Property:CSDMS meeting abstract
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Permafrost thaw is of growing concern due to its potential to weaken slope stability and influence the magnitude and frequency of rapid mass movements in an area. Therefore, modelling of permafrost distribution and dynamics is needed for mountain areas, where such events happened or might happen. For instance in Iceland, permafrost thaw has been recently recognized as a new factor influencing landslide triggering, whereas the instability and slow movement of the unstable rock slope Mannen in southwestern Norway might be connected to permafrost conditions.
CryoGrid is a suite of permafrost models for solving different problems related to permafrost processes. This presentation will show examples of application of the two model schemes, the one-dimensional model CryoGrid 2 (Westermann et al., 2013) and the two-dimensional model CryoGrid 2D (Myhra et al., 2017). Both models solve the transient heat diffusion equation that additionally accounts for the latent heat effects due to ground freezing or thawing. The CryoGrid 2 model is forced with time series of air/ground surface temperature and snow depth data, whereas the CryoGrid 2D model is forced with ground surface temperature.
We applied CryoGrid 2 to the regional modelling of permafrost dynamics in Iceland for the last six decades. To account for snow redistribution, we ran the model for three scenarios of the snow depth, having a large impact on the modelled permafrost distribution.
CryoGrid 2D was employed to model ground temperature in the Mannen rock slope since the 1860s. Our preliminary results show that permafrost can occur in the Mannen slope in areas with considerably reduced snow depth, i.e. under steep parts of the slope.
REFERENCES
Myhra, K. S., Westermann, S., & Etzelmüller, B. (2017). Modelled distribution and temporal evolution of permafrost in steep rock walls along a latitudinal transect in Norway by CryoGrid 2D. Permafrost and Periglacial Processes, 28(1), 172-182. DOI: 10.1002/ppp.1884
Westermann, S., Schuler, T., Gisnås, K., & Etzelmüller, B. (2013). Transient thermal modeling of permafrost conditions in Southern Norway. The Cryosphere, 7(2), 719-739. DOI: 10.5194/tc-7-719-2013
Planktic foraminifera abundances and distributions, i.e. faunal assemblages, have been used to reconstruct oceanographic conditions from Earth's ancient past. Here we examine the utility of Species Distribution Models (SDMs) in characterizing the ecology of modern foraminifera and how this can inform reconstructions of past oceanographic states from Earth’s climatic history. Standard faunal assemblage proxy reconstructions often reduce multidimensional environmental data into a single variable, typically temperature. However, when environmental covariates feature strong spatial autocorrelation, these traditional methods may incorrectly interpret information from other variables as a temperature signal. Using modern machine learning-based SDMs we show that while temperature is an unquestionably important control on foraminifera distribution, other environmental factors appear to play a non-trivial role. This has implications not only for temperature values derived from standard assemblage-based reconstructions, but also may help reconcile apparent mismatches between proxies and climate model simulations +
Post-fire debris flow is a common hazard in the western United States. However, after decades of efforts in the debris flow research community, universally applicable post-fire debris flow predict methods are still lacking. Large discrepancies in the post-fire debris flow initiation mechanism are the main source that limits the predictive accuracy of debris flow. Improve and understanding these discrepancies is significant to possibly improve the debris flow modeling. In this work, we propose a data-driven, physics-informed machine learning approach for reconstructing and predicting debris flows. By using a classic supervising modern learning technique based on logistics regression, the logistics regression functions are trained by existing direct field measurements and debris flow numerical simulations from Las Lomas after 2016 Fish fire and then used to predict debris flow in different drainage basin where data are not available. The proposed method is evaluated by two classes of simulations: sediment transport model and runoff model. In runoff simulations, five drainage basins are considered: Las Lomas, Arroyo Seco, Dunsmore 1, Dunsmore 2, Big Tujunga. In sediment transport model, Las Lomas and Arroyo Seco watersheds are applied. Excellent predictive performances were observed in both scenarios, demonstrating the capabilities of the proposed method. +
Post-wildfire debris flows are a major source of geomorphic change that by nature of the large amounts of mass they mobilize can be deadly and destructive. These landslides are triggered by the interaction of fire-induced changes to both hydrologic and geomorphic responses. Representing the cascading effects of fire on landslides requires linking information from hydrologic models and debris flow models and presents both technical and theoretical challenges. Statistical models of debris flows have been used successfully for decades to assist in disaster prevention and mitigation. However, physically-based models that may provide additional insight into underlying processes and behavior under extreme conditions are rarely used.
