Property:CSDMS meeting abstract
From CSDMS
This is a property of type Text.
2
Subglacial hydraulics significantly affects the ice dynamics in Greenland and Antarctic ice sheets, however, has been poorly understood due to the lack of data. Here we present an OpenFOAM-based one-dimensional subglacial model, conduitFoam, to study the hydraulics and ice dynamics of polar ice sheets. This model solves the coupled mass conservation equations for ice and water, the momentum and energy conservation equations for water, with a lake-conduit or moulin-conduit system as constraint boundaries. The model is validated using the theoretical solution applied in early melting stage and lake melting stage of the Greenland ice sheet and can be used to infer the subglacial conduit properties and the ice sheet dynamics in both seasonal and diurnal melting situations. +
Submarine slope failure is a ubiquitous process and dominant pathway for sediment and organic carbon flux from continental margins to the deep sea. Slope failure occurs over a wide range of temporal and spatial scales, from small (10e4-10e5 m3/event), sub-annual failures on heavily sedimented river deltas to margin-altering and tsunamigenic (10-100 km3/event) open slope failures occurring on glacial-interglacial timescales. Despite their importance to basic (closing the global source-to-sink sediment budget) and applied (submarine geohazards) research, submarine slope failure frequency and magnitude on most continental margins remains poorly constrained. This is primarily due to difficulty in 1) directly observing events, and 2) reconstructing age and size, particularly in the geologic record. The state of knowledge regarding submarine slope failure preconditioning and triggering factors is more qualitative than quantitative; a vague hierarchy of factor importance has been established in most settings but slope failures cannot yet be forecasted or hindcasted from a priori knowledge of these factors.<br><br>A new approach to address the knowledge gaps outlined above is using machine learning to quantitatively identify triggering and preconditioning factors that are most strongly correlated with submarine slope failure occurrence. This occurs in three general steps: 1) compile potential predictors of slope failure occurrence gridded and interpolated at desired resolution, 2) compile predictands (specific values that we wish to predict), and 3) recursively test predictor/predictand correlation with observed data until the strongest correlations are found. Potential predictors can be parsed into categories such as morphology (gradient, curvature, roughness), geology (clay fraction, grain size, sedimentation rate, fault proximity), and triggers (seismicity, significant wave height, river discharge). Predictands (i.e. training data) are various proxies for slope failure occurrence, including depth change between bathymetric surveys and sediment shear strength. The initial test sites are heavily sedimented, societally important river deltas, as they host both frequent slope failures and ample predictor/predictand measurements. Once predictors that strongly correlate with submarine slope failure occurrence are identified, this approach can be applied in more data-poor settings to further our current understanding of global submarine slope failure distribution, frequency, and magnitude.
Subsurface flow dynamics are largely controlled by pressure gradients generated by surface flow and differences in permeability. In most models, surface and subsurface flows are decoupled, with effects on one another only considered over relatively large time scales. However, at smaller time scales, these two flows interact and modify each other's structures and properties. In this study, we developed a fully-coupled free-surface/subsurface Large Eddy Simulation model to investigate the spatiotemporal variations in velocity and pressure, particularly near the bed surface. We validated our model by comparing it to experimental data from a laboratory simulation of open channel flow on a simulated salmon redd bed made of coarse granular sediment, using non-toxic index-matched fluid and stereo Particle Image Velocimetry (PIV). Our model accurately captured subsurface flow lines, velocity magnitude and direction, and superficial velocity profiles throughout the water column. With our validated model, we investigated the effects of subsurface hydraulic conductivity on the whole flow field. +
Surface processes are constantly reworking the landscape of our planet with perhaps the most diverse and beautiful patterns of sediment displacement known to humanity. Capturing this diversity is important for advancing our knowledge of systems, and for sustainable exploitation of natural resources by future generations. From a modeler's perspective, great diversity comes with great uncertainty. Although it is understandably very hard to quantify uncertainty about geological events that happened many years ago, we argue that modeling this uncertainty explicitly is crucial to improve our understanding of subsurface heterogeneity, as stratigraphy is direct function of surface processes. In this modeling work (and code), we aim to build realistic stratigraphic models that are constrained to local data (e.g. from wells, or geophysics) and that are, at the same time, subject to surface processes reflected in flume records. Experiments have improved tremendously in recent years, and the amount of data that they generate is posing new challenges to the surface processes community, who is asking more often the question "How do we make use of all this?" Traditional models based on differential equations and constitutive laws are not flexible enough to digest this information, nor were they created with this purpose. The community faces this limitation where the models cannot be conditioned on experiments, and even after exhaustive manual calibration of unobserved input parameters, these models often show poor predictive power. Our choice of inverse modeling and (geo)statistics (a.k.a. data science) was thus made knowing that these disciplines can provide the community with what we need: the ability to condition models of stratigraphy to measurements taken on a flume tank. +
Suspended sediment concentration, flux, and river discharge are essential indicators of river ecosystem health and reflect watershed-scale processes. Monitoring these variables is labor-intensive, leading to sparse and geographically biased observations and the development of models to fill in the observational gaps. These models generally use either climatological data or satellite images to estimate one of these variables. In this work, we present a novel deep learning model that can leverage multiple data sources with different temporal characteristics to produce continuous daily estimates of suspended sediment concentration (SSC), suspended sediment flux (SSF), and discharge. The model first encodes daily hydrological data from the ERA5-Land reanalysis using a Long Short-Term Memory network and water color data from Landsat satellites using a Multi-Layer Perceptron network, then merge these encoded data sources using a cross-attention decoder. We train and test the model on a large dataset of in- situ observations from 630 river sites over 43 years in the contiguous United States, covering a wide range of watersheds and conditions. We produce SSC, SSF, and discharge predictions with respective relative errors of 54%, 73%, and 28%, and relative bias of -15%, -19%, and -3%. We use our model to create a dataset of continuous daily SSC, SSF, and discharge for all large rivers in the contiguous United States. This new model architecture provides a valuable tool for monitoring river systems, addressing limitations of single-source models and offering a framework applicable to other Earth systems monitoring problems where integrating diverse data streams may be useful. +
Suspended sediment concentrations and fluxes on continental shelves impact light attenuation and primary productivity, as well as geomorphology and incorporation of particles into sea ice. As Arctic permafrost thaws, increasing riverine delivery and shoreline erosion, it is particularly important to understand how sediment sources influence suspended sediment concentrations and transport. To investigate these topics, this study analyzed results from a coupled hydrodynamic - sediment transport numerical model, namely the Regional Ocean Modeling System (ROMS) - Community Sediment Transport Modeling System (CSTMS). The model was implemented for the Alaskan Beaufort Sea Shelf for the 2020 open water (nearly ice-free) season, and accounted for processes such as riverine delivery, winds, larger-scale currents, and sediment erosion, transport and deposition. Building on previous work, we categorized riverine and seabed sediments into 26 distinct classes that allow us to distinguish among material originating from different rivers and sections of the seabed. Analysis focused on the spatial distribution of riverine and nearshore sediments over the course of an open water season, as well as the extent to which each sediment class contributed to high turbidity events. Preliminary results suggested that aggregated mud delivered by rivers during an open water season stayed within water depths of 0 – 10 m for at least a month and a half, while some unaggregated mud was transported to deeper regions during this time. Additionally, high turbidity events mainly occurred due to local resuspension, as opposed to riverine plumes, for both aggregated and unaggregated mud. Ongoing and future work include analysis of uncertainty due, for example, to sediment settling velocity and other properties. Overall, these findings suggest that high turbidity events are driven by sediment delivered to the continental shelf during previous open water seasons or winters, as opposed to new terrestrial/riverine inputs.
A
Sustainability of the anthroposphere is a result of a multitude of decisions made concerning social, economic and environmental questions. Decision makers who would like to ensure sustainable development as an emerging characteristic of humanity are challenged by the complexity of a planetary system re-engineered by an increasingly powerful global species. Examples of such problems are sustainable urban growth and the food-water-energy nexus. Tools to reliably assess the consequences of decisions from local to global level are not readily available.
In particular, current capabilities for assessing the various impacts of climate variability and change, as well as other changes are inadequate. The Group on Earth Observation (GEO) recognized this emergency and promoted several initiatives that can help address this shortcoming. One of them is the GEO Model Web initiative. The goal of this initiative is to develop a dynamic modelling consultative infrastructure of intercommunicating models and datasets to serve researchers, managers, policy makers and the general public. It focuses on enhancing interoperability of existing models and making them and their outputs more accessible. The development of the Model Web holds the promise of more decision support tools becoming available. These tools would allow decision makers to ask “What if” questions prior to the implementation of decisions and support adaptive management and responsive design. The Model Web will also benefit researchers by making it easier to run model experiments and model comparisons or ensembles, as well as help highlight areas needing further development. The Model Web would support a synchronization across different spatial and temporal scales and across the languages of different disciplines, thus making the System of System (SoS) more intelligent. The beauty of having a SoS like this is that it amplifies the signal. An immediate application is the emerging geodesign approach to the design of sustainable built environments.
The Model Web is developed in the framework of the Global Earth Observation System of Systems (GEOSS) implemented by GEO. The observing, modelling and other systems that contribute to GEOSS must be interoperable so that the data and information they generate can be used effectively. The Committee on Earth Observation Satellites (CEOS) is promoting interoperability through the Virtual Constellations concept, the Sensor Web approach, and by facilitating model interoperability and access via the Model Web concept.
The Model Web is a concept for a system of interoperable models and data capacities communicating primarily via web services. It would consist of an open-ended, distributed, multidisciplinary network of independent, interoperating models plus related datasets. Models and datasets would be maintained and operated and served by a dynamic network of participants. In keeping with the SoS approach, the Model Web initiative will explore the interoperability arrangements necessary to integrate multi-disciplinary environmental model resources. The approach of loosely coupled models that interact via web services, and are independently developed, managed, and operated has many advantages over tightly coupled, closed, integrated systems, which require strong central control, lack flexibility, and provide limited access to products.
Developing a long-term perspective, a logical next step would be the Internet of Models (IOM). Comparable to the already developing Internet of Things (IOT), which is predicted to connect by 2020 more than 50 billion “things” talking to each other without human interaction (or even knowledge), the IOM would have models talking to each other when needed without human interaction. If we compare the IOT to the nerve system of a human body, then the IOM would be the brain of the human being. Key to the development of IOT and IOM are standards that allow “things” and “models” to communicate when needed and to exchange information as needed (similar to the role of standards in the success of the WWW). Frameworks for model interactions are already developing (e.g. Object Modelling System, ModCom, the Invisible Modelling Environment, the Open Modelling Interface: OpenMI, the Spatial Modelling Environment: SME, Tarsier, Interactive Component Modelling System: ICMS, Earth System Modeling Framework: ESMF, SEAMLESS-IF , CSDMS, etc.), but they are not sufficient to achieve the Model Web (or the IOM). A major effort to develop the standards for the IOT is under way, and a similar effort to needed for the IOM standards. The combination of IOT and IOM would greatly enhance science capabilities, early warning, assessments of impacts, etc.
2
A
TITLE: Growth and Abandonment: Quantifying First-order Controls on Wave Influenced Deltas
AUTHORS: Jaap Nienhuis12, Andrew D Ashton1, Liviu Giosan1
INSTITUTIONS: 1. Geology & Geophysics, Woods Hole Oceanographic Institution, Woods Hole, MA, United States. 2. Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
ABSTRACT BODY:
River delta evolution is characterized by cyclical progradation and transgression: the delta cycle. We investigate the growth and decay of the individual or main lobes of deltas with strong wave influence with the aim to quantitatively compare marine to terrestrial controls.
We apply a model of plan-view shoreline evolution to simulate the evolution of a deltaic environment. The fluvial domain is represented by deposition of sediment along the shoreline, developing along a predefined single or multi-channel fluvial network. We investigate the influence of wave climate, fluvial sediment input and network geometry.
For growing deltas, we present a sediment-flux-based approach to quantify the relative influence of fluvial versus marine (wave) controls on morphology. Wave domination requires that the magnitude of the fluvial bedload flux to the nearshore region be less than the alongshore sediment transport capacity of waves removing sediment from the mouth. Fluvial dominance occurs when fluvial sediment input exceeds the wave-sustained alongshore sediment transport for all potential shoreline orientations, both up- and downdrift of the river mouth. For a single delta (or delta lobe), this transition depends not only on the fluvial river sediment flux and wave energy, but also on the directional wave climate.
Channel bifurcation is critical; it splits the sediment discharge from the river, while the potential alongshore sediment flux per channel remains equal. Fluvial dominance persists until sufficient bifurcations have split the fluvial sediment flux among the channels or until the occurrence of a river avulsion. This simplified model allows us to quantify the transition from fluvial to wave dominance and enables comparisons with natural examples near this transition, such as the Tinajones lobe of the Sinu River Delta, Colombia, and the Po Delta, Italy.
During delta abandonment, model results suggest littoral sediment transport can result in four characteristic modes of wave reworking, ranging from diffusional smoothing of the delta (or delta lobe) to the development of downdrift-extending recurved spits. The directional characteristics of the wave climate, along with the pre-abandonment delta shape, determine the mode of reworking. Simple analysis of pre-abandonment delta shape and wave characteristics provides a framework for predicting the mode of delta reworking; model predictions agree with the observed morphology of historically abandoned delta lobes, including the Nile, Ebro, and Rhone. These results provide insight into the potential evolution of active delta environments facing near elimination of fluvial sediment input.
Tectonic strain localization creates spatially anisotropic mechanical strength patterns that are reflected by landscape. Strain in the frictional-brittle crust produces predictable anisotropic cohesion and grain size distribution fabrics that influence spatial strain induced (SI) erodibility patterns where exposed at the surface. We assume that bedload impact is the primary mechanism for bedrock incision and erodibility is an inverse function of cohesion, which can be reduced by more than 2 orders of magnitude at the meter scale due to fragmentation and grain size reduction. The density, position, and orientation of SI anisotropies depends on the magnitude of strain and the tectonic horizontal/vertical shear stress ratio. The influence of tectonic strain on landscape becomes apparent by incorporating 3D strain induced crustal failure in a landscape evolution model. Natural observations and model results suggest naturally occurring SI anisotropy exerts a first order influence on geomorphic metrics for active orogens, including incision rate, 3D stream network geometry, and topographic evolution. Rates of vertical incision and knickpoint migration are orders of magnitude faster along SI anisotropy exposures. Shallowly dipping faults produced in a dip-slip regime are largely protected from vertical incision by unstrained overburden while a steeply dipping fault produced in a strike-slip regime is largely exposed to vertical incision. The strain field controls hydraulic geometry by influencing 1) the spatial distribution of discharge by establishing anisotropic erodibility patterns and 2) slope changes at erodibility transitions and differential uplift in a watershed. The influence of tectonic strain on landscape increases with the horizontal/vertical shear stress ratio because more steeply dipping and interconnected faults are produced. SI anisotropy controls channel network geometry by amplifying long wavelength tortuosity where fault-bound channels connect and muting short wavelength tortuosity along faults. Both effects increase with increasing tectonic horizontal shear strain. Channel width becomes constricted by the width of SI cohesion reduction, causing channel width to become a function of strain rather than reflecting only the hydraulics of a drainage basin.
2
Terrestrial cosmogenic nuclides (TCN) are commonly used to assess denudation rates in soil-mantled uplands. The estimation of an inferred denudation rate (Dinf) from TCN concentrations typically relies on the assumptions of steady denudation rates during TCN accumulation and negligible impact from soil chemical erosion on soil mineral abundances. However, in many landscapes, denudation rates are not steady, and the composition of soil is markedly affected by chemical erosion, adding complexity to the analysis of TCN concentrations. We introduce a landscape evolution model that computes transient changes in topography, soil thickness, soil mineralogy, and soil TCN concentrations. With this model, we explored TCN responses in transient landscapes by imposing idealized perturbations in tectonically (bedrock uplift rate) and climatically sensitive parameters (soil production efficiency, hillslope transport efficiency, and mineral dissolution rate) on synthetic, steady-state landscapes. The experiments on synthetic landscapes delivered important insights about TCN responses in transient landscapes. Results showed that responses of Dinf to tectonic perturbations differ from those to climatic perturbations, indicating that spatial and temporal trends in Dinf serve as indicators of perturbation type and magnitude. Also, if soil chemical erosion is accounted for, basin-averaged Dinf inferred from TCN in stream sediment closely tracks actual basin-averaged denudation rate, showing that Dinf is a reliable representation of actual denudation rate, even in many transient landscapes. In addition, we demonstrate how this model can be applied to a real landscape in the Oregon Coast Range and how model predictions can be compared to field measurements of cosmogenic nuclides and chemical depletion in sediments. Overall, landscape evolution models infused with cosmogenic nuclides can be used to scrutinize methodological assumptions, reveal potential real-world patterns in transient landscapes, and deepen the comprehension of field data.
A
Texas has historically faced severe hurricanes with Ike being the most recent major storm example. It is believed that coastal wetlands might reduce the impact of the storm surge on coastal areas, acting as a natural protection against hurricane flooding, especially for small hurricanes and tropical storms. Numerical analysis is an important instrument for predicting and simulating the flooding extent and magnitude in coastal areas. In recent years, improvements on the understanding of the physics of storm surges have led to the development of physically based numerical models capable of reasonably representing the storm surges caused from hurricanes. Wetlands are represented in the numerical model through their influence on the frictional resistance proprieties and bathymetric changes. To characterize the wetland types and their spatial distribution along the coast, we used six different land use databases from the National Land Cover Dataset (NLCD) (1992, 2001), the National Wetlands Inventory (NWI) (1993) and the Coastal Change Analysis Program (C-CAP) (1996, 2001, 2006). The analyses was conducted for Corpus Christi Bay using a pre-validated, physically based, hydrodynamic model (ADCIRC) and a wind and pressure field model (PBL) representing the physical properties of historical hurricane Bret. The calculations were performed using an unstructured numerical grid with 3.3 million nodes covering part of the Atlantic Ocean and the entire Gulf of Mexico (resolution from 2000 km to 50 meters at the coast). Considering the expected rise in the mean sea level, wetland composition and spatial distribution are also expected to change as the environmental conditions change along the coast. We analyzed a range of Intergovernmental Panel on Climate Change (IPCC) projections for sea level rise (SLR) to simulate wetland alterations and evaluate their impact on hurricane storm surge. The wetland degradation by SLR was spatially simulated using empirical relations for water levels/tides and ecosystem resilience. The choice of wetland database resulted in surge variations of less than 0.1 m in locations inside Corpus Bay. Preliminary studies considering IPCC scenarios (B1, A1F1, B1FI) for 2030 and 2080 plus predicted local subsidence showed that, although the SLR scenarios for 2030 did not affect surge considerably inside the bay (SLR increase removed after simulation), the greater degradation of the wetlands caused by SLR on the 2080 scenarios (0.80 m SLR + subsidence) resulted in surges on the order of 0.3 m higher for Hurricane Bret in selected locations. Future work includes performing analyses using different storm conditions (forward speed, central pressure and storm radius), additional and less conservative SLR scenarios, damage assessment and also include the effects of waves using the coupled version of ADCIRC with UNSWAN.
2
The ADCIRC finite element coastal ocean model is used in real time decision support services for coastal and riverine hydrodynamics, tropical cyclone winds, and ocean wave modelling for public sector agencies including NOAA, FEMA, Coast Guard, and the US Army Corps of Engineers, among others. Recent developments in ADCIRC's real time automation system, the ADCIRC Surge Guidance System (ASGS), have now enabled real time modelling of active flood control scenarios (manipulation of pumps and flood gates) for decision support during riverine floods and tropical cyclone events. During these events, the results are presented to official decision makers with the Coastal Emergency Risks Assessment (CERA) web application, an intuitive and interactive tool that integrates model data with measured data to provide situational awareness across the area of responsibility. Case study events will be described, including official decisions that have been made with ADCIRC in North Carolina (Irene 2011), Louisiana (Mississippi River flooding in 2016), and during the 2017 and 2018 hurricane seasons for Hurricanes Harvey, Irma, Maria, Florence, and Michael. +
The Amazon River Basin is the largest river system in the world, accounting for one-fifth of global freshwater discharge and supplying 40% of the Atlantic Ocean’s sediment flux. Though the Amazon is most often recognized for its rich biological diversity, it also performs a suite of ecosystem functions such as river flow regulation, local climate modulation, and carbon sequestration. Despite its ecological importance, the Amazon experiences thousands of kilometers of deforestation annually with recent rates increasing to levels unseen since the late 2000s. These increased rates of deforestation within the basin have led to changes in sediment concentration within its river systems, affecting not only the ecological balance within the system but also the availability of water to those dependent on river flows. Furthermore, sediment plays an important role in river channel morphology and landscape development, effectively influencing the future topography of the basin. Therefore, it is important to closely examine the relationship between deforestation and suspended sediment in order to characterize the extent of influence anthropogenic activities, such as deforestation, have on rivers.
In this study, we analyze the impact of deforestation from 2001 to 2020 on suspended sediment throughout the Amazon River Basin. These effects are studied by quantifying the spatiotemporal relationships between observed suspended sediment (at gage sites and using a basin wide remote sensing product) and changes in land cover over time. We hypothesize that deforestation will lead to significant increases in suspended sediment flux in adjacent streams and that the effect of deforestation on suspended sediment flux will decrease significantly downstream. We then apply these relationships to developing a new parameter within an existing global-scale sediment flux model, WBMsed. +
The Amazon River Basin stands out as the most biodiverse system in the world. However, the period when the river started flowing from the Andes to the Atlantic Ocean is still in large debate. Different studies have dated this period from the Late Miocene to the Pleistocene. The lack of a Miocene sedimentary record in the area, along with the limitations of geochronological tools that reach this timescale for shallow deposits, makes it difficult to date the event using onshore sediments, which suggests a possible Miocene age. In this study, we seek to contribute to this knowledge gap by using the landscape to reconstruct the timing of transcontinentalization. We hypothesize that when the proto-Amazon River (eastern) integrated with the western Amazon River system, it led to an increase in drainage area at the Lower Amazon River. This increase must have caused the incision of its substrate, thus lowering the base level of its tributary basins in eastern Amazonia. The lowering caused the propagation of knickpoints in the tributary basins that have been adjusting to the new base level. Widespread waterfalls at common elevations and area-corrected distances in basins provide geomorphic evidence of such base level fall. We collected 20 samples of Be-10 and 6 of Al-26, which can be used to infer erosion rates in these tributary basins. These data reveal erosion rates from 8 to 12 m/Ma. Combining these rates with the channel steepness, we obtained a value of erosional efficiency (stream power’s K) for the region. This value of 2.28 m0.1 yr-1 was used in a linear inversion of the stream power equation to obtain a base level fall age. Preliminary results showed an age estimated around 5 to 10 Myr for the formation of knickpoint clusters, thus within the time window of the commonly proposed Amazon River’s formation. However, this approach relies on linearity to calculate the stream power erosion, and it is best suited for bedrock rivers. We explore how these assumptions (i.e. n=1 and detachment-limited), influence our results by inverting a landscape model using the SPACE 1.0 component (transport and detachment-limited river-based modeling) applied to the Simulation-based inference toolkit to find best-fit sediment-transport and deposition parameters. We then compare the age solutions with the age of the linear inversion, ultimately to assess the uncertainty around the age of the latest base-level fall in the Amazon River.
