Geology topics

Thomas Wahl

Publications and source records attributed to Thomas Wahl.

8 recordsLinked to original sources

USGS2026

Challenges and opportunities for national-scale projections of future coastal landscape change

Local to global scale projections of future coastal landscape change are essential to improve land and resource management decisions that aim to prepare for and reduce risk exposure to impending coastal hazards. However, the availability of actionable knowledge is often limited due to the complexity of drivers of change, their consequences, and uncertainties that span disciplines. Varying spatial and temporal scales of physical processes that occur on landscapes with differing levels of coastal development and modification complicate decision-making. This paper reviews advances in observational approaches, modeling, and quantification of uncertainty to support the next generation of coastal change projections. We also highlight transdisciplinary opportunities for making this information more accessible and useful to support decision-making in the coastal zone. This includes consideration of different levels of user engagement to address specific needs, as well as resources and mechanisms that can support knowledge transfer broadly. A central recommendation is the development of a coordinated framework to support research integration including three components: accounting for relevant drivers, processes, and feedbacks; working with users to identify their information needs; and generating meaningful projections and uncertainty. Actions that would support such frameworks include the evaluation of models through coordinated research approaches and multi-model comparisons; ongoing investments in observations and data-driven approaches necessary to understand this complex environment; expansion of modeling capabilities using novel techniques; prioritization of co-development efforts between researchers and users; and continued training and support for transdisciplinary research.

Alaska, California, Florida, New York↗

USGS2026

Compounding of 100-year coastal floods by rainfall in an urban environment

Coastal and pluvial flooding are both becoming more prevalent and severe due to climate change and urbanization in floodplains. The co-occurrence of these flood drivers is generally assumed to exacerbate the resulting flood impacts, a result referred to as compound flooding. However, few observational or modeling studies have investigated the circumstances under which this occurs. Here, we study the impacts of these combined flood drivers and evaluate the implicit hypothesis of official flood maps, which is that rainfall has a negligible impact on the flood depth and flooded area due to a 100 year coastal flood. A coastal system model, configured to capture coastal and pluvial flood drivers, is used. We evaluate the flooding for different urban landform types, including coastal landfill (human-made land), convergent areas (topographic depressions) and other urban terrain, within a model domain covering the Jamaica Bay watershed of New York City. A scenario-based strategy is adopted with a 100 year coastal flood as a control simulation, to which we add a set of realistic scenarios of rainfall data from historical tropical cyclones. We also apply a joint probability analysis framework with historical data to evaluate the probability of these compound coastal-pluvial scenarios. Results reveal cases where the pluvial driver compounds the coastal flood through expansion of the flood zone, with a 17% chance of rainfall increasing the flood area by 6%–38%, and a 5% chance of an increase of 61%–73%. It is rare that floods are significantly deepened but when deepening occurs, it is more common for the convergent zone than for the coastal landfill. These findings quantitatively assess the potential of the pluvial driver to exacerbate flooding, which may influence emergency management strategies such as evacuation plans, shelter arrangements, and related preparedness measures.

New York↗

USGS2025

Observations reveal changing coastal storm extremes around the United States

Understanding extreme storm surge events that threaten low-lying coastal communities is key to effective flood mitigation/adaptation measures. However, observational estimates are sparse and highly uncertain along most coastal regions with a lack of observational evidence about long-term underlying trends and their contribution to overall extreme sea-level changes. Here, using a spatiotemporal Bayesian hierarchical framework, we analyse US tide gauge record for 1950–2020 and find that observational estimates have underestimated likelihoods of storm surge extremes at 85% of tide gauge sites nationwide. Additionally, and contrary to prevailing beliefs, storm surge extremes show spatially coherent trends along many widespread coastal areas, providing evidence of changing coastal storm intensity in the historical monitoring period. Several hotspots exist with regionally significant storm surge trends that are comparable to trends in mean sea-level rise and its key components. Our findings challenge traditional coastal design/planning practices that rely on estimates from discrete observations and assume stationarity in surge extremes.

Nature Climate Change↗

USGS2023

Understanding uncertainties in contemporary and future extreme wave events for broad-scale impact and adaptation planning

Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ( H s 50 "> H 50 s 50 ) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary H s 50 "> H 50 s 50 estimates dominate projected 21st-century changes in H s 50 "> H 50 s 50 across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.

Science Advances↗

USGS2019

Combining numerical and statistical models to predict storm-induced dune erosion

Dune erosion is an important aspect to consider when assessing coastal flood risk, as dune elevation loss makes the protected areas more susceptible to flooding. However, most advanced dune erosion numerical models are computationally expensive, which hinders their application in early-warning systems. Based on a combination of probabilistic and process-based numerical modeling, we develop an efficient statistical tool to predict dune erosion during storms. The analysis focuses on Dauphin Island, AL in the northern Gulf of Mexico, where we combine synthetic sea storms with a calibrated and validated XBeach model to develop and test a range of different surrogate models for their ability to predict barrier-island geometric parameters under storm conditions. Surrogate models are developed by combining the oceanographic forcing from 100 optimally sampled sea-storm events covering the entire multivariate parameter space (used as XBeach input) and associated changes in the dune system (XBeach output). We test four surrogate models using a k-fold approach for validation. All models perform well in predicting changes in dune elevation, barrier-island area, and width, but are less accurate in predicting alterations in the cross-shore locations of dune morphological features. Multivariate adaptive regression splines are identified as the best surrogate model based on their fast development and good performance, attaining a modified Mielke index of 0.81 for dune crest height. As demonstrated at Dauphin Island, our approach shows potential to be used in an operational framework to predict dune response (in particular crest elevation change) when water level and wave forecasts are available.

Alabama↗

USGS2019

Surrogate model development for coastal dune erosion under storm conditions

Early coastal dune erosion predictions are essential to avoid potential flood consequences but most dune erosion numerical models are computationally expensive, hence their application in Early Warning Systems is limited. Here, based on a combination of optimally sampled synthetic sea storms with a calibrated and validated XBeach model, we develop a surrogate model capable of producing fast and accurate dune erosion predictions under storm conditions when water level and wave forecasts are available. The analysis is performed on Dauphin Island, AL, where we train Multiple Linear Regression Models with oceanographic forcing from the selected sea storms (i.e., XBeach input) and predicted changes in the dune system (i.e., XBeach output). Surrogate model performance is assessed with a rigorous k-fold cross validation. Although changes in the location of dune features are not well predicted, the model attains good performance when predicting changes in dune elevation, barrier-island width and volume.

Alabama↗

USGS2016

Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

We assess erosion and flooding risk in the northern Gulf of Mexico by identifying interdependencies among oceanographic drivers and probabilistically modeling the resulting potential for coastal change. Wave and water level observations are used to determine relationships between six hydrodynamic parameters that influence total water level and therefore erosion and flooding, through consideration of a wide range of univariate distribution functions and multivariate elliptical copulas. Using these relationships, we explore how different our interpretation of the present-day erosion/flooding risk could be if we had seen more or fewer extreme realizations of individual and combinations of parameters in the past by simulating 10,000 physically and statistically consistent sea-storm time series. We find that seasonal total water levels associated with the 100 year return period could be up to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the observational records. Impact hours of collision and overwash—where total water levels exceed the dune toe or dune crest elevations—could be on average 70% (collision) and 100% (overwash) larger than inferred from the observations. Our model accounts for non-stationarity in a straightforward, non-parametric way that can be applied (with little adjustments) to many other coastlines. The probabilistic model presented here, which accounts for observational uncertainty, can be applied to other coastlines where short record lengths limit the ability to identify the full range of possible wave and water level conditions that coastal mangers and planners must consider to develop sustainable management strategies.

Journal of Geophysical Research C: Oceans↗

USGS2015

Changes in erosion and flooding risk due to long-term and cyclic oceanographic trends

We assess temporal variations in waves and sea level, which are driving factors for beach erosion and coastal flooding in the northern Gulf of Mexico. We find that long-term trends in the relevant variables have caused an increase of ~30% in the erosion/flooding risk since the 1980s. Changes in the wave climate—which have often been ignored in earlier assessments—were at least as important as sea level rise (SLR). In the next decades, SLR will likely become the dominating driver and may in combination with ongoing changes in the wave climate (and depending on the emission scenario) escalate the erosion/flooding risk by up to 300% over the next 30 years. We also find significant changes in the seasonal cycles of sea level and significant wave height, which have in combination caused a considerable increase of the erosion/flooding risk in summer and decrease in winter relative to long-term trends.

Alabama↗