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Lacustrine sediments have been widely used to investigate past climatic and environmental changes on millennial to seasonal time scales. Sedimentary archives of lakes in mountainous regions may also record non-climatic events such as earthquakes. We argue herein that a set of 64 annual laminae couplets reconciles a stratigraphically inconsistent accelerator mass spectrometry (AMS) C-14 chronology in a similar to 4-m-long sediment core from Lake Mengda, in the north-eastern Tibetan Plateau. The laminations suggest the lake was formed by a large landslide, triggered by the 1927 Gulang earthquake (M = 8.0). The lake sediment sequence can be separated into three units based on lithologic, sedimentary, and isotopic characteristics. Starting from the bottom of the sequence, these are: (1) unweathered, coarse, sandy valley-floor deposits or landslide debris that pre-date the lake, (2) landslide-induced, fine-grained soil or reworked landslide debris with a high organic content, and (3) lacustrine sediments with low organic content and laminations. These annual laminations provide a high-resolution record of anthropogenic and environmental changes during the twentieth century, recording enhanced sediment input associated with two phases of construction activities. The high mean sedimentation rates of up to 4.8 mm year(-1) underscore the potential for reconstructing such distinct sediment pulses in remote, forested, and seemingly undisturbed mountain catchments.
Quantitative estimates of sea-level rise in the Mediterranean Basin become increasingly accurate thanks to detailed satellite monitoring. However, such measuring campaigns cover several years to decades, while longer-term sea-level records are rare for the Mediterranean. We used a data archeological approach to reanalyze monthly mean sea-level data of the Antalya-I (1935–1977) tide gauge to fill this gap. We checked the accuracy and reliability of these data before merging them with the more recent records of the Antalya-II (1985–2009) tide gauge, accounting for an eight-year hiatus. We obtain a composite time series of monthly and annual mean sea levels spanning some 75 years, providing the longest record for the eastern Mediterranean Basin, and thus an essential tool for studying the region's recent sea-level trends. We estimate a relative mean sea-level rise of 2.2 ± 0.5 mm/year between 1935 and 2008, with an annual variability (expressed here as the standard deviation of the residuals, σresiduals = 41.4 mm) above that at the closest tide gauges (e.g., Thessaloniki, Greece, σresiduals = 29.0 mm). Relative sea-level rise accelerated to 6.0 ± 1.5 mm/year at Antalya-II; we attribute roughly half of this rate (~3.6 mm/year) to tectonic crustal motion and anthropogenic land subsidence. Our study highlights the value of data archeology for recovering and integrating historic tide gauge data for long-term sea-level and climate studies.
Modern natural hazards research requires dealing with several uncertainties that arise from limited process knowledge, measurement errors, censored and incomplete observations, and the intrinsic randomness of the governing processes. Nevertheless, deterministic analyses are still widely used in quantitative hazard assessments despite the pitfall of misestimating the hazard and any ensuing risks.
In this paper we show that Bayesian networks offer a flexible framework for capturing and expressing a broad range of uncertainties encountered in natural hazard assessments. Although Bayesian networks are well studied in theory, their application to real-world data is far from straightforward, and requires specific tailoring and adaptation of existing algorithms. We offer suggestions as how to tackle frequently arising problems in this context and mainly concentrate on the handling of continuous variables, incomplete data sets, and the interaction of both. By way of three case studies from earthquake, flood, and landslide research, we demonstrate the method of data-driven Bayesian network learning, and showcase the flexibility, applicability, and benefits of this approach.
Our results offer fresh and partly counterintuitive insights into well-studied multivariate problems of earthquake-induced ground motion prediction, accurate flood damage quantification, and spatially explicit landslide prediction at the regional scale. In particular, we highlight how Bayesian networks help to express information flow and independence assumptions between candidate predictors. Such knowledge is pivotal in providing scientists and decision makers with well-informed strategies for selecting adequate predictor variables for quantitative natural hazard assessments.
Natural and human-induced erosion supplies high amounts of soil organic carbon (OC) to terrestrial drainage networks. Yet OC fluxes in rivers were considered in global budgets only recently. Modern estimates of annual carbon burial in inland river sediments of 0.6 Gt C, or 22% of C transferred from terrestrial ecosystems to river channels, consider only lakes and reservoirs and disregard any long-term carbon burial in hillslope or floodplain sediments. Here we present the first assessment of sediment-bound OC storage in Central Europe from a synthesis of similar to 1500 Holocene hillslope and floodplain sedimentary archives. We show that sediment storage increases with drainage-basin size due to more extensive floodplains in larger river basins. However, hillslopes retain hitherto unrecognized high amounts of eroded soils at the scale of large river basins such that average agricultural erosion rates during the Holocene would have been at least twice as high as reported previously. This anthropogenic hillslope sediment storage exceeds floodplain storage in drainage basins <10(5) km(2), challenging the notion that floodplains are the dominant sedimentary sinks. In terms of carbon burial, OC concentrations in floodplains exceed those on hillslopes, and net OC accumulation rates in floodplains (0.70.2 g C m(-2)a(-1)) surpass those on hillslopes (0.40.1 g C m(-2)a(-1)) over the last 7500 years. We conclude that carbon burial in floodplains and on hillslopes in Central Europe exceeds terrestrial carbon storage in lakes and reservoirs by at least 2 orders of magnitude and should thus be considered in continental carbon budgets.
