Search Results (281)

Glacier forelands are well-suited for investigating vegetation development and plant colonization dynamics in previously uninhabited areas. The areas exposed by retreating glaciers represent sites without seed banks; thus, plant colonization almost always represents a true primary succession. Vegetation dynamics relies on diaspores brought in from the surrounding. A common approach for studying long-term vegetation developments is the employment of so-called chronosequences, deriving temporal sequences from spatially different sites of various site ages. In glacier forelands, datable traces of former glacier extents are generally used for site age estimation. In 2009 and 2010, vegetation sampling for ten points in time since deglaciation was carried out in the foreland of Schwarzenbergferner, Ötz valley, Austria. The study area extends between the recent glacier terminus and the terminal moraine left by the Little Ice Age glacier during the maximum extent (around 1850). The vegetation survey is supplemented by a one-year long measurement of soil temperature (1 September 2010–31 August 2011), which allows not only for an assessment of the seasonal course of the topsoil thermal conditions across the chronosequence, but also for the determination of the snow cover duration and the length of the vegetation period. This article summarizes the vegetation development and microclimatic conditions along the chronosequence and discusses the results, considering the interplay between the time since deglaciation and elevation being a microclimatological proxy. Full article

Winter ice-cover periods pose substantial risks to Apostichopus japonicus aquaculture in northern China. We combined field monitoring of three representative aquaculture ponds between 2014 and 2017 with controlled single-factor laboratory exposures to low temperature, hypoxia, and hyposalinity in adult and juvenile sea cucumbers. Field observations revealed pronounced temporal, vertical, spatial, and interannual variability in water temperature, salinity, and dissolved oxygen (DO) during freezing and ice-melting periods. All three stressors caused clear deterioration in physiological condition, with juveniles generally exhibiting greater short-term sensitivity than adults under low-temperature and hypoxic exposure, whereas severe hyposalinity caused pronounced deterioration in both life stages. Lactate, malondialdehyde (MDA), and glutathione (GSH) showed distinct treatment- and time-dependent responses, consistent with stress-associated changes in anaerobic metabolism, lipid peroxidation, and glutathione-associated antioxidant status. Integration of field observations with laboratory responses indicated that water temperatures approaching 0 °C and salinities approaching approximately 20‰ represent environmentally relevant conditions associated with elevated overwintering risk. DO concentrations approaching approximately 3 mg/L may warrant intensified monitoring and management intervention, whereas 2 mg/L represents a more severe experimental hypoxia condition. The more extreme treatments of −2 °C and 15‰ should similarly be regarded as severe experimental scenarios rather than commonly occurring field conditions. These environmental values should therefore be interpreted as preliminary management-oriented risk references rather than definitive physiological thresholds. Overall, the study provides an empirical field-to-laboratory basis for biologically informed winter environmental monitoring and risk-based overwintering management of A. japonicus aquaculture. Full article

Permafrost degradation is intensifying differential settlement, structural deformation, and secondary periglacial hazards along linear infrastructure in cold regions, underscoring the need for monitoring approaches that integrate corridor-scale screening with fine-scale quantification. This study investigated highways, railways, transmission tower foundations, and buried pipelines in the permafrost region of Northeast China using multi-temporal UAV-borne LiDAR point clouds and synchronous visible-light imagery acquired by a DJI Matrice 300 unmanned aerial vehicle equipped with a DJI Zenmuse L1 sensor (DJI, Shenzhen, China). A synergistic optical–LiDAR framework was developed for distress identification and multidimensional quantification. The overall root mean square errors (RMSEs) at flight altitudes of 50 m and 100 m were 3.25 cm and 4.13 cm, respectively. By integrating texture and boundary information from synchronous visible-light imagery, elevation and volumetric metrics from LiDAR-derived digital elevation models (DEMs) and digital surface models (DSMs), and structural attitude parameters extracted from three-dimensional (3D) models, the framework enabled the parametric quantification of pavement cracking, differential shoulder settlement, railway embankment slump, transmission tower inclination, thaw settlement and ponding in pipeline trenches, and secondary icing. Snow-depth retrievals agreed well with field measurements (R² = 0.87, RMSE = 1.32 cm), indicating that UAV-LiDAR can extend monitoring into snow-covered periods. These findings provide a methodological basis for distress detection, screening of hazard-prone sections, and risk-informed operation and maintenance of linear infrastructure in permafrost regions. Full article

The spring floods that occurred in 2024 in western and northern Kazakhstan caused extensive damage. Our understanding of runoff formation processes under frozen-soil conditions remains limited. In this study, we apply a coupled hydrothermal model as a case study to explicitly simulate vertical heat and water transport, phase transitions, snow dynamics, and reduced infiltration capacity due to cryogenic pore blockage (ice-filled pores). The model is based on regular meteorological data from 65 stations in five regions covering the full hydrological cycle (August–May) of 2021 and 2024. A multilevel diagnostic check showed that soil temperature is reproduced with a median $R^2$ of 0.962 and NSE of 0.888, the frozen/thawed surface condition corresponds to WMO (World Meteorological Organization) standards on approximately 91% of days, and water balance agreement reaches 86.2% (56 out of 65 stations). The model reflects the regional variability of the 2024 flood. In the northern regions (Kostanay, North Kazakhstan), snowfall was above average, and modeled runoff increased compared to 2021 (for example, at the Sergeevka station, it increased by a factor of four). In the western regions, the trends were mixed: the strongest relative increase in runoff was recorded in the Atyrau region (+119%), whilst in the West Kazakhstan region, the increase was more modest (+19%), and in the Aktobe region, runoff increased by 60%. The key mechanism—the time lag between rapid snowmelt and delayed soil thaw—is clearly evident: peaks in snowmelt occur when the soil remains frozen, infiltration capacity decreases, and the runoff potential index (RPI) exceeds 1 for extended periods. Although the model does not simulate the river channel, its ability to diagnose runoff generation conditions at the slope scale offers a diagnostic framework for identifying runoff-conducive conditions in regions with limited data, rather than a physically validated tool for flood-prone area identification. The results show that the 2024 flood period was characterized by abnormally high water inflow and hydrothermal conditions consistent with a temporal mismatch between water supply and the recovery of soil infiltration capacity. Because the RPI is a diagnostic indicator constructed from water input and infiltration capacity, these results should be interpreted as evidence of conditions conducive to runoff generation rather than as an independent causal verification of the flood mechanism. Full article

Cyanobacterial blooms occur frequently in lakes worldwide, disrupting aquatic ecosystem balance and directly threatening drinking water safety and fisheries production. Establishing a reliable bloom early-warning system has therefore become an urgent priority for lake water management. This study adopts a structured narrative review approach to synthesize the major early-warning methods, including indicator threshold methods, statistical and empirical models, mechanistic models, machine learning, and remote sensing monitoring. These methods are compared in terms of their fundamental principles, data requirements, predictive capabilities, applicability, interpretability, and computational and maintenance requirements. Emerging trends in multi-source data fusion, multi-model integration, and the development of integrated early-warning systems are also summarized. The findings indicate that each method has distinct strengths and limitations with respect to forecasting lead time, spatial coverage, process interpretation, and operational costs, and that no single method can simultaneously meet the requirements of multiscale bloom monitoring and forecasting. Integrating multi-source data from in situ monitoring, remote sensing observations, and meteorological and hydrological measurements, while coordinating statistical models, mechanistic models, and artificial intelligence algorithms according to specific forecasting objectives, represents an important pathway for improving the robustness and operational applicability of early-warning systems. Given the pronounced seasonal ice cover, substantial hydrological variability, limited monitoring data, and marked regional heterogeneity of some cold and arid region lakes, future research should strengthen high-frequency monitoring during critical periods, promote coordination between remote sensing and in situ observations, and conduct local calibration of early-warning thresholds and model parameters. Season-specific models should also be developed to account for environmental differences among ice-covered, ice-off transition, and open-water periods. Overall, early warning of cyanobacterial blooms in lakes is evolving from the application of individual methods toward the integration of multi-source monitoring, multi-model integration, and decision support, thereby providing a reference for bloom risk prevention and water environment management across different types of lakes. Full article

Ice–snow tourism in Northeast China is developing rapidly. Against this backdrop, revealing the spatial network structure of ice–snow tourism cities and assessing their disturbance resistance capacity is of great significance for achieving high-quality development of regional ice–snow tourism. This study takes 25 cities across the three northeastern provinces as network nodes, using data covering the period from January 2024 to March 2025. Integrating a complex network analysis framework, this paper comprehensively employs an accessibility model, tourism symbiotic linkage intensity model, and core–periphery model to distinguish core and peripheral cities within the network, analyze its structural characteristics and spatial patterns, and evaluate network vulnerability by simulating two scenarios: random attacks and deliberate attacks. The results indicate that: (1) Accessibility presents a concentric zonal pattern that attenuates gradually from the center to the periphery, accompanied by pronounced north–south disparities. Urban symbiosis intensity is strongly influenced by transportation distance, exhibiting a distinct proximity symbiosis pattern. (2) An ice–snow tourism symbiotic network has initially taken shape among northeastern cities. The network displays small-world properties; however, urban development is unbalanced, with marked hierarchical differentiation. Based on geographic location and resource endowments, the network can be divided into four cohesive subgroups. (3) The symbiotic network proves robust under random attacks, whereas connectivity declines sharply under deliberate attacks, embodying typical “robust-yet-vulnerable” structural characteristics. Both expanding the scale of core nodes and optimizing inter-node connection weights can significantly enhance network robustness. The static identification and dynamic dependency evaluation framework constructed in this study can effectively identify key nodes and vulnerable links within ice–snow city networks, and can serve as a reference for the coordinated development and structural optimization of ice-snow tourism in Northeast China. Full article

This study examines why ice covers on the Churchill River in Labrador crack during winter and how weather, river flow, freezing conditions, and riverbed features contribute to these events. Using data from 2010 to 2025 and satellite imagery, the study shows that cracks most often occur in December to February when heavy snow, rapid flow changes, or long cold periods place stress on the ice. Cracking also frequently starts near sandbars where the ice is weaker. The results highlight that no single factor causes cracking. Instead, a combination of snow load, temperature, flow variability, and local river conditions determines when and where cracks form. There is also a disconnect from flow regulation since cracks also formed in 2012 before the construction of the dam began in 2015. A field survey was also carried out employing a combination of borehole jack (BHJ) testing and ground-penetrating radar (GPR) surveys to quantify spatial variations in ice strength and thickness across a portion of the lower Churchill River across two sandbars. In situ BHJ measurements were conducted at multiple sites to determine confined compressive ice strength under both floating and grounded conditions, revealing substantial local variability linked to differences in ice support and the presence of white versus black ice. Complementary GPR transects using 500 MHz and 1000 MHz systems provided high-resolution profiles of ice thickness and internal structure, enabling identification of transitions between grounded and floating ice. The integrated BHJ–GPR approach allowed direct comparison between point-scale strength measurements and spatially continuous thickness and grounding patterns, demonstrating that grounded ice and ice containing higher proportions of white ice exhibited more complex stress states and greater variability in mechanical response. Together, these measurements highlight the importance of combining geophysical surveying with in situ mechanical testing to better understand how environmental conditions control ice integrity and potentially influence ice-jam lodgement propensity along regulated subarctic rivers. Full article

Ground-surface temperature (GST) in maritime Antarctic ice-free areas is influenced by atmospheric forcing, snow cover, surface energy and topography. Previous PERMATHERMAL studies in Livingston and Deception Islands have shown changes in air and ground-surface thermal regimes, with fewer cold conditions, greater thawing influence and strong snow-cover modulation. However, the interval in which GST responds effectively to radiative and topographic forcing remains poorly explored. We characterize the station- and season-specific timing of the thermally effective GST thawing period and evaluate topographic and modeled potential controls on its thermal intensity and cumulative effect around the Spanish Antarctic Station Juan Carlos I, Hurd Peninsula, Livingston Island. Onset and end were objectively delimited by using three consecutive days with daily mean GST > 0.5 °C and daily thermal amplitude > 1.0 °C. Hourly GST records from six PERMATHERMAL stations were combined with potential radiation, potential insolation and topographic variables derived from a high-resolution UAV-based DEM. Accumulated thawing degree days were strongly influenced by period duration. Mean thermal intensity was primarily associated with elevation, while mean modeled potential radiation provided additional explanatory power only when combined with elevation. This UAV–GIS–GST approach provides a simple framework for assessing local surface–atmosphere coupling in remote Antarctic ice-free areas. Full article

The “New Arctic” regime represents a prominent climatic feature of the Arctic Ocean under global warming, characterized by persistently low summer sea ice extent, a marked reduction in sea ice thickness, and an expansion of open water areas at high latitudes. As a key indicator of the Arctic sea ice system, the spatiotemporal evolution of sea ice thickness and its underlying driving mechanisms remain incompletely understood. Using reanalysis datasets and remote sensing observations, this study identifies major abrupt shifts in Arctic sea ice thickness under the New Arctic regime, reveals the spatiotemporal distribution characteristics of winter sea ice thickness, and examines the driving factors from both thermodynamic and dynamic perspectives. The results show that the evolution of Arctic sea ice thickness can be divided into three phases: a high-level period during the “Traditional Arctic” (1979–1992), a rapid thinning period during the New Arctic transition (1993–2012), and a low-level stabilization period in the New Arctic regime (2013–2023). The first EOF mode of winter sea ice thickness depicts a spatially consistent thinning pattern across the entire Arctic, with the most significant reduction occurring in the multi-year ice regions north of the Canadian Arctic Archipelago and Greenland. The second EOF mode exhibits an out-of-phase variation between the Atlantic and Pacific sectors of the Arctic, accompanied by a shrinking amplitude and weakened regional oscillations. The coupling between surface air temperature and sea ice thickness displays distinct phase dependence: their negative correlation is strongest during the transition period (r = −0.78, p < 0.001) but becomes statistically insignificant in the New Arctic regime. Sea ice motion speed exhibits an overall accelerating trend, which extends from the marginal seasonal ice zones toward the high-latitude multi-year ice regions, accompanied by a notably enhanced sensitivity of sea ice motion to wind forcing. Sea ice volume flux through the Fram Strait is primarily controlled by ice motion speed, whose contribution to the flux is approximately 2.6 times that of ice thickness. The recovery of ice drift speed offsets the thinning of sea ice cover, leading to a partial rebound in volume flux during the New Arctic steady state. This study identifies the evolutionary patterns and drivers of Arctic sea ice thickness under the New Arctic regime, providing a scientific basis for further understanding the changes in the Arctic climate system and associated air–sea ice interactions. Full article

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines whether this enhanced physical representation of snow processes improves the accuracy of streamflow simulations. The analysis was conducted across ten watersheds in Quebec, Canada. The multilayer snow model consistently improved low-flow simulations during both calibration and validation periods and enhanced the representation of the falling limb during the calibration period. However, the monolayer snow model performs slightly better during the rising limb of the freshet season for the calibration phase. In addition, the multilayer configuration reduced the bias of the cumulative freshet volumes and annual maximum freshet discharge. Overall, the multilayer snow model achieved comparable performance to the monolayer model for high-flow simulations while outperforming it for low-flow conditions, leading to a more accurate representation of freshet volumes and falling limb dynamics. Full article

Energy harvesting is an emerging approach that supports the generation of renewable and clean energy, while also augmenting the durability and stability of infrastructure. The paper aims to review applicable energy harvesting systems deployed in various types of infrastructure, including roads and bridges, for applications such as photovoltaic noise barriers, photovoltaic cells, piezoelectric devices, and thermoelectric units. The harvested energy can be utilized to generate electricity, power wireless sensors, melt ice, and provide heating or cooling, while also assisting in monitoring structural conditions. Each energy harvesting technology is described in detail, covering operational principles, application scenarios, prototype development, and key findings from the literature. Economic feasibility studies are also examined to allow for a comparative assessment of energy output, production costs, and cost-effectiveness. This review provides a comparative feasibility framework integrating energy performance, levelized cost of electricity, payback period, and technology readiness levels for infrastructure-based energy harvesting systems. Full article

The presence of ice cover significantly alters the hydraulic characteristics of river channels, and the evolutionary law of ice drift velocity is crucial for understanding the ice-jam floods (IJFs) formation mechanism during the spring IJFs breakup period. Based on miniature ice buoy locators and Sentinel-2 satellite remote sensing data, this study systematically analyzes the channel characteristics of the upper Heilongjiang River and the regulatory effect of channel morphology on ice drift velocity. The results show that the river width of the upper Heilongjiang River exhibits a widening trend, with a variation range of 212 to 1292 m, characterized by large longitudinal dispersion and significant spatial variability. During the 2024 spring IJFs breakup period, the ice drift velocity ranges from 0.57 to 3.48 m/s with an average of 1.92 m/s, and a significant decreasing trend is observed when the ice drift passes through the entrances/exits of meandering bends and the confluences of distributaries in braided channels. The longitudinal distribution law of ice drift velocity revealed in this study can provide key data support and scientific reference for the accurate prediction of IJFs and the prevention and control of IJFs. Full article

Recent studies have shown that lidar observations from the Ice, Clouds, and Land Elevation Satellite-2 (ICESat-2) enable seasonal snow depth retrieval over land through two primary approaches. The snow-on–off method estimates snow depth by differencing surface elevations acquired during snow-covered and snow-free periods, while the pathlength method derives it from multiple-scattering photon distributions within the snowpack. In this study, we cross-validate ICESat-2-derived surface elevations and snow depths against in situ measurements from SnowEx field campaigns. ICESat-2 surface elevations agree closely with SnowEx data, which we consider closest to the truth, achieving centimeter-level accuracy (e.g., 1 cm) over flat, sparsely vegetated terrain, with larger biases in vegetated and steep areas. Snow depth estimates from both methods show comparable performance in the tundra area, with typical errors on the order of tens of centimeters; however, in vegetated or steep terrain, the pathlength method yields more reliable snow depth results, being less affected by slope and vegetation than the snow-on–off method. These findings show that ICESat-2 is a reliable tool for measuring snow depth from space. Full article