Verhaegen Y., Huybrechts P., Rybak O., Popovnin V. Modelling the evolution of Djankuat Glacier, North Caucasus, from 1752 until 2100 CE // THE CRYOSPHERE v.14 №13
Том: 14 Выпуск:13 Номер статьи: 627 Опубликовано: NOV 2020
Financial support: The contribution of Victor V. Popovnin and Oleg Rybak was supported by the Russian Foundation for Basic Research, grant RFBR no. 18-05-00420a: “The latest evolutionary tendencies in water and ice resources of the glaciers in the Caucasus”.
We use a numerical flow line model to simulate the behaviour of the Djankuat Glacier, a World Glacier Monitoring Service reference glacier situated in the North Caucasus (Republic of Kabardino-Balkaria, Russian Federation), in response to past, present and future climate conditions (1752–2100 CE). The model consists of a coupled ice flow–mass balance model that also takes into account the evolution of a supraglacial debris cover. After simulation of the past retreat by applying a dynamic calibration procedure, the model was forced with data for the future period under different scenarios regarding temperature, precipitation and debris input. The main results show that the glacier length and surface area have decreased by ca. 1.4 km (ca. −29.5 %) and ca. 1.6 km2 (−35.2 %) respectively between the initial state in 1752 CE and present-day conditions. Some minor stabilization and/or readvancements of the glacier have occurred, but the general trend shows an almost continuous retreat since the 1850s. Future projections using CMIP5 temperature and precipitation data exhibit a further decline of the glacier. Under constant present-day climate conditions, its length and surface area will further shrink by ca. 30 % by 2100 CE. However, even under the most extreme RCP 8.5 scenario, the glacier will not have disappeared completely by the end of the modelling period. The presence of an increasingly widespread supraglacial debris cover is shown to significantly delay glacier retreat, depending on the interaction between the prevailing climatic conditions, the debris input location, the debris mass flux magnitude and the time of release of debris sources from the surrounding topography.
2021Е.К. Сантьева, И.Л. Башмачников, М.А. Соколовский. Об устойчивости Лофотенского вихря Норвежского моря. Океанология, 2021, том 61, № 3, с. 353–365.
2021Moreido, Vsevolod; Gartsman, Boris; Solomatine, Dimitri P.; Suchilina, Zoya. 2021. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting. Water 13, no. 12: 1696.