Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2021
Forfattere
Peter Horvath Hui Tang Rune Halvorsen Frode Stordal Lena M. Tallaksen Terje Koren Berntsen Anders BrynSammendrag
Vegetation is an important component in global ecosystems, affecting the physical, hydrological and biogeochemical properties of the land surface. Accordingly, the way vegetation is parameterized strongly influences predictions of future climate by Earth system models. To capture future spatial and temporal changes in vegetation cover and its feedbacks to the climate system, dynamic global vegetation models (DGVMs) are included as important components of land surface models. Variation in the predicted vegetation cover from DGVMs therefore has large impacts on modelled radiative and non-radiative properties, especially over high-latitude regions. DGVMs are mostly evaluated by remotely sensed products and less often by other vegetation products or by in situ field observations. In this study, we evaluate the performance of three methods for spatial representation of present-day vegetation cover with respect to prediction of plant functional type (PFT) profiles – one based upon distribution models (DMs), one that uses a remote sensing (RS) dataset and a DGVM (CLM4.5BGCDV; Community Land Model 4.5 Bio-Geo-Chemical cycles and Dynamical Vegetation). While DGVMs predict PFT profiles based on physiological and ecological processes, a DM relies on statistical correlations between a set of predictors and the modelled target, and the RS dataset is based on classification of spectral reflectance patterns of satellite images. PFT profiles obtained from an independently collected field-based vegetation dataset from Norway were used for the evaluation. We found that RS-based PFT profiles matched the reference dataset best, closely followed by DM, whereas predictions from DGVMs often deviated strongly from the reference. DGVM predictions overestimated the area covered by boreal needleleaf evergreen trees and bare ground at the expense of boreal broadleaf deciduous trees and shrubs. Based on environmental predictors identified by DM as important, three new environmental variables (e.g. minimum temperature in May, snow water equivalent in October and precipitation seasonality) were selected as the threshold for the establishment of these high-latitude PFTs. We performed a series of sensitivity experiments to investigate if these thresholds improve the performance of the DGVM method. Based on our results, we suggest implementation of one of these novel PFT-specific thresholds (i.e. precipitation seasonality) in the DGVM method. The results highlight the potential of using PFT-specific thresholds obtained by DM in development of DGVMs in broader regions. Also, we emphasize the potential of establishing DMs as a reliable method for providing PFT distributions for evaluation of DGVMs alongside RS.
Sammendrag
Information about the distribution of a study object (e.g., species or habitat) is essential in face of increasing pressure from land or sea use, and climate change. Distribution models are instrumental for acquiring such information, but also encumbered by uncertainties caused by different sources of error, bias and inaccuracy that need to be dealt with. In this paper we identify the most common sources of uncertainties and link them to different phases in the modeling process. Our aim is to outline the implications of these uncertainties for the reliability of distribution models and to summarize the precautions needed to be taken. We performed a step-by-step assessment of errors, biases and inaccuracies related to the five main steps in a standard distribution modeling process: (1) ecological understanding, assumptions and problem formulation; (2) data collection and preparation; (3) choice of modeling method, model tuning and parameterization; (4) evaluation of models; and, finally, (5) implementation and use. Our synthesis highlights the need to consider the entire distribution modeling process when the reliability and applicability of the models are assessed. A key recommendation is to evaluate the model properly by use of a dataset that is collected independently of the training data. We support initiatives to establish international protocols and open geodatabases for distribution models.
Forfattere
Hui Tang Kjetil Schanke Aas Eirik Aasmo Finne Inge Althuizen Rosie A. Fisher Hans Tømmervik Ane Victoria Vollsnes Anders Bryn Sonya Rita Geange Sunniva Indrehus Vigdis Vandvik Jarle Werner Bjerke Terje Koren Berntsen Frode StordalSammendrag
Det er ikke registrert sammendrag
Forfattere
Wendy FjellstadSammendrag
Det er ikke registrert sammendrag
Forfattere
Wendy FjellstadSammendrag
Det er ikke registrert sammendrag
Sammendrag
Rapporten dokumenterer utvalgte eksempler på bruk av stordata (engelsk: big data) teknologi og metode i NIBIO. Det første eksemplet er knyttet til oppdatering av arealressurskartet AR5, hvor det undersøkes om stordata-tilnærming kan benyttes for å identifisere lokaliteter der kartet må oppdateres. De neste eksemplene er hentet fra fagområdet plantehelse og tar for seg mulighetene for å bruke stordata-metode for å bedre prediksjonsmodeller og gjenkjenning av for skadegjørere.
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Rik De Vreese Adina Dumitru Sebastian Eiter Laurence Jones Laura Wendling Marianne ZandersenSammendrag
Det er ikke registrert sammendrag
Forfattere
Sebastian EiterSammendrag
Det er ikke registrert sammendrag
Forfattere
Dina Stober Monika Suškevičs Sebastian Eiter Stefanie Müller Stanislav Martinat Matthias BucheckerSammendrag
There is widespread agreement that society must urgently go over to renewable energy and that effective participation is crucial for a sustainable transition. Recent literature highlights that the quality of participatory planning is decisive for the acceptance of renewable energy technologies. However, social science research on the issue is dominated by single case studies and the quality of the participatory processes has been rarely studied across wider regional contexts. We present the results of a comparative qualitative inventory of innovative practices from 25 projects in 12 countries in three supra-national European regions. We assessed participation quality according to three analytical dimensions: rationale, inclusiveness, and participation level. An instrumental rationale, broad inclusiveness, and a participation level of consulting or informing was a combination that was commonly perceived as an innovative participatory practice in most regional contexts. We found a tendency towards more advanced practices in Western Europe, compared to Southern, and Eastern Europe. However, practices that were considered to be innovative in the latter regions still provide important lessons within their geographical contexts.