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Sediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel-1 data, we analyze sediment transport regimes for the south-central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope-to-fluvial and (b) a fluvial-to-alluvial transition. While transitions within a given basin can be well-constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment-wide erosion rates and to topographic metrics used to indicate long-term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation-free settings at event to decadal timescales.
High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved.
Atmospheric dynamics of extreme discharge events from 1979 to 2016 in the southern Central Andes
(2020)
During the South-American Monsoon season, deep convective systems occur at the eastern flank of the Central Andes leading to heavy rainfall and flooding. We investigate the large- and meso-scale atmospheric dynamics associated with extreme discharge events (> 99.9th percentile) observed in two major river catchments meridionally stretching from humid to semi-arid conditions in the southern Central Andes. Based on daily gauge time series and ERA-Interim reanalysis, we made the following three key observations: (1) for the period 1940-2016 daily discharge exhibits more pronounced variability in the southern, semi-arid than in the northern, humid catchments. This is due to a smaller ratio of discharge magnitudes between intermediate (0.2 year return period) and rare events (20 year return period) in the semi-arid compared to the humid areas; (2) The climatological composites of the 40 largest discharge events showed characteristic atmospheric features of cold surges based on 5-day time-lagged sequences of geopotential height at different levels in the troposphere; (3) A subjective classification revealed that 80% of the 40 largest discharge events are mainly associated with the north-northeastward migration of frontal systems and 2/3 of these are cold fronts, i.e. cold surges. This work highlights the importance of cold surges and their related atmospheric processes for the generation of heavy rainfall events and floods in the southern Central Andes.
The purity of the analysed samples (e.g. quartz) with respect to chemical composition and radionuclide contamination is essential for geomorphologic applications using so-called terrestrial cosmogenic nuclides (TCNs). To guarantee this, numerous cleaning and dissolution procedures have been developed. At the DREsden Accelerator Mass Spectrometry (DREAMS) facility, we also work on enhancing the chemical quartz-enrichment methodology from bulk rock and dissolution of quartz. Repeated exposure of the bulk material to acid mixtures (HCl/H2SiF6) at room temperature for cleaning and its monitoring by optical microscopy works for most quartz-rich samples. The quartz dissolution in HF under rather mild conditions (at room temperature on a shaker-table) has the advantage to leave difficult-to-dissolve minerals (e.g., tourmaline, zircon, rutile, sillimanite, kyanite, chromite, corundum), not separated by other physical methods before, as residue. Our comparison with a high-temperature dissolution method (in a microwave) indicates an additional amount of interfering elements, such as in average about 3 mg of Ti, more than 7 mg of Al, and about 22 mu g of Be (for 50 g SiO2), is added to the sample, hence showing the superiority of our mild method. This way, we reduce problems for chemistry and AMS, but also ensure better comparability to production rates of cleaner stoichiometric quartz from calibration sites.
Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.
Snow meltwaters account for most of the yearly water budgets of many catchments in High Mountain Asia (HMA). We examine trends in snow water equivalent (SWE) using passive microwave data (1987 to 2009). We find an overall decrease in SWE in HMA, despite regions of increased SWE in the Pamir, Kunlun Shan, Eastern Himalaya, and Eastern Tien Shan. Although the average decline in annual SWE across HMA (contributing area, 2641 x 10(3) km(2)) is low (average, -0.3%), annual SWE losses conceal distinct seasonal and spatial heterogeneities across the study region. For example, the Tien Shan has seen both strong increases in winter SWE and sharp declines in spring and summer SWE. In the majority of catchments, the most negative SWE trends are found in mid-elevation zones, which often correspond to the regions of highest snow-water storage and are somewhat distinct from glaciated areas. Negative changes in SWE storage in these mid-elevation zones have strong implications for downstream water availability.
Deciphering the response of sediment routing systems to climatic forcing is fundamental for understanding the impacts of climate change on landscape evolution. In the Kangra Basin (northwest Sub-Himalaya, India), upper Pleistocene to Holocene alluvial fills and fluvial terraces record periodic fluctuations of sediment supply and transport capacity on timescales of 10(3) to 10(5) yr. To evaluate the potential influence of climate change on these fluctuations, we compare the timing of aggradation and incision phases recorded within remnant alluvial fans and terraces with climate archives. New surface-exposure dating of six terrace levels with in-situ cosmogenic Be-10 indicates the onset of incision phases. Two terrace surfaces from the highest level (T1) sculpted into the oldest preserved alluvial fan (AF1) date back to 53.4 +/- 3.2 ka and 43.0 +/- 2.7 ka (1 sigma). T2 surfaces sculpted into the remnants of AF1 have exposure ages of 18.6 +/- 1.2 ka and 15.3 +/- 0.9 ka, while terraces sculpted into the upper Pleistocene-Holocene fan (AF2) provide ages of 9.3 +/- 0.4 ka (T3), 7.1 +/- 0.4 ka (T4), 5.2 +/- 0.4 ka (T5) and 3.6 +/- 0.2 ka (T6). Together with previously published OSL ages yielding the timing of aggradation, we find a correlation between variations in sediment transport with oxygen-isotope records from regions affected by the Indian Summer Monsoon. During periods of increased monsoon intensity and post-Last Glacial Maximum glacial retreat, aggradation occurred in the Kangra Basin, likely due to high sediment flux, whereas periods of weakened monsoon intensity or lower sediment supply coincide with incision. (C) 2016 Elsevier B.V. All rights reserved.
Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings.