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Roads at risk
(2015)
Globalisation and interregional exchange of people, goods, and services has boosted the importance of and reliance on all kinds of transport networks. The linear structure of road networks is especially sensitive to natural hazards. In southern Norway, steep topography and extreme weather events promote frequent traffic disruption caused by debris flows. Topographic susceptibility and trigger frequency maps serve as input into a hazard appraisal at the scale of first-order catchments to quantify the impact of debris flows on the road network in terms of a failure likelihood of each link connecting two network vertices, e.g. road junctions. We compute total additional traffic loads as a function of traffic volume and excess distance, i.e. the extra length of an alternative path connecting two previously disrupted network vertices using a shortest-path algorithm. Our risk metric of link failure is the total additional annual traffic load, expressed as vehicle kilometres, because of debris-flow-related road closures. We present two scenarios demonstrating the impact of debris flows on the road network and quantify the associated path-failure likelihood between major cities in southern Norway. The scenarios indicate that major routes crossing the central and north-western part of the study area are associated with high link-failure risk. Yet options for detours on major routes are manifold and incur only little additional costs provided that drivers are sufficiently well informed about road closures. Our risk estimates may be of importance to road network managers and transport companies relying on speedy delivery of services and goods.
The use of topographic metrics for estimating the susceptibility to, and reconstructing the characteristics of, debris flows has a long research tradition, although largely devoted to humid mountainous terrain. The exceptional 2010 monsoonal rainstorms in the high-altitude mountain desert of Ladakh and Zanskar, NW India, were a painful reminder of how susceptible arid regions are to rainfall-triggered flash floods, landslides, and debris flows. The rainstorms of August 4-6 triggered numerous debris flows, killing 182 people, devastating 607 houses, and more than 10 bridges around Ladakh's capital of Leh. The lessons from this disaster motivated us to revisit methods of predicting (a) flow parameters such as peak discharge and maximum velocity from field and remote sensing data, and (b) the susceptibility to debris flows from catchment morphometry. We focus on quantifying uncertainties tied to these approaches. Comparison of high-resolution satellite images pre- and post-dating the 2010 rainstorm reveals the extent of damage and catastrophic channel widening. Computations based on these geomorphic markers indicate maximum flow velocities of 1.6-6.7 m s(-1) with runout of up to similar to 10 km on several alluvial fans that sustain most of the region's settlements. We estimate median peak discharges of 310-610 m(3) s(-1), which are largely consistent with previous estimates. Monte Carlo-based error propagation for a single given flow-reconstruction method returns a variance in discharge similar to one derived from juxtaposing several different flow reconstruction methods. We further compare discriminant analysis, classification tree modelling, and Bayesian logistic regression to predict debris-flow susceptibility from morphometric variables of 171 catchments in the Ladakh Range. These methods distinguish between fluvial and debris flow-prone catchments at similar success rates, but Bayesian logistic regression allows quantifying uncertainties and relationships between potential predictors. We conclude that, in order to be robust and reliable, morphometric reconstruction of debris-flow properties and susceptibility requires careful assessment and reporting of errors and uncertainties. (C) 2015 Elsevier B.V. All rights reserved.
Sediments in rivers record the dynamics of erosion processes. While bulk sediment fluxes are easily and routinely obtained, sediment caliber remains underexplored when inferring erosion mechanisms. Yet sediment grain size distributions may be the key to discriminating their origin. We have studied grain size-specific suspended sediment fluxes in the Kali Gandaki, a major trans-Himalayan river. Two strategically located gauging stations enable tracing of sediment caliber on either side of the Himalayan orographic barrier. The data show that fine sediment input into the northern headwaters is persistent, while coarse sediment comes from the High Himalayas during the summer monsoon. A temporally matching landslide inventory similarly indicates the prominence of monsoon-driven hillslope mass wasting. Thus, mechanisms of sediment supply can leave strong traces in the fluvial caliber, which could project well beyond the mountain front and add to the variability of the sedimentary record of orogen erosion.
Mass wasting is an important process for denuding hillslopes and lowering ridge crests in active mountain belts such as the Himalaya-Karakoram ranges (HKR). Such a high-relief landscape is likely to be at its mechanical threshold, maintained by competing rapid rock uplift, river incision, and pervasive slope failure. We introduce excess topography, Z(E), for quantifying potentially unstable rock-mass volumes inclined at angles greater than a specified threshold angle. We find that Z(E) peaks along major fluvial and glacial inner gorges, which is also where the majority of 492 large (>0.1 km(2)) rock-slope failures occur in the Himalaya's largest cluster of documented Pleistocene to Holocene bedrock landslides. Our data reveal that bedrock landslides in the HKR chiefly detached from near or below the median elevation, whereas glaciers and rock glaciers occupy higher-elevation bands almost exclusively. Less than 10% of the area of the HKR is upslope of glaciers, such that possible censoring of evidence of large bedrock landslides above the permanent snow line barely affects this finding. Bedrock landslides appear to preferentially undermine topographic relief in response to fluvial and glacial incision along inner gorges, unless more frequent and smaller undetected failures, or rigorous (peri-)glacial erosion, compensate for this role at higher elevation. Either way, the distinct patterns of excess topography and large bedrock landsliding in the HKR juxtapose two stacked domains of landslide and (peri-)glacial erosion that may respond to different time scales of perturbation. Our findings call for more detailed analysis of vertical erosional domains and their geomorphic coupling in active mountain belts.
In this study, we investigate how immersive 3D geovisualization can be used in higher education. Based on MacEachren and Kraak's geovisualization cube, we examine the usage of immersive 3D geovisualization and its usefulness in a research-based learning module on flood risk, called GEOSimulator. Results of a survey among participating students reveal benefits, such as better orientation in the study area, higher interactivity with the data, improved discourse among students and enhanced motivation through immersive 3D geovisualization. This suggests that immersive 3D visualization can effectively be used in higher education and that 3D CAVE settings enhance interactive learning between students.
The 2008 eruption of Chaiten volcano in southern Chile severely impacted several densely forested river catchments by supplying excess pyroclastic sediment to the channel networks. Our aim is to substantiate whether and how channel geometry and forest stands changed in the Rayas River following the sudden input of pyroclastic sediment. We measured the resulting changes to channel geometry and riparian forest stands along 17.6 km of the impacted gravel-bed Rayas River (294 km(2)) from multiple high-resolution satellite images, aerial photographs, and fieldwork to quantify yield volume characteristics of the forest stands. Limited channel changes during the last 60 years before the eruption reflect a dynamic equilibrium condition of the river corridor, despite the high annual precipitation and the sediment supply from Chaiten and Michinmahuida volcanoes in the headwaters. Images taken in 1945, 2004, and 2005 show that total size of the vegetated channel islands nearly doubled between 1945 and 2004 and remained unchanged between 2004 and 2005. Pyroclastic sediment entering the Rayas River after the 2008 eruption caused only minor average channel widening (7%), but killed all island vegetation in the study reach. Substantial shifts in the size distribution of in-channel vegetation patches reflect losses in total island area of 46% from 2005 to 2009 and an additional 34% from 2009 to 2012. The estimated pulsed release of organic carbon into the channel, mainly in the form of large wood from obliterated island and floodplain forests, was 78-400 tC/km/y and surpasses most documented yields from small mountainous catchments with similar rainfall, forest cover, and disturbance history, while making up between 20% and 60% of the annual carbon burial rate of fluvial sediments in the northern Patagonian fjords. We conclude that the carbon footprint of the 2008 Chaiten eruption on the Rayas River was more significant than the measured geomorphic impacts on channel geometry for the first five years following disturbance. The modest post-eruptive geomorphic response in this river is a poor indicator of its biogeochemical response. (C) 2015 Elsevier B.V. All rights reserved.
