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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 Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
Mixed sand- and gravel-bed rivers record erosion, transport, and fining signals in their bedload size distributions. Thus, grain-size data are imperative for studying these processes. However, collecting hundreds to thousands of pebble measurements in steep and dynamic high-mountain river settings remains challenging. Using the recently published digital grain-sizing algorithm PebbleCounts, we were able to survey seven large (>= 1,000 m2) channel cross-sections and measure thousands to tens-of-thousands of grains per survey along a 100-km stretch of the trunk stream of the Toro Basin in Northwest Argentina. The study region traverses a steep topographic and environmental gradient on the eastern margin of the Central Andean Plateau. Careful counting and validation allows us to identify measurement errors and constrain percentile uncertainties using large sample sizes. In the coarse >= 2.5 cm fraction of bedload, only the uppermost size percentiles (>= 95th) vary significantly downstream, whereas the 50th and 84th percentiles show less variability. We note a relation between increases in these upper percentiles and along-channel junctions with large, steep tributaries. This signal is strongly influenced by lithology and geologic structures, and mixed with local hillslope input. In steep catchments like the Toro Basin, we suggest nonlinear relationships between geomorphic metrics and grain size, whereby the steepest parts of the landscape exert primary control on the upper grain-size percentiles. Thus, average or median metrics that do not apply weights or thresholds to steeper topography may be less predictive of grain-size distributions in such settings.
Sediment transport domains in mountain landscapes are characterized by fundamentally different processes and rates depending on several factors, including geology, climate, and biota. Accurately identifying where transitions between transport domains occur is an important step to quantify the past, present, and future contribution of varying erosion and sedimentation processes and enhance our predictive capabilities. We propose a new methodology based on time series of synthetic aperture radar (SAR) interferometric coherence images to map sediment transport regimes across arid and semiarid landscapes. Using 4 years of Sentinel-1 data, we analyze sediment transport regimes for the south-central Andes in northwestern Argentina characterized by steep topographic and climatic gradients. We observe seasonally low coherence during the regional wet season, particularly on hillslopes and in alluvial channels. The spatial distribution of coherence is compared to drainage areas extracted from digital topography to identify two distinct transitions within watersheds: (a) a hillslope-to-fluvial and (b) a fluvial-to-alluvial transition. While transitions within a given basin can be well-constrained, the relative role of each sediment transport domain varies widely over the climatic and topographic gradients. In semiarid regions, we observe larger relative contributions from hillslopes compared to arid regions. Across regional gradients, the range of coherence within basins positively correlates to previously published millennial catchment-wide erosion rates and to topographic metrics used to indicate long-term uplift. Our study suggests that a dense time series of interferometric coherence can be used as a proxy for surface sediment movement and landscape stability in vegetation-free settings at event to decadal timescales.
The Upper Indus Basin (UIB), which covers a wide range of climatic and topographic settings, provides an ideal venue to explore the relationship between climate and topography. While the distribution of snow and glaciers is spatially and temporally heterogeneous, there exist regions with similar elevation-snow relationships. In this work, we construct elevation-binned snow-cover statistics to analyze 3415 watersheds and 7357 glaciers in the UIB region. We group both glaciers and watersheds using a hierarchical clustering approach and find that (1) watershed clusters mirror large-scale moisture transport patterns and (2) are highly dependent on median watershed elevation. (3) Glacier clusters are spatially heterogeneous and are less strongly controlled by elevation, but rather by local topographic parameters that modify solar insolation. Our clustering approach allows us to clearly define self-similar snow-topographic regions. Eastern watersheds in the UIB show a steep snow cover-elevation relationship whereas watersheds in the central and western UIB have moderately sloped relationships, but cluster in distinct groups. We highlight this snow-cover-topographic transition zone and argue that these watersheds have different hydrologic responses than other regions. Our hierarchical clustering approach provides a potential new framework to use in defining climatic zones in the cyrosphere based on empirical data.
Insolation differences play a primary role in controlling microclimate and vegetation cover, which together influence the development of topography. Topographic asymmetry (TA), or slope differences between terrain aspects, has been well documented in small-scale, field-based, and modeling studies. Here we combine a suite of environmental (e.g., vegetation, temperature, solar insolation) and topographic (e.g., elevation, drainage network) data to explore the driving mechanisms and markers of TA on a global scale. Using a novel empirical TA analysis method, we find that (1) steeper terrain has higher TA magnitudes, (2) globally, pole-facing terrain is on average steeper than equator-facing terrain, especially in mid-latitude, tectonically quiescent, and vegetated landscapes, and (3) high-elevation and low-temperature regions tend to have terrain steepened toward the equator. We further show that there are distinct differences in climate and vegetation cover across terrain aspects, and that TA is reflected in the size and form of fluvial drainage networks. Our work supports the argument that insolation asymmetries engender differences in local microclimates and vegetation on opposing terrain aspects, which broadly encourage the development of asymmetric topography across a range of lithologic, tectonic, geomorphic, and climatic settings.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.
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
High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
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.
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.
High Mountain Asia (HMA) is dependent upon both the amount and timing of snow and glacier meltwater. Previous model studies and coarse resolution (0.25° × 0.25°, ∼25 km × 25 km) passive microwave assessments of trends in the volume and timing of snowfall, snowmelt, and glacier melt in HMA have identified key spatial and seasonal heterogeneities in the response of snow to changes in regional climate. Here we use recently developed, continuous, internally consistent, and high-resolution passive microwave data (3.125 km × 3.125 km, 1987–2016) from the special sensor microwave imager instrument family to refine and extend previous estimates of changes in the snow regime of HMA. We find an overall decline in snow volume across HMA; however, there exist spatially contiguous regions of increasing snow volume—particularly during the winter season in the Pamir, Karakoram, Hindu Kush, and Kunlun Shan. Detailed analysis of changes in snow-volume trends through time reveal a large step change from negative trends during the period 1987–1997, to much more positive trends across large regions of HMA during the periods 1997–2007 and 2007–2016. We also find that changes in high percentile monthly snow-water volume exhibit steeper trends than changes in low percentile snow-water volume, which suggests a reduction in the frequency of high snow-water volumes in much of HMA. Regions with positive snow-water storage trends generally correspond to regions of positive glacier mass balances.
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.
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.
Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite - sensors the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987-2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E, 2002-2011), AMSR2 (2012-2015), and the Global Precipitation Measurement (GPM, 2014-2015) - in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multiyear snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low (<5%), maximal snow depth dominates the uncertainty signal. In our regression across HMA, we find that forest fraction is the strongest control on SWE variability (75.8%), followed by maximal multi-year snow depth (7.82%), 90th percentile 10-m wind speed of a 10-year December-January-February (DJF) time series (5.64%), 25th percentile DJF 10-m wind speed (5.44%), and hillslope angle (5.24%). Elevation, relief, and terrain aspect show very low influence on SWE variability (<1%). We find that the GPM sensor provides the most robust regression results, and can be reliably used to estimate SWE in our study region. While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas. (C) 2016 Elsevier Inc. All rights reserved.
Rivers draining the southern Himalaya provide most of the water supply for the densely populated Indo-Gangetic plains. Despite the importance of water resources in light of climate change, the relative contributions of rainfall, snow and glacier melt to discharge are not well understood, due to the scarcity of ground-based data in this complex terrain. Here, we quantify discharge sources in the Sutlej Valley, western Himalaya, from 2000 to 2012 with a distributed hydrological model that is based on daily, ground-calibrated remote-sensing observation. Based on the consistently good model performance, we analyzed the spatiotemporal distribution of hydrologic components and quantified their contribution to river discharge. Our results indicate that the Sutlej River's annual discharge at the mountain front is sourced to 55% by effective rainfall (rainfall reduced by evapotranspiration), 35% by snow melt and 10% by glacier melt. In the high-elevation orogenic interior glacial runoff contributes ∼30% to annual river discharge. These glacier melt contributions are especially important during years with substantially reduced rainfall and snowmelt runoff, as during 2004, to compensate for low river discharge and ensure sustained water supply and hydropower generation. In 2004, discharge of the Sutlej River totaled only half the maximum annual discharge; with 17.3% being sourced by glacier melt. Our findings underscore the importance of calibrating remote-sensing data with ground-based data to constrain hydrological models with reasonable accuracy. For instance, we found that TRMM (Tropical Rainfall Measuring Mission) product 3B42 V7 systematically overestimates rainfall in arid regions of our study area by a factor of up to 5. By quantifying the spatiotemporal distribution of water resources we provide an important assessment of the potential impact of global warming on river discharge in the western Himalaya. Given the near-global coverage of the utilized remote-sensing datasets this hydrological modeling approach can be readily transferred to other data-sparse regions.
The Greater and Lesser Caucasus mountains and their associated foreland basins contain similar rock types, experience a similar two-fold, along-strike variation in mean annual precipitation, and were affected by extreme base-level drops of the neighboring Caspian Sea. However, the two Caucasus ranges are characterized by decidedly different tectonic regimes and rates of deformation that are subject to moderate (less than an order of magnitude) gradients in climate, and thus allow for a unique opportunity to isolate the effects of climate and tectonics in the evolution of topography within active orogens. There is an apparent disconnect between modern climate, shortening rates, and topography of both the Greater Caucasus and Lesser Caucasus which exhibit remarkably similar topography along-strike despite the gradients in forcing. By combining multiple datasets, we examine plausible causes for this disconnect by presenting a detailed analysis of the topography of both ranges utilizing established relationships between catchment-mean erosion rates and topography (local relief, hillslope gradients, and channel steepness) and combining it with a synthesis of previously published low-temperature thermochronologic data. Modern climate of the Caucasus region is assessed through an analysis of remotely-sensed data (TRMM and MODIS) and historical streamflow data. Because along-strike variation in either erosional efficiency or thickness of accreted material fail to explain our observations, we suggest that the topography of both the western Lesser and Greater Caucasus are partially supported by different geodynamic forces. In the western Lesser Caucasus, high relief portions of the landscape likely reflect uplift related to ongoing mantle lithosphere delamination beneath the neighboring East Anatolian Plateau. In the Greater Caucasus, maintenance of high topography in the western portion of the range despite extremely low (<2-4 mm/y) modern convergence rates may be related to dynamic topography from detachment of the north-directed Greater Caucasus slab or to a recent slowing of convergence rates. Large-scale spatial gradients in climate are not reflected in the topography of the Caucasus and do not seem to exert any significant control on the tectonics or structure of either range. (C) 2016 Elsevier B.V. All rights reserved.
In this study, we provide a comprehensive analysis of trends in the extremes during the Indian summer monsoon (ISM) months (June to September) at different temporal and spatial scales. Our goal is to identify and quantify spatiotemporal patterns and trends that have emerged during the recent decades and may be associated with changing climatic conditions. Our analysis primarily relies on quantile regression that avoids making any subjective choices on spatial, temporal, or intensity pattern of extreme rainfall events. Our analysis divides the Indian monsoon region into climatic compartments that show different and partly opposing trends. These include strong trends toward intensified droughts in Northwest India, parts of Peninsular India, and Myanmar; in contrast, parts of Pakistan, Northwest Himalaya, and Central India show increased extreme daily rain intensity leading to higher flood vulnerability. Our analysis helps explain previously contradicting results of trends in average ISM rainfall.
Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.
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)
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.
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 sediment flux through Himalayan rivers directly impacts water quality and is important for sustaining agriculture as well as maintaining drinking-water and hydropower generation. Despite the recent increase in demand for these resources, little is known about the triggers and sources of extreme sediment flux events, which lower water quality and account for extensive hydropower reservoir filling and turbine abrasion. Here, we present a comprehensive analysis of the spatiotemporal trends in suspended sediment flux based on daily data during the past decade (2001-2009) from four sites along the Sutlej River and from four of its main tributaries. In conjunction with satellite data depicting rainfall and snow cover, air temperature and earthquake records, and field observations, we infer climatic and geologic controls of peak suspended sediment concentration (SSC) events. Our study identifies three key findings: First, peak SSC events (a parts per thousand yen 99th SSC percentile) coincide frequently (57-80%) with heavy rainstorms and account for about 30% of the suspended sediment flux in the semi-arid to arid interior of the orogen. Second, we observe an increase of suspended sediment flux from the Tibetan Plateau to the Himalayan Front at mean annual timescales. This sediment-flux gradient suggests that averaged, modern erosion in the western Himalaya is most pronounced at frontal regions, which are characterized by high monsoonal rainfall and thick soil cover. Third, in seven of eight catchments, we find an anticlockwise hysteresis loop of annual sediment flux variations with respect to river discharge, which appears to be related to enhanced glacial sediment evacuation during late summer. Our analysis emphasizes the importance of unconsolidated sediments in the high-elevation sector that can easily be mobilized by hydrometeorological events and higher glacial-meltwater contributions. In future climate change scenarios, including continuous glacial retreat and more frequent monsoonal rainstorms across the Himalaya, we expect an increase in peak SSC events, which will decrease the water quality and impact hydropower generation.
Seasonal precipitation gradients and their impact on fluvial sediment flux in the Northwest Himalaya
(2010)
Precipitation in the form of rain and snowfall throughout the Himalaya controls river discharge and erosional processes and, thus, has a first-order control on the fluvial sediment flux. Here, we analyze daily precipitation data (1998-2007) of 80 weather stations from the northwestern Himalaya in order to decipher temporal and spatial moisture gradients. In addition, suspended sediment data allow assessment of the impact of precipitation on the fluvial sediment flux for a 10(3)-km(2) catchment (Baspa). We find that weather stations located at the mountain front receive similar to 80% of annual precipitation during summer (May-Oct), whereas stations in the orogenic interior, i.e., leeward of the orographic barrier, receive similar to 60% of annual precipitation during winter (Nov-Apr). In both regions 4-6 rainstorm days account for similar to 40% of the summer budgets, while rainstorm magnitude-frequency relations, derived from 40-year precipitation time-series, indicate a higher storm variability in the interior than in the frontal region. This high variability in maximum annual rainstorm days in the orogenic interior is reflected by a high variability in extreme suspended sediment events in the Baspa Valley, which strongly affect annual erosion yields. The two most prominent 5-day-long erosional events account for 50% of the total 5-year suspended sediment flux and coincide with synoptic-scale monsoonal rainstorms. This emphasizes the erosional impact of the Indian Summer Monsoon as the main driving force for erosion processes in the orogenic interior, despite more precipitation falling during the winter season.
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.
The spatial pattern of extreme precipitation from 40 years of gauge data in the central Himalaya
(2022)
The topography of the Himalaya exerts a substantial control on the spatial distribution of monsoonal rainfall, which is a vital water source for the regional economy and population. But the occurrence of short-lived and high-intensity precipitation results in socio-economic losses. This study relies on 40 years of daily data from 204 ground stations in Nepal to derive extreme precipitation thresholds, amounts, and days at the 95th percentile. We additionally determine the precipitation magnitude-frequency relation. We observe that extreme precipitation amounts follow an almost uniform band parallel to topographic contour lines in the southern Himalaya mountains in central and eastern Nepal but not in western Nepal. The relationship of extreme precipitation indices with topographic relief shows that extreme precipitation thresholds decrease with increasing elevation, but extreme precipitation days increase in higher elevation areas. Furthermore, stations above 1 km elevation exhibit a power-law relation in the rainfall magnitude-frequency framework. Stations at higher elevations generally have lower values of power-law exponents than low elevation areas. This suggests a fundamentally different behaviour of the rainfall distribution and an increased occurrence of extreme rainfall storms in the high elevation areas of Nepal.
We modeled the two most extreme highstands of Lake Naivasha during the last 175 k.y. to estimate potential precipitation/ evaporation changes in this basin. In a first step, the bathymetry of the paleolakes at f135 and 9 k.y. BP was reconstructed from sediment cores and surface outcrops. Second, we modeled the paleohydrologic budget during the highstands using a simplified coupled energy mass-balance model. Our results show that the hydrologic and hence the climate conditions at f135 and 9 k.y. BP were similar, but significantly different from today. The main difference is a f15% higher value in precipitation compared to the present. An adaptation and migration of vegetation in the cause of climate changes would result in a f30% increase in precipitation. The most likely cause for such a wetter climate at f135 and 9 k.y. BP is a more intense intertropical convergence and increased precipitation in East Africa.
Hydrological modelling of a Pleistocene landslide-dammed lake in the Santa Maria Basin, NW Argentina
(2001)
Integration of digital elevation models and satellite images to investigate geological processes.
(2006)
In order to better understand the geological boundary conditions for ongoing or past surface processes geologists face two important questions: 1) How can we gain additional knowledge about geological processes by analyzing digital elevation models (DEM) and satellite images and 2) Do these efforts present a viable approach for more efficient research. Here, we will present case studies at a variety of scales and levels of resolution to illustrate how we can substantially complement and enhance classical geological approaches with remote sensing techniques. Commonly, satellite and DEM based studies are being used in a first step of assessing areas of geologic interest. While in the past the analysis of satellite imagery (e.g. Landsat TM) and aerial photographs was carried out to characterize the regional geologic characteristics, particularly structure and lithology, geologists have increasingly ventured into a process-oriented approach. This entails assessing structures and geomorphic features with a concept that includes active tectonics or tectonic activity on time scales relevant to humans. In addition, these efforts involve analyzing and quantifying the processes acting at the surface by integrating different remote sensing and topographic data (e.g. SRTM-DEM, SSM/I, GPS, Landsat 7 ETM, Aster, Ikonos…). A combined structural and geomorphic study in the hyperarid Atacama desert demonstrates the use of satellite and digital elevation data for assessing geological structures formed by long-term (millions of years) feedback mechanisms between erosion and crustal bending (Zeilinger et al., 2005). The medium-term change of landscapes during hundred thousands to millions years in a more humid setting is shown in an example from southern Chile. Based on an analysis of rivers/watersheds combined with landscapes parameterization by using digital elevation models, the geomorphic evolution and change in drainage pattern in the coastal Cordillera can be quantified and put into the context of seismotectonic segmentation of a tectonically active region. This has far-reaching implications for earthquake rupture scenarios and hazard mitigation (K. Rehak, see poster on IMAF Workshop). Two examples illustrate short-term processes on decadal, centennial and millennial time scales: One study uses orogen scale precipitation gradients derived from remotely sensed passive microwave data (Bookhagen et al., 2005a). They demonstrate how debris flows were triggered as a response of slopes to abnormally strong rainfall in the interior parts of the Himalaya during intensified monsoons. The area of the orogen that receives high amounts of precipitation during intensified monsoons also constitutes numerous landslide deposits of up to 1km<sup>3 volume that were generated during intensified monsoon phase at about 27 and 9 ka (Bookhagen et al., 2005b). Another project in the Swiss Alps compared sets of aerial photographs recorded in different years. By calculating high resolution surfaces the mass transport in a landslide could be reconstructed (M. Schwab, Universität Bern). All these examples, although representing only a short and limited selection of projects using remote sense data in geology, have as a common approach the goal to quantify geological processes. With increasing data resolution and new sensors future projects will even enable us to recognize more patterns and / or structures indicative of geological processes in tectonically active areas. This is crucial for the analysis of natural hazards like earthquakes, tsunamis and landslides, as well as those hazards that are related to climatic variability. The integration of remotely sensed data at different spatial and temporal scales with field observations becomes increasingly important. Many of presently highly populated places and increasingly utilized regions are subject to significant environmental pressure and often constitute areas of concentrated economic value. Combined remote sensing and ground-truthing in these regions is particularly important as geologic, seismicity and hydrologic data may be limited here due to the recency of infrastructural development. Monitoring ongoing processes and evaluating the remotely sensed data in terms of recurrence of events will greatly enhance our ability to assess and mitigate natural hazards. <hr> Dokument 1: Foliensatz | Dokument 2: Abstract <hr> Interdisziplinäres Zentrum für Musterdynamik und Angewandte Fernerkundung Workshop vom 9. - 10. Februar 2006
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.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.
Orogenic plateaus are extensive, high-elevation areas with low internal relief that have been attributed to deep-seated and/or climate-driven surface processes. In the latter case, models predict that lateral plateau growth results from increasing aridity along the margins as range uplift shields the orogen interior from precipitation. We analyze the spatiotemporal progression of basin isolation and filling at the eastern margin of the Puna Plateau of the Argentine Andes to determine if the topography predicted by such models is observed. We find that the timing of basin filling and reexcavation is variable, suggesting nonsystematic plateau growth. Instead, the Airy isostatically compensated component of topography constitutes the majority of the mean elevation gain between the foreland and the plateau. This indicates that deep-seated phenomena, such as changes in crustal thickness and/or lateral density, are required to produce high plateau elevations. In contrast, the frequency of the uncompensated topography within the plateau and in the adjacent foreland that is interrupted by ranges appears similar, although the amplitude of this topographic component increases east of the plateau. Combined with sedimentologic observations, we infer that the low internal relief of the plateau likely results from increased aridity and sediment storage within the plateau and along its eastern margin.
Metamorphic dome complexes occur within the internal structures of the northern Himalaya and southern Tibet. Their origin, deformation, and fault displacement patterns are poorly constrained. We report new field mapping, structural data, and cooling ages from the western flank of the Leo Pargil dome in the northwestern Himalaya in an attempt to characterize its post-middle Miocene structural development. The western flank of the dome is characterized by shallow, west-dipping pervasive foliation and WNW-ESE mineral lineation. Shear-sense indicators demonstrate that it is affected by east-west normal faulting that facilitated exhumation of high-grade metamorphic rocks in a contractional setting. Sustained top-to-northwest normal faulting during exhumation is observed in a progressive transition from ductile to brittle deformation. Garnet and kyanite indicate that the Leo Pargil dome was exhumed from the mid-crust. Ar- 40/Ar-39 mica and apatite fission track (AFT) ages constrain cooling and exhumation pathways front 350 to 60 degrees C and suggest that the dome cooled in three stages since the middle Miocene. Ar-40/Ar-39 white mica ages of 16-14 Ma suggest a first phase of rapid cooling and provide minimum estimates for the onset of dome exhumation. AFT ages between 10 and 8 Ma suggest that ductile fault displacement had ceased by then, and AFT track-length data from high-elevation samples indicate that the rate of cooling had decreased significantly. We interpret this to indicate decreased fault displacement along the Leo Pargil shear zone and possibly a transition to the Kaurik-Chango normal fault system between 10 and 6 Ma. AFT ages from lower elevations indicate accelerated cooling since the Pliocene that cannot be related to pure fault displacement, and therefore may reflect more pronounced regionally distributed and erosion-driven exhumation
Along the Southern Himalayan Front (SHF), areas with concentrated precipitation coincide with rapid exhumation, as indicated by young mineral cooling ages. Twenty new, young ( < 1-5 Ma) apatite fission track (AFT) ages have been obtained from the Himalayan Crystalline Core along the Sutlej Valley, NW India. The AFT ages correlate with elevation, but show no spatial relationship to tectonic structures, such as the Main Central Thrust or the Southern Tibetan Fault System. Monsoonal precipitation in this region exerts a strong influence on erosional surface processes. Fluvial erosional unloading along the SHF is focused on high mountainous areas, where the orographic barrier forces out > 80% of the annual precipitation. AFT cooling ages reveal a coincidence between rapid erosion and exhumation that is focused in a similar to 50-70-km-wide sector of the Himalaya, rather than encompassing the entire orogen. Assuming simplified constant exhumation rates, the rocks of two age vs. elevation transects were exhumed at similar to 1.4 +/- 0.2 and similar to 1.1 +/- 0.4 mm/a with an average cooling rate of similar to 40-50degreesC/Ma during Pliocene-Quarternary time. Following other recently published hypotheses regarding the relation between tectonics and climate in the Himalaya, we suggest that this concentrated loss of material was accommodated by motion along a back-stepping thrust to the south and a normal fault zone to the north as part of an extruding wedge. Climatically controlled erosional processes focus on this wedge and suggest that climatically controlled surface processes determine tectonic deformation in the internal part of the Himalaya. (C) 2004 Elsevier B.V. All rights reserved
Whether variations in the spatial distribution of erosion influence the location, style, and magnitude of deformation within the Himalayan orogen is a matter of debate. We report new Ar-40/Ar-39 white mica and apatite fission- track (AFT) ages that measure the vertical component of exhumation rates along an similar to 120-km-wide NE-SW transect spanning the greater Sutlej region of northwest India. The Ar-40/Ar-39 data indicate that first the High Himalayan Crystalline units cooled below their closing temperature during the early to middle Miocene. Subsequently, Lesser Himalayan Crystalline nappes cooled rapidly, indicating southward propagation of the orogen during late Miocene to Pliocene time. The AFT data, in contrast, imply synchronous exhumation of a NE-SW-oriented similar to 80 x 40 km region spanning both crystalline nappes during the Pliocene-Quaternary. The locus of pronounced exhumation defined by the AFT data correlates with a region of high precipitation, discharge, and sediment flux rates during the Holocene. This correlation suggests that although tectonic processes exerted the dominant control on the denudation pattern before and until the middle Miocene; erosion may have been the most important factor since the Pliocene
