A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 μg L−1. Most of the observations are from Case I nonpolar waters, and ∼20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal: noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.

/pdf/ocean-color-chlorophyll-algorithms-for-seawifs-68regm2asj.pdf

Ocean Color Chlorophyll Algorithms for SEAWIFS

BOAK, E.H. and TURNER, I.L., 2005. Shoreline Definition and Detection: A Review. Journal of Coastal Research, 21(4), 688‐703. West Palm Beach (Florida), ISSN 0749-0208. Analysis of shoreline variability and shoreline erosion-accretion trends is fundamental to a broad range of investigations undertaken by coastal scientists, coastal engineers, and coastal managers. Though strictly defined as the intersection of water and land surfaces, for practical purposes, the dynamic nature of this boundary and its dependence on the temporal and spatial scale at which it is being considered results in the use of a range of shoreline indicators. These proxies are generally one of two types: either a feature that is visibly discernible in coastal imagery (e.g., highwater line [HWL]) or the intersection of a tidal datum with the coastal profile (e.g., mean high water [MHW]). Recently, a third category of shoreline indicator has begun to be reported in the literature, based on the application of imageprocessing techniques to extract proxy shoreline features from digital coastal images that are not necessarily visible to the human eye. Potential data sources for shoreline investigation include historical photographs, coastal maps and charts, aerial photography, beach surveys, in situ geographic positioning system shorelines, and a range of digital elevation or image data derived from remote sensing platforms. The identification of a ‘‘shoreline’’ involves two stages: the first requires the selection and definition of a shoreline indicator feature, and the second is the detection of the chosen shoreline feature within the available data source. To date, the most common shoreline detection technique has been subjective visual interpretation. Recent photogrammetry, topographic data collection, and digital image-processing techniques now make it possible for the coastal investigator to use objective shoreline detection methods. The remaining challenge is to improve the quantitative and process-based understanding of these shoreline indicator features and their spatial relationship relative to the physical land‐water boundary.

/pdf/shoreline-definition-and-detection-a-review-41qp0ofrkp.pdf

Shoreline Definition and Detection: A Review

Do Not Cite, Quote or Distribute 4-1 Total pages: 139 1 Chapter 4: Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities 2 3 Coordinating Lead Authors: Michael Oppenheimer (USA), Bruce Glavovic (New Zealand), Tuhin Ghosh 4 (India) 5 6 Lead Authors: Amro Abd-Elgawad (Egypt), Rongshuo Cai (China), Miguel Cifuentes-Jara (Costa Rica), 7 Rob Deconto (USA), John Hay (Cook Islands), Jochen Hinkel (Germany), Federico Isla (Argentina), 8 Alexandre K. Magnan (France), Ben Marzeion (Germany), Benoit Meyssignac (France), Zita Sebesvari 9 (Hungary), AJ Smit (South Africa), Roderik van de Wal (Netherlands) 10 11 Contributing Authors: Maya Buchanan (USA), Gonéri Le Cozannet (France), Catia Domingues 12 (Australia), Virginie Duvat (France), Tamsin Edwards (UK), Miguel D. Fortes (Philippines), Thomas 13 Frederikse (Netherlands), Jean-Pierre Gattuso (France), Robert Kopp (USA), Erwin Lambert (Netherlands), 14 Elizabeth McLeod (USA), Mark Merrifield (USA), Siddharth Narayan (US), Robert J. Nicholls (UK), 15 Fabrice Renaud (UK), Jonathan Simm (UK), Jon Woodruff (USA), Poh Poh Wong (Singapore), Siyuan Xian 16 (USA) 17 18 Review Editors: Ayako Abe-Ouchi (Japan), Kapil Gupta (India), Joy Pereira (Malaysia) 19 20 Chapter Scientist Maya Buchanan (USA) 21 22 Date of Draft: 20 April 2018 23 24 Notes: TSU Compiled Version 25 26

/pdf/sea-level-rise-and-implications-for-low-lying-islands-coasts-1ptd3trc7d.pdf

Sea Level Rise and Implications for Low Lying Islands, Coasts and Communities

Author Posting. © Annual Reviews, 2007. This is the author's version of the work. It is posted here by permission of Annual Reviews for personal use, not for redistribution. The definitive version was published in Annual Review of Earth and Planetary Sciences 36 (2008): 601-647, doi:10.1146/annurev.earth.35.031306.140139.

