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  • 1.
    Ahlberg, Jesper
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Gustafsson, David
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Distributed snow modelling integrating ground penetrating radar data for improved runoff predictions in a Swedish mountain basin2009In: EGU General Assembly 2009, 2009Conference paper (Other academic)
    Abstract [en]

    Operational forecasts of snow melt runoff in Sweden are currently running with precipitation and temperature as the main input variables and calibrated with runoff data, and there is an interest to make better use of new measurement systems for distributed snow data. At the same time, various data assimilation techniques are becoming more frequently used in hydrological modeling, in order to reduce uncertainties related to both model structure errors and errors in input and calibration data. Thus, it is important to address not only what type of snow data that can be used to improve the model predictions, but also what type of input data and model structures that are optimal in relation to the available snow data. The objective of this study is to investigate to what extent the runoff predictions can be improved by assimilation of temporal and spatially distributed snow data, and if the improvements depend on the choice of model structures, for instance the use of energy balance or day-degree snow models. In order to achieve these objectives a new distributed snow model has been implemented into the hydrological modeling framework HYSS/HYPE. This model can easily be setup with either an energy balance model or a day-degree model for the snow pack calculations, and it is easy to run the model with different spatial resolutions. In the fully distributed case, snow drift processes are implicitly included in the model through a precipitation distribution model, based on topographical information and wind direction. The model was applied to a mountain basin in northern Sweden used for hydropower production, where extensive snow measurements were taken during the last two winters 2007-2009. A climate station is located at the outlet of the regulation lake, including automated point measurements of snow depth, snow mass (snow pillow), snow wetness and snow temperature. Distributed snow cover data was sampled using ground-penetrating radar from snow mobiles. Measurements were taken at the time of the maximum snow cover, providing a data set with snow depth, snow density, snow water equivalent along 20 km long transects in representative areas of the basin. The precipitation distribution model was calibrated using the distributed SWE data from the GPR measurements. Application of the calibrated model to previous years without available snow data show that the runoff predictions was improved compared to calibrations without the distributed snow data, however the improvements were larger for the energy balance compared to the day-degree model. Further developments will include assimilation of the temporal and spatial snow data to adjust the distribution of various input variables, for instance air temperature and wind speed.

  • 2.
    Ahlberg, Jesper
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Gustafsson, David
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Snow melt runoff simulations using ensemble Kalman filter assimilation of distributed snow data2010Conference paper (Other academic)
  • 3.
    Broström, Elin
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Ahlberg, Jesper
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering (EES), Electric Power Systems.
    Modelling of ice storms and their impact applied to a part of the Swedish Transmission network2007In: 2007 IEEE LAUSANNE POWERTECH, VOLS 1-5, NEW YORK: IEEE , 2007, p. 1593-1598Conference paper (Refereed)
    Abstract [en]

    In this paper a technique of modelling severe weather for power system reliability calculations is developed. The model is based on geographically moving winds and precipitation and is suitable for transmission network. A scenario represents a weather situation with given parameters. Besides the weather model a stochastic vulnerability model for the components is required for each scenario that connects the risk of failure to the weather situation. In order to mitigate severe consequences of future ice storms in an efficient way it is essential to be able to estimate the consequences based on assumptions of the technical system and the severity of possible storms. It is assumed that the probability of a failure due to a given weather depends on load functions for wind and ice together with the component vulnerability model which is based on the design of the components. The wind load is direct and the ice load is given by a known ice accretion model. Conclusions about the reliability of the studied lines under ice storms are presented as well as a graph of the critical conditions for the studied lines with ice thickness on the x-axis and gust wind on the y-axis. The numerical examples show the impact of different weather situations on a part of the Swedish Transmission network using data both from real weather situations in Sweden and the weather model.

  • 4.
    Granlund, Nils
    et al.
    Luleå University of Technology.
    Lundberg, Angela
    Luleå University of Technology.
    Gustafsson, David
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Ahlberg, Jesper
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Wetterhall, Fredrik
    Sveriges Meteorologiska och Hydrologiska Institut.
    Towards better predictions of snow melt runoffs: Measuring Snow Depth and Density Using Ground Penetrating Radar2009Conference paper (Other academic)
    Abstract [en]

    Snow melt runoff predictions by hydrological models are essential for efficient hydropower production in the Scandinavian countries, similar to many areas with a substantial amount of snow precipitation. Operational models in Sweden are currently based on precipitation and temperature as the main input variables and calibrated with runoff data, but there is an interest to make better use of new measurement systems for distributed snow data, especially the total amount of snow in the catchment area of interest. The main objective of our project is to investigate the potential improvements in runoff predictions in relation to the choice of model structure and measurement systems, as well as measurement accuracy. This involves comparing different methods for estimating the total amount of snow in a catchment area as well as improving their accuracy. Here we present the result of such comparison based on data from case studies conducted in Sweden. Our approach involves automated single point measurements over a long period in combination with high resolution distributed measurements over a large area during critical periods. Stationary measurements are performed at a snow measurement station, with snow density and wetness estimated with a low-frequency impedance sensor band, snow depth measured using an ultrasonic depth gauge, and temperature measured at several (fixed) snow depths and at the snow surface. The station, located at Lake Korsvattnet in Swedish mountains, operates continuously during the whole winter season. Measurements of snow depth and density over large lateral distances are performed using multi-offset ground penetrating radar (GPR) operated from a snow mobile. These measurements are conducted once a year, in late winter, when the amount of snow is expected to reach its maximum before snow melt begins. Since 2007 and during the duration of the project, yearly measurements have been and will be taken in two Swedish mountain basins important for hydropower, Lake Korsvattnet and Lake Kultsjön. The radar system used is a multi-channel RAMAC/GPR system with shielded 800 and 1600 MHz antennas. The antennas are attached to a snow mobile sledge forming an array, which allows us to use the common midpoint method to calculate both radar propagation velocity and two-way travel time of radar pulses. For dry snow this gives snow density and depth via an empirical formula establishing the relationship between electrical permittivity (i.e. propagation velocity) and snow density. Note that for wet snow additional information about liquid water content in snow is required, which can be estimated, for example, from radar wave attenuation. However, for the purpose of this presentation we assume that the snow is dry. The results of GPR measurements taken from a snow mobile are compared with results obtained by two other methods. The first comparison is with manual measurements taken with traditional snow tubes along a 1000 m measurement profile at the area of Lake Korsvattnet. In this case a log-linear relationship between snow depth and density is used to interpret GPR data (note that this relationship is obtained from analysis of radar data itself). The other comparison is with GPR measurements taken from a helicopter along a 12 km transect in the area of Lake Kultsjön.

  • 5.
    Gustafsson, David
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Ahlberg, Jesper
    KTH, School of Architecture and the Built Environment (ABE), Land and Water Resources Engineering, Environmental Physics.
    Granlund, Nils
    Luleå University of Technology.
    Lindström, Göran
    Sveriges meteorologiska och hydrologiska institut.
    Wetterhall, Fredrik
    Sveriges meteorologiska och hydrologiska institut.
    Lundberg, Angela
    Luleå University of Technology.
    Distribuerade system för förbättrade snö- och avrinningsprognoser. Integration i hydrologiska modeller: Delrapport 12009Report (Other academic)
1 - 5 of 5
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