Salt pans are highly dynamic environments that are difficult to study by in situ methods because of their harsh climatic conditions and large spatial areas. Remote sensing can help to elucidate their environmental dynamics and provide important constraints regarding their sedimentological, mineralogical, and hydrological evolution. This study utilizes spaceborne multitemporal multispectral optical data combined with spectral endmembers to document spatial distribution of surface crust types over time on the Omongwa pan located in the Namibian Kalahari. For this purpose, 49 surface samples were collected for spectral and mineralogical characterization during three field campaigns (2014–2016) reflecting different seasons and surface conditions of the salt pan. An approach was developed to allow the spatiotemporal analysis of the salt pan crust dynamics in a dense time-series consisting of 77 Landsat 8 cloud-free scenes between 2014 and 2017, covering at least three major wet–dry cycles. The established spectral analysis technique Sequential Maximum Angle Convex Cone (SMACC) extraction method was used to derive image endmembers from the Landsat time-series stack. Evaluation of the extracted endmember set revealed that the multispectral data allowed the differentiation of four endmembers associated with mineralogical mixtures of the crust’s composition in dry conditions and three endmembers associated with flooded or muddy pan conditions. The dry crust endmember spectra have been identified in relation to visible, near infrared, and short-wave infrared (VNIR–SWIR) spectroscopy and X-ray diffraction (XRD) analyses of the collected surface samples. According these results, the spectral endmembers are interpreted as efflorescent halite crust, mixed halite–gypsum crust, mixed calcite quartz sepiolite crust, and gypsum crust. For each Landsat scene the spatial distribution of these crust types was mapped with the Spectral Angle Mapper (SAM) method and significant spatiotemporal dynamics of the major surface crust types were observed. Further, the surface crust dynamics were analyzed in comparison with the pan’s moisture regime and other climatic parameters. The results show that the crust dynamics are mainly driven by flooding events in the wet season, but are also influenced by temperature and aeolian activity in the dry season. The approach utilized in this study combines the advantages of multitemporal satellite data for temporal event characterization with advantages from hyperspectral methods for the image and ground data analyses that allow improved mineralogical differentiation and characterization.
Atmospheric water vapour content is a key variable that controls the development of deep convective storms and rainfall extremes over the central Andes. Direct measurements of water vapour are challenging; however, recent developments in microwave processing allow the use of phase delays from L-band radar to measure the water vapour content throughout the atmosphere: Global Navigation Satellite System (GNSS)-based integrated water vapour (IWV) monitoring shows promising results to measure vertically integrated water vapour at high temporal resolutions. Previous works also identified convective available potential energy (CAPE) as a key climatic variable for the formation of deep convective storms and rainfall in the central Andes. Our analysis relies on GNSS data from the Argentine Continuous Satellite Monitoring Network, Red Argentina de Monitoreo Satelital Continuo (RAMSAC) network from 1999 to 2013. CAPE is derived from version 2.0 of the ECMWF’s (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA-interim) and rainfall from the TRMM (Tropical Rainfall Measuring Mission) product. In this study, we first analyse the rainfall characteristics of two GNSS-IWV stations by comparing their complementary cumulative distribution function (CCDF). Second, we separately derive the relation between rainfall vs. CAPE and GNSS-IWV. Based on our distribution fitting analysis, we observe an exponential relation of rainfall to GNSS-IWV. In contrast, we report a power-law relationship between the daily mean value of rainfall and CAPE at the GNSS-IWV station locations in the eastern central Andes that is close to the theoretical relationship based on parcel theory. Third, we generate a joint regression model through a multivariable regression analysis using CAPE and GNSS-IWV to explain the contribution of both variables in the presence of each other to extreme rainfall during the austral summer season. We found that rainfall can be characterised with a higher statistical significance for higher rainfall quantiles, e.g., the 0.9 quantile based on goodness-of-fit criterion for quantile regression. We observed different contributions of CAPE and GNSS-IWV to rainfall for each station for the 0.9 quantile. Fourth, we identify the temporal relation between extreme rainfall (the 90th, 95th, and 99th percentiles) and both GNSS-IWV and CAPE at 6 h time steps. We observed an increase before the rainfall event and at the time of peak rainfall—both for GNSS-integrated water vapour and CAPE. We show higher values of CAPE and GNSS-IWV for higher rainfall percentiles (99th and 95th percentiles) compared to the 90th percentile at a 6-h temporal scale. Based on our correlation analyses and the dynamics of the time series, we show that both GNSS-IWV and CAPE had comparable magnitudes, and we argue to consider both climatic variables when investigating their effect on rainfall extremes.
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.
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.