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  • Autor
    • Reinstaller, Stefan
    • Krebs, Gerald
    • Pichler, Markus
    • Muschalla, Dirk
  • TitelIdentification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments
  • Datei
  • DOI10.3390/w14121973
  • Erschienen inWater
  • Band14
  • Erscheinungsjahr2022
  • Heft12
  • LicenceCC BY 4.0
  • ISSN2073-4441
  • ZugriffsrechteCC-BY
  • Download Statistik81
  • Peer ReviewJa
  • AbstractClimate change, as well as increasing urbanization, lead to an increase in urban flooding events around the world. Accurate urban flood models are an established tool to predict flooding areas in urban as well as peri-urban catchments, to derive suitable measures to increase resilience against urban flooding. The high computational cost and complex processes of urban flooding with numerous subprocesses are the reason why many studies ignore the discussion of model uncertainties as well as model calibration and validation. In addition, the influence of steep surface (hillside) conditions on calibration parameters such as surface roughness are frequently left out of consideration. This study applies a variance-based approach to analyze the impact of three uncertainty sources on the two variables—flow and water depth—in a steep peri-urban catchment: (i) impact of DEM validation; (ii) calibration of the model parameter; (iii) differences between 1D/2D and 2D models. The results demonstrate the importance of optimizing sensitive model parameters, especially surface roughness, in steep catchments. Additional findings of this work indicate that the sewer system cannot be disregarded in the context of urban flood modeling. Further research with real heavy storm events is to be pursued to confirm the main results of this study.