ABSTRACT This study analyses how renewable energy transitions, intertwined with rural revitalisation, evolved into energy tourism at a hot spring (onsen) tourism destination in rural Japan, where onsen water has long been part of local culture and everyday life. Using social practice theory, the study demonstrates how geothermal energy was integrated into tourism not by introducing disruptive new infrastructure but by building on traditional onsen practices, such as bathing, snow melting, and building heating. Our results reveal that traditional onsen practices were reinterpreted as sustainability initiatives and assembled into a coherent form of geothermal energy tourism. This process enabled the onsen village to link renewable energy transitions to rural revitalisation through destination branding, while preserving local culture and livelihoods. The study advances tourism planning by illustrating how renewable energy transitions and rural revitalisation can emerge through continuity and adaptation within long-standing, locally embedded tourism practices, leading to a form of energy tourism.

ABSTRACT Global temperature changes affect alpine regions unevenly, shaped by gradients in elevation and land cover. Yet, runoff modelling primarily relies on temperature-index methods with constant degree-day factor, limiting representation of melt variability. This study develops spatiotemporal melt factor formulation that incorporates nonlinear relationships among temperature, snow density, shortwave and longwave radiation. The resulting formulation employs empirical coefficients to represent integrated basin-scale melt sensitivity. The formulation is integrated into the modified Spatial Processes in Hydrology (SPHY) model to simulate runoff dynamics in transboundary Satluj River Basin. The model reproduces daily discharge with high accuracy (R2 values of 0.91–0.93) and improves seasonal runoff representation relative to conventional constant degree-day factor approach by reducing melt overestimation. Projections under CMIP6 scenarios indicate increasing rainfall contributions and gradual shift from melt-driven runoff to rainfall-influenced regimes. The proposed formulation provides a physical approach for improving runoff simulations in alpine regions under changing climate conditions. Graphical abstract

Chloromonas typhlos is a cosmopolitan alpine snow alga distributed across continents, and its blooming accelerates snow melting by decreasing the amount of snow albedo. To elucidate the genetic traits underlying the adaptation of C. typhlos to the alpine habitat, we combined PacBio sequencing and Hi-C to generate a high-quality chromosome-level genome assembly (contig N50: 1.29 Mb; scaffold N50: 7.23 Mb) with 31 chromosomes and a genome size of 200.86 Mb. Repetitive elements constituted 11.05% of the genome, and 16,133 protein-coding genes were predicted, of which 82% were functionally annotated. This study provides a set of omics resources both for snow algae and the genus Chloromonas.

Colored snow caused by green algae (Chlorophyceae) is well known, but melting snowpacks can also harbor golden-brown blooms consisting of Chrysophyceae. We collected 14 samples of cryoflora in the Austrian and Swiss Alps, the High Tatras in Slovakia, and in Arctic Svalbard. Eight laboratory unicellular flagellated strains were established from eight sites and phylogenetic analyses (18S rRNA and rbcL gene sequences) revealed new taxa belonging to the order Hydrurales (Chrysophyceae). Some formed tetrahedral swarmers; others were capsoid or amoeboid forms. Characteristics of vegetative cells and molecular markers, including the ITS2 rRNA region, supported the description of eight species: Hydrurus novisii sp. nov., H. klavenessii sp. nov., H. tatrae sp. nov., H. pulcher sp. nov., H. pascheri sp. nov., H. svalbardensis sp. nov., H. nivalis sp. nov. and H. nemcovae sp. nov. Pulse-amplitude-modulate (PAM) fluorometry indicated that the photosystem II of Arctic populations was adapted to high light conditions. Abundant polyunsaturated fatty acids supported cell survival at temperatures around 0°C, and the composition of these acidsdiffered among species. The cells contained compatible solutes that could act as antifreeze agents. The main carotenoid fucoxanthin caused the overall golden-brown pigmentation. The closest relatives of the new species were reported from snow and cold mountain streams and lakes, indicating that these Hydruralian microalgae prefer low temperatures and elevated irradiation. The large number of new species discovered during this limited sampling campaign suggests the underestimated diversity of phototrophic microbes in melting snow. Consequently, the genus Hydrurus shows a similar high relevance for snow algae blooms as Chloromonas does within the green algae.

Carbon-fiber-grid heated pavement for deicing and snow melting: The role of design parameters and environmental conditions

Study on temperature control and snow melting effects of a PCM-SHPs composite embankment structure

This study assesses future hydrological responses of the Chitral River Basin (CRB), a high-mountain, glacier-fed catchment in northern Pakistan, under climate change. The Soil and Water Assessment Tool (SWAT) was forced with bias-corrected outputs from three CORDEX regional climate models under Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5) scenarios for the period 2010-2099. Projected temperature increases range from 2.34°C to 5.23°C by the late century, while precipitation changes vary between 2.42% and 6%. These changes induce a shift in seasonal streamflow, with peak discharge advancing to June-July. Simulated streamflow responses indicate that warming may alter seasonal runoff timing through enhanced snow and ice melt processes. However, because the adopted SWAT configuration assumes static glacier area, projected late-century reductions should be interpreted as climate-driven hydrological responses rather than direct simulations of progressive glacier depletion. The results highlight substantial uncertainty in future annual flow magnitude across climate models and bias-correction methods, while consistently indicating sensitivity of runoff timing to climatic warming. These findings underline the need for adaptive water-management strategies in the Chitral River Basin.

Rapid mid-winter and early spring warming events are emerging as a key but under-recognized driver of carbon loss from cold-region ecosystems. In mountain peatlands, their influence remains largely unknown. Here, we quantify the impact of chinook wind events-warm, dry downslope winds that can raise air temperatures by over 20°C within hours-on ecosystem respiration in a montane peatland on the eastern slopes of the Canadian Rockies. Using eddy covariance carbon dioxide (CO2) flux measurements and meteorological data, 13 chinook events were identified between February and May 2021 and segmented each into pre-, during-, and post-event phases. A generalized additive mixed model accounting for temporal autocorrelation showed that CO2 emission rates increased significantly during chinook events and remained elevated afterward. CO2 emissions during snow-covered events in February-April were most likely driven by physical degassing from melting snow and thawing surface peat, whereas snow-free events in April and May likely reflected enhanced microbial activity in thawing peat. Our findings demonstrate that regularly occurring cold-season warming events can trigger substantial but short-lived CO2 releases from mountain peatlands, revealing a climate-sensitive carbon loss pathway likely to intensify as snowpack duration shortens and freeze-thaw regimes shift in mountain regions world-wide.

Climate change is a global phenomenon that requires collective intervention if humanity is to survive its catastrophic effects. The effects of climate change are palpable the world over, particularly in the rising of temperatures and sea levels and the snow melt and heavy rains that cause severe flooding followed by drought. It has been argued that climate change is a result, or consequence, of a lack of environmental awareness that is anthropogenic in nature. Of course, there are other causes of climate change that are not anthropogenic; there are natural causes too. However, these debates have tended to be dominated and led by extra-African scholars and activists, not that there are no African scholars and activists on climate change and related phenomena. This has created an illusion that Africa’s agency on climate change debates, and possible solutions, is hampered and curtailed. The article interrogates Africa’s agency on the climate change debates proffered thus far. Furthermore, it amplifies African and Africa’s voices on climate change. More importantly, this article seeks to articulate Africa’s positionality on climate change. The article uses a qualitative research methodology that allows for the interpretation of primary and secondary sources.

Stable isotopes δD-H2O, δ18O-H2O, δ15N-NO3-, δ18O-NO3- and hydrochemistry of the volcanic catchments and the influence of continental runoff on the environment of Eastern Kamchatka.

Climate warming induced wildfires are rapidly increasing at high latitudes, yet their climate impacts remain poorly understood. These deeply smoldering fires may release long-stored carbon and thus perturbate the global carbon cycle and further emit light-absorbing carbonaceous particles enhancing snow and ice melt after deposition. We newly investigate the carbon isotopic and light-absorbing characteristics of carbonaceous particles produced in laboratory combustion experiments on Arctic-boreal peats and compare these with biomass from boreal forest and savanna environments. We provide the first observational evidence that boreal and especially Arctic peat smoldering may release millennial-aged carbon into the atmosphere, which upsets radiocarbon-based source attribution, separating fossil-fuel-derived sources from modern biomass. Moreover, above- and below-ground material combust differently, and hence the fraction of modern carbon (F14C), i.e., the average age, of the original biomass and the produced carbonaceous particles may differ. Furthermore, we show that peat smoldering produces significant amounts of Brown Carbon, which absorbs light at a similar magnitude to Black Carbon in these samples. Our results indicate that the increasing number of Arctic-boreal peat fires may exacerbate Arctic warming more than previously estimated.

The paper considers the problem of water and air movement in melting snow and surface soil layers, based on the equations of non-isothermal two-phase filtration. The study uses the equations of mass conservation, two-phase filtration, and heat balance. The proposed mathematical model incorporates the phase transition in the extended region, the evolving filtration properties of the surface soil layer, and the capillarity effects. Numerical experiments demonstrate that soil porosity and water saturation fully comply with the physical maximum principle. It is found that snow cover influences the formation of a layer with reduced permeability in the soil. It affects the soil absorption capacity and the distribution of surface and ground runoff during periods of intensive snowmelt. The proposed mathematical model can be used to estimate the volume of surface and subsurface runoff during intensive snowmelt.

Abstract Quantification of black carbon on snow in the Cascade Range is needed due to increasing wildfire intensity and frequency. Here, the benzenepolycarboxylic acid (BPCA) molecular method was used to measure dissolved black carbon (DBC) in snow, nearby rivers, streams, and supraglacial melt collected in 2022 and 2023 from Mount Baker and Mount Rainier. The average DBC concentration in snow was 9 ± 4 μg‐C/L and 10 ± 6 μg‐C/L in stream, river, and supraglacial meltwater samples. The DBC method provides black carbon source identification via BPCA characterization. DBC concentrations and BPCA proportions were compared to modeled smoke deposition from the Navy Aerosol Analysis and Prediction System reanalysis model. In both years, total deposition from May through October was approximately 670 mg/m 2 . However, early season smoke deposition (May through July) was four times higher in 2023 than 2022, indicating seasonal variability in the timing of deposition. Dry deposition accounted for over 80 percent of total late season smoke deposition (August through October) in both 2022 and 2023, while wet deposition accounted for 75 and 30 percent of total early season deposition in 2022 and 2023, respectively. The largest smoke deposition events on Mount Baker coincided with precipitation events and enrichment of benzenepentacarboxylic acid, a marker of biomass burning, in snow. Using the Snow, Ice, and Aerosol Radiative model, we estimated an average albedo of 0.68 ± 0.03. The resulting instantaneous radiative forcing attributable to the presence of BC in snow ranged from 3 to 16 W/m 2 , with an average of 7.47 ± 3.3 W/m 2 .

The Urumqi Glacier No.1 (UGN1) and Dongkemadi Glacier (DG) catchments in China, both highly glacierized alpine catchments, provide critical water resources for downstream rivers and are sensitive to climate change impacts. Accurate simulation of glacier runoff processes remains challenging due to limited observational data. Here, we introduce FLEX G -iso, an isotope-aided glacier hydrological model that couples stable water isotopes with glacier mass balance (GMB) to simulate runoff generation and partition runoff components in these catchments. We also assess the transferability of model structures and parameters between UGN1 and DG catchments. Results demonstrate that incorporating stable water isotopes and GMB data enhances the identification of parameters controlling snow and ice accumulation and melt. FLEX G -iso provides more robust partitioning of runoff components, distinguishing throughflow and groundwater, even with limited tracer availability, and improves runoff simulation accuracy (KGE > 0.8) when transferring optimal parameter sets. These findings highlight the potential of isotope-aided modeling to improve both calibration and transferability of glacier hydrological models, offering a robust framework for understanding runoff dynamics in high mountain regions under climate change. • An isotope-aided glacier hydrological model (FLEX G -iso) was developed and validated. • The FLEX G -iso model quantifies runoff components with limited tracer data. • Coupling stable isotopes and GMB enhances model robustness and transferability.

