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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.
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 ∼ 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 ∼ 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 earthquake-induced 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 ∼ 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 10 Be 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 10 Be sample in catchments with source areas > 10 000 km 2 to reduce the method mean bias to below ∼ 20 % of the long-term erosion.
A local and flexible definition of the monsoon season based on hydrological evidence is important for the understanding and management of Himalayan water resources. Here, we present an objective statistical method to retrieve seasonal hydrometeorological transitions. Applied to daily rainfall data (1951-2015), this method shows an average longitudinal delay of similar to 15 days, with later monsoon onset and earlier withdrawal in the western Himalaya, consistent with the continental progression of wet air masses. This delay leads to seasons of different length along the Himalaya and biased precipitation amounts when using uniform calendric monsoon boundaries. In the Central Himalaya annual precipitation has increased, due primarily to an increase of premonsoon precipitation. These findings highlight issues associated with a static definition of monsoon boundaries and call for a deeper understanding of nonmonsoonal precipitation over the Himalayan water tower. <br /> Plain Language Summary Precipitation in the Himalayas determines water availability for the Indian foreland with large socioeconomic implications. Despite its importance, spatial and temporal patterns of precipitation are poorly understood. Here, we estimate the long-term average and trends of seasonal precipitation at the scale of individual catchments draining the Himalayas. We apply a statistical method to detect the timing of hydrometeorological seasons from local precipitation measurements, focusing on monsoon onset and withdrawal. We identify longitudinal and latitudinal delays, resulting in seasons of different length along and across the Himalayas. These spatial patterns and the annual variability of the monsoon boundaries mean that oft-used, fixed calendric dates, for example, 1 June to 30 September, may be inadequate for retrieving monsoon rainfall totals. Moreover, we find that, despite its prominent contribution to annual rainfall totals, the Indian summer monsoon cannot explain the increase of the annual precipitation over the Central Himalayas. Instead, this appears to be mostly driven by changes in premonsoon and winter rainfall. So far, little attention has been paid to premonsoon precipitation, but governed by evaporative processes and surface water availability, it may be enhanced by irrigation and changed land use in the Gangetic foreland.