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Oxidation of particulate organic carbon (POC) during fluvial transit releases CO2 to the atmosphere and can influence global climate. Field data show large POC oxidation fluxes in lowland rivers; however, it is unclear if POC losses occur predominantly during in-river transport, where POC is in continual motion within an aerated environment, or during transient storage in floodplains, which may be anoxic. Determination of the locus of POC oxidation in lowland rivers is needed to develop process-based models to predict POC losses, constrain carbon budgets, and unravel links between climate and erosion. However, sediment exchange between rivers and floodplains makes differentiating POC oxidation during in-river transport from oxidation during floodplain storage difficult. Here, we isolated inriver POC oxidation using flume experiments transporting petrogenic and biospheric POC without floodplain storage. Our experiments showed solid phase POC losses of 0%-10% over similar to 10(3) km of fluvial transport, compared to similar to 7% to >50% losses observed in rivers over similar distances. The production of dissolved organic carbon (DOC) and dissolved rhenium (a proxy for petrogenic POC oxidation) was consistent with small POC lasses, and replicate experiments in static water tanks gave similar results. Our results show that fluvial sediment transport, particle abrasion, and turbulent mixing have a minimal role on POC oxidation, and they suggest that POC losses may accrue primarily in floodplain storage.
The large, shallow earthquakes at Northridge, California (1994), Chi-Chi, Taiwan (1999), and Wenchuan, China (2008), each triggered thousands of landslides. We have determined the position of these landslides along hillslopes, normalizing for statistical bias. The landslide patterns have a co-seismic signature, with clustering at ridge crests and slope toes. A cross-check against rainfall-induced landslide inventories seems to confirm that crest clustering is specific to seismic triggering as observed in previous studies. In our three study areas, the seismic ground motion parameters and lithologic and topographic features used do not seem to exert a primary control on the observed patterns of landslide clustering. However, we show that at the scale of the epicentral area, crest and toe clustering occur in areas with specific geological features. Toe clustering of seismically induced landslides tends to occur along regional major faults. Crest clustering is concentrated at sites where the lithology along hillslopes is approximately uniform, or made of alternating soft and hard strata, and without strong overprint of geological structures. Although earthquake-induced landslides locate higher on hillslopes in a statistically significant way, geological features strongly modulate the landslide position along the hillslopes. As a result the observation of landslide clustering on topographic ridges cannot be used as a definite indicator of the topographic amplification of ground shaking.
Soil degradation is a severe and growing threat to ecosystem services globally. Soil loss is often nonlinear, involving a rapid deterioration from a stable eco-geomorphic state once a tipping point is reached. Soil loss thresholds have been studied at plot scale, but for landscapes, quantitative constraints on the necessary and sufficient conditions for tipping points are rare. Here, we document a landscape-wide eco-geomorphic tipping point at the edge of the Tibetan Plateau and quantify its drivers and erosional consequences. We show that in the upper Kali Gandaki valley, Nepal, soil formation prevailed under wetter conditions during much of the Holocene. Our data suggest that after a period of human pressure and declining vegetation cover, a 20% reduction of relative humidity and precipitation below 200 mm/year halted soil formation after 1.6 ka and promoted widespread gullying and rapid soil loss, with irreversible consequences for ecosystem services.
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.
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.
Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.
Water stable isotope ratios of tropical precipitation predominantly reflect moisture source and precipitation intensity. Trees can incorporate the isotopic signals into annual tree-ring cellulose records, permitting reconstruction of the temporal changes of hydroclimate over decades to millennia. This is especially valuable in the Himalayas where the understanding of monsoon dynamics is limited by the lack of a dense and representative observational network. We have analyzed tree ring delta O-18 records from two distinct physiographic sites along the upper Kali Gandaki valley in the central Nepal Himalayas, representing the wet High-Himalayas and the Trans-Himalayan dryland to the north. Empirical correlations and regression analyses were compared to an in-situ calibrated oxygen isotope fractionation model, exploring the relationships between tree ring delta O-18 and seasonal-mean variability of hydroclimatic forcing at the different locations. For this purpose, gridded precipitation data from the Asian rain gauge dataset APHRODITE, as well as high resolution onsite observations (relative humidity, air temperature, delta O-18 of precipitation and radial tree growth) were used. We found that two distinct sets of meteorological values, reflecting pre-monsoon and monsoon conditions, are needed to reproduce the measured tree ring delta O-18 values from the High-Himalayan site, but that a single set of monsoonal values performs best for the Trans-Himalayan site. We conclude that Trans-Himalayan trees capture long-term changes in strength of the Indian summer monsoon. In contrast, High-Himalayan tree ring delta(18)Orecords a more complex hydro-climatic signal reflecting both pre-monsoon and monsoon seasons with very contrasting isotopic signatures of precipitation. This difference in the two hydroclimatic proxy records offers an opportunity to reconstruct first-order hydroclimate conditions, such as local precipitation rates, and to gain new insights into monsoon timing and seasonal water source determination across the Himalayan orographic region. (C) 2019 Elsevier B.V. All rights reserved.