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Recent studies have shown that the 1976-77 global climate shift strongly affected the South American climate. In our study, we observed a link between this climate shift and river-discharge variability in the subtropical Southern Central Andes. We analyzed the daily river-discharge time series between 1940 and 1999 from small to medium mountain drainage basins (10(2)-10(4) km(2) ) across a steep climatic and topographic gradient. We document that the discharge frequency distribution changed significantly, with higher percentiles exhibiting more pronounced trends. A change point between 1971 and 1977 marked an intensification of the hydrological cycle, which resulted in increased river discharge. In the upper Rio Bermejo basin of the northernmost Argentine Andes, the mean annual discharge increased by 40% over 7 years. Our findings are important for flood risk management in areas impacted by the 1976-77 climate shift; discharge frequency distribution analysis provides important insights into the variability of the hydrological cycle in the Andean realm.
Vegetation has long been hypothesized to influence the nature and rates of surface processes. We test the possible impact of vegetation and climate on denudation rates at orogen scale by taking advantage of a pronounced along-strike gradient in rainfall and vegetation density in the Himalaya. We combine 12 new Be-10 denudation rates from the Sutlej Valley and 123 published denudation rates from fluvially-dominated catchments in the Himalaya with remotely-sensed measures of vegetation density and rainfall metrics, and with tectonic and lithologic constraints. In addition, we perform topographic analyses to assess the contribution of vegetation and climate in modulating denudation rates along strike. We observe variations in denudation rates and the relationship between denudation and topography along strike that are most strongly controlled by local rainfall amount and vegetation density, and cannot be explained by along-strike differences in tectonics or lithology. A W-E along-strike decrease in denudation rate variability positively correlates with the seasonality of vegetation density (R = 0.95, p < 0.05), and negatively correlates with mean vegetation density (R = -0.84, p < 0.05). Vegetation density modulates the topographic response to changing denudation rates, such that the functional relationship between denudation rate and topographic steepness becomes increasingly linear as vegetation density increases. We suggest that while tectonic processes locally control the pattern of denudation rates across strike of the Himalaya (i.e., S-N), along strike of the orogen (i.e., E-W) climate exerts a measurable influence on how denudation rates scatter around long-term, tectonically-controlled erosion, and on the functional relationship between topography and denudation. (C) 2016 Elsevier B.V. All rights reserved.
The South American Andes are frequently exposed to intense rainfall events with varying moisture sources and precipitation-forming processes. In this study, we assess the spatiotemporal characteristics and geographical origins of rainfall over the South American continent. Using high-spatiotemporal resolution satellite data (TRMM 3B42 V7), we define four different types of rainfall events based on their (1) high magnitude, (2) long temporal extent, (3) large spatial extent, and (4) high magnitude, long temporal and large spatial extent combined. In a first step, we analyze the spatiotemporal characteristics of these events over the entire South American continent and integrate their impact for the main Andean hydrologic catchments. Our results indicate that events of type 1 make the overall highest contributions to total seasonal rainfall (up to 50%). However, each consecutive episode of the infrequent events of type 4 still accounts for up to 20% of total seasonal rainfall in the subtropical Argentinean plains. In a second step, we employ complex network theory to unravel possibly non-linear and long-ranged climatic linkages for these four event types on the high-elevation Altiplano-Puna Plateau as well as in the main river catchments along the foothills of the Andes. Our results suggest that one to two particularly large squall lines per season, originating from northern Brazil, indirectly trigger large, long-lasting thunderstorms on the Altiplano Plateau. In general, we observe that extreme rainfall in the catchments north of approximately 20 degrees S typically originates from the Amazon Basin, while extreme rainfall at the eastern Andean foothills south of 20 degrees S and the Puna Plateau originates from southeastern South America.
Fluvial fill terraces preserve sedimentary archives of landscape responses to climate change, typically over millennial timescales. In the Humahuaca Basin of NW Argentina (Eastern Cordillera, southern Central Andes), our 29 new optically stimulated luminescence ages of late Pleistocene fill terrace sediments demonstrate that the timing of past river aggradation occurred over different intervals on the western and eastern sides of the valley, despite their similar bedrock lithology, mean slopes, and precipitation. In the west, aggradation coincided with periods of increasing precipitation, while in the east, aggradation coincided with decreasing precipitation or more variable conditions. Erosion rates and grain size dependencies in our cosmogenic Be-10 analyses of modern and fill terrace sediments reveal an increased importance of landsliding compared to today on the west side during aggradation, but of similar importance during aggradation on the east side. Differences in the timing of aggradation and the Be-10 data likely result from differences in valley geometry, which causes sediment to be temporarily stored in perched basins on the east side. It appears as if periods of increasing precipitation triggered landslides throughout the region, which induced aggradation in the west, but blockage of the narrow bedrock gorges downstream from the perched basins in the east. As such, basin geometry and fluvial connectivity appear to strongly influence the timing of sediment movement through the system. For larger basins that integrate subbasins with differing geometries or degrees of connectivity (like Humahuaca), sedimentary responses to climate forcing are likely attenuated.
In this study, we validate and compare elevation accuracy and geomorphic metrics of satellite-derived digital elevation models (DEMs) on the southern Central Andean Plateau. The plateau has an average elevation of 3.7 km and is characterized by diverse topography and relief, lack of vegetation, and clear skies that create ideal conditions for remote sensing. At 30m resolution, SRTM-C, ASTER GDEM2, stacked ASTER L1A stereopair DEM, ALOS World 3D, and TanDEM-X have been analyzed. The higher-resolution datasets include 12m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X DEMs, and 5m ALOS World 3D. These DEMs are state of the art for optical (ASTER and ALOS) and radar (SRTM-C and TanDEM-X) spaceborne sensors. We assessed vertical accuracy by comparing standard deviations of the DEM elevation versus 307 509 differential GPS measurements across 4000m of elevation. For the 30m DEMs, the ASTER datasets had the highest vertical standard deviation at > 6.5 m, whereas the SRTM-C, ALOS World 3D, and TanDEM-X were all < 3.5 m. Higher-resolution DEMs generally had lower uncertainty, with both the 12m TanDEM-X and 5m ALOSWorld 3D having < 2m vertical standard deviation. Analysis of vertical uncertainty with respect to terrain elevation, slope, and aspect revealed the low uncertainty across these attributes for SRTM-C (30 m), TanDEM-X (12–30 m), and ALOS World 3D (5–30 m). Single-CoSSC TerraSAR-X/TanDEM-X 10m DEMs and the 30m ASTER GDEM2 displayed slight aspect biases, which were removed in their stacked counterparts (TanDEM-X and ASTER Stack). Based on low vertical standard deviations and visual inspection alongside optical satellite data, we selected the 30m SRTM-C, 12–30m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X, and 5m ALOS World 3D for geomorphic metric comparison in a 66 km2 catchment with a distinct river knickpoint. Consistent m=n values were found using chi plot channel profile analysis, regardless of DEM type and spatial resolution. Slope, curvature, and drainage area were calculated and plotting schemes were used to assess basin-wide differences in the hillslope-to-valley transition related to the knickpoint. While slope and hillslope length measurements vary little between datasets, curvature displays higher magnitude measurements with fining resolution. This is especially true for the optical 5m ALOS World 3D DEM, which demonstrated high-frequency noise in 2–8 pixel steps through a Fourier frequency analysis. The improvements in accurate space-radar DEMs (e.g., TanDEM-X) for geomorphometry are promising, but airborne or terrestrial data are still necessary for meter-scale analysis.
