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.
2019
Authors
Idoia Biurrun Jürgen Dengler Sabina Burrascano Iwona Dembicz Itziar García-Mijangos Riccardo Guarino Jutta Kapfer Remigiusz Pielech Santiago Soliveres Manuel J. Steinbauer GrassPlot ConsortiumAbstract
No abstract has been registered
Authors
Manuel Jonas Steinbauer John Arvid Grytnes Gerald Jurasinski Aino Kulonen Jonathan Lenoir Harald Pauli Christian Rixen Manuela Winkler Manfred Bardy-Durchhalter Elena Barni Anne D. Bjorkman Frank T. Breiner Sarah Burg Patryk Czortek Melissa A. Dawes Anna Delimat Stefan Dullinger Brigitta Erschbamer Vivian Astrup Felde Olatz Fernández-Arberas Kjetil Farsund Fossheim Daniel Gómez-García D. Georges Erlend T. Grindrud Sylvia Haider Siri Vatsø Haugum Hanne Henriksen Maria J. Herreros Bogdan Jaroszewicz Francesca Orinda Holl Jaroszynska R. Kanka Jutta Kapfer Kari Klanderud Ingolf Kühn Andrea Lamprecht Magali Matteodo Umberto Morra di Cella Signe Normand Arvid Odland Siri Lie Olsen Sara Palacio Martina Petey Veronika Piscová Blazena Sedlakova Klaus Steinbauer Veronika Stöckli Jens-Christian Svenning Guido Teppa Jean-Paul Theurillat Pascal Vittoz Sarah J. Woodin Niklaus E. Zimmermann Sonja WipfAbstract
No abstract has been registered
Authors
Dilli Prasad Rijal Kelsey Lorberau Peter D. Heintzman Youri Lammers Jutta Kapfer Einar Støvern Nigel Gilles Yoccoz Dorothee Ehrich Antony Brown Inger Greve Alsos Kari Anne BråthenAbstract
No abstract has been registered
Authors
Iwona Dembicz Jürgen Dengler Idoia Biurrun Manuel J. Steinbauer Thomas J. Matthews Jutta Kapfer David Storch Werner Ulrich GrassPlot ConsortiumAbstract
No abstract has been registered
Authors
Dilli Prasad Rijal Kelsey Lorberau Jutta Kapfer Leif-Einar Støvern Inger Greve Alsos Kari Anne BråthenAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Magne SætersdalAbstract
No abstract has been registered
Authors
Kaiguang Zhao Michael A. Wulder Tongxi Hu Ryan Bright Qiusheng Wu Haiming Qin Yang Li Elizabeth Toman Bani Mallick Xuesong Zhang Molly BrownAbstract
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.
Abstract
Due to the potential for land-use–land-cover change (LULCC) to alter surface albedo, there is need within the LULCC science community for simple and transparent tools for predicting radiative forcings (ΔF) from surface albedo changes (Δαs). To that end, the radiative kernel technique – developed by the climate modeling community to diagnose internal feedbacks within general circulation models (GCMs) – has been adopted by the LULCC science community as a tool to perform offline ΔF calculations for Δαs. However, the codes and data behind the GCM kernels are not readily transparent, and the climatologies of the atmospheric state variables used to derive them vary widely both in time period and duration. Observation-based kernels offer an attractive alternative to GCM-based kernels and could be updated annually at relatively low costs. Here, we present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth's Radiant Energy System (CERES) Energy Balance and Filled (EBAF) products. When constructed on a 16-year climatology (2001–2016), we find that the CERES-based albedo change kernel – or CACK – agrees remarkably well with the mean kernel of four GCMs (rRMSE = 14 %). When the novel parameterization underlying CACK is applied to emulate two of the GCM kernels using their own boundary fluxes as input, we find even greater agreement (mean rRMSE = 7.4 %), suggesting that this simple and transparent parameterization represents a credible candidate for a satellite-based alternative to GCM kernels. We document and compute the various sources of uncertainty underlying CACK and include them as part of a more extensive dataset (CACK v1.0) while providing examples showcasing its application.
Abstract
Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.