Extreme weather events, such as heat waves, are occurring more frequently and intensively, imposing new climate-adaptation demands on municipal planning. We conducted a design study across the domains of urban planning and urban climate research, and identified challenges regarding a lack of heat-related information in current planning processes, and the high complexity of effective climate data representation. To address these challenges, and so enhance the information flow between these domains, we developed Urban Climate InteracTable, an immersive interface that supports exploratory analysis of spatio-temporal climate simulation data integrated with an urban environment representation. We describe several use cases in which this interface can be utilized to assist with planning-related decision processes and to communicate heat-related phenomena. We present the feedback obtained from our collaborating domain experts and relevant external experts, and reflect on our experiences throughout the design study. From this, we offer insights for future research.

Extreme wildfires are increasingly prevalent worldwide, driving significant forest area loss and severe environmental and socioeconomic impacts (Cunningham et al. 2024). The Mediterranean, in particular, is projected to face heightened fire risks due to climate change-induced drier conditions and lower fuel moisture (de Rivera et al. 2020). However, the drivers of extreme wildfires remain poorly understood. Current fire models, typically calibrated on global fire datasets, are primarily designed to estimate annual total burned areas and struggle to capture the unique behaviours of extreme wildfires (Forrest et al. 2024). Furthermore, correlation-based approaches, which dominate current modelling efforts, may fail to identify the underlying causal drivers of these events and are poorly suited for extrapolation to changing conditions.

Causal discovery methods, which aim to identify cause-and-effect relationships from observational data, offer a promising pathway to uncover the mechanisms driving extreme wildfires. While increasingly applied in environmental sciences, their use in wildfire prediction remains limited (de Rivera et al. 2020, Zhang et al. 2024, Zhao et al. 2024).This study will use causal discovery to identify key drivers of extreme wildfire in the Mediterranean, and further integrate the causal graphs into a stand-alone model of wildfire spread. This approach aims to move beyond correlation-based models, improve our understanding of extreme wildfire behaviour and inform more robust mitigation strategies.

Study Area and Data

We will use the Mesogeos dataset (Kondylatos et al. 2023), designed for wildfire modelling in the Mediterranean region. Spanning 17 years (2006–2022) at a 1 km² spatial and daily temporal resolution, it includes meteorological variables (e.g., temperature, wind speed), vegetation indices (e.g., NDVI, LAI), and human activity indicators (e.g., population density, road proximity). Wildfire data include MODIS fire ignitions and burned areas from EFFIS.

Methods

Extreme Wildfire Definition and Sampling

In this study, we define extreme wildfires as those that are exceptionally large in size. To identify these events, we will first extract the final burned areas associated with each fire ignition recorded in the Mesogeos dataset. Since the classification of large fires is inherently subjective and varies by region, we will adopt a data-driven approach based on an absolute quantitative threshold. Specifically, we will define extreme wildfires as those exceeding the 99th percentile of fire sizes, though this threshold may be adjusted to align with extreme fire events documented in national fire reports. While this method provides a straightforward and reproducible way to define extreme events, we acknowledge its limitations. Future work will refine this approach by incorporating region-specific thresholds and additional contextual factors to improve geographic relevance.

Phase I: Causal Discovery

Using local variables from Mesogeos, averaged over final burned areas and lagged to time t, we will estimate causal graphs for extreme events via Python’s Tigramite library with the PCMCI method (Runge et al. 2019). PCMCI detects time-lagged causal associations in large nonlinear datasets through iterative conditional independence testing. To ensure robustness, we will assess graph stability across hyperparameters and selected drivers, and validate graphs through expert knowledge.

Phase II: Causal Fire Spread Model

We will develop a fire spread model incorporating causal mechanisms from Phase I. This model will integrate spatiotemporal fire dynamics, causal dependencies constraining fire spread, and dynamic weather and fuel inputs. By explicitly modeling causal interactions, it aims to improve early warning systems and risk assessments under future climate scenarios. The causal model’s performance will be benchmarked against statistical models to evaluate its predictive accuracy and robustness.

Expected Results

We expect that the data-driven approach proposed in this study will enhance the predictability of extreme wildfires by reducing confounding effects and capturing key drivers of extreme fire events. Compared to purely statistical approaches, incorporating causal structures should lead to more reliable predictions, particularly in out-of-sample applications or under changing environmental conditions. Furthermore, the causal fire spread model will provide insights into how climate, vegetation, and anthropogenic factors interact to drive fire spread, supporting fire prevention and mitigation strategies.

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.

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.

One of the commonly used visualization techniques for multivariate data is the parallel coordinates plot. It provides users with a visual overview of multivariate data and the possibility to interactively explore it. While pattern recognition is a strength of the human visual system, it is also a strength of the auditory system. Inspired by the integration of the visual and auditory perception in everyday life, we introduce an audio-visual analytics design named Parallel Chords combining both visual and auditory displays. Parallel Chords lets users explore multivariate data using both visualization and sonification through the interaction with the axes of a parallel coordinates plot. To illustrate the potential of the design, we present (1) prototypical data patterns where the sonification helps with the identification of correlations, clusters, and outliers, (2) a usage scenario showing the sonification of data from non-adjacent axes, and (3) a controlled experiment on the sensitivity thresholds of participants when distinguishing the strength of correlations. During this controlled experiment, 35 participants used three different display types, the visualization, the sonification, and the combination of these, to identify the strongest out of three correlations. The results show that all three display types enabled the participants to identify the strongest correlation — with visualization resulting in the best sensitivity. The sonification resulted in sensitivities that were independent from the type of displayed correlation, and the combination resulted in increased enjoyability during usage.

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.