Persistent growth of a young andesite lava cone: Bagana volcano, Papua New Guinea

Bagana, an andesite lava cone on Bougainville Island, Papua New Guinea, is thought to be a very young central volcano. We have tested this idea by estimating the volumes of lava extruded over different time intervals (1-, 2-, 3-, 9-, 15-, 70- years) using digital elevation models (DEMs), mainly created from satellite data. Our results show that the long-term extrusion rate at Bagana, measured over years to decades, has remained at about 1.0 m3s-1. We present models of the total edifice volume, and show that, if our measured extrusion rates are representative, the volcano could have been built in only ~300 years. It could also possibly have been built at a slower rate during a longer, earlier period of growth. Six kilometres NNW of Bagana, an andesite-dacite volcano, Billy Mitchell, had a large, caldera-forming plinian eruption 437 years ago. We consider the possibility that, as a result of this eruption, the magma supply was diverted from Billy Mitchell to Bagana. It seems that Bagana is a rare example of a very youthful, polygenetic, andesite volcano. The characteristics of such a volcano, based on the example of Bagana, are: a preponderance of lava products over pyroclastic products, a high rate of lava extrusion maintained for decades, a very high rate of SO2 emission, evidence of magma batch fractionation and location in a trans-tensional setting at the end of an arc segment above a very steeply dipping and rapidly converging subduction zone

A decreasing glacier mass balance gradient from the edge of the Upper Tarim Basin to the Karakoram during 2000-2014

In contrast to the glacier mass losses observed at other locations around the world, some glaciers in the High Mountains of Asia appear to have gained mass in recent decades. However, changes in digital elevation models indicate that glaciers in Karakoram and Pamir have gained mass, while recent laser altimetry data indicate mass gain centred on West Kunlun. Here, we obtain results that are essentially consistent with those from altimetry, but with two-dimensional observations and higher resolution. We produced elevation models using radar interferometry applied to bistatic data gathered between 2011 and 2014 and compared them to a model produced from bistatic data collected in 2000. The glaciers in West Kunlun, Eastern Pamir and the northern part of Karakoram experienced a clear mass gain of 0.043 ± 0.078~0.363 ± 0.065 m w.e. yr−1. The Karakoram showed a near-stable mass balance in its western part (−0.020 ± 0.064 m w.e. yr−1), while the Eastern Karakoram showed mass loss (−0.101 ± 0.058 m w.e. yr−1). Significant positive glacier mass balances are noted along the edge of the Upper Tarim Basin and indicate a decreasing gradient from northeast to southwest

60 Years of Glacier Elevation and Mass Changes in the Maipo River Basin, Central Andes of Chile

Glaciers in the central Andes of Chile are fundamental freshwater sources for ecosystems and communities. Overall, glaciers in this region have shown continuous recession and down-wasting, but long-term glacier mass balance studies providing precise estimates of these changes are scarce. Here, we present the first long-term (1955–2013/2015), region-specific glacier elevation and mass change estimates for the Maipo River Basin, from which the densely populated metropolitan region of Chile obtains most of its freshwater supply. We calculated glacier elevation and mass changes using historical topographic maps, Shuttle Radar Topography Mission (SRTM), TerraSAR-X add-on for Digital Elevation Measurements (TanDEM-X), and airborne Light Detection and Ranging (LiDAR) digital elevation models. The results indicated a mean regional glacier mass balance of −0.12 ± 0.06 m w.e.a−1, with a total mass loss of 2.43 ± 0.26 Gt for the Maipo River Basin between 1955–2013. The most negative glacier mass balance was the Olivares sub-basin, with a mean value of −0.29 ± 0.07 m w.e.a−1. We observed spatially heterogeneous glacier elevation and mass changes between 1955 and 2000, and more negative values between 2000 and 2013, with an acceleration in ice thinning rates starting in 2010, which coincides with the severe drought. Our results provide key information to improve glaciological and hydrological projections in a region where water resources are under pressure

Mass balance and area changes of glaciers in the Cordillera Real and Tres Cruces, Bolivia, between 2000 and 2016

Climate change has led to a significant shrinkage of glaciers in the Tropical Andes during the last decades. Recent multi-temporal quantifications of ice mass loss at mountain range to regional scale are missing. However, this is fundamental information for future water resource planning and glacier change projections. In this study, we measure temporally consistent glacier area changes and geodetic mass balances throughout the Bolivian Cordillera Real and Tres Cruces based on multi-sensor remote-sensing data. By analyzing multi-spectral satellite images and interferometric SAR data, a glacier recession of 81 ± 18 km2 (29%; 5.1 ± 1.1 km2 a−1), a geodetic mass balance of −403 ± 78 kg m−2 a−1 and a total ice mass loss of 1.8 ± 0.5 Gt is derived for 2000–2016. In the period 2013–2016, ice mass loss was 21% above the average rate. A retreat rate of 15 ± 5 km2 a−1 and a mass budget of −487 ± 349 kg m−2 a−1 are found in this more recent period. These higher change rates can be attributed to the strong El Niño event in 2015/16. The analyses of individual glacier changes and topographic variables confirmed the dependency of the mass budget and glacier recession on glacier aspect and median elevation

Changement de masse des glaciers à l’échelle mondiale par analyse spatiotemporelle de modèles numériques de terrain

Les glaciers de la planète rétrécissent rapidement, et produisent des impacts qui s'étendent de la hausse du niveau de la mer et la modification des risques cryosphériques jusqu'au changement de disponibilité en eau douce. Malgré des avancées significatives durant l'ère satellitaire, l'observation des changements de masse des glaciers est encore entravée par une couverture partielle des estimations de télédétection, et par une faible contrainte sur les erreurs des évaluations associées. Dans cette thèse, nous présentons une estimation mondiale et résolue des changements de masse des glaciers basée sur l'analyse spatio-temporelle de modèles numériques de terrain. Nous développons d'abord des méthodes de statistiques spatio-temporelles pour évaluer l'exactitude et la précision des modèles numériques de terrain, et pour estimer des séries temporelles de l'altitude de surface des glaciers. En particulier, nous introduisons un cadre spatial non stationnaire pour estimer et propager des corrélations spatiales multi-échelles dans les incertitudes d'estimations géospatiales. Nous générons ensuite des modèles numériques de terrain massivement à partir de deux décennies d'archives d'images optiques stéréo couvrant les glaciers du monde entier. À partir de ceux-ci, nous estimons des séries temporelles d'altitude de surface pour tous les glaciers de la Terre à une résolution de 100,m sur la période 2000--2019. En intégrant ces séries temporelles en changements de volume et de masse, nous révélons une accélération significative de la perte de masse des glaciers à l'échelle mondiale, ainsi que des réponses régionalement distinctes qui reflètent des changements décennaux de conditions climatiques. En utilisant une grande quantité de données indépendantes et de haute précision, nous démontrons la validité de notre analyse pour produire des incertitudes robustes et cohérentes à différentes échelles de la structure spatio-temporelle de nos estimations. Nous espérons que nos méthodes favorisent des analyses spatio-temporelles robustes, en particulier pour identifier les sources de biais et d'incertitudes dans les études géospatiales. En outre, nous nous attendons à ce que nos estimations permettent de mieux comprendre les facteurs qui régissent le changement des glaciers et d'étendre nos capacités de prévision de ces changements à toutes échelles. Ces prédictions sont nécessaires à la conception de politiques adaptatives sur l'atténuation des impacts de la cryosphère dans le contexte du changement climatique.The world's glaciers are shrinking rapidly, with impacts ranging from global sea-level rise and changes in freshwater availability to the alteration of cryospheric hazards. Despite significant advances during the satellite era, the monitoring of the mass changes of glaciers is still hampered by a fragmented coverage of remote sensing estimations and a poor constraint of the errors in related assessments. In this thesis, we present a globally complete and resolved estimate of glacier mass changes by spatiotemporal analysis of digital elevation models. We first develop methods based on spatiotemporal statistics to assess the accuracy and precision of digital elevation models, and to estimate time series of glacier surface elevation. In particular, we introduce a non-stationary spatial framework to estimate and propagate multi-scale spatial correlations in uncertainties of geospatial estimates. We then massively generate digital elevation models from two decades of stereo optical archives covering glaciers worldwide. From those, we estimate time series of surface elevation for all of Earth's glaciers at a resolution of 100,m during 2000--2019. Integrating these time series into volume and mass changes, we identify a significant acceleration of global glacier mass loss, as well as regionally-contrasted responses that mirror decadal changes in climatic conditions. Using a large amount of independent, high-precision data, we demonstrate the validity of our analysis to yield robust and consistent uncertainties at different scales of the spatiotemporal structure of our estimates. We expect our methods to foster robust spatiotemporal analyses, in particular to identify sources of biases and uncertainties in geospatial assessments. Furthermore, we anticipate our estimates to advance the understanding of the drivers that govern glacier change, and to extend our capabilities of predicting these changes at all scales. Such predictions are critically needed to design adaptive policies on the mitigation of cryospheric impacts in the context of climate change

The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images

A basic data requirement of a river flood inundation model is a Digital Terrain Model (DTM) of the reach being studied. The scale at which modeling is required determines the accuracy required of the DTM. For modeling floods in urban areas, a high resolution DTM such as that produced by airborne LiDAR (Light Detection And Ranging) is most useful, and large parts of many developed countries have now been mapped using LiDAR. In remoter areas, it is possible to model flooding on a larger scale using a lower resolution DTM, and in the near future the DTM of choice is likely to be that derived from the TanDEM-X Digital Elevation Model (DEM). A variable-resolution global DTM obtained by combining existing high and low resolution data sets would be useful for modeling flood water dynamics globally, at high resolution wherever possible and at lower resolution over larger rivers in remote areas. A further important data resource used in flood modeling is the flood extent, commonly derived from Synthetic Aperture Radar (SAR) images. Flood extents become more useful if they are intersected with the DTM, when water level observations (WLOs) at the flood boundary can be estimated at various points along the river reach. To illustrate the utility of such a global DTM, two examples of recent research involving WLOs at opposite ends of the spatial scale are discussed. The first requires high resolution spatial data, and involves the assimilation of WLOs from a real sequence of high resolution SAR images into a flood model to update the model state with observations over time, and to estimate river discharge and model parameters, including river bathymetry and friction. The results indicate the feasibility of such an Earth Observation-based flood forecasting system. The second example is at a larger scale, and uses SAR-derived WLOs to improve the lower-resolution TanDEM-X DEM in the area covered by the flood extents. The resulting reduction in random height error is significant

Improving the TanDEM-X Digital Elevation Model for flood modelling using flood extents from Synthetic Aperture Radar images

The topography of many floodplains in the developed world has now been surveyed with high resolution sensors such as airborne LiDAR (Light Detection and Ranging), giving accurate Digital Elevation Models (DEMs) that facilitate accurate flood inundation modelling. This is not always the case for remote rivers in developing countries. However, the accuracy of DEMs produced for modelling studies on such rivers should be enhanced in the near future by the high resolution TanDEM-X WorldDEM. In a parallel development, increasing use is now being made of flood extents derived from high resolution Synthetic Aperture Radar (SAR) images for calibrating, validating and assimilating observations into flood inundation models in order to improve these. This paper discusses an additional use of SAR flood extents, namely to improve the accuracy of the TanDEM-X DEM in the floodplain covered by the flood extents, thereby permanently improving this DEM for future flood modelling and other studies. The method is based on the fact that for larger rivers the water elevation generally changes only slowly along a reach, so that the boundary of the flood extent (the waterline) can be regarded locally as a quasi-contour. As a result, heights of adjacent pixels along a small section of waterline can be regarded as samples with a common population mean. The height of the central pixel in the section can be replaced with the average of these heights, leading to a more accurate estimate. While this will result in a reduction in the height errors along a waterline, the waterline is a linear feature in a two-dimensional space. However, improvements to the DEM heights between adjacent pairs of waterlines can also be made, because DEM heights enclosed by the higher waterline of a pair must be at least no higher than the corrected heights along the higher waterline, whereas DEM heights not enclosed by the lower waterline must in general be no lower than the corrected heights along the lower waterline. In addition, DEM heights between the higher and lower waterlines can also be assigned smaller errors because of the reduced errors on the corrected waterline heights. The method was tested on a section of the TanDEM-X Intermediate DEM (IDEM) covering an 11km reach of the Warwickshire Avon, England. Flood extents from four COSMO-SKyMed images were available at various stages of a flood in November 2012, and a LiDAR DEM was available for validation. In the area covered by the flood extents, the original IDEM heights had a mean difference from the corresponding LiDAR heights of 0.5 m with a standard deviation of 2.0 m, while the corrected heights had a mean difference of 0.3 m with standard deviation 1.2 m. These figures show that significant reductions in IDEM height bias and error can be made using the method, with the corrected error being only 60% of the original. Even if only a single SAR image obtained near the peak of the flood was used, the corrected error was only 66% of the original. The method should also be capable of improving the final TanDEM-X DEM and other DEMs, and may also be of use with data from the SWOT (Surface Water and Ocean Topography) satellite

Quantification and Change Assessment Benjamin Aubrey Robson 2016 Dissertation date: 31st October 2016 of Debris-Covered Glaciers using Remote Sensing

This thesis investigates how remote sensing data can be used to assess the changing state of debris-covered ice. The principal study areas are the Manaslu Region in Nepal (papers I and III) and the Hohe Tauern National Park, Austria (paper II). Clean glacier ice is straightforward to semi-automatically classify using multi-spectral satellite imagery owing to the strong spectral signature of clean ice in the visible and near-infrared sections of the electromagnetic spectrum. Since the ablation zones of clean ice glaciers are at the pressure melting point, a change in terminus position or glacier area can be directly linked to a change in climate. Debris-covered ice is however more complicated to map and to interpret temporal change. Supraglacial debris is spectrally indistinguishable from the surrounding paraglacial terrain, and requires auxiliary data such as a Digital Elevation Model (DEM), thermal band data, or flow data. Object-Based Image Analysis (OBIA) provides a framework for combining multiple datasets in one analysis, while additionally allowing shape, contextual, hierarchical and textural criteria to be used to classify imagery. Paper I combines optical (Landsat-8), topographic (void-filled SRTM) and SAR coherence (ALOS PALSAR) data within an OBIA workflow to semi-automatically classify both clean ice and debris-covered ice in the challenging area surrounding Mount Manaslu in Nepal. When compared with manually delineated outlines, the classification achieved an accuracy of 91% (93% for clean ice and 83% for debriscovered ice). The classification was affected by seasonal snow and shadows while the debris-covered ice mapping was influenced by the datasets being temporally inconsistent, and the mountainous topography causing inconsistencies in the SAR coherence data. The method compares well with other automated techniques for classifying debris-covered ice, but has two additional advantages: firstly, that SAR coherence data can distinguish active ice from stagnant ice based on whether motion or significant downwasting has occured, and secondly, that the method is applicable over a large study area using just space-borne data. Paper II explores the potential of using high-resolution (10 m) topographic data and an edge detection algorithm to morphologically map the extent of debris-covered ice. The method was applied in the Hohe Tauern National Park, Austria, using a 10 m DEM derived from airborne Light Detection and Radar (LiDAR) acquisitions. Additionally, the end-of-summer transient snowline (TSL) was also mapped, which approximates the annual Equilibrium Line Altitude (ELA). Our classification was applied on three Landsat satellite images from 1985, 2003 and 2013 and compared the results to the Austrian Glacier Inventories from 1969 and 1998 to derive decadal-scale glacial changes. A mean rate of glacier area reduction of 1.4 km2a-1 was calculated between 1969 and 2013 with a total reduction in area of 33%. The TSL rose by 92 m between 1985 and 2013 to an altitude of 3005 m. By comparing our results with manually delineated outlines an accuracy of 97.5% was determined. When a confusion matrix was calculated it could be seen that the results contained few false positives but some false negatives which were attributed to seasonal snow, shadows and misclassified debris. Our results correspond broadly with those found in other areas of the European Alps although a heterogeneity in glacier change is observable. We recommend that future glacier mapping investigations should utilise a combination of both SAR coherence data and high-resolution topographic data in order to delineate the extent of both active and stagnant glacier ice. Paper III investigates decadal scale changes in glacier area, velocity and volume in the previously undocumented Manaslu Region, Nepal. Between 2001 and 2013 the glacier area reduced by 8.2% (-0.68% a-1). Simultaneously, the glaciers lowered by -0.21 ± 0.08 m a-1 and had a slightly negative specific mass balance of -0.05 ± 016 m w.e a-1 although mass balances ranged -2.49 ± 2.24 to +0.27 ± 0.30 m w.e a-1 throughout the region. The geodetic mass balance for select glaciers covered by a Corona DEM between 1970 and 2013 was -0.24 ± 0.12 m w.e a-1 which became more negative (-0.51 ± 0.12 m w.e. a-1) between 2005 and 2013. Rates of surface lowering over debriscovered ice increasing by 168% between 1970 – 2000 (0.40 ± 0.18 m a-1) and 2005 – 2013 (1.07 ±0.48 m a-1). The rate of glacier melt varies due to presumed increases in debris thickness at the upper and lower boundaries of the ablation zone, while an area of enhanced glacier downwasting corresponds to the presence of supraglacial lakes and exposed ice. The glacier velocity varies across the region. Many glaciers have stagnant sections towards the glacier termini, and a trend of ongoing stagnation is observable. No relationship exists between trends in glacier area and glacier volume or velocity, although a weak relationship exists between trends in the changes of volume and velocity. The rates of glacier area and velocity change appear to be similar, although the number of glaciers that had records of area, velocity, and volume was few. Our results are comparable to studies looking at mean surface lowerings and geodetic mass balances in other areas of the Himalayas, and point towards heterogeneous yet pronounced mass losses across the Himalaya region

Tracking Multi-Decadal Lake Dynamics using Optical Imagery, Digital Elevation Models, and Bathymetric Datasets

