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  • 51. Tanouchi, Hiroto
    et al.
    Olsson, Jonas
    SMHI, Forskningsavdelningen, Hydrologi.
    Lindström, Göran
    SMHI, Forskningsavdelningen, Hydrologi.
    Kawamura, Akira
    Amaguchi, Hideo
    Improving Urban Runoff in Multi-Basin Hydrological Simulation by the HYPE Model Using EEA Urban Atlas: A Case Study in the Sege River Basin, Sweden2019Inngår i: HYDROLOGY, ISSN 2306-5338, Vol. 6, nr 1Artikkel i tidsskrift (Fagfellevurdert)
  • 52. Willems, P.
    et al.
    Arnbjerg-Nielsen, K.
    Olsson, Jonas
    SMHI, Forskningsavdelningen, Hydrologi.
    Nguyen, V. T. V.
    Climate change impact assessment on urban rainfall extremes and urban drainage: Methods and shortcomings2012Inngår i: Atmospheric research, ISSN 0169-8095, E-ISSN 1873-2895, Vol. 103, s. 106-118Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cities are becoming increasingly vulnerable to flooding because of rapid urbanization, installation of complex infrastructure, and changes in the precipitation patterns caused by anthropogenic climate change. The present paper provides a critical review of the current state-of-the-art methods for assessing the impacts of climate change on precipitation at the urban catchment scale. Downscaling of results from global circulation models or regional climate models to urban catchment scales are needed because these models are not able to describe accurately the rainfall process at suitable high temporal and spatial resolution for urban drainage studies. The downscaled rainfall results are however highly uncertain, depending on the models and downscaling methods considered. This uncertainty becomes more challenging for rainfall extremes since the properties of these extremes do not automatically reflect those of average precipitation. In this paper, following an overview of some recent advances in the development of innovative methods for assessing the impacts of climate change on urban rainfall extremes as well as on urban hydrology and hydraulics, several existing difficulties and remaining challenges in dealing with this assessment are discussed and further research needs are described. (C) 2011 Elsevier B.V. All rights reserved.

  • 53.
    Yang, Wei
    et al.
    SMHI, Forskningsavdelningen, Hydrologi.
    Andreasson, Johan
    SMHI, Affärsverksamhet.
    Graham, Phil
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Olsson, Jonas
    SMHI, Forskningsavdelningen, Hydrologi.
    Rosberg, Jörgen
    SMHI, Forskningsavdelningen, Hydrologi.
    Wetterhall, Fredrik
    SMHI, Forskningsavdelningen, Hydrologi.
    Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies2010Inngår i: HYDROLOGY RESEARCH, ISSN 1998-9563, Vol. 41, nr 3-4, s. 211-229Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As climate change could have considerable influence on hydrology and corresponding water management, appropriate climate change inputs should be used for assessing future impacts. Although the performance of regional climate models (RCMs) has improved over time, systematic model biases still constrain the direct use of RCM output for hydrological impact studies. To address this, a distribution-based scaling (DBS) approach was developed that adjusts precipitation and temperature from RCMs to better reflect observations. Statistical properties, such as daily mean, standard deviation, distribution and frequency of precipitation days, were much improved for control periods compared to direct RCM output. DBS-adjusted precipitation and temperature from two IPCC Special Report on Emissions Scenarios (SRESA1B) transient climate projections were used as inputs to the HBV hydrological model for several river basins in Sweden for the period 1961-2100. Hydrological results using DBS were compared to results with the widely-used delta change (DC) approach for impact studies. The general signal of a warmer and wetter climate was obtained using both approaches, but use of DBS identified differences between the two projections that were not seen with DC. The DBS approach is thought to better preserve the future variability produced by the RCM, improving usability for climate change impact studies.

  • 54.
    Yang, Wei
    et al.
    SMHI, Forskningsavdelningen, Hydrologi.
    Gardelin, Marie
    SMHI, Affärsverksamhet.
    Olsson, Jonas
    SMHI, Forskningsavdelningen, Hydrologi.
    Bosshard, Thomas
    SMHI, Forskningsavdelningen, Hydrologi.
    Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden2015Inngår i: Natural hazards and earth system sciences, ISSN 1561-8633, E-ISSN 1684-9981, Vol. 15, nr 9, s. 2037-2057Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically down-scaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.

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