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The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
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.
Resolving Earth's surface at the meter scale is essential for an improved understanding of the dynamics of mass-movement processes. In this study, we explore the applicability and potential of digital elevation models (DEMs) derived from stereophotogrammetry to detect debris-flow channels in the Quebrada del Toro in the northwestern Argentine Andes. Our analysis relies on a high-resolution (3 m) DEM created from SPOT-7 tri-stereo satellite data. We carefully validated DEM quality with ∼6,000 differential GPS points and identified optimal parameters for DEM generation in high-relief terrain. After multiple processing steps, we achieved an accuracy of 0.051 ± 1.915 m (1σ) using n = 3,139 control points with cm precision. Previous studies have used the drainage area and slope framework to identify topographic signatures of debris flows within a catchment. We built upon this and investigated individual river-channel segments using connected-component (CC) analysis on meter-scale topographic data. We define CC as segments of similar slope along the channel profile. Based on seven manually identified debris-flow catchments, we developed a debris-flow similarity index using component length and mean channel-segment slope and identified channel segments that have likely been shaped by debris flows. The presented approach has the potential to resolve intra-catchment variability of transport processes, allows to constrain the extent of debris-flow channels more precisely than slope-area analysis, and highlights the versatility of combined space- and field-based observations for natural-hazard assessments.
Birds migrating across the Himalayan region fly over the highest peaks in the world, facing immense physiological and climatic challenges. The authors show the different strategies used by birds to cope with these challenges. Many wetland avian species are seen in the high-altitude lakes of the Himalayas and the adjoining Tibetan Plateau, such as Bar-Headed Geese. Ringing programmes have generated information about origins and destinations, and this book is the first to present information on the bird's exact migratory paths. Capitalising on knowledge generated through satellite telemetry, the authors describe the migratory routes of a multitude of birds flying over or skirting the Himalayas. The myriad of threats to migratory birds and the wetland system in the Central Asian Flyway are discussed, with ways to mitigate them. This volume will inform and persuade policy-makers and conservation practitioners to take appropriate measures for the long-term survival of this unique migration
Mixed sand- and gravel-bed rivers record erosion, transport, and fining signals in their bedload size distributions. Thus, grain-size data are imperative for studying these processes. However, collecting hundreds to thousands of pebble measurements in steep and dynamic high-mountain river settings remains challenging. Using the recently published digital grain-sizing algorithm PebbleCounts, we were able to survey seven large (>= 1,000 m2) channel cross-sections and measure thousands to tens-of-thousands of grains per survey along a 100-km stretch of the trunk stream of the Toro Basin in Northwest Argentina. The study region traverses a steep topographic and environmental gradient on the eastern margin of the Central Andean Plateau. Careful counting and validation allows us to identify measurement errors and constrain percentile uncertainties using large sample sizes. In the coarse >= 2.5 cm fraction of bedload, only the uppermost size percentiles (>= 95th) vary significantly downstream, whereas the 50th and 84th percentiles show less variability. We note a relation between increases in these upper percentiles and along-channel junctions with large, steep tributaries. This signal is strongly influenced by lithology and geologic structures, and mixed with local hillslope input. In steep catchments like the Toro Basin, we suggest nonlinear relationships between geomorphic metrics and grain size, whereby the steepest parts of the landscape exert primary control on the upper grain-size percentiles. Thus, average or median metrics that do not apply weights or thresholds to steeper topography may be less predictive of grain-size distributions in such settings.
The eastern flanks of the Central Andes are characterized by deep convection, exposing them to hydrometeorological extreme events, often resulting in floods and a variety of mass movements. We assessed the spatiotemporal pattern of rainfall trends and the changes in the magnitude and frequency of extreme events (ae<yen>95th percentile) along an E-W traverse across the southern Central Andes using rain-gauge and high-resolution gridded datasets (CPC-uni and TRMM 3B42 V7). We generated different climate indices and made three key observations: (1) an increase of the annual rainfall has occurred at the transition between low (< 0.5 km) and intermediate (0.5-3 km) elevations between 1950 and 2014. Also, rainfall increases during the wet season and, to a lesser degree, decreases during the dry season. Increasing trends in annual total amounts characterize the period 1979-2014 in the arid, high-elevation southern Andean Plateau, whereas trend reversals with decreasing annual total amounts were found at low elevations. (2) For all analyzed periods, we observed small or no changes in the median values of the rainfall-frequency distribution, but significant trends with intensification or attenuation in the 95th percentile. (3) In the southern Andean Plateau, extreme rainfall events exhibit trends towards increasing magnitude and, to a lesser degree, frequency during the wet season, at least since 1979. Our analysis revealed that low (< 0.5 km), intermediate (0.5-3 km), and high-elevation (> 3 km) areas respond differently to changing climate conditions, and the transition zone between low and intermediate elevations is characterized by the most significant changes.
Effects of topographic smoothing on the simulation of winter precipitation in High Mountain Asia
(2017)
Numerous studies have projected future changes in High Mountain Asia water resources based on temperature and precipitation from global circulation models (GCMs) under future climate scenarios. Although the potential benefit of such studies is immense, coarse grid-scale GCMs are unable to resolve High Mountain Asia's complex topography and thus have a biased representation of regional weather and climate. This study investigates biases in the simulation of physical mechanisms that generate snowfall and contribute to snowpack in High Mountain Asia in coarse topography experiments using the Weather Research and Forecasting model. Regional snowpack is event driven, thus 33 extreme winter orographic precipitation events are simulated at fine atmospheric resolution with 6.67 km resolution topography and smoothed 1.85° × 1.25° GCM topography. As with many modified topography experiments performed in other regions, the distribution of precipitation is highly dependent on first-order orographic effects, which dominate regional meteorology. However, we demonstrate that topographic smoothing enhances circulation in simulated extratropical cyclones, with significant impacts on orographic precipitation. Despite precipitation reductions of 28% over the highest ranges, due to reduced ascent on windward slopes, total precipitation over the study domain increased by an average of 9% in smoothed topography experiments on account of intensified extratropical cyclone dynamics and cross-barrier moisture flux. These findings identify an important source of bias in coarse-resolution simulated precipitation in High Mountain Asia, with important implications for the application of GCMs toward projecting future hydroclimate in the region.
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.
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.