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.
2006
Sammendrag
The minirhizotron technique provides the opportunity to perform in situ measurements of fine root dynamics and obtain accurate estimates of fine root production and turnover. The objective of the present work was to determine the fine root longevity and mycorrhization in a Norway spruce chronosequence. The study was carried out on four stands of planted Norway spruce (Picea abies), approximately 10, 30, 60 and 120 years old, during 2001 and 2002. The stands were located at Nordmoen, a plain of sandy deposits in southeast Norway (60o15 N, 11o06 E). For the root turnover study, altogether 60 minirhizotrones were installed and images were processed.Individual fine roots were identified, their mycorrhization assessed, appearance and possible disappearance dated, and growth in length measured. The data set was subjected to a survival analysis, using a Kaplan-Meier product-limit approach. The minirhizotron samples were stratified according to stand age class, and Coxs F-test was used to analyze differences in survival estimates. The analysis may also be extended to consider other covariates such as tree species (spruce, pine or birch), understory vegetation, or soil depth. Typical survival function estimates will be presented, and the influence of stand age on the mycorrhization and the dynamics of the fine roots will be discussed.
2005
Sammendrag
Barmasse er sterkt knyttet til kronetetthet og til bladarealindeks (LAI), og er viktig for fjernmåling av skogens helsetilstand. Vi kan nå presentere foreløpige, men lovende resultater for måling av barmasse ved hjelp av flybåren laserskanning.
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Forfattere
Kjersti Holt HanssenSammendrag
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Forfattere
Dagang Tang Thorjørn Larssen Min Shao Xiaoshan Zhang Lei Duan Wang Yanhui Shang He Zhao Dawei Renjun Xiang Jinsong Xiao Jiahai Luo Wenche Aas Rolf David Vogt Hans Martin Seip Espen Lydersen Odd Eilertsen Haijing Liu Jixi Gao Tonje Økland Svein Solberg Valter Angell Jan MulderSammendrag
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Sammendrag
We investigate ecosystem dynamics by analyzing time series of measured variables. The information content and the complexity of these data are quantifed by methods from information theory.When applied to runoff (stream discharge) from catchments, the information/complexity relation reveals a simple non-trivial property for a large ensemble (more than 1800) of time series. This behaviour is so far not understood in hydrology.Using a multi-agent network receiving input resembling rainfall and producing output, we are able to reproduce the observed behaviour for the first time. The reconstruction is based on the identification and subsequent replacement of general patterns in the input. We thus consider runoff dynamics as the expression of an interactive learning problem of agents in an ecosystem.
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We investigate a data set of 160 river runoff time series at daily resolution from catchments in Southern Germany. Our aim is to seek spatial patterns for best parametrization of extreme value distributions to these data sets on one hand, and to analyze temporal instationarities of parameter estimates and extreme value attributes on the other. Conventional extreme value statistics and the calculation of return periods implicitly assume that the most extreme events are statistically independent. We demonstrate that this assumption is invalid, and that correlations, temporal as well as spatial, of arbitrary extent prevail instead. An important consequence is that the concept of return periods is obsolete. In order to find explanatory variables for the observed patterns, features of the waiting time distribution at a given relative threshold are correlated to catchment properties, such as size, mean runoff volume, elevation, and others. Finally, the effect of varying temporal resolution on the duration periods is exhibited. http://www.cosis.net/abstracts/EGU05/03192/EGU05-J-03192.pdf
Sammendrag
Instationarities in runoff time series are ubiquitous. However, simple trend analyses are often obscured by the presence of long-term correlations, and some instationarities are not simply changes in the mean or periodicities. Thus, wherever feasible, instationarities should be based on the full frequency distribution, or the cumulative distribution function (cdf), of the series. In this paper, we investigate the time-dependence of the empirical cdfs of 97 runoff datasets from the upper Danube basin applying a new pairwise test statistic, KSSUM, based on integrated differences of the cdfs. This is an improvement to the Kolmogorov-Smirnov (KS) test and was applied on different time scales, i.e. windows of varying size. If desired, the influence of drifts in the mean as well as heteroscedasticity can be excluded via z-transformations. The resulting time series of the KSSUM variable, either within a runoff series for different windows, or across series for the same period, is then subjected to the detection of spatiotemporal patterns with different methods. For most of the time series the underlying distributions move towards higher values in the long run. We also observed a periodic drift in the mean across all analysed gauges. It is furthermore possible to separate exceedingly variable runoff series from those with intermediate or small changes in value distribution on a regional basis, and thus to separate overall trends from local deviations at individual gauges. It is demonstrated that KSSUM is a sensitive method to investigate instationarities in sets of time series based on pairwise comparisons. An extension to a proper multivariate comparison is a possible further development. http://www.cosis.net/abstracts/EGU05/04198/EGU05-J-04198.pdf
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