Regionalization of forested landscapes in Russia to optimize regional modelling of greenhouse gas flows

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

The regionalization of Russian forest landscapes for spatial extrapolation of greenhouse gas fluxes at regional and national scales was performed. Using the method of simple linear iterative clustering based on 12 variables, the study area was divided into 78 ecoregions. The spatial data on climatic characteristics, surface topography, soil cover and vegetation were collected from open sources and resampled to a grid of 0.0025° × 0.0025°. The results of the clustering were compared with expert-defined geobotanical and physical-geographical regionalization schemes of the territory of the Russian Federation. The identified ecoregions can serve as a basis for establishing new greenhouse gas monitoring sites in forest ecosystems.

全文:

受限制的访问

作者简介

T. Kharitonova

Shirshov Institute of Oceanology, Russian Academy of Sciences; Lomonosov Moscow State University

编辑信件的主要联系方式.
Email: kharito@geogr.msu.ru
俄罗斯联邦, Moscow; Moscow

M. Krinitskiy

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: kharito@geogr.msu.ru
俄罗斯联邦, Moscow

V. Rezvov

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: kharito@geogr.msu.ru
俄罗斯联邦, Moscow

A. Maksakov

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: kharito@geogr.msu.ru
俄罗斯联邦, Moscow

A. Olchev

Shirshov Institute of Oceanology, Russian Academy of Sciences; Lomonosov Moscow State University

Email: kharito@geogr.msu.ru
俄罗斯联邦, Moscow; Moscow

S. Gulev

Shirshov Institute of Oceanology, Russian Academy of Sciences

Email: kharito@geogr.msu.ru

Corresponding Member of the RAS

俄罗斯联邦, Moscow

参考

  1. Upton S., Reichstein M., Gans F., Peters W., Kraft B., Bastos A. Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches // Atmos. Chem. Phys. 2024. № 24. P. 2555–2582.
  2. Xiao J., Chevallier F., Gomez C., Guanter L., Hicke J.A., Huete A.R., Ichii K., Ni W., Pang Y., Rahman A.F., Sun G. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years // Remote Sensing of Environment. 2019. № 233. P. 111383.
  3. Pastorello G., Trotta C., Canfora E. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data // Sci Data. 2020. V. 7(225).
  4. Jung M., Reichstein M., Bondeau A. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model // Biogeosciences. 2009. V. 6(10). P. 2001–2013.
  5. Hersbach H., Bell B., Berrisford P. et al. The ERA5 global reanalysis // Quarterly Journal of the Royal Meteorological Society. 2020. V. 146(730). P. 1999–2049.
  6. Fick S.E., Hijmans R.J. WorldClim 2: new 1 km spatial resolution climate surfaces for global land areas // International Journal of Climatology. 2017. V. 37(12). P. 4302–4315.
  7. Wikle C.K. Spatio-temporal methods in climatology // Encyclopedia of Life Support Systems (EOLSS). 2002.
  8. Yamazaki D., Ikeshima D., Tawatari R. et al. A high accuracy map of global terrain elevations // Geophysical Research Letters. 2017. V. 44(11). P. 5844–5853.
  9. Achanta R., Shaji A., Smith K. et al. SLIC superpixels compared to state-of-the-art superpixel methods //IEEE transactions on pattern analysis and machine intelligence. 2012. V. 34(11). P. 2274–2282.
  10. Golikov V., Krinitskiy M., Borisov D. Visual clustering of ocean sediment grains using a combination of unsupervised machine learning methods / Proceeding of the 6th International Workshop on Deep Learning in Computational Physics (DLCP-2022). Dubna, Russia. 2022. 429 p.
  11. Огуреева Г.Н., Леонова Н.Б., Булдакова Е.В. и др. Биомы России. Карта в серии карт природы для высшей школы. Масштаб 1:7500000. Издание 2-е, переработанное и дополненное. М.: Всемирный фонд дикой природы (WWF), 2018.
  12. Карта физико-географического районирования СССР. Гвоздецкий Н.А., Самойлова Г.С. (ред.). Масштаб 1:8000000. М.: ГУГК, 1986.
  13. Hargrove W.W., Hoffman F.M. Potential of multivariate quantitative methods for delineation and visualization of ecoregions // Environmental management. 2004. V. 34. P. S39–S60.
  14. Mackey B.G., Berry S.L., Brown T. Reconciling approaches to biogeographical regionalization: a systematic and generic framework examined with a case study of the Australian continent // Journal of Biogeography. 2007. V. 35. No. 2. P. 213–229.
  15. Ольчев А.В., Гулев С.К. Карбоновые полигоны Российской Федерации: цели, задачи, перспективы // Изменения климата: причины, риски, последствия, проблемы адаптации и регулирования / Под ред. академика РАН И.И. Мохова, члена-корреспондента РАН А.А. Макоско, к.ф.-м.н. А.В. Чернокульского. М.: РАН, 2024. С. 343–355.

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. Ecoregions of the forested territory of the Russian Federation

下载 (390KB)
索引源数据

版权所有 © Russian Academy of Sciences, 2025