Limits to lichenometry
(2015)
Lichenometry is a straightforward and inexpensive method for dating Holocene rock surfaces. The rationale is that the diameter of the largest lichen scales with the age of the originally fresh rock surface that it colonised. The success of the method depends on finding the largest lichen diameters, a suitable lichen-growth model, and a robust calibration curve. Recent critique of the method motivates us to revisit the accuracy and uncertainties of lichenometry. Specifically, we test how well lichenometry is capable of resolving the ages of different lobes of large active rock glaciers in the Kyrgyz Tien Shan. We use a bootstrapped quantile regression to calibrate local growth curves of Xanthoria elegans, Aspicilia tianshanica, and Rhizocarpon geographicum, and report a nonlinear decrease in dating accuracy with increasing lichen diameter. A Bayesian type of an analysis of variance demonstrates that our calibration allows discriminating credibly between rock-glacier lobes of different ages despite the uncertainties tied to sample size and correctly identifying the largest lichen thalli. Our results also show that calibration error grows with lichen size, so that the separability of rock-glacier lobes of different ages decreases, while the tendency to assign coeval ages increases. The abundant young (<200 yr) specimen of fast-growing X elegans are in contrast with the fewer, slow-growing, but older (200-1500 yr) R. geographicum and A. tianshanica, and record either a regional reactivation of lobes in the past 200 years, or simply a censoring effect of lichen mortality during early phases of colonisation. The high variance of lichen sizes captures the activity of rock-glacier lobes, which is difficult to explain by regional climatic cooling or earthquake triggers alone. Therefore, we caution against inferring palaeoclimatic conditions from the topographic position of rock-glacier lobes. We conclude that lichenometry works better as a tool for establishing a relative, rather than an absolute, chronology of rock-glacier lobes in the northern Tien Shan. (C) 2015 Elsevier Ltd. All rights reserved.
Many mountain belts sustain prolonged snow cover for parts of the year, although enquiries into rates of erosion in these landscapes have focused almost exclusively on the snow-free periods. This raises the question of whether annual snow cover contributes significantly to modulating rates of erosion in high-relief terrain. In this context, the sudden release of snow avalanches is a frequent and potentially relevant process, judging from the physical damage to subalpine forest ecosystems, and the amount of debris contained in avalanche deposits. To quantitatively constrain this visual impression and to expand the sparse literature, we sampled sediment concentrations of n = 28 river-spanning snow-avalanche deposits (snow bridges) in the area around Davos, eastern Swiss Alps, and inferred an orders-of-magnitude variability in specific fine sediment and organic carbon yields (1.8 to 830 t km(-2) yr(-1), and 0.04 to 131 tC km(-2) yr(-1), respectively). A Monte Carlo simulation demonstrates that, with a minimum of free parameters, such variability is inherent to the geometric scaling used for computing specific yields. Moreover, the widely applied method of linearly extrapolating plot scale sample data may be prone to substantial under- or overestimates. A comparison of our inferred yields with previously published work demonstrates the relevance of wet snow avalanches as prominent agents of soil erosion and transporters of biogeochemical constituents to mountain rivers. Given that a number of snow bridges persisted below the insulating debris cover well into the summer months, snow-avalanche deposits also contribute to regulating in-channel sediment and organic debris storage on seasonal timescales. Finally, our results underline the potential shortcomings of neglecting erosional processes in the winter and spring months in mountainous terrain subjected to prominent snow cover.
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.
Deforestation is a prominent anthropogenic cause of erosive overland flow and slope instability, boosting rates of soil erosion and concomitant sediment flux. Conventional methods of gauging or estimating post-logging sediment flux often focus on annual timescales but overlook potentially important process response on shorter intervals immediately following timber harvest. We resolve such dynamics with non-parametric quantile regression forests (QRF) based on high-frequency (3 min) discharge measurements and sediment concentration data sampled every 30-60 min in similar-sized (similar to 0.1 km(2)) forested Chilean catchments that were logged during either the rainy or the dry season. The method of QRF builds on the random forest algorithm, and combines quantile regression with repeated random sub-sampling of both cases and predictors. The algorithm belongs to the family of decision-tree classifiers, which allow quantifying relevant predictors in high-dimensional parameter space. We find that, where no logging occurred, similar to 80% of the total sediment load was transported during extremely variable runoff events during only 5% of the monitoring period. In particular, dry-season logging dampened the relative role of these rare, extreme sediment-transport events by increasing load efficiency during more efficient moderate events. We show that QRFs outperform traditional sediment rating curves (SRCs) in terms of accurately simulating short-term dynamics of sediment flux, and conclude that QRF may reliably support forest management recommendations by providing robust simulations of post-logging response of water and sediment fluxes at high temporal resolution.