We provide estimates of glacier mass changes in the High Mountain Asia (HMA) area from April2002 to August 2016 by employing a new version of gravity solutions of the Gravity Recovery and ClimateExperiment (GRACE) twi...We provide estimates of glacier mass changes in the High Mountain Asia (HMA) area from April2002 to August 2016 by employing a new version of gravity solutions of the Gravity Recovery and ClimateExperiment (GRACE) twin-satellite mission. We find a total mass loss trend of the HMA glaciers at a rateof –22.17 (±1.96) Gt/a. The largest mass loss rates of –7.02 (±0.94) and –6.73 (±0.78) Gt/a are found forthe glaciers in Nyainqentanglha Mountains and Eastern Himalayas, respectively. Although most glaciers inthe HMA area show a mass loss, we find a small glacier mass gain of 1.19 (±0.55) and 0.77 (±0.37) Gt/a inKarakoram Mountains and Western Kunlun Mountains, respectively. There is also a nearly zero massbalance in Pamirs. Our estimates of glacier mass change trends confirm previous results from the analysisof altimetry data of the ICESat (ICE, Cloud and Land Elevation Satellite) and ASTER (AdvancedSpaceborne Thermal Emission and Reflection Radiometer) DEM (Digital Elevation Model) satellites inmost of the selected glacier areas. However, they largely differ to previous GRACE-based studies which weattribute to our different post-processing techniques of the newer GRACE data. In addition, we explicitlyshow regional mass change features for both the interannual glacier mass changes and the 14-a averagedseasonal glacier mass changes. These changes can be explained in parts by total net precipitation (netsnowfall and net rainfall) and net snowfall, but mostly by total net radiation energy when compared to datafrom the ERA5-Land meteorological reanalysis. Moreover, nearly all the non-trend interannual masschanges and most seasonal mass changes can be explained by the total net radiation energy data. The massloss trends could be partly related to a heat effect due to increased net rainfall in Tianshan Mountains, QilianMountains, Nyainqentanglha Mountains and Eastern Himalayas. Our new results for the glacier mass changein this study could help improve the understanding of glacier variation in the HMA area and contribute tothe study of global change. They could also serve the utilization of water resources there and in neighboringareas.展开更多
The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies...The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.展开更多
基金This work is funded by the National Key R&D Program of China(2017YFA0603103)the National Natural Science Foundation of China(41974009,42004007)+1 种基金the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(QYZDB-SSW-DQC027,QYZDJ-SSW-DQC042)the open fund of State Key Laboratory of Geodesy and Earth's Dynamics(SKLGED2021-2-6)。
文摘We provide estimates of glacier mass changes in the High Mountain Asia (HMA) area from April2002 to August 2016 by employing a new version of gravity solutions of the Gravity Recovery and ClimateExperiment (GRACE) twin-satellite mission. We find a total mass loss trend of the HMA glaciers at a rateof –22.17 (±1.96) Gt/a. The largest mass loss rates of –7.02 (±0.94) and –6.73 (±0.78) Gt/a are found forthe glaciers in Nyainqentanglha Mountains and Eastern Himalayas, respectively. Although most glaciers inthe HMA area show a mass loss, we find a small glacier mass gain of 1.19 (±0.55) and 0.77 (±0.37) Gt/a inKarakoram Mountains and Western Kunlun Mountains, respectively. There is also a nearly zero massbalance in Pamirs. Our estimates of glacier mass change trends confirm previous results from the analysisof altimetry data of the ICESat (ICE, Cloud and Land Elevation Satellite) and ASTER (AdvancedSpaceborne Thermal Emission and Reflection Radiometer) DEM (Digital Elevation Model) satellites inmost of the selected glacier areas. However, they largely differ to previous GRACE-based studies which weattribute to our different post-processing techniques of the newer GRACE data. In addition, we explicitlyshow regional mass change features for both the interannual glacier mass changes and the 14-a averagedseasonal glacier mass changes. These changes can be explained in parts by total net precipitation (netsnowfall and net rainfall) and net snowfall, but mostly by total net radiation energy when compared to datafrom the ERA5-Land meteorological reanalysis. Moreover, nearly all the non-trend interannual masschanges and most seasonal mass changes can be explained by the total net radiation energy data. The massloss trends could be partly related to a heat effect due to increased net rainfall in Tianshan Mountains, QilianMountains, Nyainqentanglha Mountains and Eastern Himalayas. Our new results for the glacier mass changein this study could help improve the understanding of glacier variation in the HMA area and contribute tothe study of global change. They could also serve the utilization of water resources there and in neighboringareas.
基金This work was supported by the National Science Foundation[grant numbers 1526520 to AK and 0711456 to PL].
文摘The availability and quantity of remotely sensed and terrestrial geospatial data sets are on the rise.Historically,these data sets have been analyzed and quarried on 2D desktop computers;however,immersive technologies and specifically immersive virtual reality(iVR)allow for the integration,visualization,analysis,and exploration of these 3D geospatial data sets.iVR can deliver remote and large-scale geospatial data sets to the laboratory,providing embodied experiences of field sites across the earth and beyond.We describe a workflow for the ingestion of geospatial data sets and the development of an iVR workbench,and present the application of these for an experience of Iceland’s Thrihnukar volcano where we:(1)combined satellite imagery with terrain elevation data to create a basic reconstruction of the physical site;(2)used terrestrial LiDAR data to provide a geo-referenced point cloud model of the magmatic-volcanic system,as well as the LiDAR intensity values for the identification of rock types;and(3)used Structure-from-Motion(SfM)to construct a photorealistic point cloud of the inside volcano.The workbench provides tools for the direct manipulation of the georeferenced data sets,including scaling,rotation,and translation,and a suite of geometric measurement tools,including length,area,and volume.Future developments will be inspired by an ongoing user study that formally evaluates the workbench’s mature components in the context of fieldwork and analyses activities.