The goal of this research is to review the current state of long-term, multi-decadal lake dynamic monitoring and develop novel techniques for scalable analysis at local, regional, and global levels. This dissertation is comprised of three chapters formatted as journal manuscripts with each chapter progressively addressing some key limitation in current lake dynamic monitoring methodologies. Chapter 1 tracks lake dynamics (surface elevation, surface area, volume, and volume change) for a single water body, Lake McConaughy, which is the largest lake and reservoir in the state of Nebraska, using the cloud-based geospatial analysis platform Google Earth Engine. Lake dynamics were estimated using bathymetric survey data, the Shuttle Radar Topography Mission 30-meter digital elevation model, and Landsat 5 image composites for 100 time periods between 1984 and 2009. Water surface elevation was estimated and assessed for 5,994 different combinations of water indices, segmentation thresholds, water boundaries, and statistics and produced elevations as accurate as 0.768 m CI95% [0.657, 0.885] root-mean-square-error. The method also detected seasonal and long-term trends which would have major implications for regional agriculture, recreation, and water quality. Chapter 1 was published as an article in the peer-reviewed journal Water Resources Research in October 2019. Chapter 2 expands and improves upon the techniques explored in Chapter 1 in multiple ways. First, the techniques were improved to remove image contamination sources such as snow, ice, cloud cover, shadow, and sensor error for individual images using the Pixel Quality Assurance (QA) band available as a part of the Landsat 4, 5, 7, and 8 Top-of-Atmosphere Tier-1 Collection-1 archives. Using the Pixel QA band information, image contamination was removed from each image between August 1982 and December 2017 and water surface elevation was estimated with the remaining visible water boundary extents overlaying merged National Elevation Dataset digital elevation model and bathymetric survey data resampled to 30-meters which resulted in enhanced temporal resolution compared to the techniques used in Chapter 1. Second, the analysis was expanded from a single water body to fifty-two lakes/reservoirs to provide a better understanding of how the techniques generalize to imagery and water bodies encompassing a wide range of ecotypes, geologies, climates, and management strategies. A variety of common water indices, such as the Modified Normalized Difference Water Index, naïve and dynamic water indices, water boundary types, and filtering strategies were tested and individual lake accuracies are as low as 0.191m RMSE CI95%[0.129, 0.243], and 45 of the 52 lakes produced sub-meter root-mean-squared-error accuracies. Furthermore, accuracy of surface elevation estimates is highly correlated with the mean slope of surrounding terrain with low-slope shorelines having greater accuracy than high-slope shorelines such as those in canyon-filled reservoirs or in mountainous regions. Overall, the improved techniques extend our ability to track long-term lake dynamics to lakes with bathymetric datasets while lacking in-situ hydrological stations, provide a framework for scale-able analysis in Google Earth Engine, and balance a need between high-accuracy estimates and maximum temporal resolution. Bathymetric survey data, such as that used in Chapters 1 and 2 is, unfortunately, not available for most water bodies at regional and global scales. Chapter 3 introduces a method of tracking long-term lake dynamics without bathymetry data and only using available digital elevation models such as Shuttle Radar Topography Mission, the National Elevation Dataset, and Advanced Land Observing Satellite. In digital elevation models, the water surface is often, but not always, hydroflattened producing a flat surface approximating the surface of the water at the time of the data capture which precludes using water boundaries like those in Chapter 1 and Chapter 2 to estimate water level when it is lower than the hydroflattened surface in the digital elevation model. However, using hypsometric relationships developed from the digital elevation models, subsurface water dynamics can still be estimated by extrapolating the low water levels using regression, albeit with increased uncertainty compared to levels above the hydroflattened surface. Using multiple digital elevation models, the lowest hydroflattened surface can be identified for each water body which reduces uncertainty for low water levels by reducing the extrapolation distance to those values while simultaneously increasing the number of above hydroflattened surface estimates. In addition to low-level uncertainty, hypsometric techniques are highly impacted by image contamination such as cloud, cloud shadow, snow, ice, and sensor error which reduces the observable water surface area resulting in erroneous surface elevation, volume, and volume change estimates. To help alleviate this issue, a technique of using proportional hypsometry was developed to remove contamination effects. Together, using the lowest hydroflattened surface and proportional hypsometry, this research produced 12,680 additional water surface elevation estimates for 46 lakes in comparison to traditional hypsometric techniques, reduced the number of sub-hydroflattened water surface estimates by 549 or more compared to individually using any of the three digital elevation models assessed, and lays the groundwork for regional and global scale surface water dynamic research without bathymetric survey data