Search Results (314)

Snow cover has a key role in balancing the Earth’s surface temperature and can help in filling rivers and reservoirs. In this study, 8-day MOD10A2 images are employed to monitor the spatiotemporal changes in snow cover in the Sefid-Rud basin and its eleven sub-basins during 2000–2019. The non-parametric Mann–Kendall (MK) test and its associated Sen’s slope estimator are utilized to estimate the trends in annual, seasonal, and monthly snow cover changes. The Sen’s slope results show a decrease in the snow cover for the basin, statistically significant toward the central and southern parts of the basin. In the winter season, a decreasing trend is observed, where its decreasing rate is higher than the annual rate. The trends in the calendar months are like the seasons, i.e., December, January, and February exhibit a decreasing trend, like the winter season. The Goltapeh-Zarinabad and Ghorveh-Dehgolan sub-basins show decreasing snow cover rates of $-0.51$ and $-0.68$ (%/year) during 2000–2019, respectively, the only two sub-basins whose gradients are statistically significant at the 95% confidence level. The Pearson correlation analysis between elevation and snow cover for each year shows that the highest and lowest correlations are 0.81 for 2007 and 0.59 for 2017. Finally, analysis of the MCD12Q1 land cover data shows that a significant portion of non-vegetated lands have turned into grasslands, mainly in the central part of the basin, where the significant gradual snow cover decline is observed. The results can guide stakeholders and policymakers in the development of a sustainable environment in the face of climate change. Full article

Accurate land surface temperature (LST) retrieval depends on precise knowledge of the land surface emissivity (LSE). Neglecting or inaccurately estimating the emissivity introduces substantial errors and uncertainty in LST measurements. The emissivity, which varies across different surfaces and land uses, reflects material composition and surface roughness. Satellite data offer a robust means to determine LSE at large scales. This study utilises the Normalised Difference Vegetation Index Threshold Method (NDVI^THM) to produce a novel emissivity dataset spanning the last 40 years, specifically tailored for the Fennoscandian region, including Norway, Sweden, and Finland. Leveraging the long and continuous data series from the Advanced Very High Resolution Radiometer (AVHRR) sensors aboard the NOAA and MetOp satellites, an emissivity dataset is generated for 1981–2022. This dataset incorporates snow-cover information, enabling the creation of annual emissivity time series that account for winter conditions. LSE time series were extracted for six 15 × 15 km study sites and compared against the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD11A2 LSE product. The intercomparison reveals that, while both datasets generally align, significant seasonal differences exist. These disparities are attributable to differences in spectral response functions and temporal resolutions, as well as the method considering fixed values employed to calculate the emissivity. This study presents, for the first time, a 40-year time series of the emissivity for AVHRR channels 4 and 5 in Fennoscandia, highlighting the seasonal variability, land-cover influences, and wavelength-dependent emissivity differences. This dataset provides a valuable resource for future research on long-term land surface temperature and emissivity (LST&E) trends, as well as land-cover changes in the region, particularly with the use of Sentinel-3 data and upcoming missions such as EUMETSAT’s MetOp Second Generation, scheduled for launch in 2025. Full article

Albedo is a primary driver of the glacier surface energy balance and consequent melting. As glacier albedo decreases, it further accelerates glacier melting. Over the past 20 years, glaciers on the Tibetan Plateau have experienced significant melting. However, our understanding of the variations in glacier albedo and its driving factors in this region remains limited. This study used MOD10A1 data to examine the average characteristics and variations in glacier albedo on the Tibetan Plateau from 2001 to 2022; the MOD10A1 snow cover product, developed at the National Snow and Ice Data Center, was employed to analyze spatiotemporal variations in surface albedo. The results indicate that the albedo values of glaciers on the Tibetan Plateau predominantly range between 0.50 and 0.60, with distinctly higher albedo in spring and winter, and lower albedo in summer and autumn. Glacier albedo on the Tibetan Plateau decreased at an average linear regression rate of 0.06 × 10⁻² yr⁻¹ over the past two decades, with the fastest declines occurring in autumn at an average rate of 0.18 × 10⁻² yr⁻¹, contributing to the prolongation of the melting period. Furthermore, significant variations in albedo change rates with altitude were found near the snowline, which is attributed to the transformation of the snow and ice surface. The primary factors affecting glacier albedo on the Tibetan Plateau are temperature and snowfall, whereas in the Himalayas, black carbon and dust primarily influence glacier albedo. Our findings reveal a clear decrease in glacier albedo on the Tibetan Plateau and demonstrate that seasonal and spatial variations in albedo and temperature are the most important driving factors. These insights provide valuable information for further investigation into surface albedo and glacier melt. Full article

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. Full article

During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production. Full article

Many debris-covered glaciers are widely distributed on the Qinghai–Tibet Plateau. Glaciers are important freshwater resources and cause disasters such as glacier collapse and landslides. Therefore, it is of great significance to monitor the movement characteristics of large active glaciers and analyze the process of mass migration, which may cause serious threats and damage to roads and people living in surrounding areas. In this study, we chose a glacier with strong activity in Lulang County, Tibet, as the study area. The complete 4-year time series deformation of the glacier was estimated by using an improved small-baseline subset InSAR (SBAS-InSAR) technique based on the ascending and descending Sentinel-1 datasets. Then, the three-dimensional time series deformation field of the glacier was obtained by using the 3D decomposition technique. Furthermore, the three-dimensional movement of the glacier and its material migration process were analyzed. The results showed that the velocities of the Lulang glacier in horizontal and vertical directions were up to 8.0 m/year and 0.45 m/year, and these were basically consistent with the movement rate calculated from the historical optical images. Debris on both sides of the slope accumulated in the channel after slipping, and the material loss of the three provenances reached 6–9 × 10³ m³/year, while the volume of the glacier also decreased by about 76 × 10³ m³/year due to snow melting and evaporation. The correlation between the precipitation, temperature, and surface velocity suggests that glacier velocity has a clear association with them, and the activity of glaciers is linked to climate change. Therefore, in the context of global warming, the glacier movement speed will gradually increase with the annual increase in temperature, resulting in debris flow disasters in the future summer high-temperature period. Full article

Currently, the global climate system is complex and ever-changing, with multiple factors influencing climate change. The Qinghai–Tibet Plateau, known as the “Third Pole” of the Earth, is particularly sensitive to global climate change. Without timely and scientific research on the ecological environment of the Qinghai–Tibet Plateau and without summarizing relevant adaptive strategies, global climate change will impact the sustainable development of the plateau. This study utilized Landsat remote sensing images from 2000 to 2020 to extract the snow cover area and snow temperature of the Qinghai–Tibet Plateau using the snow frequency threshold method. The study analyzed the spatiotemporal characteristics of snow cover and temperature over the 20-year period and investigated some of the climate and topographical driving factors influencing their changes. The results showed that from 2000 to 2020, the permanent snow cover area in the Qinghai–Tibet Plateau region showed a fluctuating decreasing trend, reducing from approximately 12.34 thousand km² to around 9.01 thousand km²; the permanent snow temperature showed an initial increase followed by a decrease during the same period. The highest annual average snow temperature was approximately −3.478 °C, while the lowest annual average temperature was around −8.150 °C. Over the 20-year period, the snow cover area in the plateau was negatively correlated with temperature and precipitation, while snow temperature was positively correlated with temperature and precipitation. The snow cover in the weak wind areas of