Identificación de clústeres glaciares a lo largo de los Andes chilenos usando variables topoclimáticas

Caro, Alexis; Gimeno, F.; Rabatel, Antoine; Condom, Thomas; Ruiz, Jean Carlos

doi:10.5354/0719-5370.2020.59009

Cited by 2 publications

(1 citation statement)

References 57 publications

(89 reference statements)

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“…These results are in line with Ayala et al (2020) who identified significant differences (GMB, runoff contribution and glacier elevation) between the southern (DA2) and northern (DA3) watersheds inside the Río Maipo watershed. With regards to the Outer Tropics and the Wet Andes, our results show that a latitudinal classification is not possible, which is in agreement with previous studies (e.g., Caro et al, 2020;Sagredo & Lowell, 2012). Southward of 46 °S, we found different clusters, from west to east, related to the high contrast in precipitation and temperature amounts (WA3 to WA6) related to the wet western air masses originating from the Pacific Ocean (Langhamer et al, 2018).…”

Section: Clusters Without a Latitudinal Distribution Across The Andessupporting

confidence: 91%

Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

2021

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Over the last decades, glaciers across the Andes have been strongly affected by a loss of mass and surface areas. This increases risks of water scarcity for the Andean population and ecosystems. However, the factors controlling glacier changes in terms of surface area and mass loss remain poorly documented at watershed scale across the Andes. Using machine learning methods (Least Absolute Shrinkage and Selection Operator, known as LASSO), we explored climatic and morphometric variables that explain the spatial variance of glacier surface area variations in 35 watersheds (1980–2019), and of glacier mass balances in 110 watersheds (2000–2018), with data from 2,500 to 21,000 glaciers, respectively, distributed between 8 and 55°S in the Andes. Based on these results and by applying the Partitioning Around Medoids (PAM) algorithm we identified new glacier clusters. Overall, spatial variability of climatic variables presents a higher explanatory power than morphometric variables with regards to spatial variance of glacier changes. Specifically, the spatial variability of precipitation dominates spatial variance of glacier changes from the Outer Tropics to the Dry Andes (8–37°S) explaining between 49 and 93% of variances, whereas across the Wet Andes (40–55°S) the spatial variability of temperature is the most important climatic variable and explains between 29 and 73% of glacier changes spatial variance. However, morphometric variables such as glacier surface area show a high explanatory power for spatial variance of glacier mass loss in some watersheds (e.g., Achacachi with r2 = 0.6 in the Outer Tropics, Río del Carmen with r2 = 0.7 in the Dry Andes). Then, we identified a new spatial framework for hydro-glaciological analysis composed of 12 glaciological zones, derived from a clustering analysis, which includes 274 watersheds containing 32,000 glaciers. These new zones better take into account different seasonal climate and morphometric characteristics of glacier diversity. Our study shows that the exploration of variables that control glacier changes, as well as the new glaciological zones calculated based on these variables, would be very useful for analyzing hydro-glaciological modelling results across the Andes (8–55°S).

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Section: Clusters Without a Latitudinal Distribution Across The Andessupporting

confidence: 91%

Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

2021

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Revisiting glacier mass-balance sensitivity to surface air temperature using a data-driven regionalization

Fernández

Somos‐Valenzuela

2022

J. Glaciol.

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This study involves examination of glaciological mass-balance time series, glacier and climatic descriptors, the application of machine learning methods for glaciological clustering, and computation of mass-balance time series based upon the clustering and statistical analyses relative to gridded air temperature datasets. Our analysis revealed an increasingly coherent mass-balance trend but a latitudinal bias of monitoring programs. The glacier classification scheme delivered three clusters, suggesting these correspond to climate-based first-order regimes, as glacier morphometric characteristics weighed little in our multivariate analysis. We combined all available surface mass-balance data from in situ monitoring programs to study temperature sensitivity for each cluster. These aggregated mass-balance time series delivered spatially different statistical relationships to temperature. Results also showed that surface mass balance tends to have a temporal self-correlation of ~20 years. Using this temporal window to analyze sensitivity since ~ 1950, we found that in all cases temperature sensitivity, while generally negative, tended to fluctuate through time, with the largest absolute magnitudes occurring in the 1980s and becoming less negative in recent years, revealing that glacier sensitivity is non-stationary. These findings point to a scenario of a coherent signal of change no matter the glacier regime. This work provides new insights into glacier–climate relationships that can guide observational and analytical strategies.

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Identificación de clústeres glaciares a lo largo de los Andes chilenos usando variables topoclimáticas

Cited by 2 publications

References 57 publications

Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

Climatic and Morphometric Explanatory Variables of Glacier Changes in the Andes (8–55°S): New Insights From Machine Learning Approaches

Revisiting glacier mass-balance sensitivity to surface air temperature using a data-driven regionalization

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