Several thousands of moraine-dammed and supraglacial lakes spread over the Hindu Kush Himalayan (HKH) region, and some have grown rapidly in past decades due to glacier retreat. The sudden emptying of these lakes releases large volumes of water and sediment in destructive glacial lake outburst floods (GLOFs), one of the most publicised natural hazards to the rapidly growing Himalayan population. Despite the growing number and size of glacial lakes, the frequency of documented GLOFs is remarkably constant. We explore this possible reporting bias and offer a new processing chain for establishing a more complete Himalayan GLOF inventory. We make use of the full seasonal archive of Landsat images between 1988 and 2016, and track automatically where GLOFs left shrinking water bodies, and tails of sediment at high elevations. We trained a Random Forest classifier to generate fuzzy land cover maps for 2491 images, achieving overall accuracies of 91%. We developed a likelihood-based change point technique to estimate the timing of GLOFs at the pixel scale. Our method objectively detected ten out of eleven documented GLOFs, and another ten lakes that gave rise to previously unreported GLOFs. We thus nearly doubled the existing GLOF record for a study area covering similar to 10% of the HKH region. Remaining challenges for automatically detecting GLOFs include image insufficiently accurate co-registration, misclassifications in the land cover maps and image noise from clouds, shadows or ice. Yet our processing chain is robust and has the potential for being applied on the greater HKH and mountain ranges elsewhere, opening the door for objectively expanding the knowledge base on GLOF activity over the past three decades.
Quantitative estimates of sediment flux and the global cycling of sediments from hillslopes to rivers, estuaries, deltas, continental shelves, and deep-sea basins have a long research tradition. In this context, extremely large and commensurately rare sediment transport events have so far eluded a systematic analysis. To start filling this knowledge gap I review some of the highest reported sediment yields in mountain rivers impacted by volcanic eruptions, earthquake- and storm-triggered landslide episodes, and catastrophic dam breaks. Extreme specific yields, defined here as those exceeding the 95th percentile of compiled data, are similar to 10(4) t km(-2) yr(-1) if averaged over 1 yr. These extreme yields vary by eight orders of magnitude, but systematically decay with reference intervals from minutes to millennia such that yields vary by three orders of magnitude for a given reference interval. Sediment delivery from natural dam breaks and pyroclastic eruptions dominate these yields for a given reference interval. Even if averaged over 10(2)-10(3) yr, the contribution of individual disturbances may remain elevated above corresponding catchment denudation rates. I further estimate rates of sediment (re-)mobilisation by individual giant terrestrial and submarine mass movements. Less than 50 postglacial submarine mass movements have involved an equivalent of similar to 10% of the contemporary annual global flux of fluvial sediment to Earth's oceans, while mobilisation rates by individual events rival the decadal-scale sediment discharge from tectonically active orogens such as Taiwan or New Zealand. Sediment flushing associated with catastrophic natural dam breaks is non-stationary and shows a distinct kink at the last glacial-interglacial transition, owing to the drainage of very large late Pleistocene ice-marginal lakes. Besides emphasising the contribution of high-magnitude and low-frequency events to the global sediment cascade, these findings stress the importance of sediment storage for fuelling rather than buffering high sediment transport rates.
The propagation of a seismic rupture on a fault introduces spatial variations in the seismic wave field surrounding the fault. This directivity effect results in larger shaking amplitudes in the rupture propagation direction. Its seismic radiation pattern also causes amplitude variations between the strike-normal and strike-parallel components of horizontal ground motion. We investigated the landslide response to these effects during the 2016 Kumamoto earthquake (M-w 7.1) in central Kyushu (Japan). Although the distribution of some 1500 earthquake-triggered landslides as a function of rupture distance is consistent with the observed Arias intensity, the landslides were more concentrated to the northeast of the southwest-northeast striking rupture. We examined several landslide susceptibility factors: hillslope inclination, the median amplification factor (MAF) of ground shaking, lithology, land cover, and topographic wetness. None of these factors sufficiently explains the landslide distribution or orientation (aspect), although the landslide head scarps have an elevated hillslope inclination and MAF. We propose a new physics-based ground-motion model (GMM) that accounts for the seismic rupture effects, and we demonstrate that the low-frequency seismic radiation pattern is consistent with the overall landslide distribution. Its spatial pattern is influenced by the rupture directivity effect, whereas landslide aspect is influenced by amplitude variations between the fault-normal and fault-parallel motion at frequencies < 2 Hz. This azimuth dependence implies that comparable landslide concentrations can occur at different distances from the rupture. This quantitative link between the prevalent landslide aspect and the low-frequency seismic radiation pattern can improve coseismic landslide hazard assessment.
The Norwegian traffic network is impacted by about 2000 landslides, avalanches, and debris flows each year that incur high economic losses. Despite the urgent need to mitigate future losses, efforts to locate potential debris flow source areas have been rare at the regional scale. We tackle this research gap by exploring a minimal set of possible topographic predictors of debris flow initiation that we input to a Weights-of-Evidence (WofE) model for mapping the regional susceptibility to debris flows in western Norway. We use an inventory of 429 debris flows that were recorded between 1979 and 2008, and use the terrain variables of slope, total curvature, and contributing area (flow accumulation) to compute the posterior probabilities of local debris flow occurrence. The novelty of our approach is that we quantify the uncertainties in the WofE approach arising from different predictor classification schemes and data input, while estimating model accuracy and predictive performance from independent test data. Our results show that a percentile-based classification scheme excels over a manual classification of the predictor variables because differing abundances in manually defined bins reduce the reliability of the conditional independence tests, a key, and often neglected, prerequisite for the WofE method. The conditional dependence between total curvature and flow accumulation precludes their joint use in the model. Slope gradient has the highest true positive rate (88%), although the fraction of area classified as susceptible is very large (37%). The predictive performance, i.e. the reduction of false positives, is improved when combined with either total curvature or flow accumulation. Bootstrapping shows that the combination of slope and flow accumulation provides more reliable predictions than the combination of slope and total curvature, and helps refining the use of slope-area plots for identifying morphometric fingerprints of debris flow source areas, an approach used outside the field of landslide susceptibility assessments.