HAL Collection UNC (Univ. de la Nouvelle Calédonie)
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Arctic sea-ice loss drives a strong regional atmospheric response over the North Pacific and North Atlantic on decadal scales
No full textInternational audiencePrevious studies have suggested that Arctic sea-ice loss can have a profound influence on atmospheric circulation far away from the Arctic. However, there is little scientific consensus on the features of these remote responses, with the opposite impacts reported. Here we present a multi-model analysis of the decadal climate response to Arctic sea-ice loss using state-of-the-art energy conserving methodologies to isolate the impacts of sea-ice decline. We observe weakening of the Aleutian Low and development of a geopotential ridge in the North Pacific, associated with drier winter conditions over the southwest United States. Over the Atlantic, a negative NAO-like response drives wetter winter conditions across the western Mediterranean. These decadal-scale impacts substantially differ from reported centennial-scale responses to Arctic sea-ice loss simulated using non-energy conserving methodologies. Factors such as the timescale of the response and methodologies used to isolate the impacts of disappearing sea-ice cover should be carefully considered when consolidating scientific understanding on the future impacts of changing Arctic
A Century of O&G Exploration in France: What Is the Legacy for the Energy Transition?
No full textInternational audienceThe global shift towards sustainable energy solutions has sparked renewed interest in subsurface exploration, driven by the urgent need for cleaner energy resources and carbon management strategies. In this context, France’s extensive history of oil and gas (O&G) exploration offers an invaluable foundation for transitioning towards low-carbon energy systems. Over a century of petroleum exploration has generated vast datasets, advanced technologies, and a culture of subsurface innovation that now holds transformative potential for geothermal energy, CO₂ storage, and hydrogen exploration.This study examines the legacy of O&G exploration in France and its role in the energy transition. It highlights how historical exploration data and scientific advancements are being repurposed for new applications, while also addressing the inherent limitations and challenges of reusing legacy datasets for modern energy exploration. Through case studies of ongoing research and collaborative projects, the analysis illustrates how France is leveraging its past to navigate a more sustainable and resilient energy future
Assessment of spatial distribution of organic contaminants and metallic compounds on a tropical island’ coral reef fish communities
No full textInternational audienceThe New Caledonian archipelago is an important hotspot of marine biodiversity. Due to mining activities, urbanization, and industrialization, significant amounts of contaminants are discharged into the lagoon. This study analysed the concentrations, spatial distribution, and potential drivers of 14 metallic compounds and trace elements (MTEs) and 22 persistent organic pollutants (POPs) in ~400 coral reef fish sampled from various sites around New Caledonia, across a gradient from mining centers to remote, uninhabited locations. Boosted regression trees modelling explained between 61 and 86 % of the global variation in MTEs and POPs concentration. Fish body size emerged as the most important correlate of MTEs and POPs concentrations in coral reef fish. Monthly rainfalls were the second most important variable for POPs, whereas the reef area was the second variable explaining MTE concentrations. Our modelling approach allowed us to predict and map the distribution of concentrations at the fish community level for 17 contaminants (9 MTEs and 8 POPs). Predicted concentrations ranged from ~1.5 ng.g -1 (β-endosulfan) to ~11.5 μg.g -1 (Ni), and revealed a widespread contamination throughout the lagoon, from the coast to the barrier reef. Contamination by mining-related elements (Ni, Cr…) were clearly influenced by the surface area of mining registry and to lithology to a lesser extent, whereas Hg contamination strongly depended on biological variables. Our study is the largest of its kind at the archipelago scale, combining data on 36 contaminants in ~400 fish samples with a modelling framework offering insights into underlying processes and spatial data for policy use.</div
Supporting Information for: Recycling Subducted Carbon as Diamonds: An Example from the New Caledonia Forearc Ophiolite G Cubed 2025
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Variabilité climatique et stratégies d’adaptation des riziculteurs de la Basse-Casamance (Sénégal)
No full textInternational audienceFor several decades now, West Africa has been experiencing disrupted climatic conditions, marked by irregular rainfall and a significant rise in temperatures, with negative repercussions on the livelihoods of rural populations. In Basse-Casamance, Senegal, traditional rice-growing, the basis of the lineage heritage and the foundation of the Diola identity, is going through a crisis exacerbated by climate change. This study analyses the impacts of climate variability and the adaptation strategies of rice farmers in Basse-Casamance. The method combines the tools of environmental and social geography, namely the processing and analysis of climatic data, surveys using questionnaires and interview guides, and direct and participant observation sessions. Analysis of the rainfall and temperature data revealed a return to wetter conditions since the beginning of the 21st century, with high spatio-temporal variability in rainfall and a significant increase in average temperatures in Basse-Casamance. The surveys show that rice farmers are relying on their knowledge and know-how to adapt to climate variability, with public policies often out of step with local realities. Today, the rich knowledge and skills of these rice farmers are increasingly threatened by global change, limiting their capacity to adapt. The aim of this study is to contribute to the scientific debate on the knowledge, practices and innovations of local communities, and to examine the operationality of the agro-ecological transition in this context.Depuis plusieurs décennies, l’Afrique de l’Ouest connaît une perturbation des conditions climatiques marquée par une irrégularité des précipitations et une augmentation significative des températures avec des répercussions négatives sur les moyens d’existence des populations rurales. En Basse-Casamance, la riziculture traditionnelle, base du patrimoine lignager et au fondement de l’identité diola, traverse une crise exacerbée par le changement climatique. Ce travail analyse les impacts de la variabilité climatique et les stratégies d’adaptation des riziculteurs de la Basse-Casamance. La méthode combine les outils de la géographie environnementale et sociale, à savoir le traitement et l’analyse des données climatiques, les enquêtes par questionnaire et guide d’entretien et les séances d’observation directe et participante. L’analyse des données pluviométriques et de températures a permis d’observer un retour à des conditions plus humides depuis le début du XXIe siècle, avec une forte variabilité spatio-temporelle des précipitations et une augmentation significative des températures moyennes en Basse-Casamance. Il résulte des enquêtes que les riziculteurs comptent sur leurs savoirs et savoir-faire pour s’adapter à la variabilité climatique, les politiques publiques étant souvent en déphasage avec les réalités locales. Aujourd’hui, les riches connaissances et compétences de ces riziculteurs sont de plus en plus menacées par les changements globaux limitant ainsi leur capacité d’adaptation. Ainsi, ce travail vise à contribuer au débat scientifique sur les savoirs, pratiques et innovations des communautés locales et s’interroge sur l’opérationnalité de la transition agroécologique dans ce contexte
Malnutrition and Climate in Niger: Findings from Climate Indices and Crop Yield Simulations
No full textInternational audienceMalnutrition, particularly its impact on child morbidity and mortality, is one of the top five health effects of climate change. However, quantifying the portion of malnutrition attributed to climate remains challenging due to various confounding factors. This study examines the relationship between climate and acute malnutrition in Niger, a country highly vulnerable to climate change and disasters. Since climate’s effect on malnutrition is indirect, mediated by crop production, we combine rainfall data from TAMSAT satellite estimates with the SARRA-O crop model, which simulates the impact of rainfall variability on crop yields. Our analysis reveals a significant correlation between malnutrition and both rainfall and crop production from the previous year, but not within the same year. The strongest correlation (R = −0.72) was found with the previous year’s crop production. No significant links were found with temperature or intra-seasonal rainfall indices, like the start or duration of the rainy season. Although national correlations between global malnutrition, rainfall, and crop yields were stronger, they were weaker or absent at the regional level and, for Severe Acute Malnutrition crises, are less likely driven by climate variability. However, the one-year lag in the correlation allows for the prediction of future food crises, providing an opportunity to implement early intervention measures
Le chômage des fonctionnaires et la règle lex specialis derogat generali
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Five actions to close the gap between marine spatial planning research and practice
No full textInternational audienceMarine spatial planning (MSP) has emerged prominently in ocean governance literature and international guidance as a more holistic, inclusive, and proactive alternative to contemporary sectoral or ecosystem-specific management. MSP is, at its core, a governance transformation, though in practice it has often failed to catalyze transformative action. Despite MSP research and practice evolving side-by-side, much of the academic literature shows little awareness of the complex socio-political and planning dimensions of MSP, while MSP initiatives fail to uphold MSP good practices. Much current academic literature on MSP fails to properly account for the complex realities of ocean governance. As MSP becomes increasingly central to achieving global ocean conservation and sustainability goals, it is a critical time to better bridge MSP research and practice. We identify five actions that researchers and practitioners could take to help narrow this gap: (1) find allies across research and governance agencies, (2) better understand the realities of the researcher's and practitioner's day-to-day, (3) co-develop research that is fit-for-purpose to current MSP needs, (4) challenge barriers to transforming the status quo, and (5) speak truth to power by leveraging the collective, powerful voice of researchers and practitioners. By working together with intention and shared purpose, researchers and practitioners can enable MSP to reach its full potential in sustainable governance that ensures a healthy ocean for all
Combining Procedural Generation and Genetic Algorithms to Model Urban Growth
No full textInternational audienceIn this paper, we present an approach to model spatial influences in multi-agent models using procedural generation and genetic algorithms. We applied this approach in an urban growth model. In agent-based simulations, the agents make decisions based on the perception of their environment. In our context, the agents represent inhabitants who can create new buildings or extend the existing ones. Their behaviour is ruled by spatial influences (e.g., the proximity of the road increases chances of building in the surrounding areas). Procedural generation provides a good framework for representing the influences of the environment on the agent’s behaviour. Each spatial feature is associated with an influence function. Their parameters search space can be tremendous, making it difficult for field experts to set them manually. Consequently, we use a genetic algorithm to optimize the parameters of these influence functions and train the model based on three spatial measures (Chamfer distance , kernel density, and a density grid). This approach can be employed likewise to any problem where the agent decisions are wholly or partly based on location. Our experiments highlight the interest of our approach and the impact of the chosen fitness functions
Are LSTM and conceptual rainfall-runoff models able to cope with limited training datasets under diverse hydrometeorological conditions?
No full textInternational audienceAs climate change exacerbates variability and non-stationarity in rainfall patterns, it is crucial to assess the predictive capabilities of forecasting models. Previous researches on rainfall-runoff modeling have focused on the impact of training dataset size on Artificial Neural Networks (ANNs) results, with limited consideration of hydrometeorological diversity. This study first evaluates the influence of the training dataset length (1 to 15 years) on performance of a Long Short-Term Memory (LSTM) and a traditional conceptual model, Superflex, across 10 validation years. Next, training years are categorized based on hydrometeorological diversity (wetter, standard, drier). This clustering allows for experiments where models are trained on data from similar or different clusters, enhancing understanding of how data diversity, and therefore climate change, can affect model performance. Results indicate that the LSTM model is highly sensitive to training length, showing poor performance with short datasets (below three years), reaches similar performance to Superflex around six training years on average, and overperforms with 15 years of training. Conversely, Superflex maintains rather constant performance levels regardless of the dataset length. LSTM model benefits from diverse 1 training data, achieving higher accuracy and reliability when trained on years with diverse hydrological typology. Despite their potential to outperform traditional models (with six or more training years on average), LSTM models are highly dependent on the quality and diversity of training data. In climate change scenarios, caution is needed when applying LSTM models to unfamiliar conditions, as their predictive accuracy may decline more rapidly than that of more traditional hydrological models