Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers

Kraaijenbrink, Philip; Bierkens, Marc F. P.; Lutz, Arthur; Immerzeel, Walter W.

doi:10.1038/nature23878

Cited by 556 publications

(452 citation statements)

References 49 publications

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“…An obvious application of high-resolution thermal imagery is to inversely estimate debris-cover thickness (Mihalcea et al, 2008b;Foster et al, 2012;Rounce and McKinney, 2014;Schauwecker et al, 2015;Gibson et al, 2017;Kraaijenbrink et al, 2017), since thickness is an important variable in the surface energy balance of debris-covered glaciers (e.g., Benn, 2006, 2013;Ragettli et al, 2016). As we show in this study, the UAV is able to capture spatial patterns and heterogeneity in T s (Figures 7, 10).…”

Section: Applications Of Thermal Uav Imagerymentioning

confidence: 68%

“…Medium resolution thermal infrared satellite imagery [e.g., Landsat 7 (60 m) (Rounce and McKinney, 2014), ASTER (90 m) (Mihalcea et al, 2008a,b;Foster et al, 2012), and Landsat 8 (100 m) (Kraaijenbrink et al, 2017)] has been previously used to infer debris thickness using temperature inversion methods. To get an impression of the accuracy and spatial detail of such spaceborne thermal infrared imagery for analyses of the debris layer, we compared satellite and UAV-derived surface temperatures.…”

Section: Landsat 8 Thermal Infrared Comparisonmentioning

confidence: 99%

“…Surface temperatures derived from satellite-based thermal infrared (TIR) measurements have been previously used to infer debris thickness by temperature inversion methods (Mihalcea et al, 2008b;Foster et al, 2012;Rounce and McKinney, 2014;Schauwecker et al, 2015;Kraaijenbrink et al, 2017). Higher surface temperatures are assumed to correspond to thicker debris layers, as the insulating effect of the debris shields the debris surface from the cold ice.…”

Section: Introductionmentioning

confidence: 99%

“…Especially in regions with many large debriscovered glacier tongues, such as the Karakoram and Himalaya, the proportion of glacier ice mass that is covered by debris in the ablation zone reaches ∼40%. Consequently, future changes in water resources for heavily glacierized catchments in the region partly depend on the long-term melt rates of debris-covered glaciers (Kraaijenbrink et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Mapping Surface Temperatures on a Debris-Covered Glacier With an Unmanned Aerial Vehicle

et al. 2018

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A layer of debris cover often accumulates across the surface of glaciers in active mountain ranges with exceptionally steep terrain, such as the Andes, Himalaya, and New Zealand Alps. Such a supraglacial debris layer has a major influence on a glacier's surface energy budget, enhancing radiation absorption, and melt when the layer is thin, but insulating the ice when thicker than a few cm. Information on spatially distributed debris surface temperature has the potential to provide insight into the properties of the debris, its effects on the ice below and its influence on the near-surface boundary layer. Here, we deploy an unmanned aerial vehicle (UAV) equipped with a thermal infrared sensor on three separate missions over one day to map changing surface temperatures across the debris-covered Lirung Glacier in the Central Himalaya. We present a methodology to georeference and process the acquired thermal imagery, and correct for emissivity and sensor bias. Derived UAV surface temperatures are compared with distributed simultaneous in situ temperature measurements as well as with Landsat 8 thermal satellite imagery. Results show that the UAV-derived surface temperatures vary greatly both spatially and temporally, with −1.4 ± 1.8, 11.0 ± 5.2, and 15.3 ± 4.7 • C for the three flights (mean ± sd), respectively. The range in surface temperatures over the glacier during the morning is very large with almost 50 • C. Ground-based measurements are generally in agreement with the UAV imagery, but considerable deviations are present that are likely due to differences in measurement technique and approach, and validation is difficult as a result. The difference in spatial and temporal variability captured by the UAV as compared with much coarser satellite imagery is striking and it shows that satellite derived temperature maps should be interpreted with care. We conclude that UAVs provide a suitable means to acquire surface temperature maps of debris-covered glacier surfaces at high spatial and temporal resolution, but that there are caveats with regard to absolute temperature measurement.

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Section: Applications Of Thermal Uav Imagerymentioning

confidence: 68%

Section: Landsat 8 Thermal Infrared Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Surface Temperatures on a Debris-Covered Glacier With an Unmanned Aerial Vehicle

et al. 2018

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“…The GEE data catalogue contains numerous remote sensing data sets such as top and bottom of atmosphere reflectance, as well as atmospheric and meteorological data. Data processing is performed in a parallel on Google's computational infrastructure, dramatically improving processing efficiency, and opens up excellent prospects especially for multitemporal and global studies that include vegetation, temperature, carbon exchange, and hydrological processes [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Global Estimation of Biophysical Variables from Google Earth Engine Platform

et al. 2018

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This paper proposes a processing chain for the derivation of global Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fraction Vegetation Cover (FVC), and Canopy water content (CWC) maps from 15-years of MODIS data exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The retrieval chain is based on a hybrid method inverting the PROSAIL radiative transfer model (RTM) with Random forests (RF) regression. A major feature of this work is the implementation of a retrieval chain exploiting the GEE capabilities using global and climate data records (CDR) of both MODIS surface reflectance and LAI/FAPAR datasets allowing the global estimation of biophysical variables at unprecedented timeliness. We combine a massive global compilation of leaf trait measurements (TRY), which is the baseline for more realistic leaf parametrization for the considered RTM, with large amounts of remote sensing data ingested by GEE. Moreover, the proposed retrieval chain includes the estimation of both FVC and CWC, which are not operationally produced for the MODIS sensor. The derived global estimates are validated over the BELMANIP2.1 sites network by means of an inter-comparison with the MODIS LAI/FAPAR product available in GEE. Overall, the retrieval chain exhibits great consistency with the reference MODIS product (R 2 = 0.87, RMSE = 0.54 m 2 /m 2 and ME = 0.03 m 2 /m 2 in the case of LAI, and R 2 = 0.92, RMSE = 0.09 and ME = 0.05 in the case of FAPAR). The analysis of the results by land cover type shows the lowest correlations between our retrievals and the MODIS reference estimates (R 2 = 0.42 and R 2 = 0.41 for LAI and FAPAR, respectively) for evergreen broadleaf forests. These discrepancies could be attributed mainly to different product definitions according to the literature. The provided results proof that GEE is a suitable high performance processing tool for global biophysical variable retrieval for a wide range of applications.

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