Property:CSDMS meeting abstract
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Densely populated coastal deltas worldwide face cascading flood and salinization hazards associated with sea-level rise, storm surges, dwindling sediment supplies, and land subsidence. One of the greatest hurdles to hazard prediction stems from quantifying the land-subsidence component, which exhibits significant spatial and temporal variations across any given delta. Here, we present a delta-subsidence model capable of quantifying these variations. The model is built upon fundamental principles of effective stress, conservation of mass, and Darcy flow; as well as constitutive relations for porosity and edaphic factors (e.g. roots, burrows). For an input sediment column and deposition rate, we quantify the depth-profile of vertical land motion over time, allowing for direct comparison with field observations spanning various depths, timescales, and methods (e.g., GPS stations; Rod-surface-elevation tables; C14 and OSL ages). Preliminary results demonstrate the model can accurately resolve decadal-scale subsidence patterns on the Ganges-Brahmaputra delta, including subsidence hotspots associated with fine-grained lithologies, buried Pleistocene paleovalleys, and river embankments constructed in the 1950’s. This predictive subsidence model can improve assessments of coastal flood hazards on the Ganges-Brahmaputra and other deltas worldwide; and help inform ongoing billion-dollar restoration efforts facing crucial decisions as to where and when coastal barriers, sediment diversions, and settlement relocations will be implemented in the coming century. +
Deposition of sediment from upland sources has the potential to increase flood risk in downstream riverside communities by reducing the carrying capacity of rivers and causing overbank flow. However, the morphodynamic response of rivers to variable upstream sediment supply remains poorly understood, and operational flood models do not account for sediment in flood prediction.
We introduce a framework for integrating source-to-sink sediment dynamics using coupled hydrological, hydrodynamic and landscape evolution models to quantify and better predict flooding events. A Distributed Hydrology Soil Vegetation Model is used to simulate upland streamflow and land coverage over numerical grids of river networks. Modules from the Python toolkit, Landlab, generate and route sediment from mountain sources (i.e. landslides, exposed glacial till) in the same domain. Streamflow and sediment from these upland models are delivered to a Delft3D hydrodynamic, sediment transport and morphodynamic model to characterize the effects of sediment-routing on lowland, coastal floodplains and investigate the impact on flood risk. This modeling framework is tested for three Puget Sound, WA basins: the Nooksack River, Skagit River and Mt. Rainier drainage, where gage analysis performed on historic USGS indicates regional morphodynamic patterns, with potential implications on flood risk. To ensure accurate model-coupling, the model ensemble is tested in an idealized, Landlab-generated domain.
Funded by the National Science Foundation. +
Depressions are inwardly-draining regions of digital elevation models (DEMs). For modeling purposes, depressions are often removed to create a "hydrologically corrected" DEM. However, this compromises model realism and creates perfectly flat surfaces that must be handled in some other way. If depressions are not removed, the movement of water within them must be modeled. This is challenging because depressions are often deeply nested, one inside the other.
Here, we present a novel data structure – the depression hierarchy – which uses a forest of binary trees to capture and abstract the full topographic and the topologic complexity of depressions. The depression hierarchy can be used to quickly manipulate individual depressions or depression networks, as well as to accelerate dynamic models of hydrological flow, as shown in our Fill-Spill-Merge poster. While the algorithm is implemented in C++ for performance reasons, we have also developed a Python wrapper using the pybind11 library. This enables users to capitalize on the strengths of both languages. The Python wrapper also streamlines the process of integrating the depression hierarchy into the CSDMS model interfaces and Landlab.
Open source code is available on GitHub at https://github.com/r-barnes/Barnes2019-DepressionHierarchy and https://github.com/r-barnes/pydephier. +
Depressions—inwardly-draining regions—are common to many landscapes. When there is sufficient water availability, depressions take the form of lakes and wetlands; otherwise, they may be dry. Depressions can be hard to model, so hydrological flow models often eliminate them through filling or breaching, producing unrealistic results. However, models that retain depressions are often undesirably expensive to run. Our Depression Hierarchy poster shows how we began to address this by developing a data structure to capture the full topographic complexity of depressions in a region. Here, we present a Fill-Spill-Merge algorithm that utilizes depression hierarchies to rapidly process and distribute runoff. Runoff fills depressions, which then overflow and spill into their neighbors. If both a depression and its neighbor fill, they merge. In case studies, the algorithm runs 90–2,600× faster (with a 2,000–63,000× reduction in compute time) than commonly-used iterative methods and produces a more accurate output. Complete, well-commented, open-source code with 97% test coverage is available on Github and Zenodo. +
Depth averaged, adaptive, Cartesian grid models have been used effectively in the modeling of tsunamis, landslides, flooding, debris flows and other phenomena in which the computational domain can be reasonably approximated by a logically Cartesian mesh. One such code, GeoClaw (D. George, R. J. LeVeque, K. Mandli, M. Berger), is already part of the CSDMS model repository. A new code, ForestClaw, a parallel library based on adaptive quadtrees, has been extended with the GeoClaw library. This GeoClaw extension of ForestClaw gives GeoClaw users distributed parallelism and a C-interface for enhanced interoperability with other codes, while maintaining the core functionality of GeoClaw. We will describe the basic features of the ForestClaw code (www.forestclaw.org) and present results using the GeoClaw extension of ForestClaw to model the 1976 Teton Dam failure. If time permits, we will also describe on-going work to model dispersion and transport of volcanic ash using the Ash3d (H. Schweiger, R. Denlinger, L. Mastin, Cascade Volcanic Observatory, USGS) extension of ForestClaw. +
Despite the essential role sub-aerial reef islands on atolls play as home to terrestrial ecosystems and human infrastructure, the morphologic processes and environmental forcings responsible for their formation and maintenance remain poorly understood. Given that predicted sea-level rise by the end of this century is at least half a meter (Horton et al., 2014), it is important to understand how atolls and their reef islands will respond to accelerated sea-level rise for island nations where the highest elevation may be less than 5 meters (Webb and Kench, 2010). Atolls are oceanic reef systems consisting of a shallow reef platform encircling a lagoon containing multiple islets around the reef edge (Carter et al., 1994). Atolls come in a variety of shapes from circular to rectangular and size from 5 to 50 km width of the inner lagoon (Fig. 1a and 1b). I want to understand why atolls vary in their morphology and whether wave climate is the primary driver of atoll morphology. Previous work has highlighted the importance of wave energy on reef morphology and atoll morphology (Stoddart, 1965; Kench et al., 2006). Around a given atoll, the morphology of the reef islands may change significantly from small individual islets or larger continuous islets that are more suitable for human habitation (Fig. 1c and 1d). I will create a global dataset of atoll morphometrics to compare to external forcing, e.g. comparing reef width to the mean wave climate. Using Google Earth Engine, a cloud-based geospatial analysis platform to collate Landsat imagery, I can measure a range of morphometrics including atoll size and shape, reef flat width, reef island size and shape, and distribution of reef islands around an atoll. I will compare these morphometrics to global waves simulated by WaveWatch3. By compiling a global dataset of atoll morphometrics, I am able to better understand the impact of wave climate on atoll morphology and long-term evolution.
References:
Carter, R.W.G., Woodroffe, C.D.D., McLean, R.F., and Woodroffe, C.D.D., 1994, Coral Atolls, in Carter, R.W.G. and Woodroffe, C.D. eds., Coastal evolution: Late Quaternary shoreline morphodynamics, Cambridge University Press, Cambridge, p. 267–302.
Horton, B.P., Rahmstorf, S., Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6, doi: 10.1016/j.quascirev.2013.11.002.
Kench, P.S., Brander, R.W., Parnell, K.E., and McLean, R.F., 2006, Wave energy gradients across a Maldivian atoll: Implications for island geomorphology: Geomorphology, v. 81.
Stoddart, D.R., 1965, The shape of atolls: Marine Geology, v. 3.
Webb, A.P., and Kench, P.S., 2010, The dynamic response of reef islands to sea-level rise: Evidence from multi-decadal analysis of island change in the Central Pacific: Global and Planetary Change, v. 72.
Distributed systems of reservoirs (DSR) provide an alternative to large dams and reservoirs for riverine flow regulation and flood management. A DSR consists of temporary, small-in-size reservoirs, or detention ponds, spatially distributed across a watershed. A DSR can be as effective as a single large reservoir in terms of water storage and flow regulation and has overall a limited environmental impact. The effectiveness of a DSR depends, among others, on the number of reservoirs and their locations, making this approach to flood management a geographic problem.
In this work I propose a framework for reservoir modeling and siting. The main research objective is to find the optimal spatial configuration for a DSR that overall maximizes water storage capacity and minimizes reservoir footprint extent and system cost. First, reservoir models are generated on numerous locations along a river network, especially on small streams and tributaries, based on local topography. Shape, geometry and capacity is defined for each candidate reservoir. Then heuristic search is used to find an optimal subset of reservoirs given some spatial and structural cost constraints.
Preliminary results for real watersheds in northeastern Iowa suggest that, costs being equal, DSRs with many reservoirs of small average size have a higher storage capacity than DSRs with fewer reservoirs with a larger average size. That represents the necessary first step for future research on the effect of different configurations of DSRs on flood wave magnitude and propagation, assessing the scale of their benefits and comparing benefits with costs and impacts. +
Drainage capture and divide migration are critical processes that shape tectonically quiescent landscapes. However, the frequency and magnitude of drainage captures, especially under varying lithologic conditions are still poorly understood. In this context, we present the RiverCaptureFinder, a function designed for Landlab that identifies drainage capture events and extracts geomorphic metrics upstream and downstream of the capture point. To explore river captures using this function, we simulate landscape evolution using the StreamPowerEroder function in Landlab. Starting with an equilibrium landscape, we tested several transient scenarios. For scenario 1, we varied the resistant rock layer width (in map view); for scenario 2, we modified the erodibility contrast; and for scenario 3, we explored different uplift rates. In each scenario, RiverCaptureFinder was used to track drainage capture events and compute geomorphic metrics (drainage area, local relief, mean elevation, slope, chi, and ksn). Our preliminary results show that a thinner resistant layer delays drainage capture, results in lower local relief, gentler slopes, and lower ksn values, and ultimately results in smaller captured areas. A thicker resistant layer leads to earlier and larger river captures, higher local relief, steeper slopes, and elevated ksn values for a longer period. Varying erodibility contrast yields similar results, with lower contrast having capture-related geomorphic characteristics similar to the previous scenario with thinner rocks. Conversely, higher contrasts produce geomorphic responses similar to the scenarios with a wider resistant rock. Lastly, even though less significant than the latter, lower uplift delays capture and leads to lower mean elevation, whereas higher uplift increases captured drainage area but reduces mean elevation, local relief, slope, and ksn. In conclusion, the RiverCaptureFinder analysis indicates that drainage captures are more sensitive to changes in rock-resistant thickness (in map-view) and erodibility contrast, which can affect how frequent and how big the captures will be over time in tectonically quiescent landscapes.
