Rain data crowdsourcing for improving urban flood risk management : Exploring the potential in Sweden

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Karagiorgos, KonstantinosKarlstad University,Centre of Natural Hazards and Disaster Science (CNDS) (author)

Rain data crowdsourcing for improving urban flood risk management : Exploring the potential in Sweden

Article/chapterEnglish2022

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Copernicus GmbH,2022

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LIBRIS-ID:oai:lup.lub.lu.se:0cd2676b-f3ad-4a9c-8c41-a27a8f69e822
https://lup.lub.lu.se/record/0cd2676b-f3ad-4a9c-8c41-a27a8f69e822URI
https://doi.org/10.5194/egusphere-egu22-9274DOI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:kon swepub-publicationtype
Subject category:ref swepub-contenttype

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Urban flooding causes considerable societal damage and necessitates increased climate adaptation measures. Extreme rain events' rapid and local character make them difficult to observe, assess, predict, and warn about. One example is the flood event in Malm&#246; in 2014, not only in terms of rain intensities and volumes but also the fact that the more intense parts of the rainfall fell across central Malm&#246; with adverse consequences (approx. SEK 100 million for 1400 claims). &#160;These extreme hydrological events are generally predicted to become more frequent and damaging in Sweden due to the warming climate.The overarching aim of this study is to develop an approach to improve urban rain safety by establishing a participatory system for collecting data to support urban flood risk modelling for the adaptation of cities to intense rainfall. Nowadays, meteorological information to improve flood risk modelling can be obtained from non-traditional sources such as privately owned weather stations and social media. Crowdsourcing techniques linked to public engagement via citizen science are frequently used across different scientific areas to supplement traditional data collection.Our analysis compiles data from available platforms (WeatherObervationWebsite (WOW) by SMHI, Netatmo platform and WunderMap website) for different case studies. The data will be organised in databases; and validated with SMHI-certified automatic rain gauges, municipal gauges, radar data and integrated gridded products.The approach presented opens up insights into the measurement accuracy and issues in operational crowdsourcing of private rain measurements. The new type of rain data will be used for testing their applicability with flood models. The particular focus in the testing will be on the effect of model output from added spatial resolution in rain measurements.

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Mobini, ShiftehLund University,Lunds universitet,Avdelningen för Teknisk vattenresurslära,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,Division of Water Resources Engineering,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)sh1311mo (author)
Grahn, TonjeKarlstad University (author)
Gustafsson, KristinKarlstad University (author)
Larsson, RolfLund University,Lunds universitet,Avdelningen för Teknisk vattenresurslära,Institutionen för bygg- och miljöteknologi,Institutioner vid LTH,Lunds Tekniska Högskola,Division of Water Resources Engineering,Department of Building and Environmental Technology,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)tvrl-rla (author)
Olsson, JonasSwedish Meteorological and Hydrological Institute (author)
Van de Beek, RemcoSwedish Meteorological and Hydrological Institute (author)
Nyberg, LarsKarlstad University,Centre of Natural Hazards and Disaster Science (CNDS) (author)
Karlstad UniversityCentre of Natural Hazards and Disaster Science (CNDS) (creator_code:org_t)

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