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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 glacial retreat. Embedded in loose debris and surrounded by sources of falling debris and ice, many glacial lakes are expected to drain catastrophically in destructive glacial lake outburst floods (GLOFs), one of the most publicised natural hazards to the rapidly growing Himalayan population. GLOFs have killed several hundreds of people in the past decades and caused substantial damage to infrastructure, hydropower stations, livestock and farmland. However, the hypothesized response to continuous glacial recession by an increased GLOF frequency is still indeterminable with, in most cases, zero or one event per year. We explore this possible reporting bias and offer a processing chain for establishing a more complete Himalayan GLOF inventory. We make use of the full seasonal archive of Landsat images, the world’s longest continuous collection of optical remote sensing data. Our goal is to track where GLOFs left shrinking water bodies, and tails of sediment at high elevations. We apply Machine Learning techniques to produce land cover maps for several thousand Landsat images and developed a likelihood-based change point technique to estimate the timing of GLOFs at the pixel scale. For a training area covering ~10% of the HKH, our method objectively detected ten out of eleven documented GLOFs, and another ten lakes that gave rise to previously unreported GLOFs. We hence nearly doubled the existing GLOF record for this particular region, showing the potential of our processing chain for GLOF detection along the greater HKH and other mountain ranges elsewhere. We thus set the basis towards a more complete inventory for the HKH, and towards objectively filling the gaps in the hitherto censored chronology of past GLOFs.
In the wake of changing hydro-climatological, geo-physical and socio-economic conditions the magnitude, frequency and impact of certain types of natural hazards are likely bound to change as well. This is highly of utmost importance for many regions in the world where risks due to natural hazards have to be managed and mitigated and this is where the research training group “Natural hazards and risks in a changing world (NatRiskChange)” aims to foster the scientific knowledge basis. This research training group started on October 1st 2015 and is funded by the Deutsche Forschungsgemeinschaft DFG. The central goal of NatRiskChange is to pursue the development of methods to improve hazard and risk analysis and quantification based on the transient, non-stationary nature of hazards and risks in response to changing natural and anthropogenically altered components of the Earth system. Key scientific aims are the development, testing, and pilot application of studies on identification, quantification (mechanisms) and prediction of transient natural hazards and associated risks.
Within NatRiskChange, a telephone aided survey was conducted in October and November 2017 among companies, which were affected from heavy rainfall or flash flood events in 2014 to 2016. We aim at gathering information about the companies experiences with severe weather warning systems, the type and extent of the damage as well es the state of recovery. Results shall identify improved mitigation measures for the management of eavy rainfall events. We thank all participants of the survey for their support!