Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2017
Authors
Synnøve Rivedal Samson Øpstad Sverre Heggset Sissel Hansen Trond Børresen Torbjørn Haukås Johannes Deelstra Peter DörschAbstract
No abstract has been registered
Authors
Synnøve Rivedal Samson Øpstad Sverre Heggset Trond Børresen Torbjørn Haukås Sissel Hansen Peter Dörsch Johannes DeelstraAbstract
No abstract has been registered
Abstract
Shallow (<1 m deep) snowpacks on agricultural areas are an important hydrological component in many countries, which determines how much meltwater is potentially available for overland flow, causing soil erosion and flooding at the end of winter. Therefore, it is important to understand the development of shallow snowpacks in a spatially distributed manner. This study combined field observations with spatially distributed snow modelling using the UEBGrid model, for three consecutive winters (2013–2015) in southern Norway. Model performance was evaluated by comparing the spatially distributed snow water equivalent (SWE) measurements over time with the simulated SWE. UEBGrid replicated SWE development at catchment scale with satisfactory accuracy for the three winters. The different calibration approaches which were necessary for winters 2013 and 2015 showed the delicacy of modelling the change in shallow snowpacks. Especially the refreezing of meltwater and prohibited runoff and infiltration of meltwater by frozen soils and ice layers can make simulations of shallow snowpacks challenging.
Authors
Michael Roleda Udo Nitscke Anna Gietl Celine Rebours Jorunn Skjermo Hélène Marfaing Ronan Pierre Annelise Sabine Chapman Dagmar StengelAbstract
No abstract has been registered
Authors
Nina Trandem Arne Stensvand Joséphine Rehnfeldt Karin Westrum Dan H. Christensen Aksel Døving Jan Karstein HenriksenAbstract
No abstract has been registered
Authors
Daniel Muluwork Atsbeha Dadi Kristofersson Kyrre RickertsenAbstract
No abstract has been registered
Authors
Kirsten Tørresen Jevgenija Necajeva J. Soukup Peter Kryger Jensen Friederike De Mol Garifalia Economou Alireza Taab S Babaei Anna Bochenek Agnieszka Synowiec E. Jakubiak Ahmet Uludag Alistair Murdoch Aritz Royo-EsnalAbstract
No abstract has been registered
Authors
Kirsten Tørresen Jevgenija Necajeva J. Soukup Friederike De Mol Garifalia Economou Alireza Taab S Babaei Anna Bochenek Agnieszka Synowiec E. Jakubiak Ahmet Uludag Alistair Murdoch Aritz Royo-EsnalAbstract
No abstract has been registered
Authors
Signe Kynding Borgen Gry Alfredsen Johannes Breidenbach Lise Dalsgaard Gunnhild Søgaard Aaron SmithAbstract
No abstract has been registered
Authors
Svetlana Saarela Johannes Breidenbach Pasi Raumonen Anton Grafström Göran Ståhl Mark J. Ducey Rasmus AstrupAbstract
This study presents an approach for predicting stand-level forest attributes utilizing mobile laser scanning data collected as a nonprobability sample. Firstly, recordings of stem density were made at point locations every 10th metre along a subjectively chosen mobile laser scanning track in a forest stand. Secondly, kriging was applied to predict stem density values for the centre point of all grid cells ina5m×5m lattice across the stand. Thirdly, due to nondetectability issues, a correction term was computed based on distance sampling theory. Lastly, the mean stem density at stand level was predicted as the mean of the point-level predictions multiplied with the correction factor, and the corresponding variance was estimated. Many factors contribute to the uncertainty of the stand-level prediction; in the variance estimator, we accounted for the uncertainties due to kriging prediction and due to estimating a detectability model from the laser scanning data. The results from our new approach were found to correspond fairly well to estimates obtained using field measurements from an independent set of 54 circular sample plots. The predicted number of stems in the stand based on the proposed methodology was 1366 with a 12.9% relative standard error. The corresponding estimate based on the field plots was 1677 with a 7.5% relative standard error.