The effect of global warming is triggering natural disasters to occur more often and of greater severity, prompting the need for enhanced disaster management measures. Particularly dangerous to people's lives, property, and economy are floods, as recent devastating events in Germany and other countries have shown. Static flood forecast maps are a primary tool used in conventional flood management methods. However helpful, these maps frequently lack real-time data integration and are dependent on assumptions. This study addresses these shortcomings by introducing a cloud-based platform that uses dynamic Geographic Information Systems (GIS) and enhanced flooding modeling to improve catastrophe preparedness and response.

Near-real-time flood simulations are produced by the suggested method, which combines high-performance computers, real-time sensor data, and weather forecasts. This makes it possible for decision-makers to evaluate risks, visualize changing flood situations, and plan efficient actions. The platform offers an integrated and interactive environment for stakeholders to assist all aspects of disaster management, including mitigation, readiness, response, and recovery.

A communication platform that promotes data sharing and cooperation between first responders and other stakeholders, is one of the system's essential elements. To provide a thorough and current operating picture, it integrates information from social media, sensors, and geographic data. With the help of the flood simulation component, which is based on cutting-edge hydrological models and real-time data, users may plan evacuation routes, assess potential effects, and forecast the course of flooding. The platform offers a new degree of operational efficiency during emergencies thanks to its near-real-time functioning, which is a major improvement over conventional technique.

The implementation of this technology at various stages of flooding occurrences is also examined in this article. Through 2D and 3D simulations, the technology helps with data collecting and analysis prior to flooding, providing reliable flood forecasts. The platform's high-performance processing capabilities enable quick simulation updates during flooding, giving decision-makers vital information. The technology helps with recovery planning and damage assessment after floods by helping to coordinate reconstruction operations and visualizing the effects of the storm.

All things considered, a holistic approach to catastrophe management is provided by the combination of dynamic GIS, real-time flooding simulation, and sophisticated communication technologies. By improving situational awareness and decision-making, this technology eventually helps mitigate the negative effects of disasters on communities. The significance of mentioned aspects helps in building more resilient communities capable of successfully handling the challenges posed by disasters resulting from climate change is emphasized in the end of this paper.

Incoherence in coordination between institutions that address climate
change adaptation (CCA), disaster risk reduction (DRR) and food security (FS)
policy areas exacerbates FS challenges in most developing countries, including
South Africa. To address such incoherencies, this article seeks to develop a
coordination mechanism that could help in aligning FS functions undertaken by
CCA, DRR and FS institutions. The coordination mechanism was developed
using aspects from the functional resonance analysis method (input, time,
preconditions, resources, control and output) and data gathered through policy
analysis, key informant interviews, and written responses. The coordination
mechanism allows for simultaneous participation of relevant stakeholders
through a systematic characterisation of FS functions. It allows policymakers
and practitioners to identify variabilities, challenges, and opportunities that
should be considered before any FS activity is executed.

The passability of a terrain is understood as the possibility of traversing it cross-country, outside the regular road network, taking into account weather and soil conditions. The analysis of passability is mainly applicable in the planning of military operations, which very often take place in roadless areas. The issue of passability is also very relevant in the emergency management process, especially in less developed areas, where there is a need for emergency vehicles to reach facilities located away from the regular road network via roadless roads.

The presentation will outline the factors to be taken into account in the process of passability modelling and the system being developed at the Military University of Technology for the automated generation of terrain passability maps, which can also be used in the crisis management process. In presented project, the problem of terrain classification to the respective category of passability was solved (among others) by applying artificial neural networks to generate (calculate) the Index of Passability (IOP). The main methodological assumption of the conducted research was to refer the index of passability of the terrain to the primary fields of various shapes and sizes.

The basis for calculating IOP are elements of land cover, weather and soil type that exist in the given primary field. The results show a comprehensive analysis of the reliability of the neural network parameters, considering the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions. The Authors assumed that the values of indices of passability obtained with use of the algorithms may differ, even if the same methods and source data are used, depending on the type of the primary field used, i.e., its shape and size. Considering the above, the Authors analyzed the influence of the shape and size of the primary field on the results of automated terrain classification for the purposes of developing passability maps. The Authors determined indices of passability for square primary fields of various side lengths ranging from 1 m to 10 km. The Authors has demonstrated that terrain classification for passability purposes may also be performed with use both: military and civilian data sources.

What is important developed system makes enable of using terrain passability maps for generating graphs that enable the determination of the optimum route between two points. The proposed methodology enables the determination of different variants of routes: a longer route that passes all terrain obstacles or a route that is shorter but more difficult to pass.

The results obtained allow the conclusion to be drawn that the modelling of terrain passability allows the rescue operation to be planned more efficiently, which in the case of emergency management can be crucial to the success of the overall operation.

The coastline of the Oeiras municipality, Portugal is quite popular all year round among residents and tourists, especially due to the beaches. Previous research conducted by the authors (Santos at al., 2022) shows that a tsunami similar to the 1755 event would inundate the beaches. Moreover, the same study shows the first tsunami wave arrived at Paço de Arcos beach 31 minutes after the earthquake, inundating the beach up to 4.4 m high. For this reason, many people could die if they do not evacuate the tsunami inundation zone immediately after the earthquake and before the arrival of the first tsunami wave. In addition, this beach is very interesting to study because it has 5 beach accesses and high ground nearby. On the other hand, the pandemic situation in 2020-2021 allowed a unique opportunity to conduct a detailed analysis of the present population in the Paço de Arcos beach, with the use and collection of access turnstiles control data (CMO, 2021). Thus, the objective of this study is to conduct a tsunami evacuation plan for Paço de Arcos beach.

The research was developed on a GIS (Geographic Information System) environment, on which the cartography of the inundated area was considered, as well as the beach access locations. In addition, the Safe Area was identified; this is an area that must be located on high ground and outside the tsunami inundation zone. Moreover, the number of beach users was evenly distributed over the beach area, and the low-cost paths were calculated by using the Network Analyst tolls for the roads’ network. Finally, the calculation of the beach evacuation time and the total evacuation time was carried out. The population data at the beach consisted on a 24 h records during the summer months of June to September 2021 (Fernandes, 2023). The data is important for this research because there is no available data of the number of present population at the beach, before and after 2021.

The main results show the human carrying capacity of the Paço de Arcos beach was 1000 users, but the data of access turnstiles control data show the maximum occupation was recorded on August 15, which is a national holiday, at 5 pm, with 611 people (Fernandes, 2023). On the other hand, the Safe Area near the beach has a capacity of 1236 people. Therefore, with or without social distance the safe areas are large enough to accommodate the beach users of Paço de Arcos. The results also show the beach can be evacuated very quickly, in less than 3 minutes. The Safe Area can be reached between 8 and 12 minutes, given a total evacuation time of about 10 to 17 minutes, which is less than the tsunami travel time of 31 minutes. However, if people do not evacuate immediately after the earthquake, the total evacuation time can range between 16 to 43 minutes. Therefore, delays in the evacuation may lead to a chaotic evacuation causing unnecessary fatalities.

Against the background of modern climate warming, the intensity of atmospheric precipitations, melting of glaciers, the arrival of landslides and rock avalanches, as well as floods and mudflow, droughts and related forest fires have intensified in the world. This is the main issue for the world's climate warming management policy.

Georgia is also distinguished by the frequency of such natural events, especially its mountainous part. It should be noted that the melting of glaciers, waterfalls and floods have become more active against the background of climate warming. The dammed lakes is also connected with these processes. Their breakthrough is accompanied by catastrophic floods. There have been examples of them in Georgia in the past and it is actively taking place now, a classic example is August 2023 in Racha, Shovi resort. (S. Gorgijanidze2023).

This was preceded by the melting of glaciers, which is taking place in all those areas where there is an intensity of global climatic warming. Topographic maps were prepared, and with their help, the action of the glacier in the entire Buba River valley was investigated during that period. (T.gorgodze 2023). Military units of the Ministry of Defense helped in the rescue process. Soldiers searched for people based on studying the topographical map and using modern techniques.

It is important to learn to manage them. It is important to mention the relations between the National Environmental Agency of Georgia and the international consulting Swiss company "GEOTEST AG". As a result, an early warning system has been installed on the Devodrak glacier.

It should be noted that currently monitoring and observation are not carried out everywhere. In 2017, in June, on the 56th kilometer of the Pshaveli-Abano-Omalo highway, at the place of Nashliani, about landslides of mass fell, which in fact completely blocked the Alazani River of Gometsri, and created dam lake Khiso. The danger is high, because every time it rains, the lake level rises, covering the highway.

The Tsaneri lake on the Tsaneri glacier is of such a new origin. However, this lake is a geographical object formed in the moraines and depressions there during the retreat of the glacier over time. Currently, the lake is not fully studied, although periodic observations are being made. As Levan Tielidze (2021) notes, the lake can burst and flood at any time, (GLOF).It is important to study all the maps of this region, and the hydrographic situation. It is important to install early warning systems in all critical areas. Channels and drains should be made taking into account the mechanism of natural occurrence.

References (optional)

Tengiz Gordeziani*, Zurab Laoshvili, Gocha Gudzuadze,*, Tedo Gorgodze, Manana Sharashenidze, Gocha Jincharadze, Mariam Gagoshashvili . Heoretical cartography structure, connections, functions. Abstracts of the ICA. Olomoutsi. 2023

Gorgijanidze, S. M., Jincharadze, G. A., Silagadze, M. M., & Tchintcharauli, I. R. (2023). The Geography of Risks of Breakthrough of Glacial Lakes and Valleys. Journals of Georgian Geophysical Society, 26(2). https://doi.org/10.60131/ggs.2.2023.7442

Glaciers of the Greater Caucasus- levan tielidze 2021

Crisis management encompasses activities focused on preventing, preparing for, responding to, and recovering from crisis situations, with particular attention to their spatial dimensions. Access to detailed spatial information is crucial for determining optimal access routes to danger zones, especially in remote areas without established transportation infrastructure. This research addresses the challenge of planning emergency routes in such off-road areas and presents practical solutions to this problem. Specifically, the study demonstrates the potential use of a previously developed methodology for identifying access routes to hard-to-reach locations outside the regular transport network. By integrating the existing road network with terrain passability maps, high-resolution digital terrain models, and vehicle traction parameters, the approach enables a detailed analysis of microrelief, ensuring that inaccessible areas are effectively excluded from potential routes. This comprehensive method enhances the ability to navigate challenging terrains during crisis situations, thereby improving the overall effectiveness of crisis management efforts.

A key factor in crisis situations is the speed of reaching the destination. Challenges arise when the destination lies outside the established road network, requiring rescue vehicles to traverse difficult terrain. In this study, the process of generating access routes is divided into two stages. First, the route is determined using the existing paved road network. The second stage involves determining the route from the nearest paved road to the destination, using passability maps developed with an automated passability map generation system.

The methodology operates as follows: first, a passability map is created based on land cover data. Next, the starting and ending points for the route are identified. If the destination is situated away from the road network, an additional point is selected on a paved road, positioned as close as possible to the destination. With all essential points determined, the algorithm calculates the most efficient route between the starting point and the additional point on the paved road. The final stage involves mapping out a cross-country route using a graph with traversal costs derived from the terrain passability map, while excluding impassable areas based on detailed microrelief analysis.

The methodology was applied in a case study conducted in Warsaw-West County, Poland. It focused on two single-family homes situated far from paved roads, complicating access for rescue operations. It modelled a fire emergency scenario requiring intervention by a fire truck from the local County Fire Station (MAN TGM 15.290 BL vehicle), highlighting the challenges of reaching these remote locations. The results were further validated through terrain verification.

In conclusion, effective crisis management depends on accurate spatial information to ensure swift access to emergency sites, especially in remote areas. This study showcases a methodology for determining access routes to hard-to-reach locations by combining passability maps, terrain models, and vehicle traction parameters. The approach, validated through a case study in Warsaw-West County, involves first mapping routes via paved roads and then optimizing cross-country access. This methodology improves navigation through challenging terrains, enhancing overall emergency response effectiveness.

The increasing frequency and intensity of natural disasters lead to justified expectations of new concepts for solving them and significant improvement in disaster risk reduction [DRR] supported by new methodologies and technologies. The abstract's author (further author) reflects on selected questions against the background of key global initiatives and
clarifies some approaches, especially those socially oriented, associated with improving the whole process of DRR. The Sendai Framework [SF], Agenda 2030 (Sustainable Development Goals-SDGs), or COPERNICUS are key enablers respecting the era of Big Data and its Geospatial one, and other available sources of digital data (GEO, GEOSS, GGIM, DBAR, and INSPIRE). World scientific and professional organizations such as ICA (especially the Commission on Cartography for Early Warning and Disaster Risk Management) are introducing new approaches to deal with Early Warning [EW], disaster risk management [DRM], and DRR, respecting the main tasks of SF global objectives and formulating and fulfilling its global indicators. Several are related to the umbrella approach of the International Society of Digital Earth [ISDE]. In connection with the Digital Earth concept. Annoni et al. (2023) and Annoni (2022) described the aspects of the „Digital transformation of global society“, which can be characterized by the „continuous development of Digital Channels, Digital Analytics, and Digital Business model(s)“. There is a huge demand for disaster risk management using digitalization as a key enabler for effective and efficient disaster risk management systems. Digital and intelligence technologies can help solve key aspects of the disaster management cycle. An urgent requirement is the incorporation of the latest approaches from cutting-edge scientific studies into practice in the form of comprehensible, well-thought-out applications (Ariyachandra & Wedawatta, 2023; Ford &
Wolf, 2020; Kremers, 2022; Lienert et al., 2022; Lienert et al., 2021). The effectiveness of realising contemporary challenges depends on investigating relations between Geographical Space and newly established Cyberspace (Chen et al., 2023). The geographical space humankind is living, using, and still investigating and recognizing everyday challenges, but it has a new, strong, and, in many aspects, different partner - Cyberspace.
Artificial Intelligence [AI], Digital Analytics and Visualization, and Digital Twins play an important role in its development. AI is developing through Capabilities-based AI (narrow, general, and super ones) and Functionality AI (reactive machines, limited memory, theory of mind, and self-awareness). Cyberspace is generally considered a global domain within the information environment consisting of the interdependent network of information technology
infrastructures, including the internet, telecommunication networks, computer systems, and embedded processors and controllers. (Kirwan & Zhiyong, 2020)
This transformation brings opportunities and challenges to obtain valuable insights into how these two spaces (geographical space and cyberspace) can be mapped and interact with each other. The review of current challenges and future directions offered by close interactions between the two spaces, as illustrated in Fig. 1.
[...].

IntroductionThe integration of machine learning (ML) in Geographic Information Systems (GIS) has opened new avenues for spatial analysis, enabling more efficient and accurate geospatial data processing. The performance of ML models is often considered highly dependent on the granularity of input data, but the effects of different granularity levels have not been fully clear. In emergency applications, granularity may significantly affect the resulting actions. For example, a better understanding of the watercourse network is crucial in many applications and even lifesaving, e.g. flood modelling and preparedness (Petty, 2016). The National Land Survey of Finland (NLS) is currently renewing its watercourse mapping from high-resolution point clouds. In this study, we present a comparative analysis of six distinct datasets, varying in point cloud density and spatial resolution, to evaluate their effectiveness as input data for ML.

Methods

This study was conducted across two 36 km² study areas in central Finland, one near Ristijärvi and the other near Heinävesi. We trained six ML models with different input datasets, varying in point cloud density and spatial resolution. The datasets consisted of elevation models generated from lidar point clouds with densities of 20 points per square meter (20p) and 5 points per square meter (5p), at three spatial resolutions: 0.25 meters, 0.50 meters, and 1.00 meter. The NLS produced the datasets.

The primary objective was to assess the performance of these datasets as input for ML models in the research of watercourse networks. We implemented various ML training runs to achieve this, systematically altering several key parameters. Specifically, we varied the tile side length, cropping strategies, and the number of training epochs to observe how these factors influenced model performance.

We recorded multiple metrics to evaluate the models during the ML runs, including F1-score, precision, recall, and runtime. These metrics provided a comprehensive view of the effectiveness and efficiency of each dataset when used in ML tasks. We also calculated [KC1] relaxed recall, precision, and F1-score, with a spatial toleration of 1 meter, to better assess the F1-scores in relation to feature detection. The results from each run were carefully documented and analyzed.

After completing the ML runs, the outcomes were further analyzed and discussed within our group of four researchers to interpret the results and identify the datasets that yielded the best performance with visual analysis. Hillshading, relative topographic position, NLS orthophotos and the NLS topographic map were used in the visual analysis. This discussion also focused on understanding trade-offs between data granularity, computational efficiency, and the accuracy of the models.

Tropical cyclones (TCs) displace millions annually, ranking second only to floods in terms of people displaced by natural hazards. While TCs impose significant hardships and pose threats to lives, their negative impacts can be mitigated through anticipatory actions, such as evacuation, emergency protection, and coordinated humanitarian aid. Impact-based forecasting offers a valuable tool for planning these actions by providing detailed information on the numbers and locations of people at risk of displacement.

We present a fully open-source, globally consistent, and regionally calibrated TC-related displacement forecasting system. This system integrates meteorological forecasts with spatial data on population exposure and vulnerability, offering a cost-effective solution for predicting displacement risks. Through a case study of TC Yasa, which struck Fiji in December 2020, we illustrate the practical application of this forecast system. We highlight the importance of accounting for uncertainties in hazard, exposure, and vulnerability through a comprehensive global uncertainty analysis, which reveals a wide range of potential outcomes.

Additionally, we conducted a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to better understand the influence of uncertain inputs on forecast accuracy. Our findings suggest that for longer lead times, decision-making should prioritize meteorological uncertainties, whereas closer to landfall, the focus should shift towards the vulnerability of local communities. Our open-source code and methodologies are easily adaptable to other hazards and impact scenarios, making them valuable tools for broader applications in anticipatory humanitarian action.

Heavy rainfall events have become more frequent in recent years and in many cases the large amount of precipitation within a very short period of time leads to severe flooding and damage. Recent studies on climate change support this observation and show increasing trends in the frequency of extreme weather events, which include these high-intensity precipitation events.

In Germany, the high intensity as well as the extent and damaging impacts of flooding gain increasing attention among the society and the federal and state governments. Attention focus shifts towards heavy rainfall risk management and prevention of catastrophic harm and damage to people and property.

Contributing to an optimal preparation for the consequences of heavy rainfall events the Federal Agency for Cartography and Geodesy (BKG) introduces a nationwide scenario-based high-resolution pluvial flood map showing simulated information. The map evolves in close partnership with federal and state authorities and is currently being successively made available to politicians, authorities and the general public as OpenData for damage prevention and civil protection (Wimmer et al. 2023).

The foundation of the hydronumerical two-dimensional modelling are high-resolution geoinformation provided by the federal and state governments. A one-meter resolution Digital Terrain Model (DTM) is modified with culverts, 3D buildings, land cover data and further geoinformation to achieve a hydrologically effective and realistic discharge.

The resulting map provides two flood scenarios in line with the heavy rainfall index by Schmitt et al. (2018) which classifies the hazardous character of heavy rainfall events based on the return period. It is increasingly used in risk communication towards the general public in Germany. Based on a dataset of regionalized precipitation heights as a function of precipitation duration and annularity (KOSTRA) by the German Meteorological Service (DWD), a 100-year event is simulated. Additionally, the result of a heavy rainfall event with a precipitation of 100 mm/h is published. Both scenarios are visualized with flood depths, flow velocities and flow directions (geoportal.de/map.html?map=tk_04-starkregengefahrenhinweise-nrw).

The successively published data are used as initial assessment of risk potential. Combining the map with local expertise and government action guidelines simplifies the planning of measures. The map serves as an important and nationwide tool for identifying areas at risk of pluvial flooding. Local authorities, planners and emergency services are enabled to derive appropriate measures, both preventively and immediately before the event.

Building on the initial map, the BKG is currently developing a dynamic tool being part of the Digital Twin Germany project. It aims to provide event-related information on flooding caused by heavy rainfall. By simulating the effects of changes in infrastructure on the extent of pluvial flooding, users will be able to virtually test possible measures in advance in order to successfully minimize the impact.

Stream water temperatures influence water quality with effects on aquatic biodiversity, drinking water provision, electricity production, agriculture, and recreation. More frequent challenges from changes in water temperature are expected as we undergo the effects of the human-induced climate crisis. The current evidence on observed and projected long-term stream water temperature regional warming trends is often insufficient for stakeholders that stand to benefit from an operational forecasting service providing timely information from individual streams at high temporal resolution. Here we do so with deep learning models that efficiently generate multi-horizon probabilistic forecasts across multiple stations at once.

We train and evaluate several state-of-the-art models using 10 years of data from 54 stations across Switzerland. Static catchment features, time of the year, meteorological observations from the past 64 days, and their ensemble forecasts for the following 32 days are included as predictors in the models to estimate daily maximum water temperature over the next 32 days. The considered meteorological variables are daily average near surface air temperature, precipitation, and daily fraction of sunshine duration. Gridded observations of these variables are provided by MeteoSwiss at a spatial resolution of 2 km, and their forecasts correspond to a downscaled version of the Extended-range forecasts from the European Centre for Medium-Range Weather Forecasts.

Results show that the Temporal Fusion Transformer (TFT) model performs best with a Continuous Rank Probability Score (CRPS) of 0.70 ºC averaged over all lead times, stations, and 90 forecasts distributed over 1 year. The TFT is followed by the Recurrent Neural Network Encoder – Decoder with a CRPS of 0.74 ºC, and the Neural Hierarchical Interpolation for Time Series with a CRPS of 0.75 ºC. These deep learning models outperform other simpler models trained at each station: Random Forest (CRPS = 0.80 ºC), Multi-layer Perceptron neural network (CRPS = 0.81 ºC), and Autoregressive linear model (CRPS = 0.96 ºC). The average CRPS of the TFT degrades from 0.38 ºC at lead time of 1 day to 0.90 ºC at lead time of 32 days, largely driven by the uncertainty of the meteorological ensemble forecasts.