We present a case study to begin addressing these challenges, focusing on a basin burned by the Thomas Fire in southern California in 2017. Soil water content maps and sediment fluxes produced by the Distributed Hydrology Soil Vegetation Model (DHSVM) in areas at risk for debris flows are compared with times and locations of known landslides. The degree of correspondence between modelled debris flow risk factors is compared for different potential methods of representing fire in DHSVM, including: changes to soil depth, soil infiltration characteristics, vegetation cover, and vegetation properties. Finally, future challenges of linking information across hydrologic and landslide models are discussed, towards more accurately representation of the spectrum of debris flow processes. +
Predicting river hydrodynamics through computational models is critical for advancing science and engineering practices to manage rivers and floodplains. Traditional hydrodynamic models pose computational challenges, often demanding extensive processing time for large-scale 2D flood simulations. While data-driven algorithms have shown promise in improving simulation efficiency, existing efforts have primarily concentrated on generating inundation maps only at event peaks. In this research, we introduce a novel deep learning model designed to provide accurate and rapid simulation of the temporal evolution of floods, providing 2D prediction of both water depth and flood inundation maps across an entire event. We trained and evaluated this model based on a dataset that was developed using HEC-RAS, a physics-based model, for a segment of Ninnescah River, in Kansas. This was done using a deep learning model to integrate the spatial advantages of a convolutional neural network along with the temporal sequence capabilities of a long-short term memory network. The hybrid model demonstrates remarkable proficiency in capturing the dynamic nature of flood events. Evaluation of the inundation maps, at the highest testing peak, exhibited exceptional performance, with precision exceeding 0.99 and an F1-score approaching 0.98. Moreover, this hybrid model showed robust performance in predicting water depth maps, with RMSE values of 0.03 m on average during testing and 0.08 m at the highest peak time-step. This study represents a significant advancement in our ability to conduct long-term simulations of hydrodynamics and sediment transport. +
Predictive understanding of the variation and distribution of substrates at large spatial extents in aquatic systems is severely lacking. This hampers efforts to numerically predict the occurrence and distribution of specific benthic habitats, which must be observed in the field. Existing survey methods are limited in scale, require heavy and technically sophisticated survey equipment, or are prohibitively expensive for surveying and mapping. Recreation-grade side scan sonar (SSS) instruments, or fishfinders, have demonstrated their unparalleled value in a lightweight and easily-to-deploy system to image benthic habitats efficiently at the landscape-level. Existing methods for generating geospatial datasets from these sonar systems require a high-level of interaction from the user and are primarily closed-source, limiting opportunities for community-driven enhancements. We introduce PING-Mapper, an open-source and freely available Python-based software for generating geospatial benthic datasets from recreation-grade SSS systems. PING-Mapper is an end-to-end framework for surveying and mapping aquatic systems at large spatial extents reproducibly, with minimal intervention from the user. Version 1.0 of the software (Summer 2022) decodes sonar recordings from any existing Humminbird® side imaging system, export plots of sonar intensities and sensor-derived bedpicks and generates georeferenced mosaics of geometrically corrected sonar imagery. Version 2.0 of the software, to be released Summer 2023, extends PING-Mapper functionality by incorporating deep neural network models that automatically locate and mask sonar shadows, calculate independent bedpicks from both side scan channels, and classify substrates at the pixel level. The widespread availability of substrate information in aquatic systems will facilitate development of the next generation of in-stream models for routing flows of sediment and water, as well as more sophisticated simulations of specific habitats.
