier Plain, Louisiana. M.S. Thesis, Univ. of 1942 Louisiana, Lafayette. 1943

Foret. J.D. 2001. Nutrient limitation of tidal marshes on the Chenier Plain, Louisiana. Ph.D. 1944 Dissertation, Univ. of Louisiana, Lafayette. 1945

Jarvis, Jessie. 2010. Vertical accretion rates in coastal Louisiana: A review of the scientific 1946 literature: 1-14. 1947

Morris, J.T., P.V. Sundareshwar, C.T. Nietch, B. Kjerfve, and D.R. Cahoon. 2002. Responses of 1948 coastal wetlands to rising sea level. Ecology. 83:2869-2877. 1949

Nyman, J.A., R.J. Walters, R.D. DeLaune, and W.H. Patrick, Jr. 2006. Marsh vertical accretion 1950 via vegetative growth. Estuarine, Coastal and Shelf Science 69:370-380. 1951

Phillips, L.A. 2002. Vertical accretion and marsh elevation dynamics on the Chenier Plain, 1952 Louisiana. M.S. Thesis, Univ. of Louisiana, Lafayette. 1953

Piazza, S.C., G.D. Steyer, K.F. Cretini, C.E. Sasser, J.M. Visser, G. O. Holm, Jr., L. A. Sharp, D. 1954 E. Evers, and J. R. Meriwether. In press. Geomorphic and ecological effects of 1955 Hurricanes Katrina and Rita on Coastal Louisiana Marsh Communities: U.S. Geological 1956 Survey Open File Report. 1957

Steyer, G.D. 2008. Landscape analysis of vegetation change in coastal Louisiana following 1958 Hurricanes Katrina and Rita. Louisiana State Univ. Ph.D. Dissertation. 158p. 1959

Turner, R.E., E.M. Swenson, and C.S. Milan. 2001. Organic and inorganic contributions to 1960 vertical accretion in salt marsh sediments. Pgs. 583-595. In: M. Weinstein and K. Kreeger 1961 (eds.) Concepts and Controversies in Tidal Marsh Ecology. Kluwer Academic 1962 Publishing, Drodrecht, Netherlands. 1963

1964

60

Appendix D: Detailed Procedure for Incorporating Sea-Level Rise into Louisiana Coastal Project 1965 Planning and Design 1966

1967 The following is an example of how relative sea-level rise would be calculated using the 1968 recommendations in the main report and can be used as a stand-alone instruction manual. 1969 1970 To predict the future relative sea-level rise for programmatic planning and project-level 1971 engineering and design, we recommend the following four-step procedure. To describe this 1972 more fully we use a hypothetical wetland restoration project on Marsh Island State Wildlife 1973 Management Area as an example, with a construction date of 2015 and a period of study that 1974 carries through 2100. The recommendations presented in the Technical Report begin with a 1975 three-step process to define relative sea-level rise (RSLR), represented by the generalized 1976 equation 1977 1978

E(t) = (a*t + b*t2) + S*t (Eqn. D1) 1979 1980

where E is RSLR over the time increment t, 1981 a is the historical linear rate of global sea-level rise (GSLR) (Step 1), 1982 b is the acceleration constant for predicted GSLR (Step 2), and 1983 S is rate of subsidence (or uplift in areas of glacial rebound) (Step 3). 1984

1985 Specifically, the equation illustrates that in order to calculate RSLR through 2065, the first 1986 component needed is a prediction of the change in sea (Gulf of Mexico) surface elevation. 1987 Subsidence is added to this value to define the total change in elevation of that location relative 1988 to the water surface. 1989 1990 The first two steps of the recommendations apply to the change in elevation of the Gulf of 1991 Mexico sea surface, and define the values for the function in Eqn. D1 represented by (a*t + b*t2). 1992 However, for actual predictions of future RSLR at a specific location, we use a detailed equation 1993 that acknowledges that most sea-level predictions use a starting point in 1986 (the starting point 1994 for most GSLR scenarios). 1995 1996

