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.
2025
Forfattere
Carl-Fredrik Johannesson H. Ilvesniemi O. Janne Kjønaas K.S. Larsen A. Lehtonen Jenni Nordén D. Paré Hanna Marika Silvennoinen J. Stendahl I. Stupak L. Vesterdal Lise DalsgaardSammendrag
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Forfattere
Randi Berland FrøsethSammendrag
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Forfattere
Ellen ElverlandSammendrag
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Forfattere
Ellen ElverlandSammendrag
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Sammendrag
Long-term monitoring of ecosystems is the only direct method to provide insights into the system dynamics on a range of timescales from the temporal resolution to the duration of the record. Time series of typical environmental variables reveal a striking diversity of trends, periodicities, and long-range correlations. Using several decades of observations of water chemistry in first-order streams of three adjacent catchments in the Harz mountains in Germany as example, we calculate metrics for these time series based on ordinal pattern statistics, e.g. permutation entropy and complexity, Fisher information, or q-complexity, and other indicators like Tarnopolski diagrams. The results are compared to those obtained for reference statistical processes, like fractional Brownian motion or ß noise. After detrending and removing significant periodicities from the time series, the distances of the residuals to the reference processes in this space of metrics serves as a classification of nonlinear dynamical behavior, and to judge whether inter-variable or rather inter-site differences are dominant. The classification can be combined with knowledge about the processes driving hydrochemistry, elucidating the connections between the variables. This can be the starting point for the next step, constructing causal networks from the multivariate dataset.
Sammendrag
In terrestrial ecosystems, forest stands are the primary drivers of atmospheric moisture and local climate regulation, making the quantification of transpiration (T) at the stand level both highly relevant and scientifically important. Stand-level T quantification complements evapotranspiration monitoring by eddy-covariance systems, providing valuable insight into the water use efficiency of forested ecosystems in addition to serving as important inputs for the calibration and validation of global transpiration monitoring products based on satellite observations. Stand level T estimates are typically obtained by scaling up individual tree estimates of water movement within the xylem – or sap flow. This movement affects the radius of a tree stem, whose fluctuations over the diel cycle provide pertinent information about tree water relations which can be readily detected by point (or precision) dendrometers. While sap flow measurements have greatly advanced our understanding of water consumption (T) at the level of individual trees, deploying conventional sap flow monitoring equipment to quantify T at the level of entire forested stands (or ecosystems) can quickly become costly since sap flow measurements from many trees are required to reduce the uncertainty of the upscaling. Using a boreal old-growth Norway spruce stand at an ICOS site in Southern Norway as a case study, we assess the potential of augmenting conventional sap flow monitoring systems with sap flow modeling informed by point dendrometer measurements to reduce the uncertainty of stand level T estimation at the daily resolution. We test the hypothesis that the uncertainty reduction afforded by a boosted tree sample size more than offsets the propagation of uncertainty originating from the point dendrometer-based sap flow estimates.