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.
2004
Abstract
Sampling the catchment outlet generally is assumed to be a convenient way to infer information about a variety of biogeochemical processes at the catchment scale as it provides a spatial and temporal integral of the predominating catchment output fluxes for a number of chemical compounds of interest.Moreover, the short-term dynamics and long-term trends of the hydrograph and of solute concentrations in the catchment runoff can provide information about the predominating processes at the catchment scale and can be used to refine conceptual and mathematical models.Additional measurements inside the catchment, e.g., of soil solution, groundwater, and stream water at different sites, are used to relate the findings to within-catchment processes and thus to further constrain hypotheses and models.
Abstract
Using Singular System Analysis (SSA), we extract a collection of significant long-term components (with dominant periods of at least 3 years) for a large number of river runoff records.At first glance, these long-term modes are a distinct feature of this variable, not contained in precipitation and temperature, and not easily correlated to commonly known long-term indices (NAO, SOI, NHT, SUN, etc.). However, low-pass filtered versions of these time series exhibit strikingly similar behavior, like common maxima, within a region (such as Southern Germany), pointing to a common origin.Although not an unequivocal example for synchronization, we quantify the degree of synchronization as a function of the regional extent of the data and propose a mechanism, stochastic resonance, discussed in climate dynamics, which is able to produce this collective behavior despite the lack of deterministic drivers. We also comment on air pressure-induced teleconnections between the different large scale oscillations in the climate system.
Abstract
Ecosystems as objects of natural sciences are often difficult to understand, as an object of traditional management they are sometimes easy to utilize. Computer-based modeling offers new tools to study this apparent paradox.We propose an interactive framework from which the traditional approach based on dynamic system theory can be challenged for living systems: Models derived on the basis of the state concept have not (yet?) allowed predictions that derive novel management competence relevant for the altered boundary conditions of ecosystems. Here a concept of interaction as currently used in information sciences serves as starting point for deriving models more appropriate for ecosystems.An application and test of this concept consists in a search for signatures of interaction in environmental and ecological time series. Confronted with the notorious lack of detailed process understanding, it is plausible to rely on time series analysis techniques. The intricate nature of typical multivariate data sets from ecosystems immediately suggests a preference for nonlinear techniques, and among them temporally local methods, able to detect even subtle changes in the underlying dynamics.We shortly introduce a couple of these methods, which have been demonstrated to be appropriate for time series exceeding minimal length requirements. This is exemplified by recurrence quantification analysis. In addition we present methods to quantify the memory content (Hurst analysis) and complexity of data sets (defined in an informationtheoretic context).Time series analysis of extended environmental and ecological data sets can give detailed structural insights, monitors subtle changes undetectable otherwise, forms the basis for further inferences and provides rigorous model testing on all scales. The success of dynamic system theory when applied to non-living environmental data is strikingly contrasted by the difficulties of the same method when dealing with ecological data We conjecture that this difference reflects the extent to which interaction has been disregarded for ecosystems.
Abstract
The process of model building in the environmental sciences and when dealing with ecosystems is discussed. Two types of modeling approaches need to be distinguished: An algorithmic one, which has been used traditionally in physics, meteorology, and other branches where biological degrees of freedom are either absent or neglectable; and an interactive one, which is a new framework in computer science and seems to be most suitable in cases where organisms (including humans) as agents in ecosystems are to be taken into account. The first modeling approach is exemplified by state models in dynamic systems theory and expresses the correspondence imposed by Natural Law between inferential entailment in a formal system and causal entailment in natural systems. Modeling is to be separated from simulation. Simulation is a less restrictive type of modeling in which the description of non-interactive behaviour is the purpose and no constraints on the correspondence to internal states are imposed. The second (new) modeling approach is exemplified by interactive simulation models. It is able to express the correspondence in behaviour imposed by engineering standards (or cultural norms in general) between documentation, training and application in interactive choice situations such as games or ecosystem management. It generalises the notion of simulation for interactive problems. In an idealised situation the strictest correspondence between behaviour in a natural and a virtual system is expressed as bisimulation. The principles for model building are shortly demonstrated with examples.
Abstract
The forest stand growth simulator TRAGIC (tree response to acidification in groundwater in C) which has been developed to serve as a decision support system and a visualisation tool for scientists and forestry practitioners is introduced. TRAGIC places an emphasis upon visualisation techniques while at the same time providing detailed information on tree physiology and related parameters. The model is calibrated numerically to growth history data from two different European sites.Next, due to the importance of the visual component of the model, its ability to reproduce forest stand spatial structure is investigated, using an application of the theory of marked point processes. This analysis is applied to different experimental data sets for stands of different age, revealing information on planting schemes and the extent of significant spatial correlations.The spatial structure of the two model calibrations is then explored with the same methods. The point process analysis turns out to be a powerful diagnostic for model quality assessments, since spatial distribution is an indirect result of competition between trees for light.
Abstract
The notion of an ecological damage has so far neither been given a proper theoretical nor a pragmatic or operational foundation. Yet one of the most widespread motivations for the scientific study of ecosystems is a “protectional” one by which an improved scientific understanding is sought in order to be able to prevent future ecological damages. We review the possibilities of valuating changes in the environment, in health or in ecosystems as a damage. The conceptual separation of potential from actual behaviour/structure is a prerequisite to any of them. The critical point here is the formal and empirical basis for the knowledge about these potentials. We contrast the dynamic systems theory approach derived in physics with an interactive computing approach recently developed in computer science. The former requires to distinguish facts and values and leads to notorious difficulties when applied at the ecosystem level. The latter and novel approach opens the possibility for a consistent definition of a damage at the ecosystem level whenever a tradition of (sustainable) utilization of such systems is available. The documentation, actualisation and dissemination of the tacit (expert) knowledge can be improved by the use of interactive simulations in which a virtual standard can defined by the respective experts themselves.
Authors
Svein Solberg Kjell Andreassen Nicholas Clarke Kjetil Tørseth Ole Einar Tveito Geir-Harald Strand Stein Michael TomterAbstract
No abstract has been registered
2003
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
The rationale for stand growth modelling is often either grounded in a search for improved scientific understanding or in support for management decisions. The ultimate goal under the first task is seen in mechanistic models, i.e. models that represent the stand structure realistically and predict future growth as a function of the current status of the stand. Such mechanistic models tend to be over-parameterized with respect to the data actually available for a given stand. Calibration of these models may lead to non-unique representations and unreliable predictions. Empirical models, the second major line of growth modelling, typically match available data sets as well as do process-based models. They have less degrees of freedom, hence mitigate the problem of non-unique calibration results, but they employ often parameters without physiological or physical meaning. That is why empirical models cannot be extrapolated beyond the existing conditions of observations. Here we argue that this widespread dilemma can be overcome by using interactive models as an alternative approach to mechanistic (algorithmic) models. Interactive models can be used at two levels: a) the interactions among trees of a species or ecosystem and b) the interactions between forest management and a stand structure, e.g. in thinning trials. In such a model data from a range of sources (scientific, administrative, empirical) can be incorporated into consistent growth reconstructions. Interactive selection among such growth reconstructions may be theoretically more powerful than algorithmic automatic selection. We suggest a modelling approach in which this theoretical conjecture can be put to a practical test. To this end growth models need to be equipped with interactive visualization interfaces in order to be utilized as input devices for silvicultural expertise. Interactive models will not affect the difficulties of predicting forest growth, but may be at their best in documenting and disseminating silvicultural competence in forestry.