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
Streams commonly respond to base-level fall by localizing erosion within steepened, convex knickzone reaches. Localized incision causes knickzone reaches to migrate upstream. Such migrating knickzones dictate the pace of landscape response to changes in tectonics or erosional efficiency and can help quantify the timing and source of base-level fall. Identification of knickzones typically requires individual selection of steepened reaches: a process that is tedious and subjective and has no efficient means to measure knickzone size. We construct an algorithm to automate this procedure by selecting the bounds of knickzone reaches in a -space (drainage-area normalized) framework. An automated feature calibrates algorithm parameters to a subset of knickzones handpicked by the user. The algorithm uses these parameters as consistent criteria to identify knickzones objectively, and then the algorithm measures the height, length, and slope of each knickzone reach. We test the algorithm on 1, 10, and 30m resolution digital elevation models (DEMs) of six catchments (trunk-stream lengths: 2.1-5.4km) on Santa Cruz Island, southern California. On the 1m DEM, algorithm-selected knickzones confirm 93% of handpicked knickzone positions (n=178) to a spatial accuracy of 100m, 88% to an accuracy within 50m, and 46% to an accuracy within 10m. Using 10 and 30m DEMs, accuracy is similar: 88-86% to 100m and 82% to 50m (n=38 and 36, respectively). The algorithm enables efficient regional comparison of the size and location of knickzones with geologic structures, mapped landforms, and hillslope morphology, thereby facilitating approaches to characterize the dynamics of transient landscapes. Plain Language Summary The shape of rivers reflects the environments that they flow through and the environments that they link together: mountains and oceans. Anywhere along the length of a river, changes in environmental conditions are propagated upstream and downstream as the river changes its morphology to match the new environmental conditions. Commonly, rivers steepen as land uplifts faster in regions of high tectonic convergence. The steepening of river gradients is propagated upstream and can be mapped to trace zones of high tectonic activity across landscapes and estimate the source and timing of environmental change. Such insights may indicate regions where earthquakes have become more frequent in the recent past and how rivers respond to these changes. In this submission, we detail an algorithm that can use digital topographic data (similar to google earth), to automatically map and measure anomalously steep river reaches across continental scales. This technology can highlight areas that have experienced recent sustained changes in environmental conditions, evident by changes in the morphology of rivers. Such environmental conditions could be changes in tectonic uplift and earthquake activity, changes in sea level, changes in land-use, or changes in climate, all factors that can produce measurable differences in river morphology over time.
High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
Fluvial planation surfaces, such as straths, commonly serve as recorders of climatic and tectonic changes and are formed by the lateral erosion of rivers, a process that remains poorly understood. Here we present a study of kilometer-wide, fluvially eroded, low-relief surfaces on rapidly uplifting folds in the foreland of the southwestern Tian Shan. A combination of field work, digital elevation model analysis, and dating of fluvial deposits reveals that despite an arid climate and rapid average rock-uplift rates of 1-3mm/yr, rivers cut extensive (>1-2km wide) surfaces with typical height variations of <6m over periods of >2-6kyr. The extent of this beveling varies in space and time, such that different beveling episodes affect individual structures. Between times of planation, beveled surfaces are abandoned, incised, and deformed across the folds. In a challenge to models that link strath cutting and abandonment primarily to changes in river incision rates, we demonstrate that lateral erosion rates of antecedent streams crossing the folds have to vary by more than 1 order of magnitude to explain the creation of beveled platforms in the past and their incision at the present day. These variations do not appear to covary with climate variability and might be caused by relatively small (much less than an order of magnitude) changes in sediment or water fluxes. It remains uncertain in which settings variations in lateral bedrock erosion rates predominate over changes in vertical erosion rates. Therefore, when studying fluvial planation and strath terraces, variability of both lateral and vertical erosion rates should be considered.
The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of precipitation over central Asia over the year April 2005 through March 2006. Experiments are performed at 6.7 km horizontal grid spacing, with an emphasis on winter and summer precipitation over the Himalaya. The model and the Tropical Rainfall Measuring Mission show a similar inter-seasonal cycle of precipitation, from extratropical cyclones to monsoon precipitation, with agreement also in the diurnal cycle of monsoon precipitation. In winter months, WRF compares better in timeseries of daily precipitation to stations below than above 3-km elevation, likely due to inferior measurement of snow than rain by the stations, highlighting the need for reliable snowfall measurements at high elevations in winter. In summer months, the nocturnal precipitation cycle in the foothills and valleys of the Himalaya is captured by this 6.7-km WRF simulation, while coarser simulations with convective parameterization show near zero nocturnal precipitation. In winter months, higher resolution is less important, serving only to slightly increase precipitation magnitudes due to steeper slopes. However, even in the 6.7-km simulation, afternoon precipitation is overestimated at high elevations, which can be reduced by even higher-resolution (2.2-km) simulations. These results indicate that WRF provides skillful simulations of precipitation relevant for studies of water resources over the complex terrain in the Himalaya.
Understanding the evolution of continental deformation zones relies on quantifying spatial and temporal changes in deformation rates of tectonic structures. Along the eastern boundary of the Pamir-Tian Shan collision zone, we constrain secular variations of rock uplift rates for a series of five Quaternary detachment- and fault-related folds from their initiation to the modern day. When combined with GPS data, decomposition of interferometric synthetic aperture radar time series constrains the spatial pattern of surface and rock uplift on the folds deforming at decadal rates of 1-5mm/yr. These data confirm the previously proposed basinward propagation of structures during the Quaternary. By fitting our geodetic rates and previously published geologic uplift rates with piecewise linear functions, we find that gradual rate changes over >100kyr can explain the interferometric synthetic aperture radar observations where changes in average uplift rates are greater than similar to 1 mm/yr among different time intervals (similar to 10(1), 10(4-5), and 10(5-6) years).
