Improved Precipitation Information for Hydrological Problem Solving - Focus on Open Data and Simulation

Tero Niemi

Research output: ThesisDoctoral ThesisCollection of Articles

Abstract

Precipitation acts as the starting point and the driving force in nearly every field of hydrology. Rainfall-runoff models in particular require accurate rainfall input data in order to provide accurate runoff results. The data requirements are emphasized in urban environments due to small sizes and rapid runoff responses of urban catchments. In recent years, the amount of open precipitation data has increased due to changes in governmental policies and legislations. However, since measuring everything is ultimately impossible, there remains a need for precipitation simulation models no matter how much data is (openly) available. This thesis studied the benefits of improved precipitation information in hydrological assessments by addressing the following questions: 1) How can open precipitation data be utilized more extensively in hydrological research? 2) How can simulation models be improved via more realistic spatial description of precipitation fields? The feasibility of open weather radar and rain gauge data for urban hydrological assessments was studied by conducting high-resolution rainfall-runoff simulations at small Finnish catchments utilizing open precipitation data and rainfall-runoff data collected at the catchments. The open gauge data performs well, given that the gauge is located at the studied catchment or close to it. When the distance to the gauge increases, gauge corrected radar data can give superior results even when the studied catchment is much smaller than the radar data spatial resolution. A new method was developed to quantify the anisotropic shape of precipitation fields and the evolution of the shape during storm events utilizing the linear Generalized Scale Invariance formalism. The shape description was implemented into a state-of-the art stochastic precipitation generator to provide a parsimonious way for a more realistic description of precipitation features. Impact of the field shape on the catchment response was studied by conducting rainfall-runoff simulations replicating an extreme storm event. While the description of anisotropy allows for creating stochastic precipitation events that produce the desired rainfall accumulations without sacrificing other event characteristics such as storm advection or storm evolution, its effect was attenuated when exploring the catchment response. This thesis lays groundwork for future advances in understanding the precipitation process from coarse radar scales to detailed urban scales by utilizing the open precipitation data more comprehensively. Amongst other things, the open data enables studying precipitation features across scales. The presented anisotropy quantification method allows for building better stochastic precipitation simulation models capable of reproducing more realistic precipitation fields for situations where measurement data, open or not, is unavailable.
Translated title of the contributionParannettu sadantainformaatio hydrologisessa ongelmanratkaisussa – painopisteinä avoin data ja simulointi
Original languageEnglish
QualificationDoctor's degree
Awarding Institution
  • Aalto University
Supervisors/Advisors
  • Koivusalo, Harri, Supervisor
  • Kokkonen, Teemu, Advisor
Publisher
Print ISBNs978-952-60-7512-9
Electronic ISBNs978-952-60-7511-2
Publication statusPublished - 2017
MoE publication typeG5 Doctoral dissertation (article)

Keywords

  • design storm
  • GSI
  • open data
  • precipitation
  • precipitation simulation
  • rainfall
  • rain gauge
  • urban hydrology
  • weather radar

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  • Prizes

    Student prize: Aalto University School of Engineering Award for Best Doctoral Dissertation 2017

    Tero Niemi (Student prize recipient), 28 Feb 2018

    Prize: Award or honor granted for a specific work

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