Classifying the 2021 ‘Ahrtal’ flood event using hermeneutic interpretation, natural language processing, and instrumental data analyses

Abstract

Extreme weather events and severe flash floods during July 2021 caused numerous deaths and massive ecological disasters across Europe. The regionally overstrained environmental and socio-cultural resilience triggered an intensive discussion about cause and effect, responsibilities and public denouncement, and the financial consequences of climate-induced extreme events. In this article we analyze the flood event by four methodological approaches: (1) hermeneutics, with an analog interpretation of printed newspapers and sources; (2) text mining and natural language processing of digital newspaper articles available online; (3) precipitation and discharge models based on instrumental data; and (4) how the findings can be linked to the historical extreme floods of 1804 and 1910, based on documentary source analysis. These four approaches are used to compare and evaluate their consistency by tracking the course, consequences, and aftermaths of the flood disaster. The study shows a high consistency between the analog, digital, and instrumental data analysis. A combination of multidisciplinary methods and their application to historical events enables the evaluation of modern events. It enables to answer the question of return periods and intensities, which are indispensable for today's risk assessments and their social contextualization, a desideratum in historical and modern climatology.

Enhanced trends in spectral greening and climate anomalies across Europe

“…The discussion about the cause and effect of the local and regional anthropogenic overprint on the ecosystem’s functionalities, fuelled by global climate change feedbacks, has been reinforced by the strong gradient of continental European flood and drought spells (Ciais et al, 2005 ; Harris, 2010 ; Herrmann & Hutchinson, 2005 ; Lin et al, 2020 ; Zahradníček et al, 2015 ). Particularly, the severe drought episodes in 2003, 2010, between 2018 and 2020 (and this year, 2022), and followed by dramatic flooding events across Europe in summer 2021 have entered the political and economic debate (Brun et al, 2020 ; Büntgen et al, 2021 ; Cramer et al, 2018 ; Dirmeyer et al, 2021 ; Hanewinkel et al, 2022 ; Hari et al, 2020 ; Kahle et al, 2022 ; Kempf & Glaser, 2020 ). Temperature is constantly increasing across Europe and particularly over terrestrial surfaces.…”

“…Particularly, long-term drought spells, hot drought events, flash droughts, and mega-heatwaves like in 2003, 2010, between 2018 and 2020, and recently in summer 2022 provoke persistent landcover transformations that amplify faunal and floral aggravation and land degradation, and consequently force socio-cultural adaptation (Barriopedro et al, 2011;Fischer et al, 2021;Ionita & Nagavciuc, 2021;Lin et al, 2020;Luterbacher et al, 2004;Miralles et al, 2014;Pereira et al, 2017;Quesada et al, 2012;Rasmijn et al, 2018;Schumacher et al, 2019;Shah et al, 2022;Sousa et al, 2020;Zhou et al, 2019). Parallel to this, the number of severe flood events and the frequency of wet spells increased over the past decades with significant regional variability across Europe (Breinl et al, 2020;Dai et al, 1998;Dietze et al, 2022;Kahle et al, 2022;Zolina et al, 2010). The development of rapidly occurring and long-lasting extremes poses particularly high challenges on the ecosystem's adaptive mechanisms.…”

A systematic review of natural language processing applications for hydrometeorological hazards assessment

“…Despite the proven adverse financial, economic, and humanitarian effects of floods, databases including measures to reduce flood risk are either sparse in detail, have a limited scope, or are owned privately by commercial entities. However, given that the amount of Internet data is constantly increasing, several studies from this review highlighted the emergence of information extraction methods on unstructured text (Kahle et al 2022 ; Lai et al 2022 ). For example, to extract information from newspapers, Lai et al ( 2022 ) used NLP to build a hybrid Named Entity Recognition (NER) model that uses a domain-specific machine learning model, linguistic characteristics, and rule-based matching.…”

“…NLP can support extreme weather events data challenges by analyzing large amounts of weather data for patterns and trends (Kahle et al 2022 ). These data can provide more accurate forecasts and early warning systems for extreme weather events (Kitazawa and Hale 2021 ; Rossi et al 2018 ; Vayansky et al 2019 ; Zhou et al 2022 ).…”