/pdf/coastal-impacts-due-to-sea-level-rise-3e2h8eh3jm.pdf

Coastal Impacts Due to Sea-Level Rise

One of the most certain consequences of global warming is an increase of global (eustatic) sea level. The resulting inundation from rising seas will heavily impact low-lying areas; at least 100 million persons live within one meter of mean sea level and are at increased risk in the coming decades. The very existence of some island states and deltaic coasts is threatened by sea level rise. An additional threat affecting some of the most heavily developed and economically valuable real estate will come from an exacerbation of sandy beach erosion. As the beach is lost, fixed structures nearby are increasingly exposed to the direct impact of storm waves, and will ultimately be damaged or destroyed unless expensive protective measures are taken. It has long been speculated that the underlying rate of long-term sandy beach erosion is two orders of magnitude greater than the rate of rise of sea level, so that any significant increase of sea level has dire consequences for coastal inhabitants. We present in this paper an analytical treatment that indicates there is a highly multiplicative association between long-term sandy beach erosion and sea level rise, and use a large and consistent data base of shoreline position field data to show that there is reasonable quantitative agreement with observations of 19th and 20th century sea levels and coastal erosion. This result means that the already-severe coastal erosion problems witnessed in the 20th century will be exacerbated in the 21st century under plausible global warming scenarios.

Global Warming and Coastal Erosion

The United States Federal Emergency Management Agency (FEMA) is assessing technical methodologies and procedures for the collection, analysis, and computation of coastal erosion rates. This assessment is being performed to determine the feasibility of generating such data for use as a basis for administering Section 544 of the Housing and Community Development Act of 1987 (commonly known as the Upton-Jones amendment to the National Flood Insurance Program). It is likely that the methodology selected will involve the use of historical shoreline data compared with current shoreline information. This fundamental approach of predicting shoreline location based on extrapolation of past changes has been utilized by several states in developing data to support setback programs. Requirements contemplated for data development include: (1) standardization of a methodology for developing erosion rate data, (2) consistency in application, (3) capability of being applied in a timely manner, and (4) accuracy commensurate with program needs. This paper largely addresses the last issue by examining sources of errors inherent in the raw data. Source data include historical and recent National Ocean Service (NOS) T-sheets (produced ca. 1840's to present) and air photos (taken ca. late 1930's to present) as well as any other types of accurate map and photographic data. A study of this nature requires extensive preparation of the required source material prior to digitization. For example, the high water line (HWL) or bluff line must be identified and annotated on the air photos and maps. Furthermore, the source material must undergo extensive pre-digitization and post-data compilation accuracy checks and reliability assessments necessary to screen inaccurate maps and air photos. For example, older historical maps with obsolete coordinates must be updated to current standards and analyzed to insure that distortion has not rendered the map inaccurate and unusable. In addition, computerized techniques (e.g., Metric Mapping, analytical stereoplotter) or manual techniques (e.g., Zoom Transfer Scope) must be used to correct for distortion inherent in aerial photography.

/pdf/historical-shoreline-change-error-analysis-and-mapping-152v3h1zy2.pdf

Historical Shoreline Change: Error Analysis and Mapping Accuracy

Coastal development is proceeding rapidly in the United States while at the same time coastal erosion is almost ubiquitous. The result is that dollar losses due to coastal storms and flooding are reaching economically intolerable levels. Avoiding even larger losses in the future requires effective land use management practices. These typically take the form of building setbacks to serve as protection for a time comparable to the expected lifetime of new coastal structures, usually 30 or 60 years. However, determining adequate setbacks requires estimating long-term shoreline change trends from temporally poorly sampled historical shoreline position data bases. As such, it is important to recognize these data limitations so that faulty analyses and potentially costly errors of prediction can be minimized or avoided. Since there is a well-known relationship between long-term shore retreat and sea level rise, we have used temporally complete sea level records as surrogate data sets to evaluate various shoreline prediction algorithms (e.g., end point method, linear regression, and a technique based on the minimum description length criterion). Predictions from subsets of sea level data sampled temporally to mimic shoreline data sets are compared to actual sea level values. It has been found that in a clear majority of cases, linear regression gives superior results. Predictions shaped or influenced by higher-order polynomial schemes can sometimes be superior to those obtained from linear regressions, but they can also be extremely inaccurate.