This study demonstrates the potential of using single-pass TanDEM-X (TDX) radar imagery to analyse inter- and intra-annual glacier changes in mountainous terrain. Based on SAR images acquired in February 2012, March 2013 and November 2013 over the Inylchek Glacier, Kyrgyzstan, we discuss in detail the processing steps required to generate three reliable digital elevation models (DEMs) with a spatial resolution of 10 m that can be used for glacial mass balance studies. We describe the interferometric processing steps and the influence of a priori elevation information that is required to model long wavelength topographic effects. We also focus on DEM alignment to allow optimal DEM comparisons and on the effects of radar signal penetration on ice and snow surface elevations. We finally compare glacier elevation changes between the three TDX DEMs and the C-band shuttle radar topography mission (SRTM) DEM from February 2000. We introduce a new approach for glacier elevation change calculations that depends on the elevation and slope of the terrain. We highlight the superior quality of the TDX DEMs compared to the SRTM DEM, describe remaining DEM uncertainties and discuss the limitations that arise due to the side-looking nature of the radar sensor. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
The cryosphere in mountain regions is rapidly declining, a trend that is expected to accelerate over the next several decades due to anthropogenic climate change. A cascade of effects will result, extending from mountains to lowlands with associated impacts on human livelihood, economy, and ecosystems. With rising air temperatures and increased radiative forcing, glaciers will become smaller and, in some cases, disappear, the area of frozen ground will diminish, the ratio of snow to rainfall will decrease, and the timing and magnitude of both maximum and minimum streamflow will change. These changes will affect erosion rates, sediment, and nutrient flux, and the biogeochemistry of rivers and proglacial lakes, all of which influence water quality, aquatic habitat, and biotic communities. Changes in the length of the growing season will allow low-elevation plants and animals to expand their ranges upward. Slope failures due to thawing alpine permafrost, and outburst floods from glacier-and moraine-dammed lakes will threaten downstream populations.Societies even well beyond the mountains depend on meltwater from glaciers and snow for drinking water supplies, irrigation, mining, hydropower, agriculture, and recreation. Here, we review and, where possible, quantify the impacts of anticipated climate change on the alpine cryosphere, hydrosphere, and biosphere, and consider the implications for adaptation to a future of mountains without permanent snow and ice.
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.
High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
In this study, we validate and compare elevation accuracy and geomorphic metrics of satellite-derived digital elevation models (DEMs) on the southern Central Andean Plateau. The plateau has an average elevation of 3.7 km and is characterized by diverse topography and relief, lack of vegetation, and clear skies that create ideal conditions for remote sensing. At 30m resolution, SRTM-C, ASTER GDEM2, stacked ASTER L1A stereopair DEM, ALOS World 3D, and TanDEM-X have been analyzed. The higher-resolution datasets include 12m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X DEMs, and 5m ALOS World 3D. These DEMs are state of the art for optical (ASTER and ALOS) and radar (SRTM-C and TanDEM-X) spaceborne sensors. We assessed vertical accuracy by comparing standard deviations of the DEM elevation versus 307 509 differential GPS measurements across 4000m of elevation. For the 30m DEMs, the ASTER datasets had the highest vertical standard deviation at > 6.5 m, whereas the SRTM-C, ALOS World 3D, and TanDEM-X were all < 3.5 m. Higher-resolution DEMs generally had lower uncertainty, with both the 12m TanDEM-X and 5m ALOSWorld 3D having < 2m vertical standard deviation. Analysis of vertical uncertainty with respect to terrain elevation, slope, and aspect revealed the low uncertainty across these attributes for SRTM-C (30 m), TanDEM-X (12–30 m), and ALOS World 3D (5–30 m). Single-CoSSC TerraSAR-X/TanDEM-X 10m DEMs and the 30m ASTER GDEM2 displayed slight aspect biases, which were removed in their stacked counterparts (TanDEM-X and ASTER Stack). Based on low vertical standard deviations and visual inspection alongside optical satellite data, we selected the 30m SRTM-C, 12–30m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X, and 5m ALOS World 3D for geomorphic metric comparison in a 66 km2 catchment with a distinct river knickpoint. Consistent m=n values were found using chi plot channel profile analysis, regardless of DEM type and spatial resolution. Slope, curvature, and drainage area were calculated and plotting schemes were used to assess basin-wide differences in the hillslope-to-valley transition related to the knickpoint. While slope and hillslope length measurements vary little between datasets, curvature displays higher magnitude measurements with fining resolution. This is especially true for the optical 5m ALOS World 3D DEM, which demonstrated high-frequency noise in 2–8 pixel steps through a Fourier frequency analysis. The improvements in accurate space-radar DEMs (e.g., TanDEM-X) for geomorphometry are promising, but airborne or terrestrial data are still necessary for meter-scale analysis.
High Mountain Asia (HMA) - encompassing the Tibetan Plateau and surrounding mountain ranges - is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications - such as agriculture, drinking-water generation, and hydropower - rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season - defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3-5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade 1 over the 29-year study period (5-25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002-2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers - such as the Karakoram and Kunlun Shan - see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.
In this study, we validate and compare elevation accuracy and geomorphic metrics of satellite-derived digital elevation models (DEMs) on the southern Central Andean Plateau. The plateau has an average elevation of 3.7 km and is characterized by diverse topography and relief, lack of vegetation, and clear skies that create ideal conditions for remote sensing. At 30m resolution, SRTM-C, ASTER GDEM2, stacked ASTER L1A stereopair DEM, ALOS World 3D, and TanDEM-X have been analyzed. The higher-resolution datasets include 12m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X DEMs, and 5m ALOS World 3D. These DEMs are state of the art for optical (ASTER and ALOS) and radar (SRTM-C and TanDEM-X) spaceborne sensors. We assessed vertical accuracy by comparing standard deviations of the DEM elevation versus 307 509 differential GPS measurements across 4000m of elevation. For the 30m DEMs, the ASTER datasets had the highest vertical standard deviation at > 6.5 m, whereas the SRTM-C, ALOS World 3D, and TanDEM-X were all < 3.5 m. Higher-resolution DEMs generally had lower uncertainty, with both the 12m TanDEM-X and 5m ALOSWorld 3D having < 2m vertical standard deviation. Analysis of vertical uncertainty with respect to terrain elevation, slope, and aspect revealed the low uncertainty across these attributes for SRTM-C (30 m), TanDEM-X (12-30 m), and ALOS World 3D (5-30 m). Single-CoSSC TerraSAR-X/TanDEM-X 10m DEMs and the 30m ASTER GDEM2 displayed slight aspect biases, which were removed in their stacked counterparts (TanDEM-X and ASTER Stack). Based on low vertical standard deviations and visual inspection alongside optical satellite data, we selected the 30m SRTM-C, 12-30m TanDEM-X, 10m single-CoSSC TerraSAR-X/TanDEM-X, and 5m ALOS World 3D for geomorphic metric comparison in a 66 km2 catchment with a distinct river knickpoint. Consistent m = n values were found using chi plot channel profile analysis, regardless of DEM type and spatial resolution. Slope, curvature, and drainage area were calculated and plotting schemes were used to assess basin-wide differences in the hillslope-to-valley transition related to the knickpoint. While slope and hillslope length measurements vary little between datasets, curvature displays higher magnitude measurements with fining resolution. This is especially true for the optical 5m ALOS World 3D DEM, which demonstrated high-frequency noise in 2-8 pixel steps through a Fourier frequency analysis. The improvements in accurate space-radar DEMs (e. g., TanDEM-X) for geomorphometry are promising, but airborne or terrestrial data are still necessary for meter-scale analysis.
