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Am Abend des 29. Mai 2016 wurde der Ort Braunsbach im Landkreis Schwäbisch-Hall (Baden-Württemberg) von einer Sturzflut getroffen, bei der mehrere Häuser stark beschädigt oder zerstört wurden. Die Sturzflut war eine der Unwetterfolgen, die im Frühsommer 2016 vom Tiefdruckgebiet Elvira ausgelöst wurden. Der vorliegende Bericht ist der zweite Teil einer Doppelveröffentlichung, welche die Ergebnisse zur Untersuchung des Sturzflutereignisses im Rahmen des DFG-Graduiertenkollegs “Naturgefahren und Risiken in einer sich verändernden Welt” (NatRiskChange, GRK 2043/1) der Universität Potsdam präsentiert. Während Teil 1 die meteorologischen und hydrologischen Ereignisse analysiert, fokussiert Teil 2 auf die geomorphologischen Prozesse und die verursachten Gebäudeschäden. Dazu wurden Ursprung und Ausmaß des während des Sturzflutereignisses mobilisierten und in den Ort getragenen Materials untersucht. Des Weiteren wurden zu 96 betroffenen Gebäuden Daten zum Schadensgrad sowie Prozess- und Gebäudecharakteristika aufgenommen und ausgewertet. Die Untersuchungen zeigen, dass bei der Betrachtung von Hochwassergefährdung die Berücksichtigung von Sturzfluten und ihrer speziellen Charakteristika, wie hoher Feststofftransport und sprunghaftes Verhalten insbesondere in bebautem Gelände, wesentlich ist, um effektive Schutzmaßnahmen ergreifen zu können.
The Tibetan Plateau, the world's largest orogenic plateau, hosts thousands of lakes that play prominent roles as water resources, environmental archives, and sources of natural hazards such as glacier lake outburst floods. Previous studies have reported that the size of lakes on the Tibetan Plateau has changed rapidly in recent years, possibly because of atmospheric warming. Tracking these changes systematically with remote sensing data is challenging given the different spectral signatures of water, the potential for confusing lakes with glaciers, and difficulties in classifying frozen or partly frozen lakes. Object-based image analysis (OBIA) offers new opportunities for automated classification in this context, and we have explored this method for mapping lakes from LANDSAT images and Shuttle Radar Topography Mission (SRTM) elevation data. We tested our algorithm for most of the Tibetan Plateau, where lakes in tectonic depressions or blocked by glaciers and sediments have different surface colours and seasonal ice cover in images obtained in 1995 and 2015. We combined a modified normalised difference water index (MNDWI) with OBIA and local topographic slope data in order to classify lakes with an area > 10 km(2). Our method derived 323 water bodies, with a total area of 31,258 km(2), or 2.6% of the study area (in 2015). The same number of lakes had covered only 24,892 km(2) in 1995; lake area has increased by -26% in the past two decades. The classification had estimated producer's and user's accuracies of 0.98, with a Cohen's kappa and F-score of 0.98, and may thus be a useful approximation for quantifying regional hydrological budgets. We have shown that our method is flexible and transferable to detecting lakes in diverse physical settings on several continents with similar success rates.
The northern edge of the western central Tien Shan range is bounded by the Issyk-Ata fault situated south of Bishkek, the capital of Kyrgyzstan. Contraction in this thick-skinned orogen occurs with low-strain accumulation and long earthquake recurrence intervals. In the nineteenth to twentieth centuries, a sequence of large earthquakes with magnitudes between 6.9 and 8 affected the northern Tien Shan but left nearly the entire extent of the Issyk-Ata fault unruptured. Here, the only known historic earthquake ruptured in A.D. 1885 (M6.9) along the western end of the Issyk-Ata fault. Because earthquakes in low-strain regions often tend to cluster in time and may promote failure along nearby structures, the earthquake history of the northern Tien Shan represents an exceptional structural setting for studying fault behavior affected by an intraplate earthquake sequence. We present a paleoseismological study from one site (Belek) along the Issyk-Ata fault located east of the A.D. 1885 epicentral area. Our analysis combines a range of tools, including photogrammetry, differential Global Positioning System, 3D visualization, and age modeling with different dating methods (infrared stimulated luminescence, radiocarbon, U-series) to improve the reliability of an event chronology for the trench stratigraphy and fault geometry. We were able to distinguish three different surfacerupturing paleoearthquakes; these affected the area before 10.5 +/- 1.1 cal ka B.P., at similar to 5.6 +/- 1.0 cal ka B.P., and at similar to 630 +/- 100 cal B.P., respectively. Associated paleomagnitudes for the last two earthquakes range between M6.7 and 7.4, with a cumulative slip rate of 0.7 +/- 0.32 mm/yr. We did not find evidence for the A.D. 1885 event at Belek. Our study yielded two main overall results: first, it extends the regional historic and paleoseismic record; second, the documented rupture events along the Issyk-Ata fault suggest that this fault was not affected in its entirety; instead, these events indicate segmented rupture behavior.
