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. In active mountain belts with steep terrain, bedrock landslidingis a major erosional agent. In the Himalayas, landsliding is driven by annualhydro-meteorological forcing due to the summer monsoon and by rarer,exceptional events, such as earthquakes. Independent methods yield erosionrate estimates that appear to increase with sampling time, suggesting thatrare, high-magnitude erosion events dominate the erosional budget.Nevertheless, until now, neither the contribution of monsoon and earthquakesto landslide erosion nor the proportion of erosion due to rare, giantlandslides have been quantified in the Himalayas. We address these challengesby combining and analysing earthquake- and monsoon-induced landslideinventories across different timescales. With time series of 5 m satelliteimages over four main valleys in central Nepal, we comprehensively mappedlandslides caused by the monsoon from 2010 to 2018. We found no clearcorrelation between monsoon properties and landsliding and a similar meanlandsliding rate for all valleys, except in 2015, where the valleys affectedby the earthquake featured ∼5 –8 times more landsliding than thepre-earthquake mean rate. The long-term size–frequency distribution ofmonsoon-induced landsliding (MIL) was derived from these inventories and froman inventory of landslides larger than ∼0.1  km 2 that occurredbetween 1972 and 2014. Using a published landslide inventory for the Gorkha2015 earthquake, we derive the size–frequency distribution forearthquake-induced landsliding (EQIL). These two distributions are dominatedby infrequent, large and giant landslides but under-predict an estimatedHolocene frequency of giant landslides (> 1 km 3 ) which wederived from a literature compilation. This discrepancy can be resolved whenmodelling the effect of a full distribution of earthquakes of variablemagnitude and when considering that a shallower earthquake may cause largerlandslides. In this case, EQIL and MIL contribute about equally to a totallong-term erosion of ∼ 2 ± 0.75  mm yr −1 in agreement with most thermo-chronological data.Independently of the specific total and relative erosion rates, theheavy-tailed size–frequency distribution from MIL and EQIL and the very largemaximal landslide size in the Himalayas indicate that mean landslide erosionrates increase with sampling time, as has been observed for independenterosion estimates. Further, we find that the sampling timescale required to adequately capture the frequency of the largest landslides, which isnecessary for deriving long-term mean erosion rates, is often much longerthan the averaging time of cosmogenic 10Be methods. Thisobservation presents a strong caveat when interpreting spatial or temporalvariability in erosion rates from this method. Thus, in areas where a verylarge, rare landslide contributes heavily to long-term erosion (as theHimalayas), we recommend 10Be sample in catchments with sourceareas > 10 000 km 2 to reduce the method mean bias to below ∼20  % of the long-term erosion.

Long-term erosion of the Nepal Himalayas by bedrock landsliding: the role of monsoons, earthquakes and giant landslides