/pdf/on-forecasting-future-u-s-shoreline-positions-a-test-of-3qjbnpsto5.pdf

On Forecasting Future U.S. Shoreline Positions: A Test of Algorithms

CROWELL, M.; COULTON, K.; JOHNSON, C.; WESTCOTT, J.; BELLOMO, D.; EDELMAN, S., and HIRSCH, E., 2010. An estimate of the U.S. population living in 100-year coastal flood hazard areas. Journal of Coastal Research, 26(2), 201– 211. West Palm Beach (Florida), ISSN 0749-0208. The Federal Emergency Management Agency (FEMA) recently completed a coastal demographics study of the United States and U.S. territories. As part of this study, FEMA estimated the United States population subject to the 1% annual chance (100 y) coastal flood hazard as mapped by FEMA. This determination followed a three-step process: (1) create a national digital flood hazard database by compiling the best available coastal-proximate, digital flood-hazard-area data using FEMA data sets; (2) develop a systematic method to separate coastal and riverine flood hazard areas and incorporate this boundary into the digital flood hazard database; and (3) combine the year 2000 census data with the digital flood hazard database using a geographic information system. This enabled estimates of the U.S. population subject to the 1% annual chance coastal flood. The analysis was conducted at the census block-group level, with census block-group populations (permanent residents) assumed to be uniformly distributed across each block group. The results demonstrate that approximately 3.0% of the U.S. population lives in areas subject to the 1% annual chance coastal flood hazard. It must be emphasized, however, that these numbers are based on the 1% annual chance (100 y) coastal flood. Historical coastal floods less frequent than the 1% chance annual flood have occurred in the U.S. on numerous occasions. If less-frequent coastal flood events were considered in this study, such as the 0.2% annual chance (500 y) coastal flood or, if seasonal (vacations) population were considered, then a much greater percentage of the U.S. population would be determined as subject to coastal flooding.

https://bioone.org/journals/Journal-of-Coastal-Research/volume-2010/issue-262/JCOASTRES-D-09-00076.1/An-Estimate-of-the-US-Population-Living-in-100-Year/10.2112/JCOASTRES-D-09-00076.1.pdf

An Estimate of the U.S. Population Living in 100-Year Coastal Flood Hazard Areas

CROWELL et al. (1997), using series of sparsely sampled sea-level values as surrogate data for shoreline change evaluated several well-known shoreline position prediction algorithms. They concluded that in the absence of physical changes such as opening of inlets or shore engineering, linear regression over the longest possible period was the most reliable predictor of shoreline trends for extended intervals (30+ years). They also noted that shorelines, like sea-level, have unpredictable interannual and longer quasi-periodic fluctuations that can mask an underlying trend for many years. Thus an effective prediction algorithm for predicting shoreline position at all temporal scales must reflect persistence of these variations while at the same time correctly accounting for the underlying long-term trend. Successful interpretation of shoreline behavior and prediction of future position requires knowledge of the nature and impact of past erosional events, particularly due to major storms. A simple mathematical model that mimics many of the characteristics of shoreline position variation and real shoreline position data from Delaware between 1845 -1993 are employed to illustrate the difficulties of the prediction problem. The northeaster of March 1962, the largest in this century, provides a revealing case study of the response of a shoreline to a severe storm event. The effect of this storm, which lasted through five high tides, was to "overshoot" the long-term trend of erosion by a very large amount, with subsequent accretion taking place for a decade or longer back toward the position predicted by the underlying long-term (~150 year) trend. Thus for a long time, the beach appeared to be accreting rather than eroding. Long-term planning, such as for 30 or 60 year building setbacks, requires the most careful attention to the long-term erosion trend and the historical record of storms, including their impacts on the shoreline position and beach recovery.

/pdf/considerations-for-shoreline-position-prediction-vqufpwu6by.pdf

Considerations for shoreline position prediction

https://bioone.org/journals/journal-of-coastal-research/volume-23/issue-5/07A-0017.1/How-Many-People-Live-in-Coastal-Areas1/10.2112/07A-0017.1.pdf

How Many People Live in Coastal Areas?1

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