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Long-term estimates of the dissolution of silicate rock are generally derived from a range of isotopic proxies, such as the radiogenic strontium isotope ratio (Sr-87/Sr-86), which are preserved in sediment archives. For these systems to fairly represent silicate weathering, the changes in isotopic ratios in terrestrial surface waters should correspond to changes in the overall silicate dissolution. This assumes that the silicate mineral phases that act as sources of a given isotope dissolve at a rate that is proportional to the overall silicate weathering. Bedrock landsliding exhumes large quantities of fresh rock for weathering in transient storage, and rapid weathering in these deposits is controlled primarily by dissolution of the most reactive phases. In this study, we test the hypothesis that preferential weathering of these labile minerals can decouple the dissolution of strontium sources from the actual silicate weathering rates in the rapidly eroding Western Southern Alps (WSA) of New Zealand. We find that rapid dissolution of relatively radiogenic calcite and biotite in landslides leads to high local fluxes in strontium with isotopic ratios that offer no clear discrimination between sources. These higher fluxes of radiogenic strontium are in contrast to silicate weathering rates in landslides that are not systematically elevated. On a mountain belt scale, radiogenic strontium fluxes are not coupled to volumes of recent landslides in large (>100 km(2)) catchments, but silicate weathering fluxes are. Such decoupling is likely due first to the broad variability in the strontium content of carbonate minerals, and second to the combination of radiogenic strontium released from both biotite and carbonate in recent landslides. This study supports previous work suggesting the limited utility of strontium isotopes as a system to study silicate weathering in the WSA. Crucially however, in settings where bedrock landsliding is a dominant erosive process there is potential for both random and systematic bias in isotope proxies if the most reactive phases exposed for dissolution by landslides disproportionately contribute to the proxy of choice. This clearly suggests that the isotopic composition of marine Sr is a proxy for periods of rapid mountain uplift and erosion rather than for the associated enhanced silicate weathering. (C) 2017 Elsevier Ltd. All rights reserved.
A link between chemical weathering and physical erosion exists at the catchment scale over a wide range of erosion rates(1,2). However, in mountain environments, where erosion rates are highest, weathering may be kinetically limited(3-5) and therefore decoupled from erosion. In active mountain belts, erosion is driven by bedrock landsliding(6) at rates that depend strongly on the occurrence of extreme rainfall or seismicity(7). Although landslides affect only a small proportion of the landscape, bedrock landsliding can promote the collection and slow percolation of surface runoff in highly fragmented rock debris and create favourable conditions for weathering. Here we show from analysis of surface water chemistry in the Southern Alps of New Zealand that weathering in bedrock landslides controls the variability in solute load of these mountain rivers. We find that systematic patterns in surface water chemistry are strongly associated with landslide occurrence at scales from a single hillslope to an entire mountain belt, and that landslides boost weathering rates and river solute loads over decades. We conclude that landslides couple erosion and weathering in fast-eroding uplands and, thus, mountain weathering is a stochastic process that is sensitive to climatic and tectonic controls on mass wasting processes.
Erosion and tectonic uplift are widely thought to be coupled through feedbacks involving orographic precipitation, relief development, and crustal weakening. In many orogenic systems, it can be difficult to distinguish whether true feedbacks exist, or whether observed features are a consequence of tectonic forcing. To help elucidate these interactions, we examine Gongga Shan, a 7556 m peak on the eastern margin of the Tibetan Plateau where cosmogenic Be-10 basin-wide erosion rates reach >5 mm/yr, defining a region of localized rapid erosion associated with a restraining bend in the left-lateral Xianshuihe Fault. Erosion rates are consistent with topography, thermochronometry, and geodetic data, suggesting a stable pattern of uplift and exhumation over at least the past 2-3 My. Transpression along the Xianshuihe Fault, orographically enhanced precipitation, thermally weakened crust, and substantial local relief all developed independently in the Gongga region and existed there prior to the uplift of Gongga Shan. However, only where all of these conditions are present do the observed topographic and erosional extremes exist, and their relative timing indicates that these conditions are not a consequence of rapid uplift. We conclude that their collocation at 3-4 Ma set into motion a series of feedbacks between erosion and uplift that has resulted in the exceptionally high topography and rapid erosion rates observed today.
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.
Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area-frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area-frequency distribution, resulting from amalgamation, may be up to 200 and 50 %, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m(2), tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7-3.6% incorrectly amalgamated landslides missed and 3.9-4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
Inventories of individually delineated landslides are a key to understanding landslide physics and mitigating their impact. They permit assessment of area–frequency distributions and landslide volumes, and testing of statistical correlations between landslides and physical parameters such as topographic gradient or seismic strong motion. Amalgamation, i.e. the mapping of several adjacent landslides as a single polygon, can lead to potentially severe distortion of the statistics of these inventories. This problem can be especially severe in data sets produced by automated mapping. We present five inventories of earthquake-induced landslides mapped with different materials and techniques and affected by varying degrees of amalgamation. Errors on the total landslide volume and power-law exponent of the area–frequency distribution, resulting from amalgamation, may be up to 200 and 50%, respectively. We present an algorithm based on image and digital elevation model (DEM) analysis, for automatic identification of amalgamated polygons. On a set of about 2000 polygons larger than 1000 m2, tracing landslides triggered by the 1994 Northridge earthquake, the algorithm performs well, with only 2.7–3.6% incorrectly amalgamated landslides missed and 3.9–4.8% correct polygons incorrectly identified as amalgams. This algorithm can be used broadly to check landslide inventories and allow faster correction by automating the identification of amalgamation.
We present a new, seismologically consistent expression for the total area and volume of populations of earthquake-triggered landslides. This model builds on a set of scaling relationships between key parameters, such as landslide spatial density, seismic ground acceleration, fault length, earthquake source depth, and seismic moment. To assess the model we have assembled and normalized a catalog of landslide inventories for 40 shallow, continental earthquakes. Low landscape steepness causes systematic overprediction of the total area and volume of landslides. When this effect is accounted for, the model predicts the total landslide volume of 63% of 40 cases to within a factor 2 of the volume estimated from observations (R-2 = 0.76). The prediction of total landslide area is also sensitive to the landscape steepness, but less so than the total volume, and it appears to be sensitive to controls on the landslide size-frequency distribution, and possibly the shaking duration. Some outliers are likely associated with exceptionally strong rock mass in the epicentral area, while others may be related to seismic source complexities ignored by the model. However, the close match between prediction and estimate for about two thirds of cases in our database suggests that rock mass strength is similar in many cases and that our simple seismic model is often adequate, despite the variety of lithologies and tectonic settings covered. This makes our expression suitable for integration into landscape evolution models and application to the anticipation or rapid assessment of secondary hazards associated with earthquakes.