Parties d'ouvrages collectifs (1)
  1. 1. Diaconu, C. A., Heidler, K., Bamber, J. L., & Zekollari, H. (2025). Multi-sensor deep learning for glacier mapping. In Deep Learning for Multi-Sensor Earth Observation (pp. 287-333). Elsevier. doi:10.1016/B978-0-44-326484-9.00024-5
    2.   Articles dans des revues avec comité de lecture (20)
  2. 1. Tollenaar, V., Zekollari, H., Kittel, C., Farinotti, D., Lhermitte, S., Debaille, V., Goderis, S., Claeys, P., Joy, K. H., & Pattyn, F. (2024). Antarctic meteorites threatened by climate warming. Nature climate change, 14(4), 340-343. doi:10.1038/s41558-024-01954-y
  3. 2. Tollenaar, V., Zekollari, H., Pattyn, F., Rußwurm, M., Kellenberger, B., Lhermitte, S., Izeboud, M., & Tuia, D. (2024). Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica. Geophysical research letters, 51(3). doi:10.1029/2023GL106285
  4. 3. Løkkegaard, A., Mankoff, K. D., Zdanowicz, C., Clow, G. G., Lüthi, M. M., Doyle, S. S., Thomsen, H. H., Fisher, D., Harper, J., Aschwanden, A., Vinther, B. M., Dahl-Jensen, D., Zekollari, H., Meierbachtol, T., McDowell, I., Humphrey, N., Solgaard, A., Karlsson, N. N., Khan, S. S., Hills, B., Law, R., Hubbard, B., Christoffersen, P., Jacquemart, M., Seguinot, J., Fausto, R. R., & Colgan, W. (2023). Greenland and Canadian Arctic ice temperature profiles database. The Cryosphere, 17(9), 3829-3845. doi:10.5194/tc-17-3829-2023
  5. 4. Postnikova, T., Rybak, O., Gubanov, A., Zekollari, H., Huss, M., & Shahgedanova, M. (2023). Debris cover effect on the evolution of Northern Caucasus glaciers in the 21st century. Frontiers in Earth Science, 11, 1256696. doi:10.3389/feart.2023.1256696
  6. 5. Bolibar, J., Rabatel, A., Gouttevin, I., Zekollari, H., & Galiez, C. (2022). Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. Nature communications, 13(1), 409. doi:10.1038/s41467-022-28033-0
  7. 6. Compagno, L., Huss, M., Zekollari, H., Miles, E. S., & Farinotti, D. (2022). Future growth and decline of high mountain Asia's ice-dammed lakes and associated risk. Communications Earth and Environment, 3(1), 191. doi:10.1038/s43247-022-00520-8
  8. 7. Wiersma, P., Aerts, J., Zekollari, H., Hrachowitz, M., Drost, N., Huss, M., Sutanudjaja, E. E., & Hut, R. (2022). Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff. Hydrology and earth system sciences, 26(23), 5971-5986. doi:10.5194/hess-26-5971-2022
  9. 8. Zekollari, H., Huss, M., Farinotti, D., & Lhermitte, S. (2022). Ice-Dynamical Glacier Evolution Modeling—A Review. Reviews of geophysics, 60(2), e2021RG000754. doi:10.1029/2021RG000754
  10. 9. Compagno, L., Huss, M., Miles, E. S., McCarthy, M. J., Zekollari, H., Dehecq, A., Pellicciotti, F., & Farinotti, D. (2022). Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: an application to High Mountain Asia. The Cryosphere, 16(5), 1697-1718. doi:10.5194/tc-16-1697-2022
  11. 10. Tollenaar, V., Zekollari, H., Lhermitte, S., Tax, D. M., Debaille, V., Goderis, S., Claeys, P., & Pattyn, F. (2022). Unexplored Antarctic meteorite collection sites revealed through machine learning. Science advances, 8(4). doi:10.1126/sciadv.abj8138
  12. 11. Hanus, S., Hrachowitz, M., Zekollari, H., Schoups, G., Vizcaino, M., & Kaitna, R. (2021). Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria. Hydrology and earth system sciences, 25(6), 3429-3453. doi:10.5194/hess-25-3429-2021
    13. << Précédent 1 2 3 Suivant >>