Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.

We mapped eight monsoon-related (> 100 m²) and large (> 0.1 km²) landslides in the Nepal Himalayas since 1970. Adding inventories of Holocene landslides, giant landslides (> 1 km³), and landslides from the 2015 Gorkha earthquake, we constrain the size–frequency distribution of monsoon- and earthquake-induced landslides. Both contribute ~50 % to a long-term (> 10 kyr) total erosion of ~2 mm yr^-1, matching the long-term exhumation rate. Large landslides rarer than ¹⁰Be sampling time drive erosion.

A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs. We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle. This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.