Glaciares de Chile
- Glaciares del Volcán Melimoyu
- Glaciares del Nevado de Queulat
- Glaciares del Volcán Mentolat
- Glaciares del Volcán Cay
- Glaciares del Volcán Macá
- Glaciares del Volcán Hudson
- Glaciar Erasmo
- Glaciar San Rafael
- Glaciar San Quintín
- Campo de Hielo Norte
- Glaciar Nef
- Glaciar Colonia
- Lago Cachet II
- Glaciar Steffen
- Glaciares del Monte San Lorenzo
- Glaciar Jorge Montt
- Glaciar Lucía
- Glaciar Los Moscos
- Glaciar Bernardo
- Glaciar O’Higgins
- Glaciar Chico
- Campo de Hielo Sur
- Campo de Hielo Sur
- Glaciar Témpanos
- Glaciar Pío XI
- Glaciar Viedma
- Glaciar Perito Moreno
- Glaciar Dickson
- Glaciar Olvidado
- Glaciar Grey
- Glaciar Amalia
- Glaciar Pingo
- Glaciar Tyndall
- Glaciar Balmaceda
- Isla Desolación
- Glaciares de la Isla Santa Inés
- Seno Gabriel
- Glaciar Schiaparelli
- Glaciar Marinelli
- Fiordo Parry
- Cordillera Darwin
- Glaciar Garibaldi
- Glaciar Roncagli
- Glaciares Isla Hoste
Antártica
"On ecosystems dynamics"
Stehlik M.; P. Aguirre; S. Girard; P. Jordanova; J. Kiselak; S. Torres; Z. Sadovsky and Rivera A. (2017) : “On ecosystems dynamics” Ecological complexity, 29: 10-29.
Resumen / Abstract.
We show how a dynamical system given by a t-score function for some class of monotonic data transformations generates consistent extreme value estimators. The variation of their values increases the uncertainty of proper assessment of climate change. Two important examples illustrate the methodology: mass balance measurements on Guanaco glacier, Chile, and extreme snow loads in Slovakia. We experience singular learning of the transitions in ecosystems.
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