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Glacial lakes in the Hindu Kush–Karakoram–Himalayas–Nyainqentanglha (HKKHN) region have grown rapidly in number and area in past decades, and some dozens have drained in catastrophic glacial lake outburst floods (GLOFs). Estimating regional susceptibility of glacial lakes has largely relied on qualitative assessments by experts, thus motivating a more systematic and quantitative appraisal. Before the backdrop of current climate-change projections and the potential of elevation-dependent warming, an objective and regionally consistent assessment is urgently needed. We use an inventory of 3390 moraine-dammed lakes and their documented outburst history in the past four decades to test whether elevation, lake area and its rate of change, glacier-mass balance, and monsoonality are useful inputs to a probabilistic classification model. We implement these candidate predictors in four Bayesian multi-level logistic regression models to estimate the posterior susceptibility to GLOFs. We find that mostly larger lakes have been more prone to GLOFs in the past four decades regardless of the elevation band in which they occurred. We also find that including the regional average glacier-mass balance improves the model classification. In contrast, changes in lake area and monsoonality play ambiguous roles. Our study provides first quantitative evidence that GLOF susceptibility in the HKKHN scales with lake area, though less so with its dynamics. Our probabilistic prognoses offer improvement compared to a random classification based on average GLOF frequency. Yet they also reveal some major uncertainties that have remained largely unquantified previously and that challenge the applicability of single models. Ensembles of multiple models could be a viable alternative for more accurately classifying the susceptibility of moraine-dammed lakes to GLOFs.
"In spite of ever-increasing research into natural hazards, the reported damage from natural disasters continues to rise, increasingly disrupting human activities. We, as scientists who study the way in which the part of Earth most relevant to society- the surface-behaves, are disturbed and frustrated by this trend. It appears that the large amounts of funding devoted each year to research into reducing the impacts of natural disasters could be much more effective in producing useful results. At the same time we are aware that society, as represented by its decision makers, while increasingly concerned at the impacts of natural disasters on lives and economies, is reluctant to acknowledge the intrinsic activity of Earth's surface and to take steps to adapt societal behaviour to minimise the impacts of natural disasters. Understanding and managing natural hazards and disasters are beyond matters of applied earth science, and also involve considering human societal, economic and political decisions"
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.
Models for the predictions of monetary losses from floods mainly blend data deemed to represent a single flood type and region. Moreover, these approaches largely ignore indicators of preparedness and how predictors may vary between regions and events, challenging the transferability of flood loss models. We use a flood loss database of 1812 German flood-affected households to explore how Bayesian multilevel models can estimate normalised flood damage stratified by event, region, or flood process type. Multilevel models acknowledge natural groups in the data and allow each group to learn from others. We obtain posterior estimates that differ between flood types, with credibly varying influences of water depth, contamination, duration, implementation of property-level precautionary measures, insurance, and previous flood experience; these influences overlap across most events or regions, however. We infer that the underlying damaging processes of distinct flood types deserve further attention. Each reported flood loss and affected region involved mixed flood types, likely explaining the uncertainty in the coefficients. Our results emphasise the need to consider flood types as an important step towards applying flood loss models elsewhere. We argue that failing to do so may unduly generalise the model and systematically bias loss estimations from empirical data.
Many of the volcanic plateau margins of the eastern, formerly glaciated, foreland of the Patagonian Andes are undermined by giant landslides (>= 10(8) m(3)). One cluster of such landslides extends along the margin of the Meseta del Lago Buenos Aires (MLBA) plateau that is formed mainly by Neogene-Quaternary basalts. The dry climate is at odds with numerous >2-km long earthflows nested within older and larger compound landslides. We present a hydrological analysis, a detailed geomorphic map, interpretations of exposed landslide interiors, and radiocarbon dating of the El Mirador landslide, which is one of the largest and morphologically most representative landslide. We find that the presence of lakes on top of the plateau, causing low infiltration rates, correlates negatively with the abundance of earthflows on compound landslides along the plateau margins. Field outcrops show that the pattern of compound landslides and earthflows is likely controlled by groundwater seepage at the contact between the basalts and underlying soft Miocene molasse. Numerous peat bogs store water and sediment and are more abundant in earthflow-affected areas than in their contributing catchment areas. <br /> Radiocarbon dates indicate that these earthflows displaced metre-thick layers of peat in the late Holocene (<2.5 ka). We conclude that earthflows of the MLBA plateau might be promising proxies of past hydroclimatic conditions in the Patagonian foreland, if strong earthquakes or gradual crustal stress changes due to glacioisostatic rebound can be ruled out.
Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression-one of the most widely used susceptibility models-to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change.
Increased landslide activity on forested hillslopes following two recent volcanic eruptions in Chile
(2019)
Large explosive eruptions can bury landscapes beneath thick layers of tephra. Rivers subsequently overloaded with excess pyroclastic sediments have some of the highest reported specific sediment yields. Much less is known about how hillslopes respond to tephra loads. Here, we report a pulsed and distinctly delayed increase in landslide activity following the eruptions of the Chaiten (2008) and Puyehue-Cordon Caulle (2011) volcanoes in southern Chile. Remote-sensing data reveal that land-slides clustered in densely forested hillslopes mostly two to six years after being covered by tephra. This lagged instability is consistent with a gradual loss of shear strength of decaying tree roots in areas of high tephra loads. Surrounding areas with comparable topography, forest cover, rainfall and lithology maintained landslide rates roughly ten times lower. The landslides eroded the landscape by up to 4.8 mm on average within 30 km of both volcanoes, mobilizing up to 1.6 MtC at rates of about 265 tC km(-2) yr(-1). We suggest that these yields may reinforce the elevated river loads of sediment and organic carbon in the decade after the eruptions. We recommend that studies of post-eruptive mass fluxes and hazards include lagged landslide responses of tephra-covered forested hillslopes, to avoid substantial underestimates.
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.
Shrinking glaciers in the Hindu Kush-Karakoram-Himalaya-Nyainqentanglha (HKKHN) region have formed several thousand moraine-dammed glacial lakes(1-3), some of these having grown rapidly in past decades(3,4). This growth may promote more frequent and potentially destructive glacial lake outburst floods (GLOFs)(5-7). Testing this hypothesis, however, is confounded by incomplete databases of the few reliable, though selective, case studies. Here we present a consistent Himalayan GLOF inventory derived automatically from all available Landsat imagery since the late 1980s. We more than double the known GLOF count and identify the southern Himalayas as a hotspot region, compared to the more rarely affected Hindu Kush-Karakoram ranges. Nevertheless, the average annual frequency of 1.3 GLOFs has no credible posterior trend despite reported increases in glacial lake areas in most of the HKKHN3,8, so that GLOF activity per unit lake area has decreased since the late 1980s. We conclude that learning more about the frequency and magnitude of outburst triggers, rather than focusing solely on rapidly growing glacial lakes, might improve the appraisal of GLOF hazards.