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Unravelling the dynamical characteristics of tropical cyclones : Amphan and Nisarga using ERA5 reanalysis
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ROBIN : Reference observatory of basins for international hydrological climate change detection
No full textHuman-induced warming is modifying the water cycle. Adaptation to posed threats requires an understanding of hydrological responses to climate variability. Whilst these can be computationally modelled, observed streamflow data is essential for constraining models, and understanding and quantifying emerging trends in the water cycle. To date, the identification of such trends at the global scale has been hindered by data limitations - in particular, the prevalence of direct human influences on streamflow which can obscure climate-driven variability. By removing these influences, trends in streamflow data can be more confidently attributed to climate variability. Here we describe the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) - the first iteration of a global network of streamflow data from national reference hydrological networks (RHNs) - comprised of catchments which are near-natural or have limited human influences. This collaboration has established a freely available global RHN dataset of over 3,000 catchments and code libraries, which can be used to underpin new science endeavours and advance change detection studies to support international climate policy and adaptation
Weather warning archives reveal spatio-temporal hot spots of compound natural hazards
No full textIndividual natural hazards may be combined in different ways, leading to cascading or co-occurring effects, turning them into compound hazards. However, assessment of individual as well as compound hazards is often hampered by short or incomplete observational records of actual hazards, and records of various hazards that do not easily combine. In this study we propose an alternative way to detect potential risk of compound natural hazards via archived severe weather warnings. We investigate weather warnings in Sweden from 2011 to 2020 regarding their distributions and frequencies in time (at daily level) and space (at warning district level) from both an individual and compound perspective. We illustrate the methodology and results by focusing on compound flood-related risk, generated by combinations of heavy rainfall, high streamflow and high sea level, and contextualize with two actual compound flood events in Sweden. We find compound fluvial and coastal flood risk primarily along the southwest coast during the winter half year as well as compound fluvio-pluvial flood risk during the summer half year. The results show that severe weather warnings can be used to assess the frequency and compounding nature of natural hazards, as well as to identify actual cases for further investigation, and we encourage similar investigations elsewhere
Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach
No full textExtreme sea levels are a major global concern due to their potential to cause fatalities and significant economic losses in coastal areas. Consequently, accurate projections of these extremes for the coming century are crucial for effective coastal planning. While it is well established that relative sea level rise driven by ongoing climate change is a key factor influencing future extreme sea levels, changes in storm surges resulting from shifts in storm climatology may also play a critical role. In this study, we project future daily maximum storm tides (the combination of storm surge and tides) using a random forest machine learning approach for 59 stations around the Baltic Sea, based on atmospheric variables such as surface pressure, wind speed, and wind direction derived from climate datasets. The results suggest both positive and negative changes, with sub-regional variations, in 50-year storm tide return levels across the Baltic Sea when comparing the period of 2070-2099 to 1850-1879. Localized increases of up to 10 cm are projected along the west coast of Sweden and the northern Baltic Sea, while decreases of up to 6 cm are anticipated along the south coast of Sweden, the Gulf of Riga, and the mouth of the Gulf of Finland. Negligible levels of change are expected in other parts of the Baltic Sea. The variability in atmospheric drivers across the four climate models contributes to a high degree of uncertainty in future climate projections
Large-scale semi-distributed modelling of dissolved silica fluxes and assessment of their role in coastal eutrophication
No full textA study of the relationship between GRACE-TWSA and large-scale atmospheric-oceanic patterns
No full textWhere can rewetting of forested peatland reduce extreme flows? : Model experiment on the hydrology of Sweden
No full textLeisure boat activities and emissions
No full textThe Baltic Sea as a whole does not meet the criteria for good environmental status (GES) under descriptors 8 and 9 of the Marine Framework Directive based on a comprehensive evaluation of hazardous substances (HELCOM, 2018). Improved understanding of how human activities affect the Baltic sea is required to be able to more effectively target measures where they can have the greatest benefit. Shipping has been identified as a significant source of emissions of metals and polycyclic aromatic hydrocarbons (PAHs) in particular to the marine environment. We have developed a new activity model for Swedish leisure boats, using a new mapping of moorings in combination with Automatic Identification System (AIS) data. AIS transmitters are mandatory for larger ships, with gross tonnage over 300, as well as for passenger ships in international traffic. The use of AIS among leisure boats is therefore generally lower than for commercial traffic, but many boat owners still use AIS voluntarily, for example for safety reasons. Therefore, AIS data does not provide full temporal and spatial coverage of leisure boats, but the data can be used, among other things, to produce a generic time variation for leisure boat activity. The activity model combines AIS data for the year 2023, and a new dataset on Swedish moorings of leisure boats along the entire coast of Sweden, mapped using satellite imagery. The activity model is also based on information from the boating survey where boat owners estimated how often they use their boat, the distance sailed and what type of boat they use (Swedish Transport Agency, 2020). The model then assumes a Gaussian distribution of activity, concentrated at moorings and with a greater probability of boat activity closer to land. By modelling the activity for 232 963 leisure boats, we estimate a total fuel use of 27 330 tonnes. Antifouling paints release 18.9 tonnes of copper and 15.9 tonnes of zinc. As a complement to the activity model, Shipair has also been used (Segersson, 2013) to model the emissions of larger leisure boats. Shipair models emissions based on time series of boat positions available through AIS. AIS data also tracks longer trips, between different ports, which are not covered by the activity model. Although only about 6 000 leisure boats are equipped with AIS transponders, 11.1 % of the entire fleet's travelled distance is covered by these boats. However, the modelled fuel consumption is only 5.2% of the total fuel consumption estimated by the activity model, since a large proportion of the boats are assumed to be sailing boats.The findings presented in this study can serve as input data for dispersion modelling to estimate atmospheric and marine pollutant concentrations. Moreover, the model can be adapted to assess additional environmental pressures on the marine ecosystem, including underwater noise, physical disturbances, and unburned fuel emissions from twostroke engines. Additionally, it can be used to analyse the effects of different policy scenarios, offering valuable insights into how regulations influence emissions in both the atmosphere and marine environment.Större delen av Europas havsområden når inte upp till en tillfredsställande miljöstatus enligt deskriptor 8 (farliga ämnen) och deskriptor 9 (farliga ämnen i livsmedel) i havsmiljödirektivet. Förbättrad förståelse kring hur olika mänskliga aktiviteter påverkar olika havsområden krävs för att mer effektivt kunna rikta åtgärder där de kan göra störst nytta. Sjöfart har identifierats som en betydande källa till utsläpp av särskilt metaller och polycykliska aromatiska kolväten (PAHer) till havsmiljön. Vi har tagit fram en ny aktivitetsmodell för svenska fritidsbåtar med hjälp av en ny kartering av bryggor baserat på satellitbilder i kombination med Automatic Identification System-data (AIS). AIS-sändare är obligatoriska för större fartyg, med bruttodräktighet över 300, samt för passagerarbåtar i internationell trafik. Användandet av AIS bland fritidsbåtar är därför generellt lägre än för den kommersiella trafiken, men många båtägare använder ändå AIS frivilligt, till exempel av säkerhetsskäl. Därför ger AIS-data ingen full tidsmässig och rumslig täckning av fritidsbåtar, men uppgifterna kan bland annat användas för framtagandet av en generisk tidsvariation för fritidsbåtsaktivitet.Aktivitetsmodellen kombinerar AIS-data för år 2023, och ett nytt dataset över svenska bryggor framtagen i tidigare projekt av Chalmers där förtöjningsplatser för fritidsbåtar längs hela Sveriges kust kartlagts med hjälp av ortofoton från Lantmäteriet tagna under 2020–2022. Aktivitetsmodellen bygger även på information från Båtlivsundersökningen där båtägare skattat hur ofta de använder sin fritidsbåt, seglad sträcka och vilken typ av båt de använder (Transportstyrelsen 2020). Modellen antar sedan en Gaussisk spridning av aktiviteten, koncentrerat vid förtöjningsplatser och med större sannolikhet för båtaktivitet närmare land. Genom att modellera aktiviteten för 232 963 fritidsbåtar, uppskattar vi en total bränsleanvändning av 27 330 ton. Från bottenfärger släpps 18,9 ton koppar och 15,9 ton zink. Som ett komplement till aktivitetsmodellen har även Shipair används. Shipair modellerar emissioner baserat på tidsserier av båtpositionerna som är tillgängliga genom AIS. AIS-data visar även längre turer, mellan olika hamnar, som inte omfattas av aktivitetsmodellen. Trots att bara cirka 6 000 fritidsbåtar är utrustade med AIS-sändare, täcks 11,1 % av hela flottans färdsträcka av dessa båtar. Den modellerade bränsleförbrukningen utgör dock bara 5,2 % av den totala bränsleförbrukningen som estimeras av aktivitetsmodellen, eftersom det är en stor andel av båtarna som antas vara segelbåtar. Resultaten från modellerna kan användas som indata för spridningsmodellering för att estimera atmosfäriska och marina koncentrationer av olika föroreningar. Dessutom har modellerna potential att inkludera och utvärdera andra miljöbelastningar på det marina ekosystemet, såsom undervattensbuller, fysiska störningar och oförbrända bensinutsläpp från tvåtaktsmotorer. Dessutom kan modellerna användas för att utvärdera effekterna av olika åtgärder, vilket ger insikter om lagstiftningens inverkan på utsläpp till atmosfären och den marina miljön