• A hybrid modelling approach combining process-based and data-driven models was developed and tested to the large and hydrologically complex karst catchment of the Unica River in Slovenia. • The models were evaluated using 60 years of daily catchment discharge data with large variation and under the impact of changing climate and environmental conditions. • A comprehensive performance comparison between the models focusing on their behaviors during extreme flow conditions shows that the hybrid model clearly outperforms standalone process-based and data-driven models. Hydrological modeling of karst systems is difficult due to their unique recharge, drainage and discharge behavior, which is often highly dynamic and nonlinear. It becomes even more challenging for elevated karst catchments, where the recharge process is additionally influenced by snow accumulation and melting. In this study, an innovative modelling approach was developed that hybridizing a process-based model and a data-driven model for the karst systems influenced by seasonal snow cover and its application was tested to a large, complex karst system in the Unica River catchment in Slovenia. For this purpose, the process-based model Génie Rural à 6 paramètres Journalier, including the CemaNeige snow routine (CemaNeige GR6J), was hybridized with the Stacked Autoencoder Deep Neural Networks (SAE-DNN). A 60-year period of catchment discharge observations, from 1962 to 2021, was used for model development, testing and evaluation. The performance of the stand-alone models, CemaNeige GR6J and SAE-DNN, as well as the hybrid model CemaNeige GR6J-SAE-DNN, was systematically compared. The results show that the hybrid model clearly outperforms the stand-alone process-based model and the data-driven model, especially during the extreme flow conditions. Additionally, the hybrid model performs better for more recent modelling periods than for longer ones. This is due to changes in climate conditions in historical datasets, which the hybrid model is limited to capture. Overall, the proposed hybrid modeling approach offers an innovative way to robustly predict the daily discharge behavior of karst systems influenced by seasonal snow cover, especially during extreme flow conditions, and could be applied to other karst systems with similar complexity and characteristics to support robust decision making in karst water resource management.

• A comprehensive framework integrating hydrological modeling and remotely sensed crop data is proposed for agricultural water management in data-scarce regions. • Improving the performance of WRF-Hydro model in simulation of snow accumulation and melting by optimizing land surface model parameterization. • Improved crop type prediction by assembling multiple machine-learning algorithms using NDVI time-series data. • Machine learning algorithms have been ensembled to predict crop type maps, improve prediction accuracy and estimate irrigation water needs. Water resources are essential for human activities, with no substitutes. Agriculture consumes a significant portion of water to sustain food production. This study proposes a framework for agricultural water management by integrating hydrological modeling and remotely sensed crop type data in a data-scarce region. It focuses on the Amu River Basin (ARB), the largest watershed in northeastern Afghanistan, accounting for 57% of the region’s surface water. The WRF-Hydro stand-alone model was used to simulate daily discharge for three rivers from 2014 to 2019 and was calibrated and validated with the Global Land Data Assimilation System version 2. Statistical indicators were used to assess the model performance. The overall performance results for three rivers show a correlation coefficient (R) of 0.85–0.42, Nash-Sutcliffe Efficiency (NSE) 0.52 to −8.64, Killing-Gupta Efficiency (KGE) 0.74 to −0.56, and coefficient of determination (R 2 ) of 0.73–0.17. Three machine learning (ML) algorithms, namely, random forest, support vector machine, and gradient boosting, were ensembled via a maximum voting classifier to predict crop type maps for the study period. The model was trained using Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data from the Aqua and Terra satellites. A high-resolution 2020 crop type map from United Nation Food and Agriculture Organization (UNFAO) was used for training. The model predicted the five major crop types from 2014 to 2019 across eight elevation zones. Then irrigation water requirements (IWR) were estimated for major crops via the UNFAO Penman‒Monteith method. The estimated IWRs were combined with the spatially and temporally explicit crop maps derived from the multi-model ensemble to quantify irrigation water demand and assess its balance with available water under different management scenarios. This study addresses water management challenges in data-scarce regions, improving the performance of the WRF-Hydro model in snowmelt-influenced watersheds and enhancing ML model accuracy through ensemble techniques. The findings of the current study could provide a deeper understanding of the challenges and possible solutions in arid and semiarid climates in developing countries.

Snow is a vital water resource that regulates climate, supports ecosystems, and influences economies and transportation systems, making it essential to understand its physical properties and the seasonal timing of its presence (phenology). However, current snow classifications separate snow’s meteorological controls from its phenology, limiting its decision-relevance. This study introduces SnowMAP, a global snow classification system that combines snow meteorological and phenology classes. By integrating meteorological factors such as snowfall, temperature, and wind with seasonal dynamics like snow presence and melt timing, SnowMAP provides a more complete view of global snow conditions. The resulting 18 snow classes reflect notable variations in snow depth, geography, land cover, and infrastructure. SnowMAP provides a unified framework for describing how a snowpack forms and evolves, enabling scientists, planners, and communities to better understand snow conditions and their impact on the natural and built environments.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-44321-x.

Snow accumulation and ice formation can significantly reduce pavement friction, posing a serious threat to traffic safety during winter. Traditional snow-removal methods, including mechanical removal, chemical de-icing agents, and heated pavement systems, suffer from several limitations such as low efficiency, environmental impacts, and high operational costs. Electrically conductive asphalt concrete (ECAC) has therefore emerged as a promising active snow-melting technology. When an electric current passes through the conductive network formed within the asphalt mixture, heat is generated through the Joule heating effect. After incorporating conductive fillers, the electrical resistivity of ECAC mixtures can be reduced from approximately 106-108 Ω·cm for conventional asphalt mixtures to about 10-1-102 Ω·cm. Under an applied voltage typically ranging from 30 to 60 V, ECAC pavements can increase the surface temperature by 10-30 °C within 10-30 min, thereby enabling rapid snow melting and ice removal. Meanwhile, an optimized conductive network can maintain sufficient mechanical performance, with dynamic stability generally exceeding 3000 cycles/mm. When the conductive filler content is reasonably controlled, only a limited reduction in fatigue resistance is observed. This paper presents a comprehensive review of electrically conductive asphalt concrete technologies for snow-melting pavements. The background, underlying mechanisms, material development, system configuration, and field applications of ECAC are systematically summarized. Finally, the current challenges are discussed, including the stability of conductive networks, the trade-off between electrical conductivity and pavement performance, and electrical safety. Future research directions focusing on material optimization, intelligent power control, and long-term field performance evaluation are proposed to support the practical application of ECAC pavements in sustainable winter road maintenance.

Extreme weather events are increasing in frequency and intensity, but their ecological impacts remain less well understood than those of gradual climate change, largely owing to the challenge of studying unpredictable, short-lived events. The 2021 western North American heatwave is among the most extreme on record globally, yet a broad assessment of its ecological consequences is lacking. Here we synthesize meteorological, ecological, hydrological and wildfire data, along with process-based modelling, to quantify the heatwave and its impacts across the region. Our meta-analysis of 32 terrestrial and marine taxa reveals that over 75% were negatively impacted, but species responses ranged widely, from 99% declines to 89% increases. This variability reflects differences in organisms' thermal sensitivities, response capacities and exposures, with the latter dependent on geography, microclimate and refugia. Impacts tended to be greater for sessile marine invertebrates, algae and plants than for birds and mammals. At the ecosystem scale, changes in gross primary productivity ranged from 30% increases in cooler, wetter areas to 75% decreases in warmer, arid ones. Streamflow from snow and ice melt increased 40% during the heatwave before dropping below average, whereas wildfire activity surged 37% during the heatwave and 395% the following week. Our results underscore the urgent need for enhanced coordinated approaches to predict, detect and manage increasing heatwaves.

Forest treatments such as prescribed burns, mastication, and thinning are widely implemented across the western USA to reduce fuels and enhance wildfire resilience. These practices also influence snow accumulation and melt, which, in turn, affect snow storage and duration. Since many regions depend on seasonal snow for water resources, it is essential that forest management practices preserve or even enhance snow storage as a buffer against the impacts of climate change. To test the hypothesis that thinning and canopy gap creation can maximize snow storage, particularly on north-facing slopes, experimental forest treatments representing a range of thinning intensities were implemented on Cle Elum Ridge in the headwaters of the Yakima River Basin, Washington, USA. Ground-based snow observations, combined with pre-treatment (2021) and post-treatment (2023) snow-on lidar, show that canopy thinning increased snow depth and storage by 30% on north-facing slopes and by 16% on south-facing slopes. Snow depth was positively related to canopy openness, as measured by sky view fraction and canopy edge metrics, with stronger effects on north-facing slopes. In contrast, there was no clear relationship between snow depth and degree of thinning as measured by forest basal area, a common forestry metric used to plan treatment prescriptions. Using canopy edge metrics and sky view fraction relationships, we estimated the hydrologic benefit of thinning during 2023 at 12.3 acre-feet of water storage per 100 acres of north-facing forest and 5.1 acre-feet on south-facing slopes. These findings highlight the potential to incorporate hydrologic resilience as a co-benefit when planning fuel reduction strategies.