Ski tourism is a major sector of mountain regions economy, which is under the threat of long-term climate change. Snow management, and in particular grooming and artificial snowmaking, has become a routine component of ski resort operations, holding potential for counteracting the detrimental effect of natural snow decline. However, conventional snowmaking can only operate under specific meteorological conditions. Whether snowmaking is a relevant adaptation measure under future climate change is a widely debated issue in mountainous regions, with major implications on the supply side of this tourism industry. This often lacks comprehensive scientific studies for informing public and private decisions in this sector. Here we show how climate change influences the operating conditions of one of the main ski tourism markets worldwide, the French Alps. Our study addresses snow reliability in 129 ski resorts in the French Alps in the 21st century, using a dedicated snowpack model explicitly accounting for grooming and snowmaking driven by a large ensemble of adjusted and downscaled regional climate projections, and using a geospatial model of ski resorts organization. A 45% snowmaking fractional coverage, representative of the infrastructures in the early 2020s, is projected to improve snow reliability over grooming-only snow conditions, both during the reference period 1986-2005 and below 2 °C global warming since pre-industrial. Beyond 3 °C of global warming, with 45% snowmaking coverage, snow conditions would become frequently unreliable and induce higher water requirements.
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http://dx.doi.org/10.1038/s41598-019-44068-8 | DOI Listing |
Sci Rep
January 2025
UNESCO Centre of Water Law, Policy & Science, University of Dundee, Dundee, UK.
Understanding snow and ice melt dynamics is vital for flood risk assessment and effective water resource management in populated river basins sourced in inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning and deep learning techniques framed as alternative 'computational scenarios, leveraging both physical processes and data-driven insights for enhanced predictive capabilities. The standalone deep learning model (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart machine learning and glacio-hydrological model equivalents.
View Article and Find Full Text PDFEcol Lett
January 2025
Department of Biological Sciences, Texas Tech University, Lubbock, Texas, USA.
Accurately representing the relationships between nitrogen supply and photosynthesis is crucial for reliably predicting carbon-nitrogen cycle coupling in Earth System Models (ESMs). Most ESMs assume positive correlations amongst soil nitrogen supply, leaf nitrogen content, and photosynthetic capacity. However, leaf photosynthetic nitrogen demand may influence the leaf nitrogen response to soil nitrogen supply; thus, responses to nitrogen supply are expected to be the largest in environments where demand is the greatest.
View Article and Find Full Text PDFEcol Evol
January 2025
Département de Biologie, Chimie et Géographie Université du Québec à Rimouski Rimouski Quebec Canada.
This study presents the first movement analysis of snow leopards () using satellite telemetry data, focusing on the northeastern Himalayas of Nepal. By examining GPS-based satellite collar data between 2013 and 2017 from five collared snow leopards (effectively three individuals), the research uncovered distinct movement patterns, activity budgeting and home range utilisation from one adult male and two sub adult females. Hidden Markov models (HMMs) revealed three behavioural states based on the movement patterns-slow (indicative of resting), moderate and fast (associated with travelling) and demonstrated that the time of day influenced their behavioural state.
View Article and Find Full Text PDFEcol Evol
December 2024
Wildlife Conservation Society New York New York USA.
Sensors (Basel)
December 2024
Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.
This study proposed a robust lane-keeping algorithm designed for snowy road conditions, utilizing a snow tire track detection model based on machine learning. The proposed algorithm is structured into two primary modules: a snow tire track detector and a lane center estimator. The snow tire track detector utilizes YOLOv5, trained on custom datasets generated from public videos captured on snowy roads.
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