Efficient flood risk assessment and communication are essential for responding to increasingly recurrent flash floods. However, access to high-end data center computing is limited for stakeholders. This study evaluates the accuracy-speed trade-off of a hydraulic model by (i) assessing the potential acceleration of high-performance computing in PCs versus server-CPUs and GPUs, (ii) examining computing time evaluation and prediction indicators, and (iii) identifying variables controlling the computing time and their impact on the 2D hydrodynamic models' accuracy using an actual flash flood event as a benchmark. GPU-computing is found to be 130× and 55× faster than standard and parallelized CPU-computing, respectively, saving up to 99.5% of the computing time. The model's number of elements had the most significant impact, with <150,000 cells showing the best accuracy-speed trade-off. Using a PC equipped with a GPU enables almost real-time hydrodynamic information, democratizing flood data and facilitating interactive flood risk analysis.

Conducting fieldwork in polar regions presents a multifaceted challenge not only because of the remoteness of the environment, but also potential geopolitical disputes, language barriers, divergent national policies, and disparities in emergency healthcare access. This review addresses the climate crisis and reevaluates the ethical considerations of polar fieldwork in alignment with broader social responsibilities, with particular emphasis on the challenges faced by Early Career Researchers (ECRs). Ongoing climate change and its associated impacts and effects (e.g., reduced snow and ice cover, thawing permafrost, intensified fires, and increased wildlife interactions) will undoubtedly compound the aforementioned challenges. ECRs, often with a heightened awareness for contending with issues pertaining to environmental conservation and sustainability, face greater career stakes than tenured researchers, which can lead to innovation in addressing safety concerns regarding polar fieldwork. This review summarizes current challenges faced by ECRs in polar fieldwork, elaborates on how these may change during this century, and presents possible solutions. To address the aforementioned challenges, we propose a comprehensive set of recommendations, including innovative data collection methods using improved technology and emphasizing meaningful remote local collaborations to minimize the travel and environmental impact and risk of disease contagion. We advocate for reducing the redundancy among research groups by promoting data sharing. Additionally, we suggest enhancing cooperation by integrating (traditional) Indigenous knowledge while respecting the rights of Arctic communities. Such recommendations highlight the intricate dynamics of polar fieldwork safety and logistics in a changing climate, emphasizing the need for adaptability, sustainability, and inclusivity in research practices. A call for action towards revising current practices is clear, emphasizing that ECRs can be key agents in forging and establishing responsible, comprehensive, and adaptive protocols toward enhancing safe and successful fieldwork in the polar sciences.

Flash floods can cause great geomorphological changes in ephemeral fluvial systems and result in particularly severe damages for the unprepared population exposed to it. The flash flood in the Storelva river in Utvik (western Norway) on 24 July 2017 was witnessed and documented. This study assessed the causes and effects of the 2017 flood and provides valuable information for the calibration and validation of future modelling studies. The flooded area at peak discharge, maximum wetted and dry areas during the entire event, critical points and main flow paths were reconstructed using on-site and post-event (i) visual documentation, such as photographs and videos, and (ii) aerial surveying, such as orthophotographs and laser scanning, of the lowermost reach. The steep longitudinal slope together with the loose material forming the valley and riverbed contributed to a large amount of sediment transport during this extreme event. Steep rivers such as the Storelva river have very short response times to extreme hydrologic conditions, which calls for exhaustive monitoring and data collection in case of future events, as well as modelling tools that can emulate the hydro-morphodynamics observed during events such as the 2017 flash flood.

