Environmental contexts mediate the dual impacts of snow cover on vegetation green-up: A key challenge of phenological prediction under climate change

ABSTRACT Photovoltaic (PV) systems are an invaluable green power solution to the energy requirements in the world yet as they are placed outside, they are subject to numerous issues and environmental challenges that may lead to loss of efficiency in the systems and even system failures. Thus, early and accurate fault diagnosis is necessary to minimize downtime and costs of maintenance. This paper proposes an automated PV-based fault diagnosis system with the aid of a pre-trained image-based InceptionV3 convolutional neural network, which can find faults in panels. The model was trained and tested on a diverse dataset of 885 images to classify six categories of conditions, including physical degradation, electrical degradation, bird droppings, dust, snow coverage, and fault-free panels. Using transfer learning and extensive data augmentation, the model achieved a good overall test-based accuracy of 85.71%. The comprehensive analysis of the performance showed that it was highly suitable for detecting Dusty, Clean, and Bird-drop classes with high F1-scores. However, the model struggled with the lower recall Physical-Damage class, indicating that it was not fully reliable in detecting subtle physical defects such as cracks. The results, complemented by ROC curves and confusion matrices, determine the extent to which the InceptionV3 architecture is appropriate for this task and highlight some diagnostic limitations. This article is important in providing more reliable and cost-effective solar energy production by creating a clear and methodologically rigorous standard for automated PV fault detection.

ABSTRACT In remote Canada, sparse meteorological stations hinder reliable snow data collection, especially for assessing historical records. Yet, these data are crucial to evaluate the effect of climate change on ecosystems and species. Changes in snow cover influence wildlife ecology and can impact body condition, survival, or reproduction. This study validated the SNOWPACK model, forced with North American Regional Reanalysis (NARR) data, to estimate snow cover data for Ram Mountain, Alberta. We used snow depth data from five stations covering the 2022–2023 and 2023–2024 snow seasons for validation. We adjusted NARR radiation and precipitation data and SNOWPACK phase change temperature to improve model fit with snow depth and end date observations. The simulation, after bias calibration, showed an average error of less than 6 cm for snow depth and plus or minus sixteen days for the snow cover end date over the two winters. This parameterized model was then used to reconstruct snow cover data from 1979 to 2024. Though no clear trend in snow cover duration or start/end dates emerged, snow depth increased by an average of 5 mm/year. Our findings suggest that SNOWPACK can estimate snow cover characteristics in remote areas, making them valuable for reconstructing long-term snow cover trends in alpine environments.

Accurate prediction of the energy yield of bifacial photovoltaic (PV) modules requires a proper evaluation of albedo irradiance and the associated mismatch losses. In this work, an advanced tool for the assessment of the power production of bifacial modules is presented. The tool benefits from a refined numerical evaluation of ground-reflected irradiance performed through a view-factor-based cell-level approach within a realistic three-dimensional (3D) Sun-module-shadow geometry. This allows capturing both vertical and lateral nonuniformities in the irradiance distributions over the module surfaces, which are neglected in conventional module-level models. The irradiances incident on the cells are subsequently supplied to a circuit-based block, operating with a cell-level granularity as well, which computes the I–V characteristics and the maximum power point (MPP) at selected time instants. Simulations performed on a simplified tool variant assuming uniform albedo irradiance show that this approximation leads to a non-negligible overestimation of power output. An extensive comparison against state-of-the-art tools, including the previous version of our framework, allows us to conclude that the proposed method is especially advantageous for standalone modules or short-row configurations under medium-to-high albedo conditions. Moreover—like its previous version—the tool can handle a large variety of detrimental effects, namely, partial architectural shading, localized snow coverage, bird droppings, and faulty cells. Additionally, a non-zero elevation from the ground can be effectively described. It is also found that south-oriented 30°-tilted bifacial modules suffer from appreciable albedo-induced mismatch losses on the rear surface during summer under medium-albedo conditions, whereas vertically-mounted West- and East-oriented configurations are less affected by such losses. Experimental validation confirms the accuracy of the proposed framework.

The Altai Mountains, a complex mountain system of Central Asia, is particularly sensitive to global change. Under increasing human activities and continuing climate change, the range of animals may show expansion or contraction. In this study, we evaluated and predicted the distribution dynamics of 27 animal species and the resulted change of species richness in the Altai Mountains by using MaxEnt model in the current and future periods. The results show that most species are predicted to mainly distribute in the northwest of the Altai Mountains under current conditions. In the future, habitats located in the central region may be largely lost. Most species tend to shift their ranges towards higher altitudes or latitudes. Human activities, snow cover and precipitation of coldest quarter are the most important predictors explaining the potential distributions of most species. As global climate change continues to alter potentially suitable habitats, we recommend to establish a transboundary protected area across the four countries (China, Kazakhstan, Mongolia and Russia) in the central region of the Altai Mountains. Additionally, we suggest reducing potential anthropogenic impacts on wildlife and their habitats by regulating human activities.

The development of passive microwave sensors traces back to Robert Dicke's pioneering experiments in the 1940s. Since then, microwave radiometry has evolved into a key tool for Earth observation, strengthened by data from multiple satellite missions operating across different wavelengths. This paper reviews the state of the art in microwave radiometry for monitoring land surfaces. After introducing the theoretical foundations underpinning current missions, we present an overview of major satellite instruments. We then examine early theoretical advances in retrieving soil moisture and snow properties, two applications that contributed to the future development of satellite microwave radiometry missions for the observation of surface variables. Particular attention is given to radiative transfer theory and its solutions, which model the effects of roughness, vegetation, and snow cover. These approaches form the basis of today's retrieval algorithms and remain central to future missions. Subsequent sections highlight the use of passive microwave data for estimating a variety of surface variables, the role of passive microwave in data assimilation systems and forthcoming missions dedicated to land monitoring. The review concludes with key achievements, ongoing challenges, and open issues-such as soil moisture retrieval under dense vegetation or snow property retrieval in melting conditions. Addressing these limitations is critical to fully exploiting microwave radiometry in the context of climate research and mitigation strategies.

Increasing temperature and decreasing precipitation threaten the extent, persistence, and dynamics of snow across spatial scales, particularly ephemeral snow in Mediterranean mountain regions. This study estimates ephemeral snow cover and snow water equivalent (SWE) in the Sierra de San Pedro Mártir, Baja California, Mexico, using open-access datasets and remote sensing. Camera trap images and limited in situ data were used to calibrate the normalized difference snow index (NDSI) for snow detection and to estimate SWE and topographic effects on SWE from 2002 to 2023, encompassing wet, dry, and normal years. The optimal NDSI threshold for snow detection was 6.4 for MODIS Terra and 5.3 for MODIS Aqua, substantially lower than thresholds commonly reported for seasonal snowpacks in forested regions. In wet years, snowfall contributed up to 20% of annual precipitation, compared with ~13% in dry years. In normal years, the average SWE is 70 mm (24% of annual precipitation). SWE increased by 30% (91 mm) during wet years and decreased by 21% (55 mm) during dry years. Eastness (aspect) was the only statistically significant topographic predictor of SWE for MTerra, with higher SWE values observed on west-facing slopes. This study provides the first quantitative assessment of ephemeral SWE dynamics in a Mexican Mediterranean mountain system and establishes a framework for monitoring marginal snowpacks under increasing climatic variability.

Abstract. LISFLOOD is a continental, operational hydrological model widely used in Europe. Among various hydrological processes, it simulates snowmelt using a degree-day approach, where the snowmelt coefficient is typically calibrated against discharge data. This study evaluates LISFLOOD’s current snow module and investigates the effects of a post-replacement of the snowmelt coefficient across nine European basins with varying snow influence. The parameter is calibrated using Earth Observation (EO) snow cover fraction (SCF). The outcomes illustrate the extent to which a parsimonious calibration of a single model component, intended to improve a specific module, affects hydrological model performance. To this purpose, we integrate Sentinel-2 and MODIS data to address issues related to gaps and misclassifications in snow detection over complex terrain and obtain an improved reference snow cover. Using EO SCF, we estimate a spatially distributed snowmelt coefficient, in contrast to the uniform values currently used in LISFLOOD. The coefficients are optimized by matching modelled and observed SCF, and their hydrological impacts are assessed while keeping all other model parameters unchanged. This enables us to test whether modifying only the snowmelt coefficient affects discharge performance, and whether the standard calibration adequately represents both snow dynamics and streamflow. Compared with EO SCF, the standard calibration showed biases from −1 % to 22 % and RMSE values from 20 % to 55 %. The EO-based proposed approach improved both bias and RMSE by up to 8 %. In general, the optimized coefficients did not significantly change the simulated discharge at the basin level in terms of KGE, but their application led to noticeable divergences in discharge within smaller upstream catchments. Moreover, the improved representation of snow cover led in some cases to shifts in the timing and magnitude of snowmelt and total runoff. These findings highlight the potential of integrating EO data to calibrate the snowmelt coefficient without changing other calibration parameters. This approach may offer practical advantages in situations that require accurate snow cover representation without the need for a complete re-calibration, which may be overly onerous. Nevertheless, our results indicate that standard calibration procedures already provide an acceptable representation of snow dynamics.

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.

Abstract. Snow cover is a key variable for climate monitoring and hydrological applications, yet optical satellite observations are strongly limited by cloud contamination, particularly during winter. The EUMETSAT H SAF H34 snow product derived from MSG-SEVIRI uses the unique 15-minute temporal resolution over the full SEVIRI disc to clear the clouds, but persistent clouds still cause substantial data gaps. In this study, we present a cloud-gap reconstruction framework that combines Numerical Weather Prediction data with machine learning to infer snow presence beneath cloud-covered pixels in the H34 product. Skin temperature, snow depth, and snow temperature fields from the Integrated Forecast System (IFS) were used as physically consistent predictors and resampled to the H34 grid, together with elevation information from SRTM. An XGBoost-based model was trained using cloud-free H34 snow observations and applied exclusively to cloud-contaminated pixels to estimate the probability of underlying snow presence. Pixels exceeding an 80% probability threshold were reclassified as snow. The approach was applied to the winter seasons of 2024 and 2025 and validated over the European Alps using in-situ snow observations from World Meteorological Organization (WMO) stations. Evaluation using probability of detection (POD), false alarm ratio (FAR), and overall accuracy (ACC) shows a clear improvement in snow detection under cloudy conditions, with a significant reduction in missing observations. Compared to conventional temporal gap-filling methods, the proposed framework reduces reliance on temporal interpolation by directly exploiting physically meaningful meteorological information, while preserving the high temporal resolution advantage of MSG-SEVIRI.

Abstract. This study evaluates the regional variability of the number concentration of ice-nucleating particles (INPs) between the two pre-Alpine central-European sites of Eriswil, Switzerland, and Hohenpeißenberg, Germany, supported by INP measurements from Melpitz, Germany, during the winter months of 2024. The aim of the study is to spatially and temporally evaluate INP availability and removal within the planetary boundary layer (PBL) during Bise situations because reasons for the lack of ice and precipitation in the supercooled clouds observed over the Swiss Plateau remain unclear and may be caused by the lack of INPs. Target scenario of the study were situations when northeasterly winds (so-called Bise winds) prevailed and layers of stratus clouds formed at the top of the PBL at temperatures down to −10 °C. In these situations, it is expected that INPs are depleted along the transport path. The main insights from INP measurements were: first, during the cold-Bise (cloud minimum temperatures as low as −10 °C) and warm-Bise (cloud minimum temperatures above 0 °C), almost no INP contrast was found between Hohenpeißenberg and Eriswil if both were within the PBL. Also, the INP concentration was overall found to be much lower during the cold-Bise than during the later warm-Bise situation. Second, when the Hohenpeißenberg site was located in the free troposphere during the cold-Bise situation, INP concentrations were much higher compared to Eriswil (still within the PBL) but similar to cloud-free Melpitz. These observations led to the conclusion that during cold-Bise situations the INP reservoir within the PBL is depleted, likely by the presence of supercooled stratus. The inversion-capped wintertime PBL, especially during periods of widespread snow cover, is apparently not capable to replenish the INP reservoir from the free troposphere.

Quantifying the impact of snowfall and snow cover on runoff in the Tibetan plateau using a modified Budyko framework.

Investigating Surface Changes in Snow Cover Concerning Land Surface Temperature, Evapotranspiration, and Vegetation Cover in the Aras Basin

X-ray fluorescence determination of fluorine in snow cover solid phase for investigation of aluminum industry emissions

Combined toxicity of chloride-based and eco-friendly deicers with nanoplastics on Lemna minor and Salvinia natans.

Competition release dominates growth recovery of four subtropical broadleaved tree species following an extreme snow event

Abstract The Arctic's rapid transformation due to climate change significantly impacts Northern Eurasia. Eastern Siberia experienced increased summer precipitation and permafrost thaw in the mid-2000s, leading to wetter surfaces and higher river runoff. Furthermore, Arctic warming is linked to winter cooling in Eurasia, indicating a major disruption in the interconnected Arctic Ocean–atmosphere–vegetation–permafrost–river system. Research on these changes in Northern Eurasia focuses on the water cycle, particularly summer rainfall and winter snowfall, which are crucial for water resources and climate feedback. Japanese research institutions have played a vital role since the 1990s, collaborating with Russian and Mongolian counterparts through projects, integrating field observations, remote sensing, and modeling. Understanding changes in Eurasian precipitation and atmospheric water vapor transport is crucial for assessing the impacts of Arctic climate change, particularly considering westerly, poleward, and southward transport. Summer precipitation is influenced by the recirculation of water vapor resulting from repeated cycles of precipitation and evapotranspiration over land areas, and potentially "Siberian Atmospheric Rivers." Decadal atmospheric circulation shifts, possibly amplified by warming, have contributed to events like the East Siberian wet period. In winter, Arctic warming paradoxically links to both less snow cover and extreme cold snaps with heavy snowfall in Eurasia due to increased evaporation from reduced Arctic sea ice along the Eurasian side. The "Warm Arctic, Cold Eurasia" (WACE) pattern is debated, with models suggesting that it may be part of a larger atmospheric variability. Eastern Siberian boreal forests, adapted to permafrost, utilize both rainwater and meltwater within the soil active layer. The wet period of 2004–2010 significantly altered surface water dynamics, initially increasing evapotranspiration but eventually causing waterlogging and shifts in vegetation and permafrost near the surface. Major Siberian rivers significantly contribute to the Arctic Ocean's freshwater inflow. Satellite data revealed an increase in terrestrial water storage in the Lena River basin during the wet period. These changes, along with permafrost dynamics, directly influence river runoff, with the wet conditions leading to summer flood peaks. Future research should consider multi-scale interactions, long-term climate change, and feedback processes to understand these complex and interconnected environmental changes in Northern Eurasia.

Abstract. Atmospheric Rivers (ARs) are narrow bands of concentrated moisture that transport water vapor from the tropics to higher latitudes. They are responsible for ∼ 90 % of poleward water vapor transport and play a vital role in water resource management along the North American west coast. While ARs significantly contribute to regional water supplies, they are also major drivers of flooding. This study investigates the extent to which ARs contribute to compound inland flooding (CIF) events where multiple drivers intensify flood risks, namely Rain on Snow (ROS) and Saturation Excess Flooding (SEF) events. Furthermore, the influence of anthropogenic climate change is investigated relative to internal climate variability. Using the CanRCM4 Large Ensemble simulations, we analyze the frequency and seasonality of AR-driven CIF events in Western North American coastal areas, with emphasis on interactions between ARs and antecedent snowpack and soil moisture. ARs are found to be dominant drivers of CIF events by contributing to the development and intensification of these events. These conditions also shape the seasonality and intensity of AR-driven CIFs. Projections suggest that internal climate variability can significantly contribute to future uncertainty in CIF frequency and intensity, complicating efforts to predict and mitigate these events. The findings underscore the importance of integrating AR-related flooding risks into flood management strategies and infrastructure design to adapt to a changing climate.

ABSTRACT This study examines changes in high-flow and flood characteristics in the upper Ialomița River catchment (Romania) through a comparative analysis of maximum discharge series (monthly and annual) recorded at nine gauging stations over two 30-year periods: 1961–1990 (reference period) and 1991–2020. Variations in key climatic parameters influencing streamflow (air temperature, precipitation, and snowpack) were also analyzed using data from five weather stations to identify potential changes that could explain the observed alterations in maximum streamflow behaviour. Results indicate an overall increase in the magnitude of annual maximum floods and monthly maximum discharges during 1991–2020 compared to the reference period, particularly in March–April, June, and August–October. The total number of months with maximum discharges exceeding specified thresholds rose by up to 20%. The highest increases (of up to 55–60%) were observed in the frequency of floods with peaks above the 3% and 10% exceedance thresholds. The identified changes in maximum streamflow can be attributed primarily to regional warming (especially during winter), and to shifts in precipitation and snow cover regimes. In the case of rivers influenced by dams and reservoirs, however, the direct climatic signal is less discernible due to their regulating effects.

Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTM). Among the various sources of uncertainty in RTM simulations, snow–ground reflectivity—used as a boundary condition—plays a critical role in influencing the accuracy of simulated backscatter. This study leverages high-resolution X- and Ku-band synthetic aperture radar (SAR) backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in situ snow pit observations in Grand Mesa, Colorado, and uses a Bayesian MCMC parameter optimization model with RTM framework to estimate the key ground parameters such as surface roughness, moisture content, and specular-to-total reflectivity ratio (STRR) governing the estimation of the snow–ground reflectivity and quantify the uncertainties associated with them. At the X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, increasing the STRR produced an additional ~1.0 dB decrease while the dielectric properties of the ground are highly sensitive to the moisture content of frozen soil, and increasing the moisture content even by 2% increased the backscatter by 2–3 dB. The retrieval sensitivity to the STRR is minimized in the 0.6–0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that the extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study provides a robust methodology for the estimation of the snow–ground backscatter boundary condition for forward modeling, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here is general and, along with the model uncertainty, is directly transferable and broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring.