Simulating and quantifying legacy topographic data uncertainty: an initial step to advancing topographic change analyses

Rapid technological advances, sustained funding, and a greater recognition of the value of topographic data have helped develop an increasing archive of topographic data sources. Advances in basic and applied research related to Earth surface changes require researchers to integrate recent high-resolution topography (HRT) data with the legacy datasets. Several technical challenges and data uncertainty issues persist to date when integrating legacy datasets with more recent HRT data. The disparate data sources required to extend the topographic record back in time are often stored in formats that are not readily compatible with more recent HRT data. Legacy data may also contain unknown error or unreported error that make accounting for data uncertainty difficult. There are also cases of known deficiencies in legacy datasets, which can significantly bias results. Finally, scientists are faced with the daunting challenge of definitively deriving the extent to which a landform or landscape has or will continue to change in response natural and/or anthropogenic processes. Here, we examine the question: how do we evaluate and portray data uncertainty from the varied topographic legacy sources and combine this uncertainty with current spatial data collection techniques to detect meaningful topographic changes? We view topographic uncertainty as a stochastic process that takes into consideration spatial and temporal variations from a numerical simulation and physical modeling experiment. The numerical simulation incorporates numerous topographic data sources typically found across a range of legacy data to present high-resolution data, while the physical model focuses on more recent HRT data acquisition techniques. Elevation uncertainties observed from anchor points in the digital terrain models are modeled using “states� in a stochastic estimator. Stochastic estimators trace the temporal evolution of the uncertainties and are natively capable of incorporating sensor measurements observed at various times in history. The geometric relationship between the anchor point and the sensor measurement can be approximated via spatial correlation even when a sensor does not directly observe an anchor point. Findings from a numerical simulation indicate the estimated error coincides with the actual error using certain sensors (Kinematic GNSS, ALS, TLS, and SfM-MVS). Data from 2D imagery and static GNSS did not perform as well at the time the sensor is integrated into estimator largely as a result of the low density of data added from these sources. The estimator provides a history of DEM estimation as well as the uncertainties and cross correlations observed on anchor points. Our work provides preliminary evidence that our approach is valid for integrating legacy data with HRT and warrants further exploration and field validation

Debris Flow Hazard Mapping Along Linear Infrastructure: An Agent Based Model and GIS Approach

Often linear infrastructure, including rail, highways, and pipelines, span large geographic areas intersecting a variety of terrain, predisposing infrastructure to a higher likelihood of geohazard interaction. Debris flow models can be particularly advantageous in remote hazard and risk mapping along linear infrastructure as runout from susceptible slopes may extend considerable distance downslope to a receptor. In this sentiment, a method is developed using an agent-based model, DebrisFlow Predictor, in combination with geographic information software (GIS), to produce regional debris flow hazard and risk profiles along widespread corridors. Thousands of debris flows upslope of a receptor(s) are simulated in the model environment (i.e., model scenarios). Outputs of the modelled scenarios provide probabilistic spatial attributes of debris flow runout and depth across a digital elevation model at a 5m resolution. The outputs of multiple scenarios are mosaiced and corrected to in-situ temporal and spatial debris flow initiation conditions in GIS. The corrected scenario outputs provide a comprehensive hazard profile along the infrastructure alignment, that in turn can facilitate quantified vulnerability and risk calculations. Thousands of modelled debris flows throughout several physiographic regions of Canada and the United States of America, calibrated to local conditions, provide substantive support for a novel methodology to identify key hazard and risk locations to major linear infrastructure

Advancing debris flow hazard and risk assessments using debris flow modeling and radar derived rainfall intensity data

Debris flow hazard and risk assessments are critical tools in mitigating and planning for these events. Existing debris flow hazard assessments can provide a rapid view of the likelihood of debris flows in recently burned watersheds and along stream segments within the watershed. Furthermore, debris flow volumes can be predicted for these watersheds and along the stream segments. Advances in modeling and remote-sensing data can add further value to the rapid assessments. Here, modeled debris flow volumes and a more detailed understanding of rainfall conditions highlight a need to reconcile debris flow probabilities and volumes using local conditions. Modeled debris flow volumes are consistently lower than even the lowest predicted volumes from empirical models used in the debris flow hazard assessments. Watershed probability and volume relations also over predict based on our probabilities derived from rainfall intensity from Multi-Radar/Multi-Sensor System 1-hr data. Probability and volume measures need to be further considered as the conservative measures of the rapid assessments have implication for risk analyses required for planning and management decisions, and ultimately for design and cost of mitigation to manage risk

What does landslide triggering rainfall mean?

Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and durations. Probabilistic distribution of landslides for a given study area and rainfall intensity can be easily derived using the binomial method from these relations

Three years of morphologic changes at a bowl blowout, Cape Cod, USA

© 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (July 2017) in accordance with the publisher’s archiving policyThis study presents measurements of blowout topography obtained with annual terrestrial laser surveys carried out over a three-year period at a single, large bowl blowout located in the Provincelands Dunes section of Cape Cod National Seashore, in Massachusetts. The study blowout was selected because its axis is aligned with northwest winds that dominate the region, and because it was seemingly interacting with a smaller saucer blowout that had recently formed on the southern rim of the primary feature. Assuming that blowouts enlarge both horizontally and vertically in response to the wind regime, the objectives of the study were to determine both the amount of horizontal growth that the blowout experiences annually and the spatial patterns of vertical change that occur within the blowout. Changes to the blowout lobe surrounding the feature were also determined for areas with sparse enough vegetation cover to allow laser returns from the sand surface. The results show that the blowout consistently expanded outward during the three years, with the greatest expansion occurring at its southeast corner, opposite the prevailing winds. The most significant occurrence was the removal, in the first year, of the ridge that separated the two blowouts, resulting in a major horizontal shift of the southern rim of the new combined blowout. This displacement then continued at a lesser rate in subsequent years. The rim also shifted horizontally along the northwest to northeast sections of the blowout. Significant vertical loss occurred along the main axis of the blowout with the greatest loss concentrated along the southeast rim. On the lobe, there were large areas of deposition immediately downwind of the high erosion zones inside the blowout. However, there were also small erosion areas on the lobe, extending downwind from eroding sections of the rim. This study shows that: 1. blowouts can experience significant areal and volumetric changes in short periods of time; 2. significant changes may occur relatively suddenly when adjacent blowouts combine into a single feature; and 3. the sediment transport paths are highly controlled by the topography. The joining of two blowouts not only creates a new larger feature, but it also releases large amounts of sediment that are then distributed across the landscape downwind, creating a potential for major changes to a landscape over the longer term

Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review

International audienceThe study of mass and energy transfer across landscapes has recently evolved to comprehensive considerations acknowledging the role of biota and humans as geomorphic agents, as well as the importance of small-scale landscape features. A contributing and supporting factor to this evolution is the emergence over the last two decades of technologies able to acquire high resolution topography (HRT) (meter and sub-meter resolution) data. Landscape features can now be captured at an appropriately fine spatial resolution at which surface processes operate; this has revolutionized the way we study Earth-surface processes. The wealth of information contained in HRT also presents considerable challenges. For example, selection of the most appropriate type of HRT data for a given application is not trivial. No definitive approach exists for identifying and filtering erroneous or unwanted data, yet inappropriate filtering can create artifacts or eliminate/distort critical features. Estimates of errors and uncertainty are often poorly defined and typically fail to represent the spatial heterogeneity of the dataset, which may introduce bias or error for many analyses. For ease of use, gridded products are typically preferred rather than the more information-rich point cloud representations. Thus many users take advantage of only a fraction of the available data, which has furthermore been subjected to a series of operations often not known or investigated by the user. Lastly, standard HRT analysis work-flows are yet to be established for many popular HRT operations, which has contributed to the limited use of point cloud data.In this review, we identify key research questions relevant to the Earth-surface processes community within the theme of mass and energy transfer across landscapes and offer guidance on how to identify the most appropriate topographic data type for the analysis of interest. We describe the operations commonly performed from raw data to raster products and we identify key considerations and suggest appropriate work-flows for each, pointing to useful resources and available tools. Future research directions should stimulate further development of tools that take advantage of the wealth of information contained in the HRT data and address the present and upcoming research needs such as the ability to filter out unwanted data, compute spatially variable estimates of uncertainty and perform multi-scale analyses. While we focus primarily on HRT applications for mass and energy transfer, we envision this review to be relevant beyond the Earth-surface processes community for a much broader range of applications involving the analysis of HRT

Debris Flow Hazard Mapping Along Linear Infrastructure: An Agent Based Model and GIS Approach

Often linear infrastructure, including rail, highways, and pipelines, span large geographic areas intersecting a variety of terrain, predisposing infrastructure to a higher likelihood of geohazard interaction. Debris flow models can be particularly advantageous in remote hazard and risk mapping along linear infrastructure as runout from susceptible slopes may extend considerable distance downslope to a receptor. In this sentiment, a method is developed using an agent-based model, DebrisFlow Predictor, in combination with geographic information software (GIS), to produce regional debris flow hazard and risk profiles along widespread corridors. Thousands of debris flows upslope of a receptor(s) are simulated in the model environment (i.e., model scenarios). Outputs of the modelled scenarios provide probabilistic spatial attributes of debris flow runout and depth across a digital elevation model at a 5m resolution. The outputs of multiple scenarios are mosaiced and corrected to in-situ temporal and spatial debris flow initiation conditions in GIS. The corrected scenario outputs provide a comprehensive hazard profile along the infrastructure alignment, that in turn can facilitate quantified vulnerability and risk calculations. Thousands of modelled debris flows throughout several physiographic regions of Canada and the United States of America, calibrated to local conditions, provide substantive support for a novel methodology to identify key hazard and risk locations to major linear infrastructure

Post-Wildfire Debris Flow and Large Woody Debris Transport Modeling from the North Complex Fire to Lake Oroville

The increase in wildfires across much of Western United States has a significant impact on the water quantity, water quality, and sediment and large woody debris transport (LWD) within the watershed of reservoirs. There is a need to understand the volume and fate of LWD transported by post-wildfire debris flows to the Lake Oroville Reservoir, north of Sacramento, California. Here, we combine debris flow modeling, hydrologic and hydraulic modeling, and large woody debris transport modeling to assess how much LWD is transported from medium and small watersheds to Lake Oroville. Debris flow modeling, triggered by a 50-year rainfall intensity, from 13 watersheds, transported 1073 pieces (1579.7 m3) of LWD to the mainstem river. Large woody debris transport modeling was performed for 1-, 2-, 5-, 25-, 50-, 100-, and 500-year flows. The transport ratio increased with discharge as expected. LWD is transported to the reservoir during a 2-year event with a transport ratio of 25% with no removal of LWD and 9% with removal of LWD greater than the cross-section width. The 500-year event produced transport ratios of 58% and 46% in our two sub scenarios