E(t2-1986)-E(t1-1986)=a([t2-1986]-[t1-1986])+b(([t2-1986]2-[t1-1986]2 (Eqn. D2) 1997 1998 where E, a and b are as defined for Figure D1, 1999

t1 is the initial year or first year of project, and 2000 t2 is the final year or last year of project. 2001 2002

Although for our scenario the t1 and t2 values for the operational equation would be 2015 and 2003 2100, respectively, we recommend building the GSLR scenario starting at 1986, to more 2004 properly define the acceleration constant (b) as well as providing a valuable check on the 2005 calculations out to 2100. 2006 2007

E(2100-1986) - E(1986-1986) = a*([2100-1986]-[1986-1986]) + b*(([2100-1986]2-2008 [1986-1986]2), 2009

2010 which simplifies to 2011

61

2012 E 2015-2100 = a*114 + b*12996. (Eqn. D3) 2013

2014 This equation becomes the base operating equation for applying the values to the variables as 2015 described in our recommendations. 2016 2017

1. To begin the two-step process for estimating the future rate of change in the absolute sea 2018 level of the Gulf of Mexico, we need to establish the current rate of sea-level rise, based 2019 on aggregate GSLR data from the satellite altimetry. As Figure D1 demonstrates, the rate 2020 of SLR varies across the coast of Louisiana from west to east by as much as 58%. 2021 2022

2023

2024 2025 Figure D1. Sea-level rise rates for points offshore of coastal Louisiana derived from 1992-2010 satellite 2026 altimetry as described in Figure 12 of the main report. The circle indicates the location of the 2027 hypothetical project at Marsh Island that is the basis of the example calculations in this appendix. 2028

2029 2030 To account for the substantial east-west variation in near-shore sea-level rise observed in 2031 the recent satellite altimetry record, we recommend using the local observations of SLR 2032 from contemporary satellite altimetry most proximate to the site of interest. In the case of 2033 the hypothetical Marsh Island project, the two most proximate offshore SLR values range 2034 from 2.62 to 2.69 mm/yr and provide a mean value of 2.66 mm/yr (rounded, or 0.00266 2035 m/yr), which is applied as the value for the variable (a) in Equation D3. Note that in the 2036 update shown below, the subsidence term is omitted, since the first two steps are only 2037 interested in calculating the anticipated change in the sea surface of the Gulf of Mexico, 2038 and not fully calculating RSLR. 2039

62

2040 E2015-2100 = 0.00266*114 + b*12996, which simplifies to 2041 2042 E2015-2100 = 0.30324 + b*12996. (Eqn. D4) 2043

2044 2. We recommend that CPRA staff assume that Gulf sea-level rise will be 1 meter (3.3’) by 2045

2100 as the most heavily-weighted project alternative. To account for variability around 2046 that prediction given the debate in the scientific community, we recommend a bounding 2047 range of 0.5 – 1.5 meters (1.6’ – 4.9’) also be considered. 2048 2049 These recommendations establish three a priori values for E. In doing so, and as a result 2050 of having defined (a) in the previous step, we must calculate the relevant acceleration 2051 constants (b) for each scenario. 2052

2053 • For the 0.5 meters scenario: 2054

2055 0.5 = 0.30324 + b*12996, which simplifies to b = 1.514 x 10-5. 2056

2057 • For the 1.0 meters scenario: 2058

2059 1.0 = 0.30324 + b*12996, which simplifies to b = 5.361 x 10-5. 2060

2061 • For the 1.5 meters scenario: 2062

2063 1.5 = 0.30324 + b*12996, which simplifies to b = 9.209 x 10-4. 2064

2065 The two steps completed so far establish the values for (a) and (b) in GSLR predictive function 2066 shown in Equation D2. We can then use that function, with the included parameter values, to 2067 define the GSLR curve from 1986 to 2100. The importance in defining that curve is to calculate 2068 the annual incremental increases in Gulf sea-surface elevation. Since Steps 1 and 2 only provide 2069 an estimate of the elevation of the Gulf of Mexico at the target point in the future, these 2070 calculations must be combined with a prediction of local subsidence in order to craft an RSLR 2071 curve that will be used to predict marsh vertical accretion and overall persistence/collapse of 2072 marsh. We recommend: 2073 2074

