The heightened awareness of heat wave impacts in high-latitude cities, particularly after the record-hot summer of 2018, highlights the need for improved understanding of heat waves and Urban Heat Island (UHI) effects. This study focuses on the interaction and future change of heat waves and UHI across southern Sweden under three specific warming levels: 0.9 °C (historical), 2 °C, and 3 °C. We utilize the HCLIM43-AROME convection-permitting regional climate model at 12 km and 3 km resolutions, and the SURFEX land surface model at 300 m resolution, employing a pseudo-global warming approach over target summers (2017, 2018 and 2022) representing different climates. Our results indicate that the UHI effects are well captured by model simulations. The nocturnal UHI weakens under climate change (assuming no changes in urbanization or greenhouse gas emissions) but intensifies (0.5 °C to 1 °C) during heat waves. During heat waves, higher sea level pressure, radiation and sensible heat flux contribute to enhanced urban warming due to higher thermal inertia. The nocturnal UHI is further accentuated by lower wind speeds and cloud fraction (indicative of weaker advection), lower moisture flux, and decreased soil moisture (associated with reduced evaporation) in urban areas during heat waves. These nuanced findings provide valuable insights for local heat stress adaptation strategies, with future research needed on the impacts of urban expansion.

As extreme weather events such as floods, storms, and heatwaves proliferate, local and regional authorities face challenges in predicting, monitoring, and assessing these events and their impacts. The introduction of impact-based warning services requires detailed, location-specific information on local vulnerability and impacts. This necessitates complementing conventional data with insights from local actors, and to explore novel methods for relevant public data monitoring through social media and news outlets. This paper presents a visual analytics pipeline that was co-developed with practitioners, aiming to detect impacts of extreme weather events, particularly floods, using Volunteered Geographic Information (VGI). The pipeline steps include: collecting VGI from social media, classifying and analysing the data, and visualizing it through an interactive interface. An empirical evaluation study was performed with meteorological and hydrological experts to assess the developed visual interface. The study collected and analysed feedback on the usability of the interface and identified interaction patterns from the experiment’s screen recordings.

Organic farming policies and guidelines actively promote sustainable farming practices emphasising tighter nutrient cycles on farms and regionally. The European Union Green Deal aims to increase organic farmland to 25% and has triggered more discussion about nutrient supply challenges in organic farming, including propositions to allow the use of more recycled phosphorus products. Via a pivotal regulatory shift, struvite was allowed for use in organic farming in 2023 but dynamics involved in adoption of this recycled P product into organic farming are not understood. This study explores the influence of policy, technology, and financial instruments on the availability, accessibility, and adoption of wastewater-based struvite in organic farming.We use a qualitative multi-methods approach and adopt a systems perspective to explore the complex interplay between key sectors and the important variables in the recycled P chain. Our analysis reveals a lack of perceived P supply risk for organic farming in the European Union. Whereas the organic farming regulation is an arbiter for inputs into organic farming, adoption of recycled P products by farmers hinges on rigorous quality assurance and financial accessibility. Moreover, there are opposing views among actors on forms of policy interventions to facilitate availability and adoption of recycled P products in organic farming. However, within existing policy frameworks leverage points are present for strategic pathways to promote recycled P use in organic farming.

Tillgång till vädervarningar med information om förväntade konsekvenser av vädret är nödvändigt för god krisberedskap hos myndigheter, kommuner, näringsliv och privatpersoner. Vidareutveckling av varningssystem som fokuserar på förväntade störningar (konsekvensbaserade varningssystem) är därför en viktig komponent i samhällets hantering av klimatförändringar. Forskningsprojektet AI för klimatanpassning (AI4CA) har analyserat möjligheter och hinder med att inkludera AI-baserad text- och bildanalys som stöd till SMHI:s konsekvensbaserade vädervarningssystem och på sikt även stödja långsiktig klimatanpassning. 

In October 2021, the Swedish Meteorological and Hydrological Institute (SMHI) launched a novel national system for impact-based weather warnings, moving from the traditional format for meteorological, hydrological, and oceanographic warnings towards an assessment process that includes collaboration and consultation with regional stakeholders. For certain types of warnings, joint assessments of the potential impacts of weather events for a specific geographic area and time frame are made in collaboration with local and regional actors. As part of this new system, local and regional administrative efforts are made to create assessment-support documentation which are collated by practitioners at the municipal or organizational level, drawing on local knowledge, and subsequently compiled by the County Administrative Board. This process aims to support the collaborative decision-making processes ahead of the publication and in the evaluation of issued weather warnings. This paper explores the potential of integrating long- and short-term perspectives in societal response to climate change impacts with focus on extreme weather events. We present a case of AI-based technology to support processes linked to the national system for impact-based weather warnings and its integration with local and regional climate adaptation processes. We explore opportunities to integrate an AI-based pipeline, employing AI-based image and text analysis of crowdsourced data, in the processes of the warning system, and analyse barriers and enablers identified by local, regional, and national stakeholders. We further discuss to what extent data and knowledge of historical extreme weather events can be integrated with local and regional climate adaptation efforts, and whether these efforts could bridge the divide between long-term adaptation strategies and short-term response measures related to extreme weather events. Thus, this study unfolds the existing and perceived barriers to this integration and discusses possible synergies and ways forward in risk management and climate adaptation practice.

The ELABORATOR project aims to support cities across Europe in their transition to climate neutrality by promoting the implementation of mobility interventions towards inclusive, sustainable, safe and affordable mobility. The project aims to provide tools and methods to support a truly collaborative and participatory approach in achieving inclusive transport infrastructure development in 12 cities in Europe. The deliverable of T2.3, the ELABORATOR Co-creation playbook provides practical guidelines to engage groups of stakeholders and citizens in the development of qualitative data collection methods, comprising community-based and citizensscience research to ensure that the final methods and tools have legitimacy for all the parties involved in new and innovative urban interventions’ design and deployment. The playbook provides a solid foundation for the cities to work with co-creation methodologies to support the involvement of stakeholders and citizens, especially focusing on the inclusion of VRUs in co-creation processes. Hopefully these guidelines will also prove fruitful for other cities working with collaborative methods. 

The study set out to investigate how the experience of creating a map-based participatory system might help identify what is needed to support the production of relevant volunteered geographic information (VGI) about urban areas exposed to impacts of adverse weather events in Trondheim, Norway. This article details the systematic approach used to collect VGI, starting from the active engagement of end users during the design and development process of the CitizenSensing participatory system, through using the system in two VGI campaigns, up to the examination of the collected data. Although the VGI examination identified exposed areas in Trondheim, for instance, those that are likely to accumulate surface water from heavy rains or meltwater, the experience gained from the use of the CitizenSensing system helped to identify some critical points regarding the production of relevant VGI. Potential practical implications justify the need for VGI. For instance, in the case of Trondheim, relevant VGI may result in better planned municipal interventions regarding city infrastructure for pedestrians, cyclists and drivers, increased public awareness and access to local knowledge about areas exposed to inundation. The study also confirmed the need for adequate system components for VGI vetting and exploration in the post-collection stage to obtain a comprehensive insight into collected VGI.

Extreme weather events, such as flooding, are expected to increase in frequency and intensity. Therefore, the prediction of extreme weather events, assessment of their local impacts in urban environments, and implementation of adaptation measures are becoming high-priority challenges for local, regional, and national agencies and authorities. To manage these challenges, access to accurate weather warnings and information about the occurrence, extent, and impacts of extreme weather events are crucial. As a result, in addition to official sources of information for prediction and monitoring, citizen volunteered geographic information (VGI) has emerged as a complementary source of valuable information. In this work, we propose the formulation of an approach to complement the impact-based weather warning system that has been introduced in Sweden in 2021 by making use of such alternative sources of data. We present and discuss design considerations and opportunities towards the creation of a visual analytics (VA) pipeline for the identification and exploration of extreme weather events and their impacts from VGI texts and images retrieved from social media. The envisioned VA pipeline incorporates three main steps: (1) data collection, (2) image/text classification and analysis, and (3) visualization and exploration through an interactive visual interface. We envision that our work has the potential to support three processes that involve multiple stakeholders of the weather warning system: (1) the validation of previously issued warnings, (2) local and regional assessment-support documentation, and (3) the monitoring of ongoing events. The results of this work could thus generate information that is relevant to climate adaptation decision making and provide potential support for the future development of national weather warning systems.

Extreme weather events are expected to increase in frequency and intensity due to global warming. During disaster events, up-to-date relevant information is crucial for early detection and response. Recently, Twitter emerged as a potentially important source of volunteered geographic information of key value for global monitoring systems and increasing situational awareness. While research on the use of machine learning approaches to automatically detect disaster events from social media is increasing, the visualization and exploration of the identified events and their contextual data are often neglected. In this paper, we address this gap by proposing a visual analytics pipeline for the identification and flexible exploration of extreme weather events, in particular floods, from Twitter data. The proposed pipeline consists of three main steps: (1) text classification, (2) location extraction, and (3) interactive visualization. We tested and assessed the performances of four classification algorithms for classifying relevant tweets as flood-related, applied an algorithm to assign location information, and introduced a visual interface for exploring their spatial, temporal, and attribute characteristics. To demonstrate our work, we present an example use case where two independent flooding events were identified and explored. The proposed approach has the potential to support real-time monitoring of events by providing data on local impacts collected from citizens and to facilitate the evaluation of extreme weather events to increase adaptive capacity.