Seasons, Storms and Seawalls: A Comparison of Constrained and Unconstrained Beaches in Groton, Connecticut

A study was done in order to evaluate the impact of a seawall on a beach in Groton, Connecticut. Literature predicts beaches containing seawalls will inhibit functioning as a normal beach and lead to increased erosion. Groton Long Point is developed and backed by a seawall while Bluff Point is located on a state park and receives much less human usage. Profiles were measured to study the effect of storms, seasonality and time on these two barrier beaches. Two transects at each beach were used to determine changes in profiles throughout the year. Profiles were then compared to previous research (Campbell, 2004) to analyze long-term change. The beach at Bluff Point reacted normally to the seasons and the storm and showed a trend of accretion over the four year period. On the other hand, Groton Long Point beach showed no change in beach features in regard to the seasons and storms and exhibited extreme variability in the long-term. While it cannot be proven the seawall at Groton Long Point is increasing erosion, there is strong evidence of erosion on the beach. The irregular system created makes it hard to predict how the beach will behave in the future

Simulating extreme total water levels using a time-dependent, extreme value approach

Coastal flood hazard zones and the design of coastal defenses are often devised using the maximum recorded water level or a ‘‘design’’ event such as the 100 year return level, usually projected from observed extremes. Despite technological advances driving more consistent instrumental records of waves and water levels, the observational record may be short, punctuated with intermittent gaps, and vary in quality. These issues in the record often preclude accurate and robust estimates of extreme return level events. Here we present a total water level full simulation model (TWL-FSM) that simulates the various components of TWLs (waves, tides, and nontidal residuals) in a Monte Carlo sense, taking into account conditional dependencies that exist between the various components. Extreme events are modeled using nonstationary extreme value distributions that include seasonality and climate variability. The resulting synthetic TWLs allow for empirical extraction of return level events and the ability to more robustly estimate and assess present-day flood and erosion hazards. The approach is demonstrated along a northern Oregon, USA littoral cell but is applicable to beaches anywhere wave and water level records or hindcasts are available. Simulations result in extreme 100 year TWL return levels as much as 90 cm higher than those extrapolated from the ‘‘observational’’ record. At the Oregon site, this would result in 30% more coastal flooding than the ‘‘observational’’ 100 year TWL return level projections. More robust estimates of extreme TWLs and tighter confidence bounds on return level events can aid coastal engineers, managers, and emergency planners in evaluating exposure to hazards

What's streamflow got to do with it? A probabilistic simulation of the competing oceanographic and fluvial processes driving extreme along-river water levels

Extreme water levels generating flooding in estuarine and coastal environments are often driven by compound events, where many individual processes such as waves, storm surge, streamflow, and tides coincide. Despite this, extreme water levels are typically modeled in isolated open-coast or estuarine environments, potentially mischaracterizing the true risk of flooding facing coastal communities. This paper explores the variability of extreme water levels near the tribal community of La Push, within the Quileute Indian Reservation on the Washington state coast, where a river signal is apparent in tide gauge measurements during high-discharge events. To estimate the influence of multiple forcings on high water levels a hybrid modeling framework is developed, where probabilistic simulations of joint still water level and river discharge occurrences are merged with a hydraulic model that simulates along-river water levels. This methodology produces along-river water levels from thousands of combinations of events not necessarily captured in the observational records. We show that the 100-year still water level event and the 100-year discharge event do not always produce the 100-year along-river water level. Furthermore, along specific sections of river, both still water level and discharge are necessary for producing the 100-year along-river water level. Understanding the relative forcing driving extreme water levels along an ocean-to-river gradient will help communities within inlets better understand their risk to the compounding impacts of various environmental forcing, which is important for increasing their resilience to future flooding events

Quantifying uncertainty in exposure to coastal hazards associated with both climate change and adaptation strategies : a U.S. Pacific Northwest alternative coastal futures analysis

Coastal communities face heightened risk to coastal flooding and erosion hazards due to sea-level rise, changing storminess patterns, and evolving human development pressures. Incorporating uncertainty associated with both climate change and the range of possible adaptation measures is essential for projecting the evolving exposure to coastal flooding and erosion, as well as associated community vulnerability through time. A spatially explicit agent-based modeling platform, that provides a scenario-based framework for examining interactions between human and natural systems across a landscape, was used in Tillamook County, OR (USA) to explore strategies that may reduce exposure to coastal hazards within the context of climate change. Probabilistic simulations of extreme water levels were used to assess the impacts of variable projections of sea-level rise and storminess both as individual climate drivers and under a range of integrated climate change scenarios through the end of the century. Additionally, policy drivers, modeled both as individual management decisions and as policies integrated within adaptation scenarios, captured variability in possible human response to increased hazards risk. The relative contribution of variability and uncertainty from both climate change and policy decisions was quantified using three stakeholder relevant landscape performance metrics related to flooding, erosion, and recreational beach accessibility. In general, policy decisions introduced greater variability and uncertainty to the impacts of coastal hazards than climate change uncertainty. Quantifying uncertainty across a suite of coproduced performance metrics can help determine the relative impact of management decisions on the adaptive capacity of communities under future climate scenarios

The Tillamook County Coastal Futures Project : Exploring alternative scenarios for Tillamook County’s coastline

This report assesses climate change impacts and associated community and ecosystem vulnerability. It was compiled by a group of Oregon State University researchers and students, outreach specialists, and coastal community members in Tillamook County, Oregon (OR). Through sustained engagement with the Tillamook County Coastal Futures Knowledge to Action Network (TCCF KTAN), a suite of alternative scenarios for exploring adaptation strategies for reducing vulnerability to coastal hazards based on a variety of drivers of change was developed. These alternative scenarios were explored using Envision, a spatially explicit multi-agent modeling platform supporting scenario-based planning to examine interactions between the coupled human and natural coastal system. At its foundation is the identification of key stakeholder desires and outcomes for the future of the coastal shore (e.g., access to the beach, resilient infrastructure, etc.). These self-expressed outcomes are the orienting principle of this KTAN driven process. Probabilistic simulations of extreme total water levels, long-term coastal change, and storm-induced dune erosion along the shoreline allowed the group to represent the variable impacts of SLR, wave climate, and the El Niño Southern Oscillation in a range of climate change scenarios through the end of the century. Additionally, researchers explored a range of alternative futures related to policy decisions and socioeconomic trends using input from KTAN participants. The impact of both policy scenarios and climate change scenarios on a range of participant defined metrics were quantified. In some scenarios, model results suggest severe reductions in beach accessibility (one metric highly valued by the TCCF KTAN) by the end of century, due to the cumulative placement of riprap backshore protection structures. Flooding and erosion to coastal buildings and infrastructure also increases on a variety of scales depending on the types of policies implemented. In general, human decisions introduced greater variability and uncertainty to the impacts to the landscape by coastal hazards than climate change uncertainty

Discovery of biphenylacetamide-derived inhibitors of BACE1 using de novo structure-based molecular design

β-Secretase (BACE1), the enzyme responsible for the first and rate-limiting step in the production of amyloid-β peptides, is an attractive target for the treatment of Alzheimer’s disease. In this study, we report the application of the de novo fragment-based molecular design program SPROUT to the discovery of a series of nonpeptide BACE1 inhibitors based upon a biphenylacetamide scaffold. The binding affinity of molecules based upon this designed molecular scaffold was increased from an initial BACE1 IC50 of 323 μM to 27 μM following the synthesis of a library of optimized ligands whose structures were refined using the recently developed SPROUT-HitOpt software. Although a number of inhibitors were found to exhibit cellular toxicity, one compound in the series was found to have useful BACE1 inhibitory activity in a cellular assay with minimal cellular toxicity. This work demonstrates the power of an in silico fragment-based molecular design approach in the discovery of novel BACE1 inhibitors

Emulation as an approach for rapid estuarine modeling

Probabilistic flood hazard assessment is a promising methodology for estuarine risk assessment but currently remains limited by prohibitively long simulation times. This study addresses this problem through the development of an emulator, or surrogate model, which replaces the simulator (in this case the coupled ADCIRC+SWAN model) with a statistical representation that is able to rapidly predict estuarine variables relevant to flooding. Emulation of water levels (WLs), non-tidal residual, and significant wave height, is explored at Grays Harbor, Washington (WA) USA using Gaussian process regression. The effectiveness of the methodology is validated at various model simplification levels to determine where error is being sourced. Emulated WLs are found to be skillful when compared to over a decade of tide gauge observations (root mean square error, RMSE, <15 cm). The largest loss of skill in the method originates with ADCIRC+SWAN attempting to reproduce observations, even when the majority of relevant physics are included. Subsequent simplifications to the simulator (input reduction techniques) and the emulator itself are found to introduce a trivial amount of error (average increase in RMSE of 1 cm). Emulated WLs are also compared to spatially varying observations and found to be equally skillful throughout the estuary. An example emulation application is explored by decomposing the relative forcing contributions to extreme WLs across the study site. Results show a compound nature of extreme estuarine WLs in that all forcing dimensions contribute to extremes, with streamflow having the least influence and tides the largest. Overall the approach is shown to be both skillful and efficient at reproducing critical hydrodynamic variables, suggesting that emulation may play a key role in improving our ability to probabilistically assess flood risk in complex environments as well as being promising in a range of other applications

Quantifying Uncertainty in Exposure to Coastal Hazards Associated with Both Climate Change and Adaptation Strategies: A U.S. Pacific Northwest Alternative Coastal Futures Analysis

Coastal communities face heightened risk to coastal flooding and erosion hazards due to sea-level rise, changing storminess patterns, and evolving human development pressures. Incorporating uncertainty associated with both climate change and the range of possible adaptation measures is essential for projecting the evolving exposure to coastal flooding and erosion, as well as associated community vulnerability through time. A spatially explicit agent-based modeling platform, that provides a scenario-based framework for examining interactions between human and natural systems across a landscape, was used in Tillamook County, OR (USA) to explore strategies that may reduce exposure to coastal hazards within the context of climate change. Probabilistic simulations of extreme water levels were used to assess the impacts of variable projections of sea-level rise and storminess both as individual climate drivers and under a range of integrated climate change scenarios through the end of the century. Additionally, policy drivers, modeled both as individual management decisions and as policies integrated within adaptation scenarios, captured variability in possible human response to increased hazards risk. The relative contribution of variability and uncertainty from both climate change and policy decisions was quantified using three stakeholder relevant landscape performance metrics related to flooding, erosion, and recreational beach accessibility. In general, policy decisions introduced greater variability and uncertainty to the impacts of coastal hazards than climate change uncertainty. Quantifying uncertainty across a suite of coproduced performance metrics can help determine the relative impact of management decisions on the adaptive capacity of communities under future climate scenarios