3. Applying these calculations to either spatially-explicit empirical observations of 2075 subsidence or a map of predicted subsidence rates. In the case of our hypothetical Marsh 2076 Island project, the range of subsidence values identified by the Master Plan revision team 2077 for that area, Zone 15 on Figure D2, is 1-15 mm/yr. Applying the Master Plan “most 2078 plausible” scenario of 20% into each zone shown in Figure D2, we calculate a value of 2079 3.8 mm/yr. Functionally, 3.8 mm are added onto the annual incremental GSLR defined 2080 after Steps 1 and 2, in order to define the annual incremental RSLR. 2081

2082 This highlights some of the concern with Figure D2 as an operational product. If instead we 2083 were to use the coastal subsidence map generated by Britsch in 2007 (Figure 27 in the Technical 2084

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2085 2086 Figure D2. Map of projected subsidence ranges for south Louisiana generated by the Subsidence Advisory Panel for the Louisiana CPRA Master 2087 Plan 2012 Update, following a meeting on 14 October 2010. 2088

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Report), we would instead use a subsidence value of 0.5 feet per 100 years, which is equivalent 2089 to 1.524 mm/yr. The Britsch value is only 39% of that shown in Figure D2, which could have 2090 significant effects on the calculation of the annual RSRL increment that is applied to the marsh 2091 vegetation accretion model described in Step 4. Much more work will need to be done to refine 2092 the subsidence estimates of subsidence at the local level for predictive modeling purposes. 2093 2094 Following steps 1-3 yields a RSLR curve for Marsh Island for each of the three acceleration 2095 scenarios discussed in Step 2. These curves will then be compared to marsh collapse values 2096 established for the Chenier Plan, as exemplified in Appendix C. For this example, we will only 2097 calculate the RSLR curve for the primary recommendation of a 1-meter GSLR by the year 2100. 2098 Part of the template spreadsheet that LACES has created for this purpose is shown in Figure D3 2099 below. 2100 2101 2102

2103 2104 Figure D3. Screen capture of the spreadsheet that LACES has drafted for calculating RSLR curves 2105 consistent with the four-step process recommended in the Technical Report. In this example, the 2106 historical linear GSLR rate (green) is combined with the acceleration constant associated with a 1-meter 2107 GSLR by 2100 (red) to establish annual incremental increases in Gulf sea-surface elevation (blue). That 2108 calculation is then combined with an estimate of subsidence from Figure D2 (brown) to establish a year-2109 to-year incremental RSLR value (yellow). 2110 2111 2112

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The annual incremental RSLR values for this example scenario range from 9.2 to 18.6 mm/yr 2113 (0.36 to 0.73 inches/yr), and the models predict a total increase in relative sea level of 0.613 2114 meters (2.01 feet) between 2015 and 2065. In order to predict the persistence the coastal 2115 wetland, and specifically the persistence of the wetland surface or conversely marsh surface 2116 collapse and drowning, a fourth step is necessary. 2117 2118

4. Use the sum of #s 1-3 above to establish the annual incremental rate of inundation for the 2119 period of analysis to predict local responses of marsh vertical accretion; essentially local 2120 coastal elevation.. Marsh vertical accretion can be inferred from scientific literature if no 2121 reliable data exist on site, or can be estimated from vegetation productivity models if 2122 available. Appendix C of the Technical Report shows information of this type that was 2123 used by CPRA and U.S. Army Corps personnel conducting the Southwest Coastal 2124 Integrated Hurricane Protection and Coastal Restoration Feasibility Study. 2125 2126 In this example, if we assume the 7 mm/yr (0.28 inches/yr) threshold for marsh plant 2127 vertical accretion used by the Southwest Coastal Feasibility Study team, the models 2128 predict that the marsh will not be able to keep up with projected RSLR. Theoretically the 2129 calculations show how much elevation needs to be added through the use of marsh 2130 creation or nourishment in order to accommodate continued marsh vertical accretion, and 2131 thus marsh persistence in the face of RSLR. 2132 2133