@article{SchwanghartWorniHuggeletal.2016,
  author    = {Schwanghart, Wolfgang and Worni, Raphael and Huggel, Christian and Stoffel, Markus and Korup, Oliver},
  title     = {Uncertainty in the Himalayan energy-water nexus: estimating regional exposure to glacial lake outburst floods},
  series = {Environmental research letters},
  volume    = {11},
  journal   = {Environmental research letters},
  publisher = {IOP Publ. Ltd.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/11/7/074005},
  pages     = {9},
  year      = {2016},
  abstract  = {Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia's soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66\% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.},
  language  = {en}
}
@misc{SchwanghartWorniHuggeletal.2016,
  author    = {Schwanghart, Wolfgang and Worni, Raphael and Huggel, Christian and Stoffel, Markus and Korup, Oliver},
  title     = {Uncertainty in the Himalayan energy-water nexus},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-97136},
  pages     = {9},
  year      = {2016},
  abstract  = {Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia's soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66\% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.},
  language  = {en}
}
@article{SchwanghartWorniHuggeletal.2016,
  author    = {Schwanghart, Wolfgang and Worni, Raphael and Huggel, Christian and Stoffel, Markus and Korup, Oliver},
  title     = {Uncertainty in the Himalayan energy-water nexus},
  series = {Environmental research letters : ERL},
  volume    = {11},
  journal   = {Environmental research letters : ERL},
  publisher = {IOP Publ.},
  address   = {Bristol},
  issn      = {1748-9326},
  doi       = {10.1088/1748-9326/11/7/074005},
  pages     = {9},
  year      = {2016},
  abstract  = {Himalayan water resources attract a rapidly growing number of hydroelectric power projects (HPP) to satisfy Asia's soaring energy demands. Yet HPP operating or planned in steep, glacier-fed mountain rivers face hazards of glacial lake outburst floods (GLOFs) that can damage hydropower infrastructure, alter water and sediment yields, and compromise livelihoods downstream. Detailed appraisals of such GLOF hazards are limited to case studies, however, and a more comprehensive, systematic analysis remains elusive. To this end we estimate the regional exposure of 257 Himalayan HPP to GLOFs, using a flood-wave propagation model fed by Monte Carlo-derived outburst volumes of >2300 glacial lakes. We interpret the spread of thus modeled peak discharges as a predictive uncertainty that arises mainly from outburst volumes and dam-breach rates that are difficult to assess before dams fail. With 66\% of sampled HPP are on potential GLOF tracks, up to one third of these HPP could experience GLOF discharges well above local design floods, as hydropower development continues to seek higher sites closer to glacial lakes. We compute that this systematic push of HPP into headwaters effectively doubles the uncertainty about GLOF peak discharge in these locations. Peak discharges farther downstream, in contrast, are easier to predict because GLOF waves attenuate rapidly. Considering this systematic pattern of regional GLOF exposure might aid the site selection of future Himalayan HPP. Our method can augment, and help to regularly update, current hazard assessments, given that global warming is likely changing the number and size of Himalayan meltwater lakes.},
  language  = {en}
}
@article{SchwanghartBernhardtStolleetal.2016,
  author    = {Schwanghart, Wolfgang and Bernhardt, Anne and Stolle, Amelie and Hoelzmann, Philipp and Adhikari, Basanta R. and Andermann, Christoff and Tofelde, Stefanie and Merchel, Silke and Rugel, Georg and Fort, Monique and Korup, Oliver},
  title     = {Repeated catastrophic valley infill following medieval earthquakes in the Nepal Himalaya},
  series = {Science},
  volume    = {351},
  journal   = {Science},
  publisher = {American Assoc. for the Advancement of Science},
  address   = {Washington},
  issn      = {0036-8075},
  doi       = {10.1126/science.aac9865},
  pages     = {147 -- 150},
  year      = {2016},
  abstract  = {Geomorphic footprints of past large Himalayan earthquakes are elusive, although they are urgently needed for gauging and predicting recovery times of seismically perturbed mountain landscapes. We present evidence of catastrophic valley infill following at least three medieval earthquakes in the Nepal Himalaya. Radiocarbon dates from peat beds, plant macrofossils, and humic silts in fine-grained tributary sediments near Pokhara, Nepal's second-largest city, match the timing of nearby M > 8 earthquakes in ~1100, 1255, and 1344 C.E. The upstream dip of tributary valley fills and x-ray fluorescence spectrometry of their provenance rule out local sources. Instead, geomorphic and sedimentary evidence is consistent with catastrophic fluvial aggradation and debris flows that had plugged several tributaries with tens of meters of calcareous sediment from a Higher Himalayan source >60 kilometers away.},
  language  = {en}
}
@article{MalinowskiHoefleKoenigetal.2016,
  author    = {Malinowski, Radostaw and H{\"o}fle, Bernhard and Koenig, Kristina and Groom, Geoff and Schwanghart, Wolfgang and Heckrath, Goswin},
  title     = {Local-scale flood mapping on vegetated floodplains from radiometrically calibrated airborne LiDAR data},
  series = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  volume    = {119},
  journal   = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0924-2716},
  doi       = {10.1016/j.isprsjprs.2016.06.009},
  pages     = {267 -- 279},
  year      = {2016},
  abstract  = {Knowledge about the magnitude of localised flooding of riverine areas is crucial for appropriate land management and administration at regional and local levels. However, detection and delineation of localised flooding with remote sensing techniques are often hampered on floodplains by the presence of herbaceous vegetation. To address this problem, this study presents the application of full waveform airborne laser scanning (ALS) data for detection of floodwater extent. In general, water surfaces are characterised by low values of backscattered energy due to water absorption of the infrared laser shots, but the exact strength of the recorded laser pulse depends on the area covered by the targets located within a laser pulse footprint area. To account for this we analysed the physical quantity of radio metrically calibrated ALS data, the backscattering coefficient, in relation to water and vegetation coverage within a single laser footprint. The results showed that the backscatter was negatively correlated to water coverage, and that of the three distinguished classes of water coverage (low, medium, and high) only the class with the largest extent of water cover (>70\%) had relatively distinct characteristics that can be used for classification of water surfaces. Following the laser footprint analysis, three classifiers, namely AdaBoost with Decision Tree, Naive Bayes and Random Forest, were utilised to classify laser points into flooded and non-flooded classes and to derive the map of flooding extent. The performance of the classifiers is highly dependent on the set of laser points features used. Best performance was achieved by combining radiometric and geometric laser point features. The accuracy of flooding maps based solely on radiometric features resulted in overall accuracies of up to 70\% and was limited due to the overlap of the backscattering coefficient values between water and other land cover classes. Our point-based classification methods assure a high mapping accuracy (similar to 89\%) and demonstrate the potential of using full-waveform ALS data to detect water surfaces on floodplain areas with limited water surface exposition through the vegetation canopy. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.},
  language  = {en}
}