Preserved in the morphology of bedrock river valleys is a recorded history of geologic and climactic conditions experienced by that river over time. A deep, narrow valley suggests that conditions favored vertical incision over lateral erosion, and the presence of a wide bedrock valley indicates that lateral erosion and valley widening outpaced vertical incision. While vertical incision and the rates and mechanisms by which it operates are relatively well understood, the processes of lateral erosion and valley widening remain more enigmatic. Utilizing bedrock valley morphology as an interpretive tool is impossible without first improving our knowledge of the valley widening process. For a bedrock valley to widen the river must first laterally erode the bedrock wall until the overlying stresses cause the wall to collapse into a talus pile on the valley bottom. Once the material has collapsed, it then must be transported away from the bedrock wall so that the river can regain access to the wall and lateral erosion can continue. In this two-step conceptual model of valley widening, the size of the individual talus blocks and the volume of the pile itself plays a large role in the rate of valley widening over time. In this study, I use numerical modeling to estimate the long-term breakdown and removal of talus material in a river through chemical and physical weathering. Inputs for the model include measured talus pile characteristics from a bedrock river with wide and narrow bedrock valleys (Buffalo River) and long-term flood simulations generated by the LandLab tool, Random Precipitation Distribution Generator. Model results may offer some insight into the potential role of talus in the bedrock valley widening process and improve our understanding of the conditions favorable for the development of wide bedrock river valleys. +
A
Previous studies have found that the ratio between valley spacing and mountain range width is relatively constant across the globe, but the processes responsible for its uniformity are not well understood. To determine the reasons for this uniform ratio, we firstly need to explore why valleys are evenly distributed in a mountain range, and what factors can impact valley spacing. Recent research has found that the critical length between hillslope and fluvial processes is an important control on the valley spacing of first order fluvial channels. In this study, we use the CHILD landscape evolution model to explore how the critical length affects valley spacing in higher order fluvial channels, and we use these results to help explain the narrow range of observations in the valley spacing ratio. We find that valley spacing has a linear relationship with critical length in higher order channels and, for a given order channel, the ratio between valley spacing and critical length is relatively constant. This relationship demonstrates that the competition between hillslope and fluvial processes influences the distribution of higher order channels across the landscape. However, we also find that valley spacing is influenced by model initial conditions and variability across the landscape, such as orographic precipitation patterns. Moreover, for a fixed domain in our model, although the critical length may vary, the ratio between the valley spacing of trunk channels and mountain width remains in the range observed in real landscapes. The reason for this is that the order of trunk channels varies with the critical length. Therefore, for a given domain size (or mountain range width), a larger critical length can produce lower order trunk channels but with the same spacing value as higher order trunk channels with a smaller critical length. This may be one of the reasons why the spacing ratio is relatively constant across diverse natural settings. +
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Proper quantification of sediment flux has always been an area of interest both for scientist and engineers involved in hydraulic engineering and management of rivers, estuaries and coastal waters. In spite of the importance of bedload flux globally, either for monitoring water quality, maintaining coastal and marine ecology or during dam construction or even for food security, bedload data, especially for large rivers, extremely scarce. This is due to the fact that bedload flux measurements are relatively expensive and time consuming and introduce large spatial and temporal uncertainties. Lack of adequate and continuous field observation is a hindrance to developing a globally accepted numerical model. We developed a new global riverine bedload flux model as an extension of the WBMsed framework. Here we present an evaluation of the model predictions using over eighty field observations for large rivers (over 1000 km2), collected from different sources. This model will be used to study various aspects of fluvial geomorphology globally, which is most common interest area for the researcher to see the impacts of different issues at global scale. Also, considering the contribution of bedload as sediment in the global level, it will elucidate the relationship between suspended sediment and bedload. The observational dataset we compiled is in itself a unique product that can be instrumental for future studies. +
Quantifying the spatial distribution of vegetation on coastal dunes is critical for understanding their morphological evolution and ecological functioning. However, effective large-scale mapping of dune vegetation presents a challenge for coastal managers at the resolution suitable for detailed analysis of these berm-dune systems. This study presents an integrated machine learning and geographic information system (GIS) methodology to accurately classify vegetation, sand, and shadows from high-resolution unmanned aerial vehicle (UAV) imagery of an undeveloped dune system in Long Branch, New Jersey. Using a Zenmuse X5S 3-band multispectral sensor and a Zenmuse X5 RGB sensor, we collected data from a single survey in October, 2023. We then combined the band data from this flight in order to develop additional model inputs of normalized difference vegetation index (NDVI) and green NDVI (gNDVI). Thirteen model runs were performed and compared using 3 to 8 inputs including spectral bands of Red, Green, Blue, and near-infrared (NIR). The spectral indices of NDVI and gNDVI, were developed using Red, Green, and NIR band data. In our research, the random forest machine learning algorithm was employed, with optimal hyperparameters determined through empirical testing. Classifiers with 350 decision trees were found to yield highest accuracy on the training data across different spectral indices combinations. The trained random forest models were then applied to map vegetation distribution on an unseen "training grid" area. Results show that model runs containing the NDVI and gNDVI yielded higher accuracy in classification than model runs only containing a combination of R, G, B, and NIR bands. Incorporating additional Red and Green bands from our multispectral sensor produced less accurate model classification as these additional inputs lead to overfitting and increased misclassification of vegetation and shadows. Incorporating NDVI and gNDVI indices significantly improved classification performance compared to model runs that only used spectral band information. The classified vegetation, sand, and shadow maps were integrated into ArcPro GIS software for visualization and validation against other established methods of manual class identification and map digitization. Rigorous accuracy assessments confirmed the robustness of the machine learning approach that can be used to quickly and accurately identify vegetation distribution in a dune complex.
