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Petter Pilesjö

Petter Pilesjö

Professor

Petter Pilesjö

Dynamic spatio‐temporal flow modeling with raster DEMs

Författare

  • Hampus Nilsson
  • Petter Pilesjö
  • Abdulghani Hasan
  • Andreas Persson

Summary, in Swedish

A user-friendly high-resolution intermediate complexity dynamic and spatially distributed flow model is crucial in urban flood modeling. Planners and consultants need to improve the accuracy of floods and estimation of risks. A new flow model will serve as a rapid tool to improve identification of these. This article provides a detailed explanation of a model based on a multiple flow algorithm. Model testing was performed on selected urban and rural areas. Additionally, a sensitivity analysis is conducted to analyze functionality. The model includes basic hydrological processes and is therefore less complex than fully physical models. The data needed to set up and run the new model include spatially and temporally distributed basic geometric and hydrologic variables (i.e., digital elevation model, precipitation, infiltration, and surface roughness). The model is implemented using open-source coding and can easily be applied to any selected area. Outputs are water volumes, depths, and velocities at different modeling times. Using GIS, results can be visualized and utilized for further analyses. The test, applied in urban as well as rural areas, demonstrates its user-friendliness, and that the estimated distributed water depths and water velocity at any time step can be saved and visualized.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • Centrum för geografiska informationssystem (GIS-centrum)
  • Mellanösternstudier

Publiceringsår

2022

Språk

Engelska

Sidor

1572-1588

Publikation/Tidskrift/Serie

Transactions in GIS

Volym

26

Issue

3

Dokumenttyp

Artikel i tidskrift

Förlag

Wiley-Blackwell

Ämne

  • Physical Geography
  • Earth and Related Environmental Sciences
  • Oceanography, Hydrology, Water Resources

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1467-9671