The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimen-sionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications.
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.
This study analyzes the discharge variability of small to medium drainage basins (10(2)-10(4) km(2)) in the southern Central Andes of NW Argentina. The Hilbert-Huang Transform (HHT) was applied to evaluate non-stationary oscillatory modes of variability and trends, based on four time series of monthly normalized discharge anomaly between 1940 and 2015. Statistically significant trends reveal increasing discharge during the past decades and document an intensification of the hydrological cycle during this period. An Ensemble Empirical Mode Decomposition (EEMD) analysis revealed that discharge variability in this region can be best described by five quasi-periodic statistically significant oscillatory modes, with mean periods varying from 1 to 20 y. Moreover, we show that discharge variability is most likely linked to the phases of the Pacific Decadal Oscillation (PDO) at multi-decadal timescales (similar to 20 y) and, to a lesser degree, to the Tropical South Atlantic SST anomaly (TSA) variability at shorter timescales (similar to 2-5 y). Previous studies highlighted a rapid increase in discharge in the southern Central Andes during the 1970s, inferred to have been associated with the global 1976-77 climate shift. Our results suggest that the rapid discharge increase in the NW Argentine Andes coincides with the periodic enhancement of discharge, which is mainly linked to a negative to positive transition of the PDO phase and TSA variability associated with a long-term increasing trend. We therefore suggest that variations in discharge in this region are largely driven by both natural variability and the effects of global climate change. We furthermore posit that the links between atmospheric and hydrologic processes result from a combination of forcings that operate on different spatiotemporal scales. (C) 2017 Elsevier B.V. All rights reserved.
Fluvial fill terraces in intermontane basins are valuable geomorphic archives that can record tectonically and/or climatically driven changes of the Earth-surface process system. However, often the preservation of fill terrace sequences is incomplete and/or they may form far away from their source areas, complicating the identification of causal links between forcing mechanisms and landscape response, especially over multi-millennial timescales. The intermontane Toro Basin in the southern Central Andes exhibits at least five generations of fluvial terraces that have been sculpted into several-hundred-meter-thick Quaternary valley-fill conglomerates. New surface-exposure dating using nine cosmogenic Be-10 depth profiles reveals the successive abandonment of these terraces with a 100 kyr cyclicity between 75 +/- 7 and 487 +/- 34 ka. Depositional ages of the conglomerates, determined by four Al-26/Be-10 burial samples and U-Pb zircon ages of three intercalated volcanic ash beds, range from 18 +/- 141 to 936 +/- 170 ka, indicating that there were multiple cut-and-fill episodes. Although the initial onset of aggradation at similar to 1 Ma and the overall net incision since ca. 500 ka can be linked to tectonic processes at the narrow basin outlet, the superimposed 100 kyr cycles of aggradation and incision are best explained by eccentricity-driven climate change. Within these cycles, the onset of river incision can be correlated with global cold periods and enhanced humid phases recorded in paleoclimate archives on the adjacent Bolivian Altiplano, whereas deposition occurred mainly during more arid phases on the Altiplano and global interglacial periods. We suggest that enhanced runoff during global cold phases - due to increased regional precipitation rates, reduced evapotranspiration, or both - resulted in an increased sediment-transport capacity in the Toro Basin, which outweighed any possible increases in upstream sediment supply and thus triggered incision. Compared with two nearby basins that record precessional (21-kyr) and long-eccentricity (400-kyr) forcing within sedimentary and geomorphic archives, the recorded cyclicity scales with the square of the drainage basin length. (C) 2017 Elsevier B.V. All rights reserved.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
High Mountain Asia provides water for more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow - the vast majority of which is not monitored by sparse weather networks. We leverage passive microwave data from the SSMI series of satellites (SSMI, SSMI/S, 1987-2016), reprocessed to 3.125 km resolution, to examine trends in the volume and spatial distribution of snow-water equivalent (SWE) in the Indus Basin. We find that the majority of the Indus has seen an increase in snow-water storage. There exists a strong elevation-trend relationship, where high-elevation zones have more positive SWE trends. Negative trends are confined to the Himalayan foreland and deeply-incised valleys which run into the Upper Indus. This implies a temperature-dependent cutoff below which precipitation increases are not translated into increased SWE. Earlier snowmelt or a higher percentage of liquid precipitation could both explain this cutoff.(1) Earlier work 2 found a negative snow-water storage trend for the entire Indus catchment over the time period 1987-2009 (-4 x 10(-3) mm/yr). In this study based on an additional seven years of data, the average trend reverses to 1.4 x 10(-3). This implies that the decade since the mid-2000s was likely wetter, and positively impacted long-term SWE trends. This conclusion is supported by an analysis of snowmelt onset and end dates which found that while long-term trends are negative, more recent (since 2005) trends are positive (moving later in the year).(3)
In this study, the spatial and temporal impacts of the Ataturk Dam on agro-meteorological aspects of the Southeastern Anatolia region have been investigated. Change detection and environmental impacts due to water-reserve changes in Ataturk Dam Lake have been determined and evaluated using multi-temporal Landsat satellite imageries and meteorological datasets within a period of 1984-2011. These time series have been evaluated for three time periods. Dam construction period constitutes the first part of the study. Land cover/use changes especially on agricultural fields under the Ataturk Dam Lake and its vicinity have been identified between the periods of 1984-1992. The second period comprises the 10-year period after the completion of filling up the reservoir in 1992. At this period, Landsat and meteorological time-series analyses are examined to assess the impact of the Ataturk Dam Lake on selected irrigated agricultural areas. For the last 9-year period from 2002 to 2011, the relationships between seasonal water-reserve changes and irrigated plains under changing climatic factors primarily driving vegetation activity (monthly, seasonal, and annual fluctuations of rainfall rate, air temperature, humidity) on the watershed have been investigated using a 30-year meteorological time series. The results showed that approximately 368 km(2) of agricultural fields have been affected because of inundation due to the Ataturk Dam Lake. However, irrigated agricultural fields have been increased by 56.3% of the total area (1552 of 2756 km(2)) on Harran Plain within the period of 1984-2011.
Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.
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.
The effect of Indian Summer Monsoon rainfall on surface water delta D values in the central Himalaya
(2018)
Stable isotope proxy records, such as speleothems, plant-wax biomarker records, and ice cores, are suitable archives for the reconstruction of regional palaeohydrologic conditions. But the interpretation of these records in the tropics, especially in the Indian Summer Monsoon (ISM) domain, is difficult due to differing moisture and water sources: precipitation from the ISM and Winter Westerlies, as well as snow- and glacial meltwater. In this study, we use interannual differences in ISM strength (2011-2012) to understand the stable isotopic composition of surface water in the Arun River catchment in eastern Nepal. We sampled main stem and tributary water (n = 204) for stable hydrogen and oxygen isotope analysis in the postmonsoon phase of two subsequent years with significantly distinct ISM intensities. In addition to the 2011/2012 sampling campaigns, we collected a 12-month time series of main stem waters (2012/2013, n = 105) in order to better quantify seasonal effects on the variability of surface water delta O-18/delta D. Furthermore, remotely sensed satellite data of rainfall, snow cover, glacial coverage, and evapotranspiration was evaluated. The comparison of datasets from both years revealed that surface waters of the main stem Arun and its tributaries were D-enriched by similar to 15 parts per thousand when ISM rainfall decreased by 20%. This strong response emphasizes the importance of the ISM for surface water run-off in the central Himalaya. However, further spatio-temporal analysis of remote sensing data in combination with stream water d-excess revealed that most high-altitude tributaries and the Tibetan part of the Arun receive high portions of glacial melt water and likely Winter Westerly Disturbances precipitation. We make the following two implications: First, palaeohydrologic archives found in high-altitude tributaries and on the southern Tibetan Plateau record a mixture of past precipitation delta D values and variable amounts of additional water sources. Second, surface water isotope ratios of lower elevated tributaries strongly reflect the isotopic composition of ISM rainfall implying a suitable region for the analysis of potential delta D value proxy records.
Glacial deposits on the high-altitude, arid southern Central Andean Plateau (CAP), the Puna in northwestern Argentina, document past changes in climate, but the associated geomorphic features have rarely been directly dated. This study provides direct age control of glacial moraine deposits from the central Puna (24 degrees S) at elevations of 3900-5000 m through surface exposure dating with cosmogenic nuclides. Our results show that the most extensive glaciations occurred before 95 ka and an additional major advance occurred between 46 and 39 ka. The latter period is synchronous with the highest lake levels in the nearby Pozuelos basin and the Minchin (Inca Huasi) wet phase on the Altiplano in the northern CAP. None of the dated moraines produced boulder ages corresponding to the Tauca wet phase (24-15 ka). Additionally, the volcanic lithologies of the deposits allow us to establish production ratios at low latitude and high elevation for five different nuclide and mineral systems: Be-10, Ne-21, and Al-26 from quartz (11 or 12 samples) and He-3 and Ne-21 from pyroxene (10 samples). We present production ratios for all combinations of the measured nuclides and cross-calibrated production rates for 21Ne in pyroxene and quartz for the high, (sub-)tropical Andes. The production rates are based on our Be-10-normalized production ratios and a weighted mean of reference 10Be production rates calibrated in the high, tropical Andes (4.02 +/- 0.12 at g(-1) yr(-1)). These are, Ne-21(qtz): 18.1 +/- 1.2 at g(-1) yr(-1) and Ne-21(px): 36.6 +/- 1.8 at g(-1) yr(-1) (En(88-94)) scaled to sea level and high latitude using the Lal/Stone scheme, with 1 sigma uncertainties. As He-3 and Al-26 have been directly calibrated in the tropical Andes, we recommend using those rates. Finally, we compare exposure ages calculated using all measured cosmogenic nuclides from each sample, including 11 feldspar samples measured for Cl-36, and a suite of previously published production rates. (C) 2018 Published by Elsevier B.V.
Along the NE Pamir margin, flights of late Quaternary fluvial terraces span actively deforming fault-related folds. We present detailed results on two terraces dated using optically stimulated luminescence (OSL) and cosmogenic radionuclide Be-10 (CRN) techniques. Quartz OSL dating of two different grain sizes (4-11 mu m and 90-180 mu m) revealed the fine-grain quartz fraction may overestimate the terrace ages by up to a factor of ten. Two-mm, small-aliquot, coarse-grain quartz OSL ages, calculated using the minimum age model, yielded stratigraphically consistent ages within error and dated times of terrace deposition to similar to 9 and similar to 16 ka. We speculate that, in this arid environment, fine-grain samples can be transported and deposited in single, turbid, and (sometimes) night-time floods that prevent thorough bleaching and, thereby, can lead to relatively large residual OSL signals. In contrast, sand in the fluvial system is likely to have a much longer residence time during transport, thereby providing greater opportunities for thorough bleaching. CRN Be-10 depth profiles date the timing of terrace abandonment to similar to 8 and similar to 14 ka: ages that generally agree with the coarse-grain quartz OSL ages. Our new terrace age of similar to 13-14 ka is broadly consistent with other terraces in the region that indicate terrace deposition and subsequent abandonment occurred primarily during glacial-interglacial transitions, thereby suggesting a climatic control on the formation of these terraces on the margins of the Tarim Basin. Furthermore, tectonic shortening rates calculated from these deformed terraces range from similar to 1.2 to similar to 4.6 mm/a and, when combined with shortening rates from other structures in the region, illuminate the late Quaternary basinward migration of deformation to faults and folds along the Pamir-Tian Shan collisional interface.