Quantitative constraints on the frequency of hazards is vital to risk assessments and appropriate mitigation strategies. The frequency of landslides, a common hazard in steep landscapes, is difficult to quantify for a number of reasons including: (1) infrequent occurrence; (2) rapid deterioration of the morphological signature of a landslide event; (3) expensive geochronological approaches are often require to obtain the age of a single event. Through the use of numerical modeling, I propose that more careful approach of using cosmogenic nuclide concentrations of alluvial sediment sourced in landslide dominated drainage basins can alleviate many of these hurdles and provide regional constraints on landslide frequency. This suggestion stems from new development of an old numerical code that quantifies the impacts of landslides on CRN concentrations in alluvial sediment. The modeling shows that quantitative insight can be obtained by measuring CRN concentrations (1) of multiple nuclides (10Be and 14C), (2) of multiple grain sizes (i.e. coarse material sourced from depth in the hillslope), and (3) over time. I will present the new model developments and results as well as discuss some strategies towards applying this in field settings. +
Rainfall intensity thresholds are used to estimate when postfire debris flows may occur in the western US. Prior research has shown that postfire debris flows are highly correlated with short-duration rainfall intensity, and that short duration rainfall thresholds (e.g., 15-minute rainfall intensity) can be estimated based on wildfire and terrain attributes. Consequently, it is possible to determine possible debris flow activity in recent burn areas in the western United States by tracking rainfall rates using publicly available rainfall data. We have developed a software (FlowAlert) and an accompanying map dashboard that monitors when and where rain gages near burn areas cross rainfall intensity thresholds. The software runs continuously on a linux server, processing more than 2500 rain gages every two hours. When rainfall rates near a burn area are higher than a rainfall threshold, symbols are updated on a map indicating possible debris flow activity. Rainfall plots are also provided on the dashboard, and via email alerts for the gages that have crossed the rainfall intensity threshold. FlowAlert can be used for situational awareness to alert authorities of potential debris flow activity in remote areas. Additionally, the data stream produced by FlowAlert can be used by managers to adjust the rainfall intensity threshold in areas following storms based on observed activity. For example, if rainfall thresholds were crossed, but no debris flows were observed, managers may choose to increase the rainfall threshold to avoid warning fatigue. This presentation will focus on the utility of the new FlowAlert software, and how it might be used for decision support in burn areas. +
A
Reactive Transport Modeling (RTM) has been developed in the past decades and used extensively to understand the coupling between fluid flow, diffusive and dispersive transport, and biogeochemical processes in the natural subsurface in a wide range of applications relevant to earth and environmental sciences. Reactive transport modeling solves conservation equations of mass, momentum, and energy. Process-based reactive transport modeling allows the regeneration of spatial and temporal propagation of tightly coupled subsurface processes at spatial scales ranging from single pores (microns) to watershed scales (kilometers). RTM can keep track of evolving porous medium properties including porosity, permeability, surface area, and mineralogical composition. In this presentation I will introduce the general framework of RTM together with its advantages and challenges. The use of RTM at different spatial and temporal scales will be illustrated using two examples. A one-dimensional chemical weathering model for soil formation in Marcellus Shale will illustrate its use in Critical Zone (CZ) processes at the time scales of tens of thousands of years. A two dimensional biogeochemical transport model will exemplify its use in understanding engineered bioremediation processes in natural, heterogeneous porous media at the time scale of months to years. +
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Recent analytical and modeling studies on the Lowermost Mississippi River (LMR) have enhanced our understanding of sand flux at key locations, providing a framework for evaluating sand as a renewable resource for coastal restoration projects. This research is part of the Louisiana Sediment Management Program (LASMP) and aims to evaluate and quantify recharge rates of borrow areas within the LMR. It also seeks to determine whether channel maintenance dredging requirements are reduced downstream of borrow areas after they are dredged.
To characterize pit evolution and calculate infill rates, time-series bathymetric surveys from two recent projects with borrow areas - Spanish Pass Ridge and Marsh Creation (borrow area at Venice Anchorage), and Upper Barataria Marsh Creation (borrow areas at Alliance Bar) - were analyzed. Existing Delft3D numerical models were updated, refined, and calibrated for each reach using these time-series bathymetric data and recent sediment data.
Observations of infilling rates within a month of dredging were approximately 225,000 m3/month, declining rapidly to approximately 74,000 m3/month in February 2022 and declining further to 40,000 m3/month through March 24, 2022. During low flow conditions, infilling was minimal, but it increased to 230,000 m³/month in January 2023. For another area, initial infilling rates in the first month were around 125,000 m³/month, rapidly decreasing to approximately 75,000 m³/month by May 23, 2022. These rates fluctuated between 1,000 and 10,000 m³/month until December 27, 2022, before rising to 110,000 m³/month in January 2023.
This analysis provides a framework to forecast sediment recharge rates in LMR borrow pits, which can be incorporated into LASMP sediment resource availability estimates. It also suggests that sand extracted from the LMR for restoration can help reduce navigation channel maintenance costs. Future research opportunities include leveraging local model results and additional observed infilling rates from various locations along the Mississippi River. This research could lead to a better understanding of the relationship between the location of borrow pits, hydrograph characteristics, and corresponding infilling rates.
Furthermore, machine learning tools could be utilized to develop this correlation. Such a relationship would provide engineers and planners with an easy-to-use tool to evaluate first-order infilling rates and the time required for infilling, which is crucial for restoration project planning, including dredging operations and determining locations for borrow pits. Ultimately, this would support and promote sustainable sand extraction for restoration projects.
Recent discovery of a well-preserved drowned bald cypress forest offshore Alabama has spurred the search for analogous sites, as they provide valuable paleoclimate proxies and potential paleohuman habitats. However, drowned forests are difficult to detect when buried beneath the seabed, and degrade rapidly when exposed to the water column.
In this study, various machine learning algorithms within NRL's Global Predictive Seabed Model (GPSM) are used to geospatially predict the location of buried ancient forests offshore Mississippi. Subsurface sediment cores containing evidence of ancient forests (wood debris) are used as training and validation data, and feature layers include modern bathymetry, paleo-topographic surfaces, and seabed substrate. The resulting maps of probability of encountering wood-bearing sediments will be used to guide future data acquisition efforts. +
A
Recent morphodynamic modeling of non-uniform turbulent transport and deposition of sediment in a standing body of water devoid of tides and waves shows that sediment caliber plays a major role in determining the shapes, cumulative number of distributaries, and wetland areas of river-dominated deltas. In this study we introduce metrics for quantifying delta shoreline rugosity and foreset dip (clinoform) variability, and explore their variation with sediment caliber. Delta shoreline rugosity is calculated using the isoperimetric quotient, IP = 4 pi A / P2, where a circle has a value of one. Clinoform complexity is calculated using the uniformity test in circular statistics wherein clinoform dip direction uniformity is the sum of the deviations of dip azimuths from a theoretical uniform distribution. Analysis of fifteen simulated deltas shows that IP increases from 0.1 to 0.5 as the normalized shear stress for re-erosion of cohesive sediment, τn, increases from 0.65 to 1. Clinoform dip azimuth uniformity decreases from 300 to 130 with increasing τn. Preliminary analysis of data from outcrops of the Cretaceous Ferron Delta and ground penetrating radar data of the Pleistocene Weber and Brigham City Deltas are consistent with these trends. These results imply that changes in sediment caliber delivered to a deltaic coastal system will profoundly change its wetland area, bathymetric hypsometry, ecological function, and interior stratigraphy. +
2