Fjords and old-growth forests store large amounts of organic carbon. Yet the role of episodic disturbances, particularly volcanic eruptions, in mobilizing organic carbon in fjord landscapes covered by temperate rainforests remains poorly quantified. To this end, we estimated how much wood and soils were flushed to nearby fjords following the 2008 eruption of Chaiten volcano in south-central Chile, where pyroclastic sediments covered >12km(2) of pristine temperate rainforest. Field-based surveys of forest biomass, soil organic content, and dead wood transport reveal that the reworking of pyroclastic sediments delivered similar to 66,500+14,600/-14,500tC of large wood to two rivers entering the nearby Patagonian fjords in less than a decade. A similar volume of wood remains in dead tree stands and buried beneath pyroclastic deposits (similar to 79,900+21,100/-16,900tC) or stored in active river channels (5,900-10,600tC). We estimate that bank erosion mobilized similar to 132,300(+21,700)/(-30,600)tC of floodplain forest soil. Eroded and reworked forest soils have been accreting on coastal river deltas at >5mmyr(-1) since the eruption. While much of the large wood is transported out of the fjord by long-shore drift, the finer fraction from eroded forest soils is likely to be buried in the fjords. We conclude that the organic carbon fluxes boosted by rivers adjusting to high pyroclastic sediment loads may remain elevated for up to a decade and that Patagonian temperate rainforests disturbed by excessive loads of pyroclastic debris can be episodic short-lived carbon sources. Plain Language Summary Fjords and old-growth forests are important sinks of organic carbon. However, the role of volcanic eruptions in flushing organic carbon in fjord landscapes remains unexplored. Here we estimated how much forest vegetation and soils were lost to fjords following the 2008 eruption ofunknownChaiten volcano in south-central Chile. Pyroclastic sediments obliterated near-pristine temperateunknownrainforest, and the subsequent reworking of these sediments delivered in less than a decade similar to 66,000 tC of large wood to the mountain rivers, draining into the nearby Patagonian fjords. A similar volume of wood remains in dead tree stands and buried beneath pyroclastic deposits or stored in active riverunknownchannels. We estimate that similar to 130,000 tC of organic carbon-rich soil was lost to erosion, thus adding to the carbon loads. While much of the wood enters the long-shore drift in the fjord heads, the finerunknownfraction from eroded forest soils is likely to be buried in the fjords at rates that exceed regional estimates by an order of magnitude. We anticipate that these eruption-driven fluxes will remain elevated forunknownthe coming years and that Patagonian temperate rainforests episodically switch from carbon sinks to hitherto undocumented carbon sources if disturbed by explosive volcanic eruptions.
Moderate to large earthquakes can increase the amount of water feeding stream flows, mobilizing excess water from deep groundwater, shallow groundwater, or the vadose zone. Here we examine the regional pattern of streamflow response to the Maule M8.8 earthquake across Chile's diverse topographic and hydro-climatic gradients. We combine streamflow analyses with groundwater flow modeling and a random forest classifier, and find that, after the earthquake, at least 85 streams had a change in flow. Discharge mostly increased () shortly after the earthquake, liberating an excess water volume of >1.1 km3, which is the largest ever reported following an earthquake. Several catchments had increased discharge of >50 mm, locally exceeding seasonal streamflow discharge under undisturbed conditions. Our modeling results favor enhanced vertical permeability induced by dynamic strain as the most probable process explaining the observed changes at the regional scale. Supporting this interpretation, our random forest classification identifies peak ground velocity and elevation extremes as most important for predicting streamflow response. Given the mean recurrence interval of ∼25 yr for >M8.0 earthquakes along the Peru–Chile Trench, our observations highlight the role of earthquakes in the regional water cycle, especially in arid environments.
Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery
(2017)
Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of >0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by >80% occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.
Regional snow-avalanche detection using object-based image analysis of near-infrared aerial imagery
(2017)
Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.