The location and magnitude of Himalayan tectonic activity has been debated for decades, and several aspects remain unknown. For instance, the spatial distribution of crustal shortening that ultimately sustains Himalayan topography and the activity of major fault zones remain unknown at Ma timescales. In this study, we address the spatial deformation pattern in the data-scarce western Himalaya. We calculated catchment averaged, normalized river-steepness indices of non-glaciated drainage basins with tributary catchment areas between 5 and 200 km(2) (n = 2138). We analyzed the spatial distribution of the relative change of river steepness both along and across strike to gain information about the regional distribution of differential uplift pattern and relate this to the activity of distinctive fault segments. For our study area, we observe a positive correlation of averaged k(sn) values with long-term exhumation rates derived from previously published thermochronologic datasets combined with thermal modeling as well as with millennial timescale denudation rates based on cosmogenic nuclide dating. Our results indicate three tectono-geomorphic segments with distinctive landscape morphology, structural architecture, and fault geometry along the western Himalaya: Garhwal-Sutlej, Chamba, and Kashmir Himalaya (from east to west). Moreover, our data recognize distinctive fault segments showing varying thrust activity along strike of the Main Frontal Thrust, the Main Boundary Thrust, and in the vicinity of the steep topographic transition between the Lesser and Greater Himalaya. In this region, we relate out-of-sequence deformation along major basement thrust ramps, such as the Munsiari Thrust with deformation along a mid-crustal ramp along the basal decollement. We suggest that during the Quaternary, all major fault zones in the Western Himalaya experienced out-of-sequence faulting and have accommodated some portion of crustal shortening.
Formation of a Rain Shadow
(2018)
We measure the oxygen and hydrogen stable isotope composition of authigenic clays from Himalayan foreland sediments (Siwalik Group), and from present day small stream waters in eastern Bhutan to explore the impact of uplift of the Shillong Plateau on rain shadow formation over the Himalayan foothills. Stable isotope data from authigenic clay minerals (<2 μm) suggest the presence of three paleoclimatic periods during deposition of the Siwalik Group, between ∼7 and ∼1 Ma. The mean δ18O value in paleometeoric waters, which were in equilibrium with clay minerals, is ∼2.5‰ lower than in modern meteoric and stream waters at the elevation of the foreland basin. We discuss the factors that could have changed the isotopic composition of water over time and we conclude that (a) the most likely and significant cause for the increase in meteoric water δ18O values over time is the “amount effect,” specifically, a decrease in mean annual precipitation. (b) The change in mean annual precipitation over the foreland basin and foothills of the Himalaya is the result of orographic effect caused by the Shillong Plateau's uplift. The critical elevation of the Shillong Plateau required to induce significant orographic precipitation was attained after ∼1.2 Ma. (c) By applying scale analysis, we estimate that the mean annual precipitation over the foreland basin of the eastern Bhutan Himalayas has decreased by a factor of 1.7–2.5 over the last 1–3 million years.
Dating growth strata and basin fill by combining Al-26/Be-10 burial dating and magnetostratigraphy
(2018)
Cosmogenic burial dating enables dating of coarse-grained, Pliocene-Pleistocene sedimentary units that are typically difficult to date with traditional methods, such as magnetostratigraphy. In the actively deforming western Tarim Basin in NW China, Pliocene-Pleistocene conglomerates were dated at eight sites, integrating Al-26/Be-10 burial dating with previously published magnetostratigraphic sections. These samples were collected from growth strata on the flanks of growing folds and from sedimentary units beneath active faults to place timing constraints on the initiation of deformation of structures within the basin and on shortening rates on active faults. These new basin-fill and growthstrata ages document the late Neogene and Quaternary growth of the Pamir and Tian Shan orogens between >5 and 1 Ma and delineate the eastward propagation of deformation at rates up to 115 km/m.y. and basinward growth of both mountain belts at rates up to 12 km/m.y.
Dating growth strata and basin fill by combining 26Al/10Be burial dating and magnetostratigraphy
(2018)
Cosmogenic burial dating enables dating of coarse-grained, Pliocene-Pleistocene sedimentary units that are typically difficult to date with traditional methods, such as magnetostratigraphy. In the actively deforming western Tarim Basin in NW China, Pliocene-Pleistocene conglomerates were dated at eight sites, integrating Al-26/Be-10 burial dating with previously published magnetostratigraphic sections. These samples were collected from growth strata on the flanks of growing folds and from sedimentary units beneath active faults to place timing constraints on the initiation of deformation of structures within the basin and on shortening rates on active faults. These new basin-fill and growthstrata ages document the late Neogene and Quaternary growth of the Pamir and Tian Shan orogens between >5 and 1 Ma and delineate the eastward propagation of deformation at rates up to 115 km/m.y. and basinward growth of both mountain belts at rates up to 12 km/m.y.
Hazards and accessibility
(2018)
The assessment of natural hazards and risk has traditionally been built upon the estimation of threat maps, which are used to depict potential danger posed by a particular hazard throughout a given area. But when a hazard event strikes, infrastructure is a significant factor that can determine if the situation becomes a disaster. The vulnerability of the population in a region does not only depend on the area’s local threat, but also on the geographical accessibility of
the area. This makes threat maps by themselves insufficient for supporting real-time decision-making, especially for those tasks that involve the use of the road network, such as management of relief operations, aid distribution, or planning of evacuation routes, among others. To overcome this problem, this paper proposes a multidisciplinary approach divided in two parts. First, data fusion of satellite-based threat data and open infrastructure data from OpenStreetMap, introducing a threat-based routing service. Second, the visualization of this data through cartographic generalization and schematization. This emphasizes critical areas along roads in a simple way and allows users to visually evaluate the impact natural hazards may have on infrastructure. We develop and illustrate this methodology with a case study of landslide threat for an area in Colombia.
In the arctic and high mountains it is common to measure vertical changes of ice sheets and glaciers via digital elevation model (DEM) differencing. This requires the signal of change to outweigh the noise associated with the datasets. Excluding large landslides, on the ice-free earth the land-level change is smaller in vertical magnitude and thus requires more accurate DEMs for differencing and identification of change. Previously, this has required meter to submeter data at small spatial scales. Following careful corrections, we are able to measure land-level changes in gravel-bed channels and steep hillslopes in the south-central Andes using the SRTM-C (collected in 2000) and the TanDEM-X (collected from 2010 to 2015) near-global 12–30m DEMs. Long-standing errors in the SRTM-C are corrected using the TanDEM-X as a control surface and applying cosine-fit co-registration to remove ∼ 1∕10 pixel (∼ 3m) shifts, fast Fourier transform (FFT) and filtering to remove SRTM-C short- and long-wavelength stripes, and blocked shifting to remove remaining complex biases. The datasets are then differenced and outlier pixels are identified as a potential signal for the case of gravel-bed channels and hillslopes. We are able to identify signals of incision and aggradation (with magnitudes down to ∼ 3m in the best case) in two > 100km river reaches, with increased geomorphic activity downstream of knickpoints. Anthropogenic gravel excavation and piling is prominently measured, with magnitudes exceeding ±5m (up to > 10m for large piles). These values correspond to conservative average rates of 0.2 to > 0.5myr−1 for vertical changes in gravel-bed rivers. For hillslopes, since we require stricter cutoffs for noise, we are only able to identify one major landslide in the study area with a deposit volume of 16±0.15×106m3. Additional signals of change can be garnered from TanDEM-X auxiliary layers; however, these are more difficult to quantify. The methods presented can be extended to any region of the world with SRTM-C and TanDEM-X coverage where vertical land-level changes are of interest, with the caveat that remaining vertical uncertainties in primarily the SRTM-C limit detection in steep and complex topography.
Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series
(2018)
The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6–12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth’s surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.
In the arctic and high mountains it is common to measure vertical changes of ice sheets and glaciers via digital elevation model (DEM) differencing. This requires the signal of change to outweigh the noise associated with the datasets. Excluding large landslides, on the ice-free earth the land-level change is smaller in vertical magnitude and thus requires more accurate DEMs for differencing and identification of change. Previously, this has required meter to submeter data at small spatial scales. Following careful corrections, we are able to measure land-level changes in gravel-bed channels and steep hillslopes in the south-central Andes using the SRTM-C (collected in 2000) and the TanDEM-X (collected from 2010 to 2015) near-global 12–30m DEMs. Long-standing errors in the SRTM-C are corrected using the TanDEM-X as a control surface and applying cosine-fit co-registration to remove ∼ 1∕10 pixel (∼ 3m) shifts, fast Fourier transform (FFT) and filtering to remove SRTM-C short- and long-wavelength stripes, and blocked shifting to remove remaining complex biases. The datasets are then differenced and outlier pixels are identified as a potential signal for the case of gravel-bed channels and hillslopes. We are able to identify signals of incision and aggradation (with magnitudes down to ∼ 3m in the best case) in two > 100km river reaches, with increased geomorphic activity downstream of knickpoints. Anthropogenic gravel excavation and piling is prominently measured, with magnitudes exceeding ±5m (up to > 10m for large piles). These values correspond to conservative average rates of 0.2 to > 0.5myr−1 for vertical changes in gravel-bed rivers. For hillslopes, since we require stricter cutoffs for noise, we are only able to identify one major landslide in the study area with a deposit volume of 16±0.15×106m3. Additional signals of change can be garnered from TanDEM-X auxiliary layers; however, these are more difficult to quantify. The methods presented can be extended to any region of the world with SRTM-C and TanDEM-X coverage where vertical land-level changes are of interest, with the caveat that remaining vertical uncertainties in primarily the SRTM-C limit detection in steep and complex topography.
With the advent of the two Sentinel-1 (S1) satellites, Synthetic Aperture Radar (SAR) data with high temporal and spatial resolution are freely available. This provides a promising framework to facilitate detailed investigations of surface instabilities and movements on large scales with high temporal resolution, but also poses substantial processing challenges because of storage and computation requirements. Methods are needed to efficiently detect short term changes in dynamic environments. Approaches considering pair-wise processing of a series of consecutive scenes to retain maximum temporal resolution in conjunction with time series analyses are required. Here we present OSARIS, the “Open Source SAR Investigation System,” as a framework to process large stacks of S1 data on high-performance computing clusters. Based on Generic Mapping Tools SAR, shell scripts, and the workload manager Slurm, OSARIS provides an open and modular framework combining parallelization of high-performance C programs, flexible processing schemes, convenient configuration, and generation of geocoded stacks of analysis-ready base data, including amplitude, phase, coherence, and unwrapped interferograms. Time series analyses can be conducted by applying automated modules to the data stacks. The capabilities of OSARIS are demonstrated in a case study from the northwestern Tien Shan, Central Asia. After merging of slices, a total of 80 scene pairs were processed from 174 total input scenes. The coherence time series exhibits pronounced seasonal variability, with relatively high coherence values prevailing during the summer months in the nival zone. As an example of a time series analysis module, we present OSARIS' “Unstable Coherence Metric” which identifies pixels affected by significant drops from high to low coherence values. Measurements of motion provided by LOSD measurements require careful evaluation because interferometric phase unwrapping is prone to errors. Here, OSARIS provides a series of modules to detect and mask unwrapping errors, correct for atmospheric disturbances, and remove large-scale trends. Wall clock processing time for the case study (area ~9,000 km2) was ~12 h 4 min on a machine with 400 cores and 2 TB RAM. In total, ~12 d 10 h 44 min (~96%) were saved through parallelization. A comparison of selected OSARIS datasets to results from two state-of-the-art SAR processing suites, ISCE and SNAP, shows that OSARIS provides products of competitive quality despite its high level of automatization. OSARIS thus facilitates efficient S1-based region-wide investigations of surface movement events over multiple years.
Ocean-land thermal feedback mechanisms in the Indian Summer Monsoon (ISM) domain are an important but not well understood component of regional climate dynamics. Here we present a O-18 record analyzed in the mixed-layer dwelling planktonic foraminifer Globigerinoides ruber (sensu stricto) from the northernmost Bay of Bengal (BoB). The O-18 time series provides a spatially integrated measure of monsoonal precipitation and Himalayan meltwater runoff into the northern BoB and reveals two brief episodes of anomalously low O-18 values between 16.30.4 and 160.5 and 12.60.4 and 12.30.4 thousand years before present. The timing of these events is centered at Heinrich event 1 and the Younger Dryas, well-known phases of weak northern hemisphere monsoon systems. Numerical climate model experiments, simulating Heinrich event-like conditions, suggest a surface warming over the monsoon-dominated Himalaya and foreland in response to ISM weakening. Corroborating the simulation results, our analysis of published moraine exposure ages in the monsoon-dominated Himalaya indicates enhanced glacier retreats that, considering age model uncertainties, coincide and overlap with the episodes of anomalously low O-18 values in the northernmost BoB. Our climate proxy and simulation results provide insights into past regional climate dynamics, suggesting reduced cloud cover, increased solar radiation, and air warming of the Himalaya and foreland areas and, as a result, glacier mass losses in response to weakened ISM. Plain Language Summary Indian Summer Monsoon rainfall and Himalayan glacier/snow melts constitute the main water source for the densely populated Indian subcontinent. Better understanding of how future climate changes will affect the monsoon rainfall and Himalayan glaciers requires a long climate record. In this study, we create a 13,000-year-long climate record that allows us to better understand the response of Indian Summer Monsoon rainfall and Himalayan glaciers to past climate changes. The focus of our study is the time window between 9,000 and 22,000 years ago, an episode where the global climate experienced large and rapid changes. Our sediment record from the northern Bay of Bengal and climate change simulation indicate that during episodes of weak monsoon, the melting of the Himalayan glaciers increases substantially significantly. This is because the weakening of the monsoon results in less cloud cover and, as a result, the surface receives more sunlight and causes glacier melting.
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.
