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  • 1.
    Forsman, Åsa
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Andersson, C.
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Hoffman, M.
    Estimation of the impact of short-term fluctuations in inputs on temporally aggregated outputs of process-oriented models2003In: Journal of Hydroinformatics, ISSN 1464-7141, E-ISSN 1465-1734, Vol. 5, p. 169-180Article in journal (Refereed)
  • 2.
    Forsman, Åsa
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Reduced models for efficient simulation of spatially integrated outputs of one-dimensional substance transport models2003In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 18, no 4, p. 319-327Article in journal (Refereed)
    Abstract [en]

    We examined under what circumstances the results of a large number of runs of the one-dimensional, physics-based SOIL/SOILN nitrate transport model can be combined into a reduced (or meta) model. We considered the total flow of nitrate from a given area and investigated when and how hidden linear structures can be extracted from the underlying model. The presence of such structures can justify the use of spatially aggregated inputs to compute spatially aggregated outputs. Extensive Monte-Carlo simulations showed that some linear structures emerged when the outputs for a long period of time were summed. Other linear structures appeared as relationships between two different components of the model outputs. However, different cropping systems respond differently to changes in anthropogenic or meteorological forcings. Therefore, we derived a reduced model of long-term leaching of nitrogen from the root zone in an agricultural area by combining each combination of soil type and cropping system. Reduced models can help make process-oriented models more transparent, and they are particularly suitable for incorporation into decision support systems.

  • 3.
    Forsman, Åsa
    et al.
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Grimvall, Anders
    Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Mathematics, Statistics .
    Scholtes, J.
    Wittgren, H. B.
    Generic structures of decision support systems for evaluation of policy measures to reduce catchment-scale nitrogen fluxes2003In: Physics and Chemistry of the Earth, ISSN 1474-7065, E-ISSN 1873-5193, Vol. 28, no 14-15, p. 589-598Article in journal (Refereed)
    Abstract [en]

    Decision support systems (DSSs) for evaluation of different policy measures have two important functions: To assess how considered policy measures may influence the behavior of actors, and to predict the effects of a given set of actions generated from the anticipated behavior. So far, almost all attempts to construct DSSs for environmental management have focused on assessing the impact of a set of actions on the environment. Here, we describe the generic structure of a DSS that enables more complete evaluation of regional or national policies to reduce nitrogen inputs to water. In particular, we expound the principles for linking models of farm economic behavior to catchment-scale models of the transport and transformation of nitrogen in soil and water. First, we define system boundaries for nitrogen fluxes through the agricultural sector and the ambient environment to create a basis for model integration. Thereafter, we show how different modules operating on different temporal and spatial scales can be interlinked. Finally, we demonstrate how statistical emulators or meta-models can be derived to reduce the computational burden and increase the transparency of the DSS. In particular, we show when and how the temporal or spatial resolution of model inputs can be reduced without significantly influencing the estimates of annual nitrogen fluxes on a catchment scale.

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