---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '22469'
abstract:
- lang: eng
  text: Accurate flood simulation remains a significant challenge in many flood-prone
    regions, particularly in developing countries and urban areas, where the availability
    of high-resolution topographic data is especially limited. While publicly available
    digital elevation model (DEM) datasets are increasingly accessible, their spatial
    resolution is often insufficient for reflecting fine-scaled elevation details,
    which hinders the ability to simulate pluvial floods in built environments. To
    address this issue, we implemented a deep-learning-based method, which efficiently
    enhances the spatial resolution of DEM data, and quantified the effect of the
    improved DEM on flood simulation. The method employs a tailored multi-source input
    module, enabling it to effectively integrate and learn from diverse data sources.
    By utilising publicly accessible global datasets, such as low-resolution DEM datasets
    (i.e. 30 m Shuttle Radar Topography Mission, SRTM) in conjunction with high-resolution
    multispectral imagery (e.g. Sentinel-2A), our approach allows us to produce a
    super-resolution DEM, which exhibits superior performance compared to conventional
    methods in reconstructing 10 m DEM data based on 30 m DEM data and 10 m multispectral
    satellite images. We evaluated the performance of the super-resolution DEM in
    flood simulations. Compared to conventional methods (e.g. bicubic interpolation),
    the simulation results demonstrated that our approach significantly improved the
    accuracy of flood simulations, with a reduction in the mean absolute error of
    floodwater depth of about 13.1 % and an increase in the intersection over union
    (IoU) for inundation area predictions of about 46 %. Accordingly, this study underscores
    the practical value of machine learning techniques that leverage publicly available
    global datasets to generate DEMs that allow for the enhancement of flood simulations.
article_processing_charge: No
article_type: original
author:
- first_name: Yue
  full_name: Zhu, Yue
  last_name: Zhu
- first_name: Paolo
  full_name: Burlando, Paolo
  last_name: Burlando
- first_name: Puay Yok
  full_name: Tan, Puay Yok
  last_name: Tan
- first_name: Christian
  full_name: Geiß, Christian
  last_name: Geiß
- first_name: Simone
  full_name: Fatichi, Simone
  id: cf8e546b-a9b0-11f0-a43b-aa89ed1b56d6
  last_name: Fatichi
citation:
  ama: Zhu Y, Burlando P, Tan PY, Geiß C, Fatichi S. Improving pluvial flood simulations
    with a multi-source digital elevation model super-resolution method. <i>Natural
    Hazards and Earth System Sciences</i>. 2025;25(7):2271-2286. doi:<a href="https://doi.org/10.5194/nhess-25-2271-2025">10.5194/nhess-25-2271-2025</a>
  apa: Zhu, Y., Burlando, P., Tan, P. Y., Geiß, C., &#38; Fatichi, S. (2025). Improving
    pluvial flood simulations with a multi-source digital elevation model super-resolution
    method. <i>Natural Hazards and Earth System Sciences</i>. Copernicus Publications.
    <a href="https://doi.org/10.5194/nhess-25-2271-2025">https://doi.org/10.5194/nhess-25-2271-2025</a>
  chicago: Zhu, Yue, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi.
    “Improving Pluvial Flood Simulations with a Multi-Source Digital Elevation Model
    Super-Resolution Method.” <i>Natural Hazards and Earth System Sciences</i>. Copernicus
    Publications, 2025. <a href="https://doi.org/10.5194/nhess-25-2271-2025">https://doi.org/10.5194/nhess-25-2271-2025</a>.
  ieee: Y. Zhu, P. Burlando, P. Y. Tan, C. Geiß, and S. Fatichi, “Improving pluvial
    flood simulations with a multi-source digital elevation model super-resolution
    method,” <i>Natural Hazards and Earth System Sciences</i>, vol. 25, no. 7. Copernicus
    Publications, pp. 2271–2286, 2025.
  ista: Zhu Y, Burlando P, Tan PY, Geiß C, Fatichi S. 2025. Improving pluvial flood
    simulations with a multi-source digital elevation model super-resolution method.
    Natural Hazards and Earth System Sciences. 25(7), 2271–2286.
  mla: Zhu, Yue, et al. “Improving Pluvial Flood Simulations with a Multi-Source Digital
    Elevation Model Super-Resolution Method.” <i>Natural Hazards and Earth System
    Sciences</i>, vol. 25, no. 7, Copernicus Publications, 2025, pp. 2271–86, doi:<a
    href="https://doi.org/10.5194/nhess-25-2271-2025">10.5194/nhess-25-2271-2025</a>.
  short: Y. Zhu, P. Burlando, P.Y. Tan, C. Geiß, S. Fatichi, Natural Hazards and Earth
    System Sciences 25 (2025) 2271–2286.
das_tickbox: '1'
date_created: 2026-07-27T12:30:23Z
date_published: 2025-07-08T00:00:00Z
date_updated: 2026-07-30T11:49:35Z
day: '08'
ddc:
- '550'
doi: 10.5194/nhess-25-2271-2025
extern: '1'
has_accepted_license: '1'
intvolume: '        25'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.5194/nhess-25-2271-2025
month: '07'
oa: 1
oa_version: Published Version
page: 2271-2286
publication: Natural Hazards and Earth System Sciences
publication_identifier:
  eissn:
  - 1684-9981
  issn:
  - 1561-8633
publication_status: published
publisher: Copernicus Publications
quality_controlled: '1'
scopus_import: '1'
status: public
title: Improving pluvial flood simulations with a multi-source digital elevation model
  super-resolution method
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 25
year: '2025'
...