The structure and organization of river networks has been used for decades to investigate the influence of climate and tectonics on landscapes. The majority of these studies either analyze rivers in profile view by extracting channel steepness or calculate planform metrics such as drainage density. However, these techniques rely on the assumption of homogeneity: that intrinsic and external factors are spatially or temporally invariant over the measured profile. This assumption is violated for the majority of Earth's landscapes, where variations in uplift rate, rock strength, climate, and geomorphic process are almost ubiquitous. We propose a method for classifying river profiles to identify landscape regions with similar characteristics by adapting hierarchical clustering algorithms developed for time series data. We first test our clustering on two landscape evolution scenarios and find that we can successfully cluster regions with different erodibility and detect the transient response to sudden base level fall. We then test our method in two real landscapes: first in Bitterroot National Forest, Idaho, where we demonstrate that our method can detect transient incision waves and the topographic signature of fluvial and debris flow process regimes; and second, on Santa Cruz Island, California, where our technique identifies spatial patterns in lithology not detectable through normalized channel steepness analysis. By calculating channel steepness separately for each cluster, our method allows the extraction of more reliable steepness metrics than if calculated for the landscape as a whole. These examples demonstrate the method's ability to disentangle fluvial morphology in complex lithological and tectonic settings.
Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds.
The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature (Td). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-RangeWeather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of Td in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and Td can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes.
The interactions between atmosphere and steep topography in the eastern south–central Andes result in complex relations with inhomogenous rainfall distributions. The atmospheric conditions leading to deep convection and extreme rainfall and their spatial patterns—both at the valley and mountain-belt scales—are not well understood. In this study, we aim to identify the dominant atmospheric conditions and their spatial variability by analyzing the convective available potential energy (CAPE) and dew-point temperature (Td). We explain the crucial effect of temperature on extreme rainfall generation along the steep climatic and topographic gradients in the NW Argentine Andes stretching from the low-elevation eastern foreland to the high-elevation central Andean Plateau in the west. Our analysis relies on version 2.0 of the ECMWF’s (European Centre for Medium-RangeWeather Forecasts) Re-Analysis (ERA-interim) data and TRMM (Tropical Rainfall Measuring Mission) data. We make the following key observations: First, we observe distinctive gradients along and across strike of the Andes in dew-point temperature and CAPE that both control rainfall distributions. Second, we identify a nonlinear correlation between rainfall and a combination of dew-point temperature and CAPE through a multivariable regression analysis. The correlation changes in space along the climatic and topographic gradients and helps to explain controlling factors for extreme-rainfall generation. Third, we observe more contribution (or higher importance) of Td in the tropical low-elevation foreland and intermediate-elevation areas as compared to the high-elevation central Andean Plateau for 90th percentile rainfall. In contrast, we observe a higher contribution of CAPE in the intermediate-elevation area between low and high elevation, especially in the transition zone between the tropical and subtropical areas for the 90th percentile rainfall. Fourth, we find that the parameters of the multivariable regression using CAPE and Td can explain rainfall with higher statistical significance for the 90th percentile compared to lower rainfall percentiles. Based on our results, the spatial pattern of rainfall-extreme events during the past ∼16 years can be described by a combination of dew-point temperature and CAPE in the south–central Andes.
Climatic observables are often correlated across long spatial distances, and extreme events, such as heatwaves or floods, are typically assumed to be related to such teleconnections(1,2). Revealing atmospheric teleconnection patterns and understanding their underlying mechanisms is of great importance for weather forecasting in general and extreme-event prediction in particular(3,4), especially considering that the characteristics of extreme events have been suggested to change under ongoing anthropogenic climate change(5-8). Here we reveal the global coupling pattern of extreme-rainfall events by applying complex-network methodology to high-resolution satellite data and introducing a technique that corrects for multiple-comparison bias in functional networks. We find that the distance distribution of significant connections (P < 0.005) around the globe decays according to a power law up to distances of about 2,500 kilometres. For longer distances, the probability of significant connections is much higher than expected from the scaling of the power law. We attribute the shorter, power-law-distributed connections to regional weather systems. The longer, super-power-law-distributed connections form a global rainfall teleconnection pattern that is probably controlled by upper-level Rossby waves. We show that extreme-rainfall events in the monsoon systems of south-central Asia, east Asia and Africa are significantly synchronized. Moreover, we uncover concise links between south-central Asia and the European and North American extratropics, as well as the Southern Hemisphere extratropics. Analysis of the atmospheric conditions that lead to these teleconnections confirms Rossby waves as the physical mechanism underlying these global teleconnection patterns and emphasizes their crucial role in dynamical tropical-extratropical couplings. Our results provide insights into the function of Rossby waves in creating stable, global-scale dependencies of extreme-rainfall events, and into the potential predictability of associated natural hazards.
Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
(2019)
Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect.
Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies.
Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.
Introducing PebbleCounts
(2019)
Grain-size distributions are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are achievable only at the 1–10 ㎡ scale. With the advent of drones and increasingly high-resolution cameras, we can now generate orthoimagery over hectares at millimeter to centimeter resolution. These scales, along with the complexity of high-mountain rivers, necessitate different approaches for photo sieving. As opposed to other image segmentation methods that use a watershed approach, our open-source algorithm, PebbleCounts, relies on k-means clustering in the spatial and spectral domain and rapid manual selection of well-delineated grains. This improves grain-size estimates for complex riverbed imagery, without post-processing. We also develop a fully automated method, PebbleCountsAuto, that relies on edge detection and filtering suspect grains, without the k-means clustering or manual selection steps. The algorithms are tested in controlled indoor conditions on three arrays of pebbles and then applied to 12 × 1 ㎡ orthomosaic clips of high-energy mountain rivers collected with a camera-on-mast setup (akin to a low-flying drone). A 20-pixel b-axis length lower truncation is necessary for attaining accurate grain-size distributions. For the k-means PebbleCounts approach, average percentile bias and precision are 0.03 and 0.09 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, and 0.07 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. The automatic approach has higher bias and precision of 0.13 and 0.15 ψ, respectively, for ∼1.16 mm pixel⁻¹ images, but similar values of −0.06 and 0.05 ψ for one 0.32 mm pixel⁻¹ image. For the automatic approach, only at best 70 % of the grains are correct identifications, and typically around 50 %. PebbleCounts operates most effectively at the 1 ㎡ patch scale, where it can be applied in ∼5–10 min on many patches to acquire accurate grain-size data over 10–100 ㎡ areas. These data can be used to validate PebbleCountsAuto, which may be applied at the scale of entire survey sites (102–104 ㎡ ). We synthesize results and recommend best practices for image collection, orthomosaic generation, and grain-size measurement using both algorithms.
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
(2019)
Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect.
Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies.
Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions.