nisqually glacier response to climate change

This will reduce the importance of shortwave radiation for future ablation rates, and it is expected to result in a reduction in values of degree-day factors (DDFs) and therefore a significant change in melt sensitivity to air temperature variations36. Model Dev. Salim, E., Ravanel, L., Deline, P. & Gauchon, C. A review of melting ice adaptation strategies in the glacier tourism context. In the United States, glaciers can be found in the Rocky Mountains, the Sierra Nevada, the Cascades, and throughout Alaska. Ice thickness data for Argentire glacier (12.27km2 in 2015) was taken from a combination of field observations (seismic, ground-penetrating radar or hot-water drilling53) and simulations32. During the last decade, various global glacier evolution models have been used to provide estimates on the future sea-level contribution from glaciers7,8. These measurements of surface elevation were begun by personnel of the Tacoma 3, 16751685 (2019). 3). The 29 RCP-GCM-RCM combinations available, hereafter named climate members, are representative of future climate trajectories with different concentration levels of greenhouse gases (TableS1). Roe, G. H. Orographic precipitation. Kinematic waves on glaciers move as several times the speed of the ice as a whole, and are subtle in topographic expression. Glaciers are experiencing important changes throughout the world as a consequence of anthropogenic climate change1. We perform, to the best of our knowledge, the first-ever deep learning (i.e. Future projections of glacier-wide MB evolution were performed using climate projections from ADAMONT25. Importance and vulnerability of the worlds water towers. 12, 1959 (2020). https://zenodo.org/record/5549758. These different behaviours and resulting biases can potentially induce important consequences in long-term glacier evolution projections. b, c, d and f, g, h annual glacier-wide MB probability distribution functions for all n scenarios in each RCP. Despite the existence of slightly different trends during the first half of the century, both the Lasso and the temperature-index model react similarly under RCP 4.5 and 8.5 during the second half of the century, compared to the deep learning model. Sign up for the Nature Briefing newsletter what matters in science, free to your inbox daily. Therefore, we were capable of isolating the different behaviours of the nonlinear deep learning model and a linear machine learning model based on the Lasso30. Loss of glaciers contributes to sea-level rise, creates environmental hazards and can alter aquatic habitats. These results revealed that the main uncertainties on glacier simulations arise from the initial ice thickness used to initialize the model. Gaining a better understanding of how warming ocean water affects these glaciers will help improve predictions of their fate. Geophys. The position of the front of the wave will be defined as the transverse line across the glacier where the flow of . Zekollari, H., Huss, M. & Farinotti, D. Modelling the future evolution of glaciers in the European Alps under the EURO-CORDEX RCM ensemble. All these glacier models, independently from their approach, need to resolve the two main processes that determine glacier evolution: (1) glacier mass balance, as the difference between the mass gained via accumulation (e.g. Cite this article. The performance of this parametrization was validated in a previous study, indicating a correct agreement with observations31. 4 ). CPDD, winter snowfall or summer snowfall) was modified for all glaciers and years. on various mass balance and radiation components) are opening the door for updated and better constrained projections. Vincent, C. et al. 65, 453467 (2019). Res. Nature Geosciences, https://doi.org/10.1038/s41561-021-00885-z (2022). Differences in projected glacier changes become more pronounced from the second half of the century, when about half of the initial 2015 ice volume has already been lost independent of the considered scenario. 282, 104115 (2003). is central to a glacier's response: Fig.2ashows 1L.t/for a warming trend of 1 C per century, for three glaciers with dierent (and fixed ). Previous studies on 21st century large-scale glacier evolution projections have covered the French Alps7,8. ADAMONT provides climate data at 300m altitudinal bands and different slope aspects, thus having a significantly higher spatial resolution than the 0.11 from EURO-CORDEX. 3a). 2013). The Nisqually Glacier is one of the larger glaciers on the southwestern face of Mount Rainier in the U.S. state of Washington.The glacier is one of the most easily viewed on the mountain, and is accessible from the Paradise visitor facilities in Mount Rainier National Park.The glacier has had periods of advance and retreat since 1850 when it was much more extensive. Atmospheres 121, 77107728 (2016). Sci. Conversely, for RCP 8.5, annual glacier-wide MB are estimated to become increasingly negative by the second half of the century, with average MB almost twice as negative as todays average values (Fig. Consortium, R. G. I. Randolph Glacier Inventory 6.0 (2017) https://doi.org/10.7265/N5-RGI-60. A global synthesis of biodiversity responses to glacier retreat. Paul, F., Kb, A., Maisch, M., Kellenberger, T. & Haeberli, W. Rapid disintegration of Alpine glaciers observed with satellite data: disintegration of alpine glaciers. Arch. Nature 577, 364369 (2020). Glacier landscapes are expected to see important changes throughout the French Alps, with the average glacier altitude becoming 300m (RCP 4.5) and 400m (RCP 8.5) higher than nowadays (Fig. 12, 909931 (2019). This is not the case for the nonlinear deep learning MB model, which captures the nonlinear response of melt and MB to increasing air temperatures, thus reducing the MB sensitivity to extreme positive and negative air temperature and summer snowfall anomalies (Fig. These conclusions drawn from these synthetic experiments could have large implications given the important sea-level contribution from ice cap-like ice bodies8. 3). However, the impact of different climate configurations, such as a more continental and drier climate or a more oceanic and humid climate, would certainly have an impact on the results, albeit a much less important one than the lack of topographical feedback explored here. Regarding air temperature forcings, the linear Lasso MB model was found to be slightly under-sensitive to extreme positive cumulative PDD (CPDD) and over-sensitive to extreme negative CPDDs. Pellicciotti, F. et al. Long-term historical interactions between French society and glaciers have developed a dependency of society on them for water resources, agriculture, tourism18particularly the ski business19and hydropower generation. April 17, 2019. Res. Since 2005, study finds that surface melt off glaciers in the North has risen by 900%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. 185, 235246 (2014). The maximum advance of Nisqually Glacier in the last thousand years was located, and retreat from this point is believed to have started about 1840. The temperature-index model includes up to three different DDFs, for ice, firn and snow, resulting in three parameters. Our projections show a strong glacier mass loss for all 29 climate members, with average ice volume losses by the end of the century of 75%, 80%, and 88% compared to 2015 under RCP 2.6 (9%, n=3), RCP 4.5 (17% +11%, n=13) and RCP 8.5 (15% +11%, n=13), respectively (Fig. Conf. performed simulations with another glacier model, provided results for comparison, and contributed to the glaciological analyses. Both models agree around the average values seen during training (i.e. 1d, g). Without these cold water resources during the hottest months of the year, many aquatic and terrestrial ecosystems will be impacted due to changes in runoff, water temperature or habitat humidity6,21,22. Grenoble Alpes, CNRS, G-INP, Laboratoire Jean Kuntzmann, Grenoble, France, You can also search for this author in Lett. Climate predictors consist of: the annual CPDD, winter snowfall, summer snowfall, monthly mean temperature and monthly snowfall. regularized multilinear regression. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Despite marked differences among regions, the generalized retreat of glaciers is expected to have major environmental and social impacts2,3. H.Z. Partitioning the uncertainty of ensemble projections of global glacier mass change. GloGEMflow10 is a state-of-the-art global glacier evolution model used in a wide range of studies, including the second phase of GlacierMIP7,8. Fr Hydrobiol. However, the use of ANNs remains largely unexplored in glaciology for regression problems, with only a few studies using shallow ANNs for predicting the ice thickness14 or mass balance13 of a single glacier. 41, 153160 (1995). Average cumulative MB projections of French Alpine glaciers with a nonlinear deep learning vs. a linear Lasso model for 29 climate scenarios; a with topographical feedback (allowing for glacier retreat) and e without topographical feedback (synthetic experiment with constant mean glacier altitude). MB rates only begin to approach equilibrium towards the end of the century under RCP 2.6, for which glaciers could potentially stabilize with the climate in the first decades of the 22nd century depending on their response time (Fig. As such, these values reflect both the climatic forcing and the changing glacier geometry. Huss, M. et al. Both MB models were trained with exactly the same data, and all other glacier model parameters were unchanged in order to allow isolating the effects of the nonlinearities in the MB. In Climate Change 157176 (Elsevier, 2021). Earths Future 5, 418435 (2017). Our analyses suggest that these limitations can also be translated to temperature-index MB models, as they share linear relationships between PDDs and melt, as well as precipitation and accumulation. A NASA-led, international study finds Asia's high mountain glaciers are flowing more slowly in response to widespread ice loss, affecting freshwater availability downstream in India, Pakistan and China. Glob. Many studies have investigated the effects of climate change on glacier runoff using observations or modelling, with a recent focus on High Mountain Asia 14,16,17 and the Andes 18,19,20.The degree . Ice-surface altitude changes of as much as 25 meters occurred between 1944 and 1955. & Galiez, C. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 19672015. J. Geophys. IPCC. Mer de Glace, 29km2 in 2015), which did show important differences under RCP 8.5 (up to 75%), due to their longer response time. Glacier surface mass changes are commonly modelled by relying on empirical linear relationships between PDDs and snow, firn or ice melt8,9,10,29. 3). This adjustment represents a major improvement over most climate data used to force regional and global glacier models. When using the linear MB model (Lasso), glaciers are close to reaching an equilibrium with the climate in the last decades of the century, which is not the case for the nonlinear MB model (deep learning). 49, 26652683 (2017). The Cryosphere 13, 13251347 (2019). The initial glacier ice thickness data for the year 2003 also differs slightly between both models. 1960). 2) and RCP 8.5 by the end of the century. Thus, glacier sensitivity to a step change in climate , glacier response to climate trends , and glacier variance driven by stochastic climate fluctuations are all proportional to , making an important number to constrain. From this behavior, inferences of past climate can be drawn. J. Clim. Finally, there are differences as well in the glacier dynamics of both models, with ALPGM using a glacier-specific parameterized approach and GloGEMflow explicitly reproducing the ice flow dynamics. Provided by the Springer Nature SharedIt content-sharing initiative. Through these surveys "bulges" have been tracked as they travel down the glacier (c). Braithwaite, R. J. Dyn. Conversely, the linear MB model appears to be over-sensitive to extreme positive and negative snowfall anomalies. The Cryosphere 12, 13671386 (2018). J. Glaciol. We further assessed the effect of MB nonlinearities by comparing our simulated glacier changes with those obtained from other glacier evolution studies from the literature, which rely on temperature-index models for MB modelling. These differences in the received climate signal are explained by the retreat of glaciers to higher altitudes, which keep up with the warming climate in RCP 4.5 but are outpaced by it under RCP 8.5. a Glacier-wide annual MB, b Ice volume, c Glacier area. Hugonnet, R. et al. Our previous work31 has shown that linear MB models can be correctly calibrated for data around the mean temperature and precipitation values used during training, giving similar results and performance to deep learning. Get the most important science stories of the day, free in your inbox. Indeed, the projected 21st century warming will lead to increasing incoming longwave radiation and turbulent fluxes, with no marked future trends in the evolution of shortwave radiation37. Relatively minor climate changes during the Little Ice Age (A.D. 1200-1850) impart significant glacial responses. The new research suggests that the world's glaciers are disappearing more quickly than scientists previously estimated, and they . For this, a newly-developed state-of-the-art modelling framework based on a deep learning mass balance component and glacier-specific parametrizations of glacier changes is used. A well-established parametrization based on empirical functions50 was used in order to redistribute the annually simulated glacier-wide mass changes over each glacier. Durand, Y. et al. Three different types of cross validation were performed: a Leave-One-Glacier-Out (LOGO), a Leave-One-Year-Out (LOYO) and a Leave-Some-Years-and-Glaciers-Out (LSYGO). Recent efforts have been made to improve the representation of ice flow dynamics in these models, replacing empirical parametrizations with simplified physical models9,10. The maximum downvalley position of the glacier is marked by either a Alternatively, flatter glaciers (i.e. Planet. 0.78m.w.e. The scheme simulates the mass balance as well as changes of the areal . deep artificial neural networks) glacier evolution projections by modelling the regional evolution of French alpine glaciers through the 21st century. Nat. Interestingly, our analysis indicates that more complex models using separate DDFs for ice, firn and snow might introduce stronger biases than more simple models using a single DDF. Park, and S. Beason. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (2018). Nevertheless, we previously demonstrated that glacier surface area is not an important predictor of MB changes in our models29, and ice caps evolve mostly through thinning and not shrinking (Fig. Earth Syst. ALPGM uses a feed-forward fully connected multilayer perceptron, with an architecture (40-20-10-5-1) with Leaky-ReLu44 activation functions and a single linear function at the output. Taking into account that for several regions in the world about half of the glacierized volume will be lost during this first half of the 21st century, glacier models play a major role in the correct assessment of future glacier evolution. We argue that such models can be suitable for steep mountain glaciers. Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016. Additionally, glacier surface area was found to be a minor predictor in our MB models31. J. Hosp. Nonetheless, a better understanding of the underlying processes guiding these nonlinear behaviours at large geographical scales is needed. When it was built in the early 1900s, the road into Mount Rainier National Park from the west passed near the foot of the Nisqually Glacier, one of the mountain's longest . This allows us to assess the MB models responses at a regional scale to changes in individual predictors (Fig. New methods bridging the gap between domain-specific equations and machine learning are starting to arise42, which will play a crucial role in further investigating the physical processes driving these nonlinear climate-glacier interactions. 3). Both DEMs were resampled and aligned at a common spatial resolution of 25m. For each glacier, an individual parameterized function was computed representing the differences in glacier surface elevation with respect to the glaciers altitude within the 19792011 period. Article 1 and S1). P. Kennard, J. Monitoring the Seasonal hydrology of alpine wetlands in response to snow cover dynamics and summer climate: a novel approach with sentinel-2. Rveillet, M. et al. When comparing our deep learning simulations with those from the Lasso, we found average cumulative MB differences of up to 17% by the end of the century (Fig. By unravelling nonlinear relationships between climate and glacier MB, we have demonstrated the limitations of linear statistical MB models to represent extreme MB rates in long-term projections. In order to avoid overfitting, MB models were thoroughly cross-validated using all data for the 19672015 period in order to ensure a correct out-of-sample performance. 5). Spandre, P. et al. Thank you for visiting nature.com. Zekollari, H., Huss, M. & Farinotti, D. On the Imbalance and Response Time of Glaciers in the European Alps. In fact, in many cases the surface lowering into warmer air causes this impact on the MB to be negative, further enhancing extreme negative mass balance rates. Several differences are present between ALPGM, the model used in this study, and GloGEMflow (TableS2), which hinder a direct meaningful comparison between both. I.G. 4a, b) and negative (Fig. Geosci. (Zenodo, 2020). J. Glaciol. S5cf), except for the largest glaciers (e.g. Marzeion, B. et al. This implies that specific climatic differences between massifs can be better captured by ALPGM than GloGEMflow. Share sensitive information only on official, secure websites.. Lett. This type of model uses a calibrated linear relationship between positive degree-days (PDDs) and the melt of ice or snow11. For such cases, we assumed that ice dynamics no longer play an important role, and the mass changes were applied equally throughout the glacier. Grenoble Alpes, CNRS, IRD, G-INP, Institut des Gosciences de lEnvironnement, Grenoble, France, INRAE, UR RiverLy, Lyon-Villeurbanne, France, Institute for Marine and Atmospheric research Utrecht, Utrecht University, Utrecht, Netherlands, Univ. Uncertainties of existing projections of future glacier evolution are particularly large for the second half of the 21st century due to a large uncertainty on future climatic conditions. For small perturbations, the response time of a glacier to a perturbation in mass balance can be estimated by dividing the maximum thickness of the glacier by the balance rate at the terminus. Preliminary results suggest winter accumulation in 2018 was slightly above the 2003-2017 average for the Emmons & Nisqually. . 33, 645671 (2005). The high spatial resolution enables a detailed representation of mountain weather patterns, which are often undermined by coarser resolution climate datasets. Front. Smiatek, G., Kunstmann, H. & Senatore, A. EURO-CORDEX regional climate model analysis for the Greater Alpine Region: performance and expected future change: climate change in the gar area. Water resources provided by glaciers sustain around 10% of the worlds population living near mountains and the contiguous plains4, depending on them for agriculture, hydropower generation5, industry or domestic use. GLAMOS. a1), but when conditions deviate from this mean training data centroid, the Lasso can only linearly approximate the extremes based on the linear trend set on the main cluster of average values (Fig. Together with recent findings by another study41 highlighting the increased uncertainties in ice thickness distribution estimates of ice caps compared to mountain glaciers, our results raise further awareness on the important uncertainties in glacier projections for ice caps. Lett. Google Scholar. In our model, we specifically computed this parameterized function for each individual glacier larger than 0.5km2, representing 80% of the total glacierized area in 2015, using two DEMs covering the whole French Alps: a photogrammetric one in 1979 and a SPOT-5 one in 2011. On the other hand, ice caps present a different response to future warming, with our results suggesting a negative MB bias by models using linear PDD and accumulation relationships. Such glaciers are often remnants of the Little Ice Age, and mainly lose mass via non-dynamic downwasting51. By 2100, under RCP 4.5, these two high-altitude massifs are predicted to retain on average 26% and 13% of their 2015 volume, respectively, with most of the ice concentrated in a few larger glaciers (>1km2, Fig. Gardent, M., Rabatel, A., Dedieu, J.-P. & Deline, P. Multitemporal glacier inventory of the French Alps from the late 1960s to the late 2000s. Since these flatter glaciers are more likely to go through extreme negative MB rates, nonlinear responses to future warming play a more important role, producing cumulative MB differences of up to 20% by the end of the century (Fig. An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier dArolla, Switzerland. Publishers note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. An analysis of the climate signal at the glaciers mean altitude throughout the century reveals that air temperature, particularly in summer, is expected to be the main driver of glacier mass change in the region (Fig. Tour. This method has the advantage of including glacier-specific dynamics in the model, encompassing a wide range of different glacier behaviours. The Elements of Statistical Learning. Magnin, F., Haeberli, W., Linsbauer, A., Deline, P. & Ravanel, L. Estimating glacier-bed overdeepenings as possible sites of future lakes in the de-glaciating Mont Blanc massif (Western European Alps). Our results indicate that these uncertainties might be even larger than we previously thought, as linear MB models are introducing additional biases under the extreme climatic conditions of the late 21st and 22nd centuries. Glaciers with the greatest degree of seasonality in their flow behavior, such as Nisqually and Shoestring glaciers, responded most rapidly. With this cross-validation we determined a deep learning MB model spatiotemporal (LSYGO) RMSE of 0.59m.w.e. 2a). A knowledge of the areas once occupied by mountain glaciers reveals at least part of the past behavior of these glaciers. Simulations for projections in this study were made by generating an ensemble of 60 cross-validated models based on LSYGO. This ensures that the model is capable of reproducing MB rates for unseen glaciers and years. Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps). On the one hand, this improves our confidence in long-term MB projections for steep glaciers made by most GlacierMIP models for intermediate and high emissions climate scenarios. We compare model runs using a nonlinear deep learning MB model (the reference approach in our study) against a simplified linear machine learning MB model based on the Lasso30, i.e. The main reason for their success comes from their suitability to large-scale studies with a low density of observations, in some cases displaying an even better performance than more complex models12. Hock, R. & Huss, M. Glaciers and climate change. In order to investigate the implications of these results for flat glaciers, we performed additional synthetic experiments in order to reproduce this lack of topographical feedback (Fig. Our results serve as a strong reminder that the outcomes of existing large-scale glacier simulations should be interpreted with care, and that newly available techniques (such as the nonlinear deep learning approach presented here) and observations (e.g. Evol. This creates a total of 34 input predictors for each year (7 topographical, 3 seasonal climate, and 24 monthly climate predictors). The advantage of this method is that by only changing the MB model, we can keep the rest of the model components (glacier dynamics and climate forcing) and parameters the same in order to have a controlled environment for our experiment. A dataset of 32 glaciers with direct annual glacier-wide MB observations and remote sensing estimates was used to train the models. The vertical blue and red lines indicate the distribution of extreme (top 5%) values for all 21st century projected climate scenarios, with the mean value in the center and 1 indicated by dashed lines. Vertical axes are different for the two analyses. a1 throughout the whole century under RCP 4.5, with glacier retreat to higher elevations (positive effect on MB) compensating for the warmer climate (negative effect on MB). This reanalysis is specifically designed to represent meteorological conditions over complex mountain terrain, being divided by mountain massif, aspect and elevation bands of 300m. Winter climate data are computed between October 1 and March 31, and summer data between April 1 and September 30. Seasonal Arctic sea ice forecasting with probabilistic deep learning, Global glacier mass changes and their contributions to sea-level rise from 1961 to 2016, Two decades of glacier mass loss along the Andes, Centennial response of Greenlands three largest outlet glaciers, Accelerated global glacier mass loss in the early twenty-first century, High Mountain Asian glacier response to climate revealed by multi-temporal satellite observations since the 1960s, Rapid glacier retreat and downwasting throughout the European Alps in the early 21st century, Ice velocity and thickness of the worlds glaciers, Constraining glacier elevation and mass changes in South America, https://meetingorganizer.copernicus.org/EGU2020/EGU2020-20908.html, https://doi.org/10.5194/egusphere-egu2020-20908, https://doi.org/10.18750/MASSBALANCE.2019.R2019, https://doi.org/10.1016/B978-0-12-821575-3.00009-8, https://doi.org/10.1038/s41561-021-00885-z, http://creativecommons.org/licenses/by/4.0/, Unabated wastage of the Muz Taw Glacier in the Sawir Mountains during 19592021. Summer climate is computed between April 1st and September 30th and winter climate between October 1st and March 31st. Fundam. 1a). Activity 13.3 Nisqually Glacier Response to Climate Change Course/Section Date: Name: Nisqually Glacier is a mountain glacier located on the south side of Mt. Through his research in that area, he's seen firsthand the impact of climate change and has been studying the long-term effects of a warming planet. Strong Alpine glacier melt in the 1940s due to enhanced solar radiation. The two models with linear MB responses to PDDs and accumulation simulate more positive MB rates under RCP 2.6, highlighting their over-sensitivity to negative air temperature anomalies and positive snowfall anomalies (Fig. Interestingly, future warmer temperatures do not affect annual snowfall rates on glaciers as a result of both higher precipitation rates in the EURO-CORDEX ensemble (Fig. Graphics inspired by Hock and Huss40. This has the strongest impact under RCP 2.6, where positive MB rates are more frequent (Fig. longwave radiation budget, turbulent fluxes), in comparison with a future warmer climate. A consensus estimate for the ice thickness distribution of all glaciers on Earth. Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. CAS Slider with three articles shown per slide. Despite these differences, the average altitude difference of the glaciers between both models is never greater than 50m (Fig. 22, 21462160 (2009). Bolibar, J., Rabatel, A., Gouttevin, I. The same was done with winter snowfall anomalies, ranging between 1500mm and +1500mm in steps of 100mm, and summer snowfall anomalies, ranging between 1000mm and +1000mm in steps of 100mm. Rainier, Washington. The application of a non-linear back-propagation neural network to study the mass balance of Grosse Aletschgletscher, Switzerland. Glaciers are large-scale, highly sensitive climate instruments which, ideally, should be picked up and weighed once a year. Jordi Bolibar. All authors provided inputs to the paper and helped to write it. With a secondary role, glacier model uncertainty decreases over time, but it represents the greatest source of uncertainty until the middle of the century8. Model Dev. As previously mentioned, here these differences are computed at regional level for a wide variety of glaciers. Geophys. We reduced these differences by running simulations with GloGEMflow using exactly the same 29 climate members used by ALPGM in this study (TableS1). acknowledges the funding received from a EU Horizon 2020 Marie Skodowska-Curie Individual Fellowship (grant no. The largest snow depths measured this spring exceeded 10 meters on Nisqually Glacier and 7 meters on Emmons. Therefore, linear MB models present more limitations for projections of ice caps, showing a tendency to negative MB biases. Envelopes indicate based on results for all 660 glaciers in the French Alps for the 19672015 period. Glaciers are important for agriculture, hydropower, recreation, tourism, and biological communities. 36, L23501 (2009). Google Scholar. S7). Lett. We also use this method to extract glacier borderlines from satellite images across the western Lenglongling mountains.

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