Recent post-tsunami field surveys show that sandy tsunami deposits usually cannot cover all of the tsunami flow inundation areas. The difference between the sandy tsunami deposits inland extent and the flow inundation limit can be used to estimate tsunami magnitude. However, the relationship between tsunami deposit inland extent and inundation limit is still not fully understood. This paper focuses on studying the relationship and its control factors by using a parameter study and field measurements. Deposition ratio is a ratio between the sediment layer inland extent and the tsunami inundation limit to quantify this relationship. In the parameter study carried by a state-of-the-art sediment transport model (GeoClaw-STRICHE), we change grain size, offshore wave height, and onshore slope. The deposition ratio for tsunami deposit extent ($\xi_0$) is not sensitive to the grain size. However, the deposition ratios for observable sediment layer inland extent ($\xi_{0.5}$ and $\xi_{1}$) are affected by the grain size, offshore wave height, and onshore slope. The deposition ratios for a 0.5 cm thick sediment layer from parameter study are consistent with field measurements from the 2011 T\={o}hoku-oki tsunami on Sendai Plain. The topography, especially onshore slope, strongly influences the deposition ratio in this case. The combination of different deposition ratios can be used to estimate tsunami inundation area from tsunami deposits and improve tsunami hazard assessments. +
Recent research has highlighted the idea that long distance particle motions can be a significant component of the hillslope sediment flux. In this situation, mathematical descriptions of hillslope sediment transport must be nonlocal. That is, the flux at a position x, is a weighted function of conditions around x. This contrasts with local conditions which state that the flux is only a function of conditions at x. There are several ways to incorporate nonlocality into a mathematical description of sediment transport. Here, we focus on implementing and testing a convolution integral-like formulation. In this case, the flux is a convolution integral of a volumetric entrainment rate and a kernel that is related to the probability distribution of particle travel distance. Computation of convolution integrals is typically done by taking advantage of the convolution theorem for Fourier transforms, where a convolution integral becomes multiplication in wavenumber domain. However, in our case, the kernel is a function of position, and therefore precludes us from taking advantage of this method. Here, we apply a method that can reduce the problem back to a proper convolution integral and therefore allows for rapid computation (Gilad and von Hardenberg, 2006). We use this method to demonstrate nonlocal transport on lateral moraines on the east side of the Sierra Nevada. This method has applications in all convolution integral-like formulations including nonlinear filtering. +
Recent trends in Earth system modeling, climate data collection, and computing architecture have opened new opportunities for machine learning to improve ESMs. First, new and cheaper satellites are generating large volumes of observational data (e.g. Arctic and Antarctic DEMs), and massive climate modeling projects are generating large volumes of simulated climate data (e.g. CMIP5, CMIP6, CESM-LE). Second, machine learning applications are driving the design of next-generation computing architectures that will accelerate applications like neural nets without ameliorating the computational bottlenecks (ref: NOAA HPC position paper) that limit existing climate models. Third, the climate science community is becoming increasingly familiar with machine learning techniques. Here, I summarize opportunities for CSDMS practitioners to use machine learning techniques to improve Earth system and Earth surface models. +
A
Recurved barrier spits occur in a wide variety of environments, from active delta complexes to rocky coasts, where spits extend depositionally from a shore that is otherwise eroding. Although controls on spit orientation are often presented in the literature a posteriori (i.e. after the spit has been observed), there surprisingly remains no general model that predicts spit shape and orientation in terms of external variables, such as wave climate, sediment supply, and embayment depth. We study spit shape controls using the Coastline Evolution Model (CEM), a numerical model that evolves the plan-view coast based upon the processes of alongshore sediment transport and barrier overwash maintaining a minimum critical barrier width. Model results demonstrate that the directional distribution of approaching waves serves as a first-order control on spit shape, with waves from multiple directions playing a vital role in spit extension and reshaping. Surprisingly, we find that boundary effects, namely the rate of change of the updrift coast location, play a similarly important role in spit shape. The depth of the platform upon which a spit grows plays another important role, with deeper platforms tending to accommodate more sharply curved spits. Every day, spits act as a type of messenger in disguise, revealing wave forcing, sediment supply, and local geometry. +
