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In active mountain belts with steep terrain, bedrock landsliding is a major erosional agent. In the Himalayas, landsliding is driven by annual hydro-meteorological forcing due to the summer monsoon and by rarer, exceptional events, such as earthquakes. Independent methods yield erosion rate estimates that appear to increase with sampling time, suggesting that rare, high-magnitude erosion events dominate the erosional budget. Nevertheless, until now, neither the contribution of monsoon and earthquakes to landslide erosion nor the proportion of erosion due to rare, giant landslides have been quantified in the Himalayas. We address these challenges by combining and analysing earthquake- and monsoon-induced landslide inventories across different timescales. With time series of 5 m satellite images over four main valleys in central Nepal, we comprehensively mapped landslides caused by the monsoon from 2010 to 2018. We found no clear correlation between monsoon properties and landsliding and a similar mean landsliding rate for all valleys, except in 2015, where the valleys affected by the earthquake featured similar to 5-8 times more landsliding than the pre-earthquake mean rate. The longterm size-frequency distribution of monsoon-induced landsliding (MIL) was derived from these inventories and from an inventory of landslides larger than similar to 0.1 km(2) that occurred between 1972 and 2014. Using a published landslide inventory for the Gorkha 2015 earthquake, we derive the size-frequency distribution for earthquakeinduced landsliding (EQIL). These two distributions are dominated by infrequent, large and giant landslides but under-predict an estimated Holocene frequency of giant landslides (> 1 km(3)) which we derived from a literature compilation. This discrepancy can be resolved when modelling the effect of a full distribution of earthquakes of variable magnitude and when considering that a shallower earthquake may cause larger landslides. In this case, EQIL and MIL contribute about equally to a total long-term erosion of similar to 2 +/- 0.75 mm yr(-1) in agreement with most thermo-chronological data. Independently of the specific total and relative erosion rates, the heavy-tailed size-frequency distribution from MIL and EQIL and the very large maximal landslide size in the Himalayas indicate that mean landslide erosion rates increase with sampling time, as has been observed for independent erosion estimates. Further, we find that the sampling timescale required to adequately capture the frequency of the largest landslides, which is necessary for deriving long-term mean erosion rates, is often much longer than the averaging time of cosmogenic Be-10 methods. This observation presents a strong caveat when interpreting spatial or temporal variability in erosion rates from this method. Thus, in areas where a very large, rare landslide contributes heavily to long-term erosion (as the Himalayas), we recommend Be-10 sample in catchments with source areas > 10 000 km(2) to reduce the method mean bias to below similar to 20 % of the long-term erosion.
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 glacier retreat. The sudden emptying of these lakes releases large volumes of water and sediment in destructive glacial lake outburst floods (GLOFs), one of the most publicised natural hazards to the rapidly growing Himalayan population. Despite the growing number and size of glacial lakes, the frequency of documented GLOFs is remarkably constant. We explore this possible reporting bias and offer a new processing chain for establishing a more complete Himalayan GLOF inventory. We make use of the full seasonal archive of Landsat images between 1988 and 2016, and track automatically where GLOFs left shrinking water bodies, and tails of sediment at high elevations. We trained a Random Forest classifier to generate fuzzy land cover maps for 2491 images, achieving overall accuracies of 91%. We developed a likelihood-based change point technique to estimate the timing of GLOFs at the pixel scale. Our method objectively detected ten out of eleven documented GLOFs, and another ten lakes that gave rise to previously unreported GLOFs. We thus nearly doubled the existing GLOF record for a study area covering similar to 10% of the HKH region. Remaining challenges for automatically detecting GLOFs include image insufficiently accurate co-registration, misclassifications in the land cover maps and image noise from clouds, shadows or ice. Yet our processing chain is robust and has the potential for being applied on the greater HKH and mountain ranges elsewhere, opening the door for objectively expanding the knowledge base on GLOF activity over the past three decades.
This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km(2) area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to +/- 17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to -63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.
This study focuses on evaluating the potential of ALOS/PALSAR time-series data to analyze the activation of deep-seated landslides in the foothill zone of the high mountain Alai range in the southern Tien Shan (Kyrgyzstan). Most previous field-based landslide investigations have revealed that many landslides have indicators for ongoing slow movements in the form of migrating and newly developing cracks. L-band ALOS/PALSAR data for the period between 2007 and 2010 are available for the 484 km2 area in this study. We analyzed these data using the Small Baseline Subset (SBAS) time-series technique to assess the surface deformation related to the activation of landslides. We observed up to ±17 mm/year of LOS velocity deformation rates, which were projected along the local steepest slope and resulted in velocity rates of up to −63 mm/year. The obtained rates indicate very slow movement of the deep-seated landslides during the observation time. We also compared these movements with precipitation and earthquake records. The results suggest that the deformation peaks correlate with rainfall in the 3 preceding months and with an earthquake event. Overall, the results of this study indicated the great potential of L-band InSAR time series analysis for efficient spatiotemporal identification and monitoring of slope activations in this region of high landslide activity in Southern Kyrgyzstan.