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  • 1.
    Olsson, Jonas
    et al.
    SMHI, Research Department, Hydrology.
    Pers, Charlotta
    SMHI, Research Department, Hydrology.
    Bengtsson, Lisa
    SMHI, Research Department, Meteorology.
    Pechlivanidis, Ilias
    SMHI, Research Department, Hydrology.
    Berg, Peter
    Körnich, Heiner
    SMHI, Research Department, Meteorology.
    Distance-dependent depth-duration analysis in high-resolution hydro-meteorological ensemble forecasting: A case study in Malmo City, Sweden2017In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 93, p. 381-397Article in journal (Refereed)
  • 2. Ouarda, T. B. M. J.
    et al.
    Charron, C.
    Hundecha, Yeshewatesfa
    SMHI, Research Department, Hydrology.
    St-Hilaire, A.
    Chebana, F.
    Introduction of the GAM model for regional low-flow frequency analysis at ungauged basins and comparison with commonly used approaches2018In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 109, p. 256-271Article in journal (Refereed)
  • 3.
    Strombäck, Lena
    et al.
    SMHI, Research Department, Hydrology.
    Pers, Charlotta
    SMHI, Research Department, Hydrology.
    Strömqvist, Johan
    SMHI, Research Department, Hydrology.
    Lindström, Göran
    SMHI, Research Department, Hydrology.
    Gustavsson, Jens
    SMHI.
    A web based analysis and scenario tool for eutrophication of inland waters for Sweden and Europe2019In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 111, p. 259-267Article in journal (Refereed)
  • 4.
    Wilcke, Renate
    et al.
    SMHI, Research Department, Climate research - Rossby Centre.
    Bärring, Lars
    SMHI, Research Department, Climate research - Rossby Centre.
    Selecting regional climate scenarios for impact modelling studies2016In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 78, p. 191-201Article in journal (Refereed)
    Abstract [en]

    In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best. (C) 2016 The Authors. Published by Elsevier Ltd.

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