Methodology for improving the analysis, interpretation, and geo-visualisation of erosion rates in coastal beaches - Andalusia, Southern Spain

Erosion is one of the major issues currently facing coastal areas. Some consequences of this process are beach loss and higher flood risk, which will likely be exacerbated given ongoing sea-level rise. With this in mind, those responsible for conservation and management decisions need appropriate tools with which to identify critical coastal areas, as well as to analyse, interpret, and visualise them with the appropriate geomorphological and environmental background. The aim of this work was to present a methodology for improving the analysis and interpretation of coastal erosion rates, as well as to guarantee wide access and dissemination of erosion data. To that end, an approach for the production, management, and dissemination of shoreline erosion data for the Andalusian coast in Southern Spain was developed. This approach enables the analysis and interpretation of the erosion rates in coasts by linking erosion rates with geomorphological and thematic information using a data model. Additionally, this methodology was proven to be a valid and appropriate tool for the design of a web-based viewer, being the best way to represent the erosion rates obtained every 50 m of shore for the entire Andalusian coast, being an exposed coastal front 917 km long. This is particularly useful for integrated coastal zone management schemes, enabling quick and easy access to valuable information

Shoreline change along sheltered coastlines: Insights from the neuse river estuary, NC, USA

Coastlines are constantly changing due to both natural and anthropogenic forces, and climate change and associated sea level rise will continue to reshape coasts in the future. Erosion is not only apparent along oceanfront areas; shoreline dynamics in sheltered water bodies have also gained greater attention. Additional estuarine shoreline studies are needed to better understand and protect coastal resources. This study uses a point-based approach to analyze estuarine shoreline change and associated parameters, including fetch, wave energy, elevation, and vegetation, in the Neuse River Estuary (NRE) at two contrasting scales, Regional (whole estuary) and Local (estuary partitioned into eight sections, based on orientation and exposure). With a mean shoreline-change rate of -0.58 m yr-1, the majority (93%) of the NRE study area is eroding. Change rates show some variability related to the land-use land-cover classification of the shoreline. Although linear regression analysis at the Regional Scale did not find significant correlations between shoreline change and the parameters analyzed, trends were determined from Local Scale data. Specifically, erosion rates, fetch, and wave exposure increase in the down-estuary direction, while elevation follows the opposite trend. Linear regression analysis between mean fetch and mean shoreline-change rates at the Local Scale provide a first-order approach to predict shoreline-change rates. The general trends found in the Local Scale data highlight the presence of underlying spatial patterns in shoreline-change rates within a complex estuarine system, but Regional Scale analysis suggests shoreline composition also has an important influence

RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression

An important task in astroparticle physics is the detection of periodicities in irregularly sampled time series, called light curves. The classic Fourier periodogram cannot deal with irregular sampling and with the measurement accuracies that are typically given for each observation of a light curve. Hence, methods to fit periodic functions using weighted regression were developed in the past to calculate periodograms. We present the R package RobPer which allows to combine different periodic functions and regression techniques to calculate periodograms. Possible regression techniques are least squares, least absolute deviations, least trimmed squares, M-, S- and τ -regression. Measurement accuracies can be taken into account including weights. Our periodogram function covers most of the approaches that have been tried earlier and provides new model-regression-combinations that have not been used before. To detect valid periods, RobPer applies an outlier search on the periodogram instead of using fixed critical values that are theoretically only justified in case of least squares regression, independent periodogram bars and a null hypothesis allowing only normal white noise. Finally, the package also includes a generator to generate artificial light curves

Importance of coastal change variables in determining vulnerability to sea- and lake-level change

This paper is not subject to U.S. copyright. The definitive version was published in Journal of Coastal Research 26 (2010): 176-183, doi:10.2112/08-1102.1.In 2001, the U.S. Geological Survey began conducting scientific assessments of coastal vulnerability to potential future sea- and lake-level changes in 22 National Park Service sea- and lakeshore units. Coastal park units chosen for the assessment included a variety of geological and physical settings along the U.S. Atlantic, Pacific, Gulf of Mexico, Gulf of Alaska, Caribbean, and Great Lakes shorelines. This research is motivated by the need to understand and anticipate coastal changes caused by accelerating sea-level rise, as well as lake-level changes caused by climate change, over the next century. The goal of these assessments is to provide information that can be used to make long-term (decade to century) management decisions. Here we analyze the results of coastal vulnerability assessments for several coastal national park units. Index-based assessments quantify the likelihood that physical changes may occur based on analysis of the following variables: tidal range, ice cover, wave height, coastal slope, historical shoreline change rate, geomorphology, and historical rate of relative sea- or lake-level change. This approach seeks to combine a coastal system's susceptibility to change with its natural ability to adapt to changing environmental conditions, and it provides a measure of the system's potential vulnerability to the effects of sea- or lake-level change. Assessments for 22 park units are combined to evaluate relationships among the variables used to derive the index. Results indicate that Atlantic and Gulf of Mexico parks have the highest vulnerability rankings relative to other park regions. A principal component analysis reveals that 99% of the index variability can be explained by four variables: geomorphology, regional coastal slope, water-level change rate, and mean significant wave height. Tidal range, ice cover, and historical shoreline change are not as important when the index is evaluated at large spatial scales (thousands of kilometers)

Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Lentz, E. E., Plant, N. G., & Thieler, E. R. Relationships between regional coastal land cover distributions and elevation reveal data uncertainty in a sea-level rise impacts model. Earth Surface Dynamics, 7(2), (2019):429-438, doi:10.5194/esurf-7-429-2019.Understanding land loss or resilience in response to sea-level rise (SLR) requires spatially extensive and continuous datasets to capture landscape variability. We investigate the sensitivity and skill of a model that predicts dynamic response likelihood to SLR across the northeastern US by exploring several data inputs and outcomes. Using elevation and land cover datasets, we determine where data error is likely, quantify its effect on predictions, and evaluate its influence on prediction confidence. Results show data error is concentrated in low-lying areas with little impact on prediction skill, as the inherent correlation between the datasets can be exploited to reduce data uncertainty using Bayesian inference. This suggests the approach may be extended to regions with limited data availability and/or poor quality. Furthermore, we verify that model sensitivity in these first-order landscape change assessments is well-matched to larger coastal process uncertainties, for which process-based models are important complements to further reduce uncertainty.This research was funded by the U.S. Geological Survey Coastal and Marine Geology Program. We thank P. Soupy Dalyander for early reviews and discussion of this paper. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government

Using a Bayesian network to predict barrier island geomorphologic characteristics

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Earth Surface 120 (2015): 2452–2475, doi:10.1002/2015JF003671.Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.U.S. Geological Survey (USGS) Coastal and Marine Geology Program; U.S. Fish and Wildlife Servic

Geologic framework of the northern North Carolina, USA inner continental shelf and its influence on coastal evolution

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Geology 348 (2014): 113-130, doi:10.1016/j.margeo.2013.11.011.The inner continental shelf off the northern Outer Banks of North Carolina was mapped using sidescan sonar, interferometric swath bathymetry, and high-resolution chirp and boomer subbottom profiling systems. We use this information to describe the shallow stratigraphy, reinterpret formation mechanisms of some shoal features, evaluate local relative sea-levels during the Late Pleistocene, and provide new constraints, via recent bedform evolution, on regional sediment transport patterns. The study area is approximately 290 km long by 11 km wide, extending from False Cape, Virginia to Cape Lookout, North Carolina, in water depths ranging from 6 to 34 m. Late Pleistocene sedimentary units comprise the shallow geologic framework of this region and determine both the morphology of the inner shelf and the distribution of sediment sources and sinks. We identify Pleistocene sedimentary units beneath Diamond Shoals that may have provided a geologic template for the location of modern Cape Hatteras and earlier paleo-capes during the Late Pleistocene. These units indicate shallow marine deposition 15–25 m below present sea-level. The uppermost Pleistocene unit may have been deposited as recently as Marine Isotope Stage 3, although some apparent ages for this timing may be suspect. Paleofluvial valleys incised during the Last Glacial Maximum traverse the inner shelf throughout the study area and dissect the Late Pleistocene units. Sediments deposited in the valleys record the Holocene transgression and provide insight into the evolutionary history of the barrier-estuary system in this region. The relationship between these valleys and adjacent shoal complexes suggests that the paleo-Roanoke River did not form the Albemarle Shelf Valley complex as previously proposed; a major fluvial system is absent and thus makes the formation of this feature enigmatic. Major shoal features in the study area show mobility at decadal to centennial timescales, including nearly a kilometer of shoal migration over the past 134 yr. Sorted bedforms occupy ~ 1000 km2 of seafloor in Raleigh Bay, and indicate regional sediment transport patterns between Capes Hatteras and Lookout that help explain long-term sediment accumulation and morphologic development. Portions of the inner continental shelf with relatively high sediment abundance are characterized by shoals and shoreface-attached ridges, and where sediment is less abundant, the seafloor is dominated by sorted bedforms. These relationships are also observed in other passive margin settings, suggesting a continuum of shelf morphology that may have broad application for interpreting inner shelf sedimentation patterns.Funding for this research was provided by the USGS Coastal and Marine Geology Program

Bridging groundwater models and decision support with a Bayesian network

Author Posting. © American Geophysical Union, 2013. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Water Resources Research 49 (2013): 6459–6473, doi:10.1002/wrcr.20496.Resource managers need to make decisions to plan for future environmental conditions, particularly sea level rise, in the face of substantial uncertainty. Many interacting processes factor in to the decisions they face. Advances in process models and the quantification of uncertainty have made models a valuable tool for this purpose. Long-simulation runtimes and, often, numerical instability make linking process models impractical in many cases. A method for emulating the important connections between model input and forecasts, while propagating uncertainty, has the potential to provide a bridge between complicated numerical process models and the efficiency and stability needed for decision making. We explore this using a Bayesian network (BN) to emulate a groundwater flow model. We expand on previous approaches to validating a BN by calculating forecasting skill using cross validation of a groundwater model of Assateague Island in Virginia and Maryland, USA. This BN emulation was shown to capture the important groundwater-flow characteristics and uncertainty of the groundwater system because of its connection to island morphology and sea level. Forecast power metrics associated with the validation of multiple alternative BN designs guided the selection of an optimal level of BN complexity. Assateague island is an ideal test case for exploring a forecasting tool based on current conditions because the unique hydrogeomorphological variability of the island includes a range of settings indicative of past, current, and future conditions. The resulting BN is a valuable tool for exploring the response of groundwater conditions to sea level rise in decision support.This work was funded by the USGS Climate and Land Use Mission Area, Research and Development Program and the USGS Natural Hazards Mission Area, Coastal and Marine Geology Program

Chalk cliff retreat in East Sussex and Kent 1870s to 2001

The retreat of chalk cliffs fringing the eastern English Channel contributes shingle to the beaches which helps to protect the cliffs and slow down erosion. Conversely, cliff retreat endangers settlements and infrastructure on the clifftop. Rates of retreat have been calculated by a variety of methods over the past century, but no attempt has been made to provide a complete coverage that allows for a true comparison of retreat rates over the entire coastline. Using historic maps and recent orthophotos, cliff retreat rates have been calculated for consecutive 50 m sections of chalk cliff along the English side of the entire eastern English Channel for a period of 125 years. The chalk cliffs of East Sussex erode at an average rate of 0.25 - 0.3 m y−1 while those in Kent at a rate of 0.1 m y−1