We find that the TFT also outperforms the other models when predicting water temperature at stations on which the models were not trained and at ungauged locations. The error at new stations increases 0.11 ºC reaching an average CRPS of 0.83 ºC, whereas it increases to 1.04 ºC when local water temperature is unavailable to the model. Furthermore, the TFT outputs feature importance and attention weights that provide valuable interpretability. In our case we find a dominant role of observed water temperature and future air temperature, while including precipitation and time of the year further improve the predictive skill.

Operational probabilistic forecasts of daily maximum water temperature for the next 32 days at 54 stations across Switzerland are generated twice per week with our TFT model and made publicly available at https://www.drought.ch/de/impakt-vorhersagen-malefix/wassertemperatur-prognosen/. Overall, this study provides insights on the extended range predictability of stream water temperature, and on the applicability of deep learning models in hydrology.

Collaborative GIS is spatial groupwork software and spatial decision support systems that bring people together in a digital map-based workspace to work on a shared geography-related topic. They have been useful in several disciplines, such as maritime spatial planning (Kettunen, 2020) and urban air mobility planning (Rönneberg, 2024). Collaborative GIS has a high potential for practical impact in emergency planning and action, as human actors can better communicate and become aware of the current spatial situation and its development scenarios for the near or more distant future.

Flooding is a common disaster worldwide, having enormous detrimental impacts on society. The frequency and intensity of floods are rising due to climate change and the consequential damage is dramatically increasing as a result of elevated exposure. With the increasing frequency and intensity of flooding, it has become imperative to mitigate flood risks effectively. A great amount of research is focused on risk assessment and mitigation strategies. The effects can be significantly reduced through the implementation of risk mitigation strategies including providing decision-makers with relevant, accurate risk assessment information that can aid in establishing effective emergency management protocols and communicating with the public to save lives and property. Flood risk assessment contributes significantly to managing floods effectively. These assessments enable us to identify possible threats at both the global and local levels, providing vital information for mapping high-risk areas. Machine Learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). Tampa Bay, Florida, is selected as a test site. The study's goal is to simulate flood risk and identify dominant FRFs using five ML models: Decision Tree (DT), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Historical flood damage data is the target variable, with 17 FRFs as predictors. RF and XGBoost outperform other models in accuracy tests. RF classifies 2.23% of Tampa Bay as very high risk and 2.55% as high risk, while XGBoost classifies 3.77% as very high risk and 1.09% as high risk. Key FRFs include low elevation, distance from water bodies, extreme precipitation, and population density. This study demonstrates an effective approach to assessing flood risk by considering a wide spectrum of FRFs using advanced supervised ML algorithms. Through this approach, we were able to identify the zones under very high and high flood risks in Tampa Bay, along with the identification of responsible FRFs, in addition to potential exposed populations. Such risk identification procedures will enable us to take precautions and to better prepare for floods at both household and institutional levels. The risk identification is believed to ultimately advance flood resilience. All of the findings contribute to flood risk management both scientifically and practically. The findings can be meaningful in the vast scientific field for flood management studies around the world and the approach will directly assist the policymakers during their decision-making regarding flood risk management.

The increasing complexities of climate change and natural hazard risks pose significant challenges for communities globally, necessitating a deeper understanding of how these risks are perceived at both societal and community levels. The negative impacts of natural hazards and climate change are often exacerbated when these risks are not adequately integrated into land use planning. Currently, New Zealand lacks a comprehensive national framework to establish tolerable risk thresholds related to life, property, and the economy. While several local councils have attempted to develop their own risk-based frameworks, these efforts are constrained by several factors: insufficient community awareness about acceptable risk levels, limited understanding and motivation among decision-makers to adopt risk-based approaches, and inherent uncertainties associated with natural hazards and climate change.

This research explores the complex relationship between individuals' perceptions of climate change and natural hazard risks, examining how these perceptions are shaped by diverse contexts and backgrounds in the societal, geographical and cultural environments. It specifically investigates the factors that influence community perceptions of these risks, focusing on the level of awareness within communities about the significance of these risks and how this awareness affects their actions in managing and mitigating them.

The literature on risk perception in New Zealand, situated within a global context, suggests that risk perception is influenced by various factors, including environmental and cultural characteristics, with significant regional variations (Fan et al., 2022). Research indicates that risk perception is shaped by social representations of the associated risks and that personal values significantly impact attitudes towards climate change (Han et al., 2022). In New Zealand, social vulnerability indicators have been developed to identify populations at risk of flooding—a common natural hazard in the region. These indicators assess community resilience by considering factors such as exposure, health and disability status, social connectedness, and housing (Mason et al., 2021). Moreover, public perceptions of risk often diverge from expert assessments, which are based on empirical data and specialised knowledge (Xiao et al., 2023). Factors influencing risk perception include gender, age, race, education, region of residence, cultural background, social status, income, knowledge of risks, disaster experience, and communication strategies (Xiao et al., 2023).

The Henderson-Massey Local Board area in the Auckland region in New Zealand was selected as a case study, with data collected through both direct household surveys and online questionnaires to assess community ‘understanding’ and ‘awareness’ of their exposure to natural hazards and climate change risks. Preliminary findings highlight a complex interaction of cognitive and social factors that shape these perceptions, subsequently influencing community decision-making and behaviour concerning risk mitigation.

This study advances the field of risk management by integrating community perspectives and providing new insights and developments relevant to both local and international contexts in mainstream climate change adaptation. The findings offer valuable implications for future policy and planning initiatives aimed at enhancing urban resilience and adaptive capacity to natural hazards and climate change exacerbated impacts.

In the complexity of international poly-crisis phenomena, the emergency response to earthquakes involves a multifaceted process, from immediate relief to long-term reconstruction. Geoinformation, cartographic, and aerial data are essential in Disaster Risk Reduction, Disaster Management, and Building Damage Assessment. However, documenting early recovery architectures remains challenging during the in-between period and the reconstruction transition, despite advancements in knowledge platforms, data management, and rapid multiscalar information sharing encouraged by international scientific programs such as the Integrated Research on Disaster Risk and the Sendai Framework for Disaster Risk Reduction 2015-2030.
Geomatic and photogrammetric techniques are central to documenting the emergency process and projects across territorial, urban, and building scales, defining a common semantic and geometric codification for mapping temporary phenomena.
This research focuses on the long-term spatial impacts of all temporary solutions, which have significantly and permanently altered the post-seismic Italian landscapes, particularly in the regions of Central Italy that have been affected by a succession of catastrophic events in the last 25 years.
The research has revealed the lack of overall cartographic documentation of these interventions, which is responsible for new urban configurations, despite the collective planning, economic and regulatory effort made, and the digital technological potential available today. Existing documentation is incomplete, fragmented, and outdated, necessitating a systematic, evidence-based approach to bridge this gap.
The methodology combines diverse data sources – satellite imagery, UAV-based photogrammetry, and temporal and economic processual data – into a unified multiscale geospatial database organized within the GIS platform. This database serves as the conceptual model for the Operative Atlas, facilitating the visualization and analysis of emergency responses and urban infrastructure.
One key aspect of the research is the implementation of attribute specification in existing official cartographic data, developing a standardized terminology and classification system for mapping temporary interventions, and making them recognizable within the national and international cartography during and after emergencies. By developing a conceptual and logical model, the research defines geometric entities in the GIS data structure to document and digitalize these provisional contexts. UAV point clouds are integrated with non-metric data to ensure detailed descriptions through a multiscalar approach. The methodology was validated in the study of Visso (Macerata), a small historical mountain village in Central Italy, where specific temporary interventions were effectively mapped and
documented. In conclusion, the research adopts an interdisciplinary and integrated approach to overcome the existing gaps in the documentation of post-disaster interventions. Combining architectural studies with geomatic tools, the Operative Atlas is valuable for managing heterogeneous geospatial emergency data. Digital visualization techniques can support different emergency phases and various stakeholders involved in post-disaster development strategies, such as local and national emergency management authorities, urban planners, and practitioners, in facilitating the transition from emergency response to permanent settlement. It offers digital visualization techniques to support, in different emergency phases, stakeholders involved in post-disaster development strategies, including local and national emergency management authorities, urban planners, and practitioners in facilitating the transition from emergency response to permanent settlement.

This work presents our current methodological efforts to explore, extract, visualize, and analyze potentially relevant information captured by the citizens' weather reports.

We started this project at the beginning of 2022 when MeteoSwiss shared with us their collection of citizens' observations on weather conditions reported by users of the MeteoSwiss mobile application. We followed an iterative and participatory design process that can be divided into three main phases. In each phase, we developed prototypes, gathered experts' requirements, designed and ran user experts' studies, and collected domain experts' feedback to improve our solution's features.

In Phase 1, we focused on the research of novel visual representations for the spatio-temporal analysis of only the citizens' reports. In Phase 2, we focused on facilitating the visual comparison of radar data and citizens' reports, exploring reports' images and extra metadata, and re-engineering the tool to make the software more accessible, modular, and robust.

Phase 3 is an ongoing effort, and its main goal is to provide meteorologists with information extracted from the images to estimate the impact and damage on the ground as reported by citizens.

A prototype of our visual tools is available at https://weatherva.ifi.uzh.ch/.

Multidimensional datasets are often represented by layering them within a single map, which can lead to an exaggeration of information. As an alternative representation, this study investigates the use of a small multiple layout for web applications, in which several maps with a common spatial area, each representing a different topic, are arranged next to each other and can be viewed simultaneously.

The area of spatial planning was used as a use case, as many different topics of a common area, which are largely obtained from the ÖREB-cadastre, have to be identified and analysed in the planning process. Spatial planning experts took part in the qualitative usability study, carrying out a fictitious task that is typical for this specialist area. The aim of the study was to analyse how the experts rated the small multiple layout in terms of usability and efficiency. The usability test was analysed using a qualitative content analysis.

The experts were able to quickly gain an overview of the topics and make an efficient cross-comparison, particularly when weighing up interests. They were able to complete their tasks efficiently and were predominantly positive about the layout during and after completing the tasks. Limitations arise if the area under consideration extends beyond the boundaries of the small multiple map window. The study concludes that small multiples layouts are a useful and efficient tool for the simultaneous visual analysis of multiple spatially coincident themes, with potential applications extending beyond spatial planning to other fields that use multidimensional spatial data.

Climate change is an ongoing environmental threat, and its mitigation poses significant societal challenges. Policymakers are tasked to make time-critical decisions rapidly, including hazard forecasting, preparedness, warning, and response to mitigate potentially harmful consequences of climate change. This often involves map-based decision-making with visualized climate data, which are confronted with various sources of uncertainty. Uncertainty is an inherently tricky concept; thus, it is challenging to communicate clearly with decision-makers and the public. Especially in the emotionally charged context of climate change, with segments of the population already showing scepticism towards climate change, the communication of uncertain information in map displays still needs deeper investigation.
Applying a mixed factorial (3x2x2) map-based, online experiment, we aimed to study the visualization of (un)certainty in static climate change forecast maps and how this might interact with map readers' emotions and attitudes. Inspired by the CH2018 Scenarios for the year 2060 (NCCS, 2018), we designed change prediction map stimuli with different climate variables in three versions: without any uncertainty information, with uncertainty visualized as black dots (Figure 1) or lines, using empirically validated depiction guidelines (Retchless & Brewer, 2016). Based on prior research, we chose the term 'certainty' instead of 'uncertainty' in our stimuli (Johannsen et al., 2018). Each map was accompanied by context information on its right-hand side. It either included a graphic character with a fitting narrative (between factor: emotion), intended to elicit an emotional response (Fig. 1a), or it just contained information equivalent to facts (Fig. 1b).

Leveraging the crowd-sourcing platform Prolific.com, we recruited 109 people (f: 53, m = 54, nb = 2; average age = 34 yrs.) to participate in this online study. We balanced the number of climate change sceptical and believing participants (between factors: attitude) using screeners in Prolific. All participants were exposed to 18 maps in random order, without any time pressure. They were asked to select the locations predicted to be most/least affected by climate change shown on the map (out of six given options, A-F in Figure 1), to assess the depicted change certainty and severity, and to indicate their amount of trust in the depicted information, on a scale from least (1) to most (7).
Preliminary results (ART ANOVA) indicate that participants reporting a sceptical climate change attitude rate the severity of the depicted change significantly lower compared to believing participants (F = 5.446, p < .05), irrespective of the emotional context. Regardless of their attitudes, participants, on average, trust maps without any certainty information significantly less (F = 167.872, p < .001) compared to when certainty is visually communicated, irrespective of the certainty visualization type.

Our initial findings not only underline the importance of deriving suitable visualization guidelines to communicate uncertainty climate change forecast maps effectively but also suggest adapting the visual communication method to population segments with varying attitudes towards climate change. Future research should deepen our understanding of emotion's role in the visual communication of climate change forecast maps and how different types of climate change scepticism might interact with uncertainty visualization.