Due to its biodiversity, ecosystem services offered and deforestation experienced since the 16th century, there are several protected areas in Atlantic Forest, such as the Juréia-Itatins Mosaic of Protected Areas (MUCJI), state of São Paulo, Brazil. Illegal deforestation in the MUCJI and surroundings have been increasing, caused by urban and agricultural expansion, reducing Atlantic Forest naturalness. This work aimed to simulate scenarios of landscape naturalness of MUCJI and neighboring municipalities for 2050 year, considering the periods 1985-2002 and 2002-2019, which correspond, respectively, to the scenarios before and after the creation of the MUCJI and National System of Protected Areas (SNUC). The landscape naturalness was evaluated by generating Distance to Nature index (D2N) maps for years 1985, 2002 and 2019, which was used as input data in simulation. The forecasting of both scenarios was conducted using cellular automata, weights of evidence and Markov chain, in Dinamica EGO environmental modeling platform. Both forecasted projections suggested that there would be a slight decrease in landscape naturalness. However, the scenario without the MUCJI implementation would reach 165.15 ha of non-natural ecotope in the study area, while the scenario with MUCJI would reach 112.77 ha. The SNUC and the creation of the MUCJI would have been contributed to maintain the naturalness of the study area, reducing losses in landscape naturalness. However, municipal planning and the MUCJI management plans should consider urban and agricultural expansion and access roads as important drivers of loss of landscape naturalness, triggering deforestation and biodiversity damages.
Keywords: Atlantic Forest; Protected Areas; Modeling; Landscape Naturalness; Distance to Nature Index. +
During storms nutrients and contaminants are washed from landscapes into rivers in the form of fine particulate matter. Once in a river, fine particles are typically treated as if they pass through the environment as wash load without interacting with the stream bed. However, laboratory and field experiments have demonstrated that fine particles can be advected towards the bed where they participate in hyporheic exchange and eventual filtration within the river bed. Irreversibly filtered particles can only be remobilized through scour and bed erosion. Therefore, understanding fine particle transport, storage, and remobilization in rivers requires coupling fine particle dynamics and sediment morphodynamics.<br>Here we analyze the dynamics of solute tracers, fine suspended particles, and bed morphodynamics within a coastal stream during baseflow and an experimental flood. These field data represent a unique set of coupled surface and subsurface observations of solute and fine particle dynamics and simultaneous time-lapse photography of sandy bedform motion. From the time-lapse photography, we use novel image analysis techniques to extract time series of bedform wavelength and celerity. In tandem, we utilize existing databases of bedform topography from laboratory experiments to determine relations between the statistical distributions of bedform wavelength, height, and the maximum scour depths. The understanding gained from the high-resolution experimental dataset allows us to create time series of bedform height and scour depth to explore how changing bedform dynamics affects solute and fine particle residence times within the stream bed. +
During the 21st century, anthropogenically modulated changes in climate and land cover will drive variations in sediment dynamics throughout rivers, reservoirs, and coastlines. These changes threaten the integrity of dams, levees, and riparian ecosystems, necessitating strategies to help mitigate their associated hazards and to detect and prevent adverse consequences of engineering solutions. To optimize these strategies, geomorphologists require calibrated, watershed-scale numerical simulations of sediment transport that can predict how fluvial networks will respond to different forcings throughout their catchments.
We aim to develop watershed-scale landscape evolution models of several U.S. rivers to explore how climate and land-use change over the coming decades to centuries will influence sediment delivery to reservoirs, locks, harbors, and coasts. The models will be calibrated by historical sediment flux data, allowing them to predict how the fluvial systems will respond to plausible scenarios of future climatic and anthropogenic forcings. The Chattahoochee River in the southeastern U.S. is an ideal catchment to begin this work due to its recent urban development and sedimentation records near its outlet at Lake Seminole. We devise procedures for processing USGS NHDPlus HR datasets (Moore et al., 2019) at the HU4 and HU8 scale for compatibility with the fluvial process components of Landlab (Hobley et al., 2017; Barnhard et al., 2020). Using NLCD land cover products (Wickham et al., 2021), NRI erosion rate estimates (USDA, 2020), and historical streamflow and sediment load data (USGS, 2021), we will leverage Landlab to construct models of the Chattahoochee catchment and test their ability to replicate sedimentation records at Lake Seminole. Here, we present preliminary results obtained by applying these procedures to the Chestatee branch of the Chattahoochee River and its outlet at Lake Lanier in northern Georgia. Future versions of this workflow will use a range of projected 21st century precipitation and land cover changes to predict potential variations in future sediment generation, transport, and storage throughout the Chattahoochee watershed and other U.S. rivers.
Earthquakes can trigger the failure of thousands or even tens of thousands of landslides throughout tectonically active landscapes. Short (<10 years) term studies of these events reveal their important place in a hillslope and fluvial hazard cascade. However, it remains unclear if these widespread catastrophic landslide events leave a long lasting impact on landscape forms, and what that impact would look like. We present landscape evolution model experimental design and some preliminary results exploring the impact of earthquakes or other widespread simultaneous landsliding events on landscapes at timescales much longer than the event return intervals. We use the Hylands Landlab component to test landslide, hillslope, and river sediment interactions, and probe landscape metrics like hilltop concavity, drainage density, slope-area relationships, and possibly valley width to test and validate our models. +
Eastern oysters (Crassostrea virginica) are reef-building organisms that occupy tidal and subtidal zones along the eastern coasts of the Americas. They provide key ecosystem services by improving water quality, providing habitat, providing food, and adding to local economies. At the population level, eastern oysters also form reefs which protect coastal habitats from storms and tidal erosion by attenuating waves. The decline of eastern oyster populations coupled with increased coastal storm intensity and rising sea level is exposing coastal habitats to higher levels of risk. One potential avenue to increase coastal protection is to use artificial reef structures that can also boost eastern oyster populations. Yet, there is little research on how oyster population dynamics influence the structure of the reef–artificial or not, and in turn, how the reef structure influences wave attenuation. Our research aims to address this gap by developing a model to simulate oyster populations in St. Augustine, Florida using an agent-based model coded in the Mesa Python framework. This will be coupled with the Landlab TidalFlowCalculator component, to simulate how reef structures affect tidal velocity and water depth. This model represents the first phase of a larger research effort, which aims to investigate the effects of climate change on the evolution of reef structures and estimate their wave attenuation performance over time. +
Ecohydrological modeling capacity of Landlab is introduced and illustrated using examples that couple components for local soil moisture and plant dynamics with spatially explicit cellular automaton-based (CA) plant establishment, mortality, fire and grazing. Several key features of arid and semiarid ecosystems are discussed. Coexistence of tree-grass cover on north facing slopes (NFS) and shrub cover on south facing slopes (SFS) in central New Mexico is attributed to the competitive advantage of trees due to their longer seed dispersal range against shrubs in cooler and more moist NFS. Incorporating a rule on the inhibitory effects of shrubs on grasses enhance modeled shrub cover, while both trees and grasses are favored when runon is included in the local soil moisture model. Feedbacks among livestock grazing, grassland fire frequency and size, resource redistribution and woody plant encroachment are investigated using different ecohydrologic model configurations. These feedbacks are manifested in a three-phase woody plant expansion processes in the model, with rates of encroachment controlled by the state transition probabilities in relation to plant susceptibility to fires, grazing, and age-related mortality. A critical area of woody plant emerges in the model with which a negative feedback between fire size and woody plant expansion begins. Our results underscore the need for developing models that emphasize local and non-local plant interactions for modeling transient ecosystems. +
Environmental change interacts with human migration in complex ways and across multiple scales. This complexity makes agent-based modeling (ABM) a powerful tool to investigate environment-migration dynamics. Here, we present results from an original ABM of environmental migration in Bangladesh. The model simulates an origin community and how a stylized environmental shock to the community impacts labor opportunities and household decisions surrounding migration. Pattern-oriented modeling is a useful approach for evaluating ABM’s by assessing a model’s ability to reproduce multiple observed patterns of phenomena. We use a pattern-oriented approach to test our model’s ability to reproduce multi-level patterns of environmental migration from the literature. Previous work used machine learning methods to calibrate our ABM by identifying regions in parameter space that successfully reproduced the observed patterns. We demonstrated that a strictly income-based migration decision method was able to reproduce patterns of interest, but inconsistently. However, the pattern-oriented approach allows us to implement more complex, behaviorally driven decision-making methods of migration and evaluate their success. In this work, we will present preliminary results implementing and comparing different decision-making methods in our ABM based on existing theories including Theory of Planned Behavior, Protection Motivation Theory, and a mobility potential framework. Ultimately, we hypothesize that a hybrid framework of migration decision-making that includes community norms, social networks, and place attachment will most successfully be able to replicate known patterns of environmental migration. +
Environmental migration is an example of a complex coupled human and natural system with dynamics that operate across multiple spatial and temporal scales. Agent-based modeling (ABM) has demonstrated potential for studying such complex systems, especially where individual decision-making is an important component. In this work, we use an original ABM of environmental shock, livelihood opportunities, and migration decisions to study dynamics of environmental migration in rural Bangladesh. As ABMs are sensitive to the decision-making methods used, we present results utilizing multiple plausible decision-making methods for households deciding whether or not to send an internal migrant. We present results using both a simple economic method based on utility maximization as well as a more behaviorally complex method based on the Theory of Planned Behavior. We hypothesized that a more behaviorally complex decision method which incorporates social networks and community norms would more successfully reproduce the patterns of migration. However, using a pattern-oriented approach to reproduce two key patterns of migration from the empirical literature, we demonstrate that an economic model can reproduce our patterns of interest with high levels of success. For both decision methods, the level of community inequality in distribution of land ownership, which impacts the number of agricultural jobs available within the community, is critically important for patterns of migration outcomes. In this way, our model suggests that community-level inequality is has significant implications of migration dynamics in this study area. +
Ephemeral, steep-side channels (known as gullies and arroyos) are fundamental elements of soil erosion that threaten agricultural lands worldwide with the associated expectation that landscape degradation will accelerate due to anthropogenic climate change. Gullies are also central to landscape evolution as they are dynamic features that intensively altered between infilling and incision phases in the recent geological past. Yet, exogenic (e.g., due to climate or land-use change) and autogenic (e.g., due to natural oscillations between erosion/deposition phases) drivers of gully formation and of changes in their widespread occurrence are incompletely understood and quantified. This is, in part, because erosional dynamics of gully landforms are complex and hard to capture due to: (1) episodic and discontinuous sediment movement in response to discrete rain events, (2) unstable channel walls with frequent mass wasting, and (3) soil and vegetation properties that vary dynamically thus altering both the hydrology and slope stability.
In this work, we focus on developing a new catchment-scale gully erosion model that enables quantification of soil erosion rates and topographic evolution in response to changes in rainfall patterns and vegetation cover over historical and longer timescales. The model includes explicit representation of rainstorm runoff and erosion over sub-minute time scales. During simulation, soil particles are transported both in suspension and as bedload in accordance with their size. Episodic bank failures and headcuts evolve based on local stability criteria derived from soil properties and failure geometry. This poster presents the model configuration, its main components, and the general modeling approach that aims to bridging gaps between event-scale hydrology, sediment dynamics and longer-term landscape evolution models using new and existing components in the Landlab modeling library. We also present preliminary results of model validation against runoff and sediment data from a field site and a sensitivity analysis on how sediment flux and landform development respond to plausible changes in rainstorm properties, landcover, and vegetation dynamics.
Eroding coasts make up the majority of the coastlines on Earth, including the west coast of the United States, and host critical infrastructure like roads, railways, and residential structures. The precarious siting of infrastructure is particularly true for Del Mar, California, where a major railway between Los Angeles and San Diego sits within just a few meters of a cliff edge that is closely backed by dense housing subdivisions. Coastal cliff retreat presents a danger to these communities that is potentially amplified under rising sea level conditions, among other factors, yet constraints on retreat rates are most often limited to those derived from historical imagn ery and maps dating back 10-100 years. These modern retreat rates are then used, in conjunction with multi-model ensembles, for forecasting cliff retreat over the next 50-100 years in order to gauge future impacts to coastal communities. Managers and policymakers make decisions for mitigation efforts based on these results, however they may not capture the full picture of cliff retreat, and the factors that influence it, over time. While nearly all of the existing forecasting models explicitly account for projected sea level rise, the majority of them ignore other factors (e.g. subtidal and subaerial weathering) that may also play a large role.
A recently developed combination of in situ-produced cosmogenic 10Be surface exposure dating in conjunction with a new numerical model of shore platform profile development that takes into account sea level rise, intertidal weathering, and wave attack on cliff retreat provides quantification of cliff retreat histories over hundreds to thousands of years via cliff-normal 10Be sample transects. Here, we use a shore-perpendicular transect of cosmogenic 10Be concentrations from the surface of a sandy claystone shore platform exposed along a narrow and sandy beach backed by a near vertical ~20-meter-tall cliff in Del Mar, California to present a long-term cliff retreat rate of 5.5 - 8 cm a-1 over the last two millennia for this site. This is the first long term cliff retreat rate for any coast in North America determined by this new methodology. Existing decadal retreat rates at and proximal to this site range from 5-20 cm a-1, suggesting that cliff retreat here may be accelerating towards the present. Preliminary modeling results suggest that uplift-corrected sea level rise in Southern California, which remained constant during the late Holocene (0.8 mm a-1) but doubled in the last century, cannot alone explain this potential increase, as modeled platform geometries and associated development rates show a dependence on the imposed weathering rate as well as wave erosion efficacy. Recent investigation into the relative influence of weathering and wave attack on observed cliff retreat at this same location also shows a roughly equal contribution for both drivers. We further explore this and other potential drivers (e.g. land use change) for this potential increase, and speculate on the implications of these results for future cliff retreat forecasting efforts.
Exchange of material across the nearshore region, extending from the shoreline to a few kilometers offshore, determines the concentrations of pathogens and nutrients near the coast and the transport of larvae, whose cross-shore positions influence dispersal and recruitment. Here, we describe a framework for estimating the relative importance of cross-shore exchange mechanisms, including winds, Stokes drift, rip currents, internal waves, and diurnal heating and cooling (Moulton et al., 2023). For each mechanism, we define an exchange velocity as a function of environmental conditions. The exchange velocity applies for organisms that keep a particular depth due to swimming or buoyancy. A related exchange diffusivity quantifies horizontal spreading of particles without enough vertical swimming speed or buoyancy to counteract turbulent velocities. This framework provides a way to determine which processes are important for cross-shore exchange for a particular study site, time period, and particle behavior. I will also describe approaches we've used to communicate the framework to different audiences, including an interactive tools developed by undergraduates.
Moulton M, Suanda S, Garwood J, Kumar N, Fewings M, Pringle J. Exchange of Plankton, Pollutants, and Particles Across the Nearshore Region. Annual Review of Marine Science. 2023 January 16; 15(1):167-202. DOI:10.1146/annurev-marine-032122-115057 +
Extensive overwash occurred on North Core Banks during Hurricane Florence (September 2018). The washover deposits were partially revegetated when, a year later, sound-side inundation and outwash caused substantial erosion during Hurricane Dorian (September 2019). Repeat aerial mapping shows that reestablishment of vegetation on deposits that partially filled the washout channels is slow to non-existent. We suggest washout site revegetation is delayed by lack of organic material, slowing dune growth, and extending vulnerability to overwash. Washout channels often erode several meters of beach, berm, and back-barrier platform and are later filled with inorganic marine sands deposited as spits, bars, and overwash. By contrast, washover deposits can contain ripped-up vegetation and (partially) bury pre-existing vegetation, providing seeds, rhizomes, and plant fragments to generate new growth. We propose a heuristic model of vegetation growth following a sigmoidal curve that depends on an initial (seed) concentration and show that simulations with this model using realistic overwash recurrence, reproduces our observations of slow revegetation in deposits on former washout channels. +
