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.
2019
Sammendrag
A robust hydrological modeling at a fine spatial resolution is a vital tool for Norway to simulate river discharges and hydrological components for climate adaptation strategies. However, it requires improvements of modelling methods, detailed observational data as input and expensive computational resources. This work aims to set up a distributed version of the HBV model with a physically based evapotranspiration scheme at 1 km resolution for mainland Norway and to calibrate/validate the model for 124 catchments using regionalized parameterizations. The Penman-Monteith equation was implemented in the HBV model and vegetation characteristics were derived from the Norwegian forest inventory combined with multi-source remote sensing data at 16 m spatial resolution. The estimated potential evapotranspiration (Ep) was compared with pan measurements and estimates from the MODerate Resolution Imaging Spectrometer (MOD16) products, the Global Land Evaporation Amsterdam Model (GLEAM) and Variable Infiltration Capacity (VIC) hydrological model. There are 5 climatic zones in Norway classified based on 4 temperature and precipitation indices. For each zone, the model was calibrated separately by optimizing a multi-objective function including the Nash-Sutcliff efficiency (NSE) and biases of selected catchments. In total, there are 85 catchments for calibration and 39 for validation. The Ep estimates showed good agreement with the measurements, GLEAM and VIC outputs. However, the MOD16 product significantly overestimates Ep compared to the other products. The discharge was well reproduced with the median daily NSE of 0.68/0.67, bias of −3%/−1%, Kling-Gupta efficiency (KGE) of 0.70/0.69 and monthly NSE of 0.80/0.78 in the calibration/validation periods. Our results showed a significant improvement compared to the previous HBV application for all catchments, with an increase of 0.08–0.16 in the median values of the daily NSE, KGE and monthly NSE. Both the temporal and spatial transferability of model parameterizations were also enhanced compared to the previous application.
Forfattere
Jan Magnusson Stephanie Eisner Shaochun Huang Cristian Lussana Giulia Mazzotti Richard Essery Tuomo Saloranta Stein BeldringSammendrag
Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions.
Forfattere
Stephanie EisnerSammendrag
Det er ikke registrert sammendrag
Forfattere
Kjersti Holt Hanssen Svein Solberg Ari Hietala Paal Krokene Jørund Rolstad Halvor Solheim Bjørn ØklandSammendrag
I Europa er det registrert økende omfang av skogskader de siste hundre år, og klimaendringer er identifisert som en viktig driver bak økningene i for eksempel vindskader, barkbilleangrep og skogbranner. Det er likevel store regionale forskjeller i Europa, med en tendens til økt vekst og produktivitet i nordlige og høyereliggende skogområder, og mer tørkestress og mortalitet i sør. Ikke are endringer i klima, men også endringer i skogskjøtsel og skogstruktur påvirker forekomsten av skader i skog...
Forfattere
B.S. Steidinger Thomas W. Crowther Jingjing Liang M. E. Van Nuland G.D.A. Werner Peter B. Reich Gert-Jan Nabuurs Sergio de-Miguel M. Zhou N. Picard Bruno Herault Xiuhai Zhao C. Zhang D. Routh Kabir G Peay Meinrad Abegg C. Yves Adou Yao Giorgio Alberti Angelica Almeyda Zambrano Esteban Alvarez-Davila Patricia Alvarez-Loayza Luciana F. Alves Christian Ammer Clara Antón Fernández Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo Aymard Timothy R. Baker Radomir Bałazy Olaf Bánki Jorcely Barroso Meredith Bastian Jean-François Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Roel J. W. Brienen Eben Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo Cesar Goran Cesljar Robin L. Chazdon Han Y. H. Chen Chelsea L. Chisholm Emil Cienciala Connie J. Clark David Clark Gabriel Colletta Richard Condit David Coomes Fernando Cornejo Valverde Jose J. Corral-Rivas Philip Crim Jonathan Cumming Selvadurai Dayanandan André L. de Gasper Mathieu Decuyper Géraldine Derroire Ben DeVries Ilija Djordjevic Amaral Iêda Aurélie Dourdain Nestor Laurier Engone Obiang Brian J. Enquist Teresa Eyre Adandé Belarmain Fandohan Tom M. Fayle Ted R. Feldpausch Leena Finér Markus Fischer Christine Fletcher Jonas Fridman Lorenzo Frizzera Javier G. P. Gamarra Damiano Gianelle Henry B. Glick David J. Harris Andy Hector Andreas Hemp Geerten Hengeveld John Herbohn Martin Herold Annika Hillers Eurídice N. Honorio Coronado Markus Huber Cang Hui Hyunkook Cho Thomas Ibanez Ilbin Jung Nobuo Imai Andrzej M. Jagodzinski Bogdan Jaroszewicz Vivian Kvist Johannsen Carlos A. Joly Tommaso Jucker Viktor Karminov Kuswata Kartawinata Elizabeth Kearsley David Kenfack Deborah Kennard Sebastian Kepfer-Rojas Gunnar Keppel Mohammed Latif Khan Timothy Killeen Hyun Seok Kim Kanehiro Kitayama Michael Köhl Henn Korjus Florian Kraxner Diana Laarmann Mait Lang Simon L. Lewis Huicui Lu Natalia Lukina Brian S. Maitner Yadvinder Malhi Eric Marcon Beatriz Schwantes Marimon Ben Hur Marimon-Junior Andrew R. Marshall Emanuel H. Martin Olga Martynenko Jorge A. Meave Omar Melo-Cruz Casimiro Mendoza Cory Merow Abel Monteagudo Mendoza Vanessa Moreno Sharif A. Mukul Philip Mundhenk Maria G. Nava-Miranda David Neill Victor Neldner Radovan Nevenic Michael Ngugi Pascal Niklaus Jacek Oleksyn Petr Ontikov Edgar Ortiz-Malavasi Yude Pan Alain Paquette Alexander Parada-Gutierrez Elena Parfenova Minjee Park Marc Parren Narayanaswamy Parthasarathy Pablo L. Peri Sebastian Pfautsch Oliver Phillips Maria Teresa Piedade Daniel Piotto Nigel Pitman Irina Polo Lourens Poorter Axel Dalberg Poulsen John R. Poulsen Hans Pretzsch Freddy Ramirez Arevalo Zorayda Restrepo-Correa Mirco Rodeghiero Samir Rolim Anand Roopsind Francesco Rovero Ervan Rutishauser Purabi Saikia Philippe Saner Peter Schall Mart-Jan Schelhaas Dmitry Schepaschenko Michael Scherer-Lorenzen Bernhard Schmid Jochen Schöngart Eric Searle Vladimír Seben Josep M. Serra-Diaz Christian Salas Douglas Sheil Anatoly Shvidenko Javier Silva-Espejo Marcos Silveira James Singh Plinio Sist Ferry Slik Bonaventure Sonké Alexandre F. Souza Krzysztof Stereńczak Jens-Christian Svenning Miroslav Svoboda Natalia Targhetta Nadezhda M. Tchebakova Hans ter Steege Raquel Thomas Elena Tikhonova Peter Umunay Vladimir Usoltsev Fernando Valladares Fons van der Plas Tran Van Do Rodolfo Vasquez Martinez Hans Verbeeck Helder Viana Simone Vieira Klaus von Gadow Hua-Feng Wang James Watson Bertil Westerlund Susan Wiser Florian Wittmann Verginia Wortel Roderick Zagt Tomasz Zawila-Niedzwiecki Zhi-Xin Zhu Irie Casimir Zo-BiSammendrag
Det er ikke registrert sammendrag
Forfattere
B. S. Steidinger T. W. Crowther J. Liang M. E. Van Nuland G. D. A. Werner P. B. Reich G. J. Nabuurs S. de-Miguel M. Zhou N. Picard B. Herault X. Zhao C. Zhang D. Routh Meinrad Abegg C. Yves Adou Yao Giorgio Alberti Angelica Almeyda Zambrano Esteban Alvarez-Davila Patricia Alvarez-Loayza Luciana F. Alves Christian Ammer Clara Antón Fernández Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo Aymard Timothy Baker Radomir Bałazy Olaf Bánki Jorcely Barroso Meredith Bastian Jean-Francois Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Frans Bongers Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Francis Q. Brearley Roel Brienen Eben Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo Cesar Goran Cesljar Robin Chazdon Han Y. H. Chen Douglas Sheil K. G. PeaySammendrag
The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools1,2, sequester carbon3,4 and withstand the effects of climate change5,6. Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species7, constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species.
Forfattere
Jari Vauhkonen Ambros Berger Thomas Gschwantner Klemens Schadauer Philippe Lejeune Jérôme Perin Mikhail Pitchugin Radim Adolt Miroslav Zeman Vivian Kvist Johannsen Sebastian Kepfer-Rojas Allan Sims Claire Bastick François Morneau Antoine Colin Susann Bender Pál Kovácsevics György Solti László Kolozs Dóra Nagy Kinga Nagy Mark Twomey John Redmond Patrizia Gasparini M. Notarangelo Maria Rizzo Kristaps Makovskis Andis Lazdins Ainars Lupikis Gintaras Kulbokas Clara Antón Fernández Francisco Castro Rego Leónia Nunes Gheorghe Marin Catalin Calota Damjan Pantić Dragan Borota Joerg Roessiger Michal Bosela Vladimír Šebeň Mitja Skudnik Patricia Adame Iciar Alberdi Isabel Cañellas Torgny Lind Renats Trubins Esther Thürig Golo Stadelmann Ben Ditchburn David Ross Justin Gilbert Lesley Halsall Markus Lier Tuula PackalenSammendrag
Det er ikke registrert sammendrag
Forfattere
Jari Vauhkonen Ambros Berger Thomas Gschwantner Klemens Schadauer Philippe Lejeune Jérôme Perin Mikhail Pitchugin Radim Adolt Miroslav Zeman Vivian Kvist Johannsen Sebastian Kepfer-Rojas Allan Sims Claire Bastick François Morneau Antoine Colin Susann Bender Pál Kovácsevics György Solti László Kolozs Dóra Nagy Kinga Nagy Mark Twomey John Redmond Patrizia Gasparini Monica Notarangelo Maria Rizzo Kristaps Makovskis Andis Lazdins Ainars Lupikis Gintaras Kulbokas Clara Antón Fernández Francisco Castro Rego Leónia Nunes Gheorghe Marin Catalin Calota Damjan Pantić Dragan Borota Joerg Roessiger Michal Bosela Vladimír Šebeň Mitja Skudnik Patricia Adame Iciar Alberdi Isabel Cañellas Torgny Lind Renats Trubins Esther Thürig Golo Stadelmann Ben Ditchburn David Ross Justin Gilbert Lesley Halsall Markus Lier Tuula PackalenSammendrag
• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh . The associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9 .
Forfattere
Clara Antón FernándezSammendrag
Det er ikke registrert sammendrag
Forfattere
Kaiguang Zhao Michael A. Wulder Tongxi Hu Ryan Bright Qiusheng Wu Haiming Qin Yang Li Elizabeth Toman Bani Mallick Xuesong Zhang Molly BrownSammendrag
Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single “best” model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics.