scholarly journals Microplastic and microfiber fluxes in the Seine River: Flood events versus dry periods

2022 ◽  
Vol 805 ◽  
pp. 150123
Author(s):  
Robin Treilles ◽  
Johnny Gasperi ◽  
Romain Tramoy ◽  
Rachid Dris ◽  
Anaïs Gallard ◽  
...  
Keyword(s):  
2018 ◽  
Vol 198 ◽  
pp. 76-90 ◽  
Author(s):  
Julian P. Sachs ◽  
Rüdiger Stein ◽  
Ashley E. Maloney ◽  
Matthew Wolhowe ◽  
Kirsten Fahl ◽  
...  

2019 ◽  
Vol 6 (4) ◽  
Author(s):  
Larisa Tarasova ◽  
Ralf Merz ◽  
Andrea Kiss ◽  
Stefano Basso ◽  
Günter Blöschl ◽  
...  
Keyword(s):  

2020 ◽  
Author(s):  
Matteo Pesce ◽  
Larisa Tarasova ◽  
Ralf Merz ◽  
Jost von Hardenberg ◽  
Alberto Viglione

<p>In the European Alps, climate change has determined changes in extreme precipitation and river flood events, which impact the population living downstream with increasing frequency. The objectives of our work are:</p><ol><li>to determine what types of precipitation extremes and river flood events occur in the Alpine Region, based on their generating mechanisms (e.g., frontal convergence storms, convective storms, snow-melt floods, rain-on-snow floods, short and long rain floods, flash floods, ...)</li> <li>to determine the spatial and seasonal distribution of these event types (e.g., their dependence on elevation, geographical location, catchment size, ...) and how precipitation extremes relate to the floods they produce (e.g., the role of snow precipitation and accumulation)</li> <li>to determine whether the event type distribution is changing and will change in the future (e.g., due to climate change).  </li> </ol><p>To these aims, we will compile and analyze historical time series of precipitation and discharge in order to identify events in terms of intensity, duration, and spatial extent. We will use the ETCCDI indices as a measure of the precipitation distribution and hydrograph separation techniques for flow events, following the methodology of Tarasova et al. (2018). We will then characterize each event in terms of generation mechanisms. Furthermore, we will analyze the frequency and magnitude of the different event types in different locations and time of the year and determine whether clusters exist by applying automatic techniques (e.g. K-means clustering algorithm). Finally, we will correlate statistics of precipitation and flood event types with climate indices related to large scale atmospheric circulation, such as Atmospheric Blocking, NAO, etc. (Ciccarelli et al. 2008). Results will be then used for the projection of future storm and flood scenarios.</p><p>We will first apply the methodology in Piedmont by comparing the station-based time series with the NWIOI dataset (ARPA Piemonte) and reanalysis datasets by ECMWF (ERA5, ERA5-Land). We will use a rainfall-runoff model at the daily and sub-daily timescale, through calibration at the regional scale, useful for the simulation of soil saturation and snowpack. We expect to find a statistical correlation between the different datasets, but with changing statistical features over space and time within the single datasets. We aim to provide a detailed picture of the different types of events according to the spatial location and season. The results will be useful, from a scientific perspective, to better understand storm and flood regimes and their change in the Alpine Region, and, from a practical perspective, to better mitigate the risk associated with the occurrence of extreme events.      </p><p>Ciccarelli, N., Von Hardenberg, J., Provenzale, A., Ronchi, C., Vargiu, A., & Pelosini, R. (2008). Climate variability in north-western Italy during the second half of the 20th century. Global and Planetary Change, 63(2-3), 185-195. https://doi.org/10.1016/j.gloplacha.2008.03.006</p><p>Tarasova, L., Basso, S., Zink, M., & Merz, R. (2018). Exploring controls on rainfall-runoff events: 1. Time series-based event separation and temporal dynamics of event runoff response in Germany. Water Resources Research, 54, 7711–7732. https://doi.org/10.1029/2018WR022587</p>


2015 ◽  
Vol 163 ◽  
pp. 162-176 ◽  
Author(s):  
C. Casse ◽  
M. Gosset ◽  
C. Peugeot ◽  
V. Pedinotti ◽  
A. Boone ◽  
...  

2021 ◽  
Author(s):  
Elco Koks ◽  
Kees Van Ginkel ◽  
Margreet Van Marle ◽  
Anne Lemnitzer

Abstract. Germany, Belgium and The Netherlands were hit by extreme precipitation and flooding in July 2021. This Brief Communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed during the first six months after the event. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that large-scale risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts. This may be the result of limited availability of validation material. As such, this study will not only help to better understand how critical infrastructure can be affected by flooding, but can also be used as validation material for future flood risk assessments.


2019 ◽  
Vol 19 (7) ◽  
pp. 1485-1498 ◽  
Author(s):  
Rosa Vicari ◽  
Ioulia Tchiguirinskaia ◽  
Bruno Tisserand ◽  
Daniel Schertzer

Abstract. Today, when extreme weather affects an urban area, huge numbers of digital data are spontaneously produced by the population on the Internet. These “digital trails” can provide insight into the interactions existing between climate-related risks and the social perception of these risks. According to this research “big data” exploration techniques can be exploited to monitor these interactions and their effect on urban resilience. The experiments presented in this paper show that digital research can amplify key issues covered by digital media and identify the stakeholders that can influence the debate, and therefore the community's attitudes towards an issue. Three corpora of Web communication data have been extracted: press articles covering the June 2016 Seine River flood, press articles covering the October 2015 Alpes-Maritimes flood, and tweets on the 2016 Seine River flood. The analysis of these datasets involved an iteration between manual and automated extraction of hundreds of key terms, aggregated analysis of publication incidence and key term incidence, graph representations based on measures of semantic proximity (conditional distance) between key terms, automated visualisation of clusters through Louvain modularity, visual observation of the graph, and quantitative analysis of its nodes and edges. Through this analysis we detected topics and actors that characterise each press dataset, as well as frequent co-occurrences and clusters of topics and actors. Profiling of social media users gave us insights into who could influence opinions on Twitter. Through a comparison of the three datasets, it was also possible to observe how some patterns change over time, in different urban areas and in different digital media contexts.


2018 ◽  
Author(s):  
Rosa Vicari ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer

Abstract. Nowadays, when extreme weather affects an urban area, huge amounts of digital data are spontaneously produced by the population on the Internet. These digital trails can provide an insight on the interactions existing between climate related risks and the social perception of these risks. According to this research big data exploration techniques can be exploited to monitor these interactions and their effect on urban resilience. The experiments presented in this paper show that digital research can bring out the most central issues in the digital media, identify the stakeholders that have the capacity to influence the debate and, therefore, the community attitudes towards an issue. Three corpora of Web communication data have been extracted: press news covering the June 2016 Seine River flood; press news covering the October 2015 Alpes-Maritimes flood; tweets on the 2016 Seine River flood. The analysis of these datasets involves an iteration between manual and automated extraction of hundreds of key terms, network representations based on key terms co-occurrences, automated cluster visualisation based on adjacency matrix, and profiling of social media users. Visual observation of the network coupled to quantitative analysis of its nodes and edges allow obtaining an in-depth understanding of the most prominent topics and actors, as well as of the connections and clusters that these topics and actors tend to form in the journalistic sphere. Through a comparison of the three datasets, it is also possible to observe how these patterns change over time, in different urban areas and in different digital media contexts.


2021 ◽  
Vol 13 (9) ◽  
pp. 4972
Author(s):  
Nabil Touili

The aim of this paper is to provide a framework to improve urban resilience independently of the nature of the disturbances. Recent disasters had a significant impact on critical infrastructures providing essential urban services such as energy, transportation, telecommunication, water and food supply or health care. Indeed, several natural and human-made hazards may lead to disruptions, and most critical infrastructures are networked and highly interdependent. Henceforth, resilience building remain focused on specific hazards or on improving the resilience, separately, of single infrastructures. In order to enhance urban resilience, this paper is based on learnings from three case studies that are the 2001 WTC terrorist attack, hurricanes Irma and Maria in 2017 and the 2016 Seine river flood in Paris. These events highlight disruptions to urban services, but also some resilience options. In light of both the literature and our case studies, a framework of unspecific resilience is provided for improving some resilience principles, namely omnivory, redundancy, buffering, high flux, homeostasis and flatness within electric energy, water and food supply and transportation networks. Rebuilding resilience within this framework is further discussed with respect to all kinds of disruptive events.


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