Search Results (228)

Snow serves as a crucial water source for vegetation growth on the Mongolian Plateau, and its temporal and spatial variations exert profound influences on terrestrial vegetation phenology. In recent years, global climate change has led to significant changes in snow and vegetation start of growing season (SOS). Therefore, it is necessary to study the mechanism of snow cover on vegetation growth and changes on the Mongolian Plateau. The study found that the spatial snow cover fraction (SCF) of the Mongolian Plateau ranged from 50% to 60%, and the snow melt date (SMD) ranged from day of the year (DOY) 88 to 220, mainly concentrated on the northwest Mongolian Plateau mountainous areas. Using different SOS methods to calculate the vegetation SOS distribution map. Vegetation SOS occurs earlier in the eastern part compared to the western part of the Mongolian Plateau. In this study, we assessed spatiotemporal distribution characteristics of snow on the Mongolian Plateau over the period from 2001 to 2023. The results showed that the SOS of the Mongolian Plateau was mainly concentrated on DOY 71-186. The Cox survival analysis model system established SCF and SMD on vegetation SOS. The SCF standard coefficient is 0.06, and the SMD standard coefficient is 0.02. The SOS_NDVI coefficient is −0.15, and the SOS_NDGI coefficient is −0.096. The results showed that the vegetation SOS process exhibited differential response characteristics to snow driving factors. These research results also highlight the important role of snow in vegetation phenology and emphasize the importance of incorporating the unique effects of vegetation SOS on the Mongolian Plateau. Full article

The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article

The reflective property (albedo) of the ground plays an important role in the performance of bifacial photovoltaic modules. Snow, as a natural light-colored surface, reflects most of the light that falls on it. However, snow does not have a fixed albedo value. Therefore, it is essential to investigate the high albedo provided by snow in bifacial panels, which are becoming increasingly common. The albedo value of snow is influenced by numerous factors, including the precipitation characteristics of the snow, its depth, and the time since the previous snowfall. This study aims to investigate the impact of snow cover and the number of days with snow cover on the energy production of bifacial panels. An innovative dynamic albedo model integrating the snow type, depth, and duration was developed to advance bifacial PV system performance analysis under various snow and climate scenarios. PVsyst simulations were conducted to analyze the annual energy yield of bifacial photovoltaic panels in Erzurum Province under various snow conditions and accumulation levels. Furthermore, the variation in the number of days with snow cover according to different climatic regions and its effect on the energy production were evaluated for seven different provinces located in seven different regions of Turkey. Full article

Snow cover phenology (SCP) serves as a critical regulator of hydrological cycles and ecosystem stability across the Mongolian Plateau (MP). Despite its importance, the spatiotemporal patterns of SCP and their climatic drivers remain poorly quantified, constrained by persistent gaps in satellite snow cover observations. Leveraging a high-resolution (500 m) daily gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover dataset combined with reanalysis climate datasets, we systematically quantified SCP dynamics and identified the dominant controls during the 2000–2022 hydrological years using trend analysis and ridge regression. Our results reveal a significant divergence in SCP parameters: snow end dates (D_e) advanced markedly across the entire plateau (0.29 days yr⁻¹, p < 0.01), accounting for 90.39% of SCP anomalies. In contrast, snow onset date (D_o) exhibited unnoticeable changes, explaining 9.58% of SCP changes. Attribution analysis demonstrates that 47.72% of D_e variability stems from increased net shortwave radiation (+0.38 Wm⁻² yr⁻¹) and rising temperatures (+0.06 °C yr⁻¹) during the melting season, with net shortwave radiation exerting stronger control (R² = 0.73) than temperature (R² = 0.63). This study establishes the first continuous, high-resolution SCP climatology for the MP, providing mechanistic insights into cryosphere–atmosphere interactions that inform adaptive water resource strategies for climate-vulnerable arid ecosystems in this region. Full article

In this review paper, we perform a comprehensive review of the current state of the art, worldwide applications, and modifications of the Snowmelt Runoff Model (SRM). Snow is a significant element of the hydrologic cycle and is sometimes regarded as the primary source of streamflow in watersheds at high latitudes and altitudes. Quantitative assessment of snowmelt runoff is crucial for real-world applications, including runoff projections, reservoir management, hydro-electricity production, irrigation techniques, and flood control, among others. Numerous hydrological modeling software have been developed to simulate snowmelt-derived streamflow. The SRM is one of the well-known modeling software developed to simulate snowmelt-derived streamflow. The SRM simulates snowmelt runoff with fewer data requirements and uses remotely sensed snow cover extent. This makes the SRM appropriate for use in data-scarce locations, particularly in remote and inaccessible mountain watersheds at higher elevations. It is a conceptual, deterministic, semi-distributed, and degree-day hydrological model that can be applied in mountainous basins of nearly any size. Recent advancements in remote sensing integration and climate model coupling have significantly enhanced the model’s ability to estimate snowmelt runoff. Additionally, numerous studies have recently improved the traditional SRM, further enhancing its capabilities. This paper highlights some of the global SRM research, focusing on the working of the model, input parameters, remote sensing data availability, and modifications to the original model. Full article

Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km² in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less). Full article

Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water depth (SD) maps are generated by tracing snow occurrence from MODIS-based images of the snow-cover area, evaluating the heating degree days (HDDs) from MODIS-derived images of the land surface temperature, computing the solar radiation, and then assimilating all the previous data in the form of the snowmelt model and ground measurements of the snowmelt water release (SWE). The results show that the average snow-cover area in the Astore river basin, in the upper indus basin, ranges from 94% in winter to 20% in summer. The maps reveal that the annual average values of the SWE range from 150 mm to 535 mm, and the SD values range from 600 mm to 2135 mm, for the snowmelt period (April–September) over the years 2010–2020. The areas linked with vegetation experience low SWE accumulation because of the low slopes in the elevated regions. The meteorological parameters and basin characteristics affect the SWE and can determine the SD values. Full article

The northern slope of the Tibetan Plateau (TP) is the crucial affected area for dust originating from the Taklimakan Desert (TD). However, few studies have focused on the meteorological element responses to TD dust over different surface types near the TP. Satellite data and the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to analyze the dust being transported from the TD to the TP and its effect from 30 July to 2 August 2016. In the TD, the middle-upper dust layer weakened the solar radiation reaching the lower dust layer, which reduced the temperature within the planetary boundary layer (PBL) during daytime. At night, the dust’s thermal preservation effect increased temperatures within the PBL and decreased temperatures at approximately 0.5 to 2.5 km above PBL. In the TP without snow cover, dust concentration was one-fifth of the TD, while the cooling layer intensity was comparable to the TD. However, within the PBL, the lower concentration and thickness of dust allowed dust to heat atmospheric continuously throughout the day. In the TP with snow cover, dust diminished planetary albedo, elevating temperatures above 6 km, hastening snow melting, which absorbed latent heat and increased the atmospheric water vapor content, consequently decreasing temperatures below 6 km. Surface meteorological element responses to dust varied significantly across different surface types. In the TD, 2 m temperature (T₂) decreased by 0.4 °C during daytime, with the opposite nighttime variation. In the TP without snow cover, T₂ was predominantly warming. In the snow-covered TP, T₂ decreased throughout the day, with a maximum cooling of 1.12 °C and decreased PBL height by up to 258 m. Additionally, a supplementary simulation of a dust event from 17 June to 19 June 2016 further validated our findings. The meteorological elements response to dust is significantly affected by the dust concentration, thickness, and surface type, with significant day–night differences, suggesting that surface types and dust distribution should be considered in dust effect studies to improve the accuracy of climate predictions. Full article

Water quality in urban streams is critical for the health of aquatic and human life, as it impacts both the environment and water availability. The strong impacts of changing climate and land use on water quality necessitate a better understanding of how stream water quality changes over space and time. To this end, four key water-quality parameters—Escherichia coli (E. coli), nitrate (NO₃⁻), sulfate (SO₄²⁻), and chloride (Cl⁻)—were collected at 12 sites along Fall Creek and Pleasant Run streams in Indianapolis, Indiana USA from 2003 to 2021 on a seasonal basis: March, July, and October each year. Two-way ANOVA tests were used to determine the impacts of seasonality and location on these parameters. Correlation and RDA (redundancy analysis) were used to determine the importance of climatic drivers. Linear regressions were used to quantify the impacts of land-use types on water quality integrating buffer zone size and sub-watershed analysis. Strong seasonal variations of the water-quality parameters were found. March had higher levels of NO₃⁻, SO₄²⁻, and Cl⁻ than other months. July had the highest E. coli concentrations compared to March and October. Seven-days antecedent snow and precipitation were found to be significantly related to Cl⁻ and log₁₀(E. coli) and can explain up to 53% and 31% of their variations, respectively. Spatially, urban built-up land in a 1000 m buffer around the sampling sites was positively correlated with the log₁₀(E. coli) variation, while lawn cover was positively related to NO₃⁻ concentrations within 500 m buffers. Conversely, NDVI (Normalized Difference Vegetation Index) values were negatively related to all variables. In conclusion, E. coli is more impacted by higher precipitation and urban land coverage, which could be related to more combined sewer overflow events in July. Cl⁻ peaking in March and its relationship with snow indicate salt runoff during snow melting events. NO₃⁻ and SO₄²⁻ increases are likely due to fertilizer input from residential lawns near streams. This suggests that Indianapolis stream water-quality changes are influenced by both changing climate and land-cover/-muse types. Full article

Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg⁻¹), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article

Timely and accurate information on forest composition is crucial for ecosystem conservation and management tasks. Information regarding the distribution and extent of forested areas can be derived through the classification of satellite imagery. However, optical data alone are often insufficient to achieve the required accuracy due to the similarity in spectral characteristics among tree species, particularly in mountainous regions. One approach to improving the accuracy of forest classification is the integration of auxiliary environmental data. This paper presents the results of research conducted in the Slyudyanskoye Forestry area in the Irkutsk Region. A dataset comprising 101 variables was collected, including Sentinel-2 bands, vegetation indices, and climatic, soil, and topographic data, as well as forest canopy height. The classification was performed using the Random Forest machine learning method. The results demonstrated that auxiliary environmental data significantly improved the performance of the tree species classification model, with the overall accuracy increasing from 49.59% (using only Sentinel-2 bands) to 80.69% (combining spectral data with auxiliary variables). The most significant improvement in accuracy was achieved through the incorporation of climatic and soil features. The most important variables were the shortwave infrared band B11, forest canopy height, the length of the growing season, and the number of days with snow cover. Full article

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, onset and end of the snow cover season across fourteen regions covering the Karakoram, Western Himalayas and Kunlun Mountains. The obtained signals exhibit considerable complexity, making it difficult to find a unique factor explaining their variability, even if elevation emerged as the most important one. The mean values of snow-covered days span from about 14 days in desert regions to about 184 days in the Karakoram region. Given the high interannual variability, the metrics show no significant trend across the study area, even if significant trends were identified in specific regions. The obtained results correlate well with the ERA5 and ERA5-Land values: the Taklamakan Desert and the Kunlun Mountains experienced a significant decrease in the snow cover extent possibly associated with an increase in temperature and a decline in precipitation. Similarly, the Karakoram and Western Himalayas region show a positive snow cover trend possibly associated with a stable temperature and a positive precipitation trend. Full article

Surface albedo measurements of snow and ice on Lake Ulansu in the Central Asian arid climate zone were conducted during the winter of 2016–2017. Observations were categorized into three stages based on the ice growth and surface condition: bare ice, snow cover, and melting. During the bare ice stage, the mean surface albedo was 0.35 with a decreasing trend due to the accumulation of wind-blown sediment on the ice surface (range: 0.99–1.87 g m⁻²). Two snowfall events occurred during the snow cover stage, significantly increasing the surface albedo to 0.91. During the melting stage, the albedo decreased at a decay rate of 0.20–0.30/day. Four existing albedo schemes were evaluated but found unsuitable for Lake Ulansu. A new surface albedo scheme was proposed by incorporating the existing albedo schemes with the measured data. This scheme incorporated the effect of sediment content on bare ice albedo for the first time. It demonstrated a modelling efficiency of 0.933 over the entire 3-month period, which was used to evaluate the fit between the predicted and observed values. When validated with albedo observations from other winters, it achieved a modelling efficiency of 0.940. The closer the value is to 1, the better the model’s predictive accuracy, indicating a higher level of reliability in the model’s performance. This scheme has potential applicability to other lakes in the Central Asian arid climate zone, which is characterized by low precipitation, frequent sandstorms, and intense solar radiation. Full article

The rising surface temperatures driven by climate change have resulted in significant reductions in snow depth and snow cover duration globally, with pronounced impacts on snow-dependent regions. This study focuses on Greece, a region where snow plays a critical role in water resources and winter tourism. Using numerical model reanalysis data spanning from 1991 to 2020, this study identifies statistically significant declining trends in snow depth and duration of snow cover across much of the country. The findings reveal considerable spatial and temporal variability, with the most pronounced reductions occurring in winter months and mountainous regions. Particularly affected are the northern and central mountainous areas, where snow cover days have decreased by up to 1.5 days per year. Ski resorts at lower elevations exhibit steeper declines in snow reliability compared to higher-altitude resorts, posing challenges to winter tourism. These trends underscore the urgency of adaptation strategies for climate resilience in snow-dependent sectors and the broader implications for water resource management in the region. Full article

Changbai Mountain is located in China’s northeastern seasonal stable snow zone and is a high-latitude water tower. The changes in snow cover have a great influence on the hydrological process and ecological balance. This study quantitatively analyzed the spatio-temporal variation in snow cover in the Changbai Mountain region and its driving factors based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. To improve the accuracy of snow cover analysis, a simple cloud removal algorithm was applied, and the locally optimal NDSI threshold was investigated. The results showed that the snow-covered area (SCA) in the Changbai Mountain region exhibited strong seasonality, with the largest SCA found in January. The SCA during the winter season showed an insignificant increasing trend ($83.88{\mathrm{km}}^2$) from 2001 to 2022. The variability in SCA observed from November to the following March has progressively decreased in recent years. The snow cover days (SCD) showed high spatial variation, with areas with decreased and increased SCD mainly found in the southern and northern regions, respectively. It was also revealed that temperature is the primary hydrometeorological factor influencing the snow variation in the study domain, particularly during the spring season or in high-elevation areas. The examined large-scale teleconnection indices showed a relatively weak correlation with SCA, but they may partially explain the abnormally low snow cover phenomenon in the winter of 2018–2019. Full article