Climate change is rapidly reshaping the research landscape in the polar regions. As such, Early Career Researchers (ECRs) face increasingly daunting challenges. These challenges include international and institutional competition for funding, shifting research demands and priorities, limited data sharing, and the need for strong mentorship. ECRs in polar research face various challenges that are common to other research fields; e.g., they can encounter difficulties in securing funding and publishing [1, 2], which can lead to elevated stress and even job loss [3, 4]. Institutional barriers include pressure on senior scientists to retire prematurely and pervasive gender and social inequalities [5]. Of the approximately 5,000 members of the Association of Polar Early Career Scientists (APECS), 57% identify as female [6]. While specific data on gender distribution among APECS ECRs is unavailable, insights from the International Association for Hydro-Environment Engineering and Research (IAHR) reveal that women constitute 25% at all career stages, with ECRs representing 33%; this proportion exhibited minimal growth in recent years [7]. Anticipated trends suggest a more balanced gender distribution among APECS ECRs relative to their counterparts at advanced career stages, with potentially greater gender imbalances in developing countries and certain research fields [7]. Barriers to interdisciplinary polar research include demanding workloads, uncertain funding and employment prospects, and limited support for work-life balance [8]. The benefits of open science practices for ECRs, including reputational gains and increased chances of publication, as well as the associated challenges have also been noted [9]. In light of these challenges, addressing the climate and biodiversity crisis necessitates a reevaluation of academia's activities to ensure a safe and just space. This reevaluation calls for a balance that respects the environmental boundaries of the planet while ensuring social equity and justice [10]. In response, we propose the following solutions: (i) Creating more funding opportunities directed toward ECR-led projects, such as the Scientific Committee on Antarctic Research (SCAR) fellowships or the research grants offered by the European Consortium for Ocean Research Drilling (ECORD), an integrated member of the International Ocean Drilling Pro

The numerical tools simulating flood events must be accurate, in order to provide useful data, and computationally efficient, to facilitate informed decision-making during a flood. One of the main limitations of modelling software at the present time is the CPU time needed to perform simulations over complex spatial domains. Recent studies show that the models lack the necessary implementations to take advantage of the parallelism available on current hardware, which hinders their use in interesting applications such as real-time flood forecasting, calibration and uncertainty analysis, or visualization and gamification of floods for improved risk communication. For instance, methods based on artificial intelligence have contributed highly to the state-of-the-art of flood simulations, providing better performance and cost-effective solutions than complex hydrodynamic fluid solvers. The current study provides an overview of strategies dedicated to the optimization of hydraulic flood simulations suitable for ungauged steep rivers, which are not extensively studied.

The 2017 flash flood affecting Storelva in Utvik (West Norway), with an average slope of >10% in the ungauged reach, was reconstructed using visual observations during the event, as well as post-event field data and remote sensing. The dataset was then used for i) roughness calibration and sensitivity analysis, ii) validation of a 2D hydrodynamic model (morphodynamic data was insufficient) and reconstruction of the maximum flood extent, critical locations, and preferential flow paths, and its comparison to other modelling studies, and iii) analysis of the impact of mesh refinement on model precision for optimal model design in IberPlus.Water levels and flow discharge were measured after the flood. The observations were used to calibrate the model in the 400m-long most downstream reach. Similarly, visual flood documentation during the event was used to model the event and validate it in the 800m-long most downstream reach.To calibrate the model, GIS-classified wet and dry areas in the computational domain were compared with wet and dry areas observed along both banks, calculating the BIAS and RMSE for each calibration Manning. According to the sensitivity analysis, the model with Manning’s roughness coefficient of 0.065 in the upper and middle reach and 0.075 downstream showed the lowest global errors (i.e. RMSE= 1.1cm), although the numerical models generally underestimated the observed water levels (i.e. -8cm <BIAS< -1cm).Two of the critical locations are located near bridges and the other two near a bank with very fine material, easy to erode. The preferential flow paths indicate that the erosion occurred mainly in the left floodplain. IberPlus simulated satisfactorily the observed maximum flood extent, i.e. F and C indices of 60%–87%. The results for the 2017 flood using IberPlus were compared to the (non-calibrated) hydraulics from literature using TELEMAC-MASCARET and FINEL2D. The IberPlus hydrodynamic model had the highest roughness coefficients from all the modelling studies. This might explain the significantly higher hydraulic values observed, in agreement with those obtained by the morphodynamic models. The paths preferred by the flow during the flood and the flood extent are resembling in all three models.The F and C indices and the incremental precision between scenarios were estimated for 44,000–11.6 million cells models with uniform and variable mesh sizes. The optimal precision-gain was at model size 700,000 cells for uniform mesh (R2 >0.94), with a precision gain limited to 5–7% at best when using a finer grid. Uncertainties in the flood mapping used for validation, the hydrodynamic model set-up and input data contributed to the offset. The model precision is limited by the on-site flood protections implemented to protect private property during the flood event. These protections were effective and reduced the flood damage by 43%, yet they could not be implemented in the numerical model. Also, the model validation was carried out against a fully water-covered area, where some local dry cells were considered wet. Remotely sensed data helps understand flood dynamics and monitor flood risk in data-scarce regions.