Geology topics

Patrick W. Limber

Publications and source records attributed to Patrick W. Limber.

13 recordsLinked to original sources

USGS2019

Dynamic flood modeling essential to assess the coastal impacts of climate change

Coastal inundation due to sea level rise (SLR) is projected to displace hundreds of millions of people worldwide over the next century, creating significant economic, humanitarian, and national-security challenges. However, the majority of previous efforts to characterize potential coastal impacts of climate change have focused primarily on long-term SLR with a static tide level, and have not comprehensively accounted for dynamic physical drivers such as tidal non-linearity, storms, short-term climate variability, erosion response and consequent flooding responses. Here we present a dynamic modeling approach that estimates climate-driven changes in flood-hazard exposure by integrating the effects of SLR, tides, waves, storms, and coastal change (i.e. beach erosion and cliff retreat). We show that for California, USA, the world’s 5 th largest economy, over $150 billion of property equating to more than 6% of the state’s GDP and 600,000 people could be impacted by dynamic flooding by 2100; a three-fold increase in exposed population than if only SLR and a static coastline are considered. The potential for underestimating societal exposure to coastal flooding is greater for smaller SLR scenarios, up to a seven-fold increase in exposed population and economic interests when considering storm conditions in addition to SLR. These results highlight the importance of including climate-change driven dynamic coastal processes and impacts in both short-term hazard mitigation and long-term adaptation planning.

Scientific Reports↗

USGS2018

Building back bigger in hurricane strike zones

Despite decades of regulatory efforts in the United States to decrease vulnerability in developed coastal zones, exposure of residential assets to hurricane damage is increasing — even in places where hurricanes have struck before. Comparing plan-view footprints of individual residential buildings before and long after major hurricane strikes, we find a systematic pattern of ‘building back bigger’ among renovated and new properties.

Nature Sustainability↗

USGS2018

A model ensemble for projecting multi‐decadal coastal cliff retreat during the 21st century

Sea cliff retreat rates are expected to accelerate with rising sea levels during the 21 st century. Here we develop an approach for a multi‐model ensemble that efficiently projects time‐averaged sea cliff retreat over multi‐decadal time scales and large (>50 km) spatial scales. The ensemble consists of five simple 1‐D models adapted from the literature that relate sea cliff retreat to wave impacts, sea level rise (SLR), historical cliff behavior, and cross‐shore profile geometry. Ensemble predictions are based on Monte Carlo simulations of each individual model, which account for the uncertainty of model parameters. The consensus of the individual models also weights uncertainty, such that uncertainty is greater when predictions from different models do not agree. A calibrated, but unvalidated, ensemble was applied to the 475 km‐long coastline of Southern California (USA), with 4 SLR scenarios of 0.5, 0.93, 1.5, and 2 m by 2100. Results suggest that future retreat rates could increase relative to mean historical rates by more than two‐fold for the higher SLR scenarios, causing an average total land loss of 19 – 41 m by 2100. However, model uncertainty ranges from +/‐ 5 – 15 m, reflecting the inherent difficulties of projecting cliff retreat over multiple decades. To enhance ensemble performance, future work could include weighting each model by its skill in matching observations in different morphological settings

Journal of Geophysical Research F: Earth Surface↗

USGS2018

Projected 21st century coastal flooding in the Southern California Bight. Part 1: Development of the third generation CoSMoS model

Due to the effects of climate change over the course of the next century, the combination of rising sea levels, severe storms, and coastal change will threaten the sustainability of coastal communities, development, and ecosystems as we know them today. To clearly identify coastal vulnerabilities and develop appropriate adaptation strategies due to projected increased levels of coastal flooding and erosion, coastal managers need local-scale hazards projections using the best available climate and coastal science. In collaboration with leading scientists world-wide, the USGS designed the Coastal Storm Modeling System (CoSMoS) to assess the coastal impacts of climate change for the California coast, including the combination of sea-level rise, storms, and coastal change. In this project, we directly address the needs of coastal resource managers in Southern California by integrating a vast range of global climate change projections in a thorough and comprehensive numerical modeling framework. In Part 1 of a two-part submission on CoSMoS, methods and the latest improvements are discussed, and an example of hazard projections is presented.

Journal of Marine Science and Engineering↗

USGS2018

Identification of storm events and contiguous coastal sections for deterministic modeling of extreme coastal flood events in response to climate change

Deterministic dynamical modeling of future climate conditions and associated hazards, such as flooding, can be computationally-expensive if century-long time-series of waves, sea level variations, and overland flow patterns are simulated. To alleviate some of the computational costs, local impacts of individual coastal storms can be explored by first identifying particular events or scenarios of interest and dynamically modeling those events in detail. In this study, an efficient approach to selecting storm events for subsequent deterministic detailed modeling of coastal flooding is presented. The approach identifies locally relevant scenarios derived from regional datasets spanning long time-periods and covering large geographic areas. This is done by identifying storm events from global climate models using a robust, yet computationally simple approach for calculating total water level proxies at the shore, assuming a linear superposition of the important processes contributing to the overall total water level. Clustering of the total water level time-series is used to define coherent coastal cells where similar return period water level extrema occur in response to region-wide storms. Results show that the more severe but rare coastal flood events (e.g., the 100-year (yr) event) typically occur from the same storm across the region, but that a number of different storms are responsible for the less severe but more frequent local extreme water levels (e.g., the 1-yr event). This new ‘storm selection’ approach is applied to the Southern California Bight, a region of varying shoreline orientations that is subject to wave refraction across complex bathymetry, and shadowing, focusing, diffraction, and dissipation of wave energy by islands. Results indicate that wave runup dominates total water level extremes at this study site, highlighting the importance of downscaling global-scale models to nearshore waves when seeking accurate projections of local coastal hazards in response to climate change.

California↗

USGS2018

Coastal knickpoints and the competition between fluvial and wave-driven erosion on rocky coastlines

Active margin coastlines are distinguished by rock erosion that acts in two different directions: waves erode the coast horizontally or landwards, a process that creates sea cliffs; and rivers and streams erode the landscape vertically via channel incision. The relative rates of each process exert a dominant control on coastline morphology. Using a model of river channel incision and sea-cliff retreat, we explore how terrestrial and marine erosion compete to shape coastal topography, and specifically what conditions encourage the development of coastal knickpoints (i.e., a river or stream channels that end at a raised sea-cliff edge). We then compare results to actual landscapes. Model results and observations show that coastal knickpoint development is strongly dependent on drainage basin area, where knickpoints typically occur in drainage basins smaller than 5 × 10 5 –6 × 10 6 m 2 , as well as channel geometry and sea-cliff retreat rate. In our study area, coastal knickpoints with persistent flow (waterfalls) are uncommon and form only within a small morphological window when 1) drainage basin area is large enough to sustain steady stream discharge, but not large enough to out-compete sea-cliff formation, 2) sea-cliff retreat is rapid, and 3) channel concavity is low so that channel slopes at the coast are high. This particular geomorphic combination can sustain sea-cliff formation even when streams tap into larger drainage basins with greater discharge and more stream power, and provides an initial explanation of why persistent coastal waterfalls are, along many coastlines, relatively rare features.

California↗

USGS2017

Can beaches survive climate change?

Anthropogenic climate change is driving sea level rise, leading to numerous impacts on the coastal zone, such as increased coastal flooding, beach erosion, cliff failure, saltwater intrusion in aquifers, and groundwater inundation. Many beaches around the world are currently experiencing chronic erosion as a result of gradual, present-day rates of sea level rise (about 3 mm/year) and human-driven restrictions in sand supply (e.g., harbor dredging and river damming). Accelerated sea level rise threatens to worsen coastal erosion and challenge the very existence of natural beaches throughout the world. Understanding and predicting the rates of sea level rise and coastal erosion depends on integrating data on natural systems with computer simulations. Although many computer modeling approaches are available to simulate shoreline change, few are capable of making reliable long-term predictions needed for full adaption or to enhance resilience. Recent advancements have allowed convincing decadal to centennial-scale predictions of shoreline evolution. For example, along 500 km of the Southern California coast, a new model featuring data assimilation predicts that up to 67% of beaches may completely erode by 2100 without large-scale human interventions. In spite of recent advancements, coastal evolution models must continue to improve in their theoretical framework, quantification of accuracy and uncertainty, computational efficiency, predictive capability, and integration with observed data, in order to meet the scientific and engineering challenges produced by a changing climate.

Journal of Geophysical Research F: Earth Surface↗

USGS2017

A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change

We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21 st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.

California↗

USGS2017

New techniques to measure cliff change from historical oblique aerial photographs and structure-from-motion photogrammetry

Oblique aerial photograph surveys are commonly used to document coastal landscapes. Here it is shown that adequate overlap may exist in these photographic records to develop topographic models with Structure-from-Motion (SfM) photogrammetric techniques. Using photographs of Fort Funston, California, from the California Coastal Records Project, imagery were combined with ground control points in a four-dimensional analysis that produced topographic point clouds of the study area’s cliffs for 5 years spanning 2002 to 2010. Uncertainty was assessed by comparing point clouds with airborne LIDAR data, and these uncertainties were related to the number and spatial distribution of ground control points used in the SfM analyses. With six or more ground control points, the root mean squared errors between the SfM and LIDAR data were less than 0.30 m (minimum 1⁄4 0.18 m), and the mean systematic error was less than 0.10 m. The SfM results had several benefits over traditional airborne LIDAR in that they included point coverage on vertical- to-overhanging sections of the cliff and resulted in 10–100 times greater point densities. Time series of the SfM results revealed topographic changes, including landslides, rock falls, and the erosion of landslide talus along the Fort Funston beach. Thus, it was concluded that SfM photogrammetric techniques with historical oblique photographs allow for the extraction of useful quantitative information for mapping coastal topography and measuring coastal change. The new techniques presented here are likely applicable to many photograph collections and problems in the earth sciences.

Journal of Coastal Research↗

USGS2017

Climate change-driven cliff and beach evolution at decadal to centennial time scales

Here we develop a computationally efficient method that evolves cross-shore profiles of sand beaches with or without cliffs along natural and urban coastal environments and across expansive geographic areas at decadal to centennial time-scales driven by 21st century climate change projections. The model requires projected sea level rise rates, extrema of nearshore wave conditions, bluff recession and shoreline change rates, and cross-shore profiles representing present-day conditions. The model is applied to the ~470-km long coast of the Southern California Bight, USA, using recently available projected nearshore waves and bluff recession and shoreline change rates. The results indicate that eroded cliff material, from unarmored cliffs, contribute 11% to 26% to the total sediment budget. Historical beach nourishment rates will need to increase by more than 30% for a 0.25 m sea level rise (~2044) and by at least 75% by the year 2100 for a 1 m sea level rise, if evolution of the shoreline is to keep pace with rising sea levels.

Conference Paper↗

USGS2016

Indications of a positive feedback between coastal development and beach nourishment

Beach nourishment, a method for mitigating coastal storm damage or chronic erosion by deliberately replacing sand on an eroded beach, has been the leading form of coastal protection in the U.S. for four decades. However, investment in hazard protection can have the unintended consequence of encouraging development in places especially vulnerable to damage. In a comprehensive, parcel-scale analysis of all shorefront single-family homes in the state of Florida, we find that houses in nourishing zones are significantly larger and more numerous than in non-nourishing zones. The predominance of larger homes in nourishing zones suggests a positive feedback between nourishment and development that is compounding coastal risk in zones already characterized by high vulnerability.

Earth's Future↗

USGS2015

Towards forecasting the retreat of California’s coastal cliffs during the 21st century

In California, sea-level rise during the 21 st century threatens to accelerate coastal cliff recession rates. To forecast such changes for managers and policymakers, models must play a key role. In this paper, we extend a ~70-year long dataset of measured historic sea cliff retreat rates in Southern California into the 21 st century using a suite of simple analytical and empirical models. Ensemble results suggest that coastal cliff recession rates could increase on average by 0.09-0.22 m yr -1 for a 0.5-1.0 m rise in sea level by 2100, 27-67% faster than historical rates. The basic models used herein will serve as a baseline against which more complex, process-based and statistical (Bayesian) forecasts will be compared. The application of different models, with varying levels of detail, to the same geomorphic problem will provide a comprehensive forecast and address the question of how to reduce model complexity while minimizing uncertainty.

California↗

USGS2007

Management applications of lidar-derived mean high water shorelines in North Carolina

The accuracy of shoreline change analysis is dependent on how the shoreline is defined and the consistency of the techniques(s) used to define it. Using the concurrent lidar (light detection and ranging) and orthophotography dataset from August and September of 2004 covering North Carolina's 516 kilometers of barrier island oceanfront, Limber et al. (2007) examined the spatial relationship between two common shoreline definitions used in shoreline change analysis, mean high water [MHW] derived from lidar data and the wet/dry line digitized from orthophotography. Here, we summarize this work and extend the analysis with a comparison between two different methods of MHW shoreline extraction from liar data: a profile-based method (Stockdon et al., 2002) and a method based on correction of the lidar data to a MHW datum (Hess et al., 2005). Potential bias generated by using these different shoreline types together can affect not only the accuracy of shoreline change analysis, but also the coastal management policies and decision that rely on it. Therefore, the implications of this study potential extend far beyond North Carolina and Atlantic Coast of the United States.

North Carolina↗