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  • (-) Organizational Unit = Mountain Hydrology and Mass Movements
  • (-) Publication Year = 2019 - 2019
  • (-) Keywords ≠ PREVAH model
  • (-) WSL Authors ≠ Malle, Johanna T.
  • (-) Journal = Water Resources Research
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Future trends in the interdependence between flood peaks and volumes: hydro‐climatological drivers and uncertainty
Brunner, M. I., Hingray, B., Zappa, M., & Favre, A. ‐C. (2019). Future trends in the interdependence between flood peaks and volumes: hydro‐climatological drivers and uncertainty. Water Resources Research, 55(6), 4745-4759. https://doi.org/10.1029/2019WR024701
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
High‐resolution snowline delineation from Landsat imagery to infer snow cover controls in a Himalayan catchment
Girona‐Mata, M., Miles, E. S., Ragettli, S., & Pellicciotti, F. (2019). High‐resolution snowline delineation from Landsat imagery to infer snow cover controls in a Himalayan catchment. Water Resources Research. https://doi.org/10.1029/2019WR024935
Influence of spatial resolution on snow cover dynamics for a coastal and mountainous region at high latitudes (Norway)
Magnusson, J., Eisner, S., Huang, S., Lussana, C., Mazzotti, G., Essery, R., … Beldring, S. (2019). Influence of spatial resolution on snow cover dynamics for a coastal and mountainous region at high latitudes (Norway). Water Resources Research, 55(7), 5612-5630. https://doi.org/10.1029/2019WR024925
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