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  • (-) Organizational Unit = Mountain Hydrology and Mass Movements
  • (-) Publication Year = 2019 - 2019
  • (-) Keywords ≠ PREVAH model
  • (-) WSL Authors ≠ Malle, Johanna T.
  • (-) WSL Authors = Jonas, Tobias
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Comparing aerial lidar observations with terrestrial lidar and snow‐probe transects from NASA's 2017 SnowEx campaign
Currier, W. R., Pflug, J., Mazzotti, G., Jonas, T., Deems, J. S., Bormann, K. J., … Lundquist, J. D. (2019). Comparing aerial lidar observations with terrestrial lidar and snow‐probe transects from NASA's 2017 SnowEx campaign. Water Resources Research, 55(7), 6285-6294. https://doi.org/10.1029/2018WR024533
Implications of observation-enhanced energy-balance snowmelt simulations for runoff modeling of Alpine catchments
Griessinger, N., Schirmer, M., Helbig, N., Winstral, A., Michel, A., & Jonas, T. (2019). Implications of observation-enhanced energy-balance snowmelt simulations for runoff modeling of Alpine catchments. Advances in Water Resources, 133, 103410 (12 pp.). https://doi.org/10.1016/j.advwatres.2019.103410
Snow depth variability in the Northern Hemisphere mountains observed from space
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., Brangers, I., … De Lannoy, G. J. M. (2019). Snow depth variability in the Northern Hemisphere mountains observed from space. Nature Communications, 10, 4629 (12 pp.). https://doi.org/10.1038/s41467-019-12566-y
Projections of Alpine snow-cover in a high-resolution climate simulation
Lüthi, S., Ban, N., Kotlarski, S., Steger, C. R., Jonas, T., & Schär, C. (2019). Projections of Alpine snow-cover in a high-resolution climate simulation. Atmosphere, 10(8), 463 (18 pp.). https://doi.org/10.3390/atmos10080463
Revisiting snow cover variability and canopy structure within forest stands: insights from airborne lidar data
Mazzotti, G., Currier, W. R., Deems, J. S., Pflug, J. M., Lundquist, J. D., & Jonas, T. (2019). Revisiting snow cover variability and canopy structure within forest stands: insights from airborne lidar data. Water Resources Research, 55(7), 6198-6216. https://doi.org/10.1029/2019WR024898
The bias detecting ensemble: a new and efficient technique for dynamically incorporating observations into physics‐based, multi‐layer, snow models
Winstral, A., Magnusson, J., Schirmer, M., & Jonas, T. (2019). The bias detecting ensemble: a new and efficient technique for dynamically incorporating observations into physics‐based, multi‐layer, snow models. Water Resources Research, 55, 613-631. https://doi.org/10.1029/2018WR024521
Estimating below‐canopy light regimes using airborne laser scanning: an application to plant community analysis
Zellweger, F., Baltensweiler, A., Schleppi, P., Huber, M., Küchler, M., Ginzler, C., & Jonas, T. (2019). Estimating below‐canopy light regimes using airborne laser scanning: an application to plant community analysis. Ecology and Evolution, 9(16), 9149-9159. https://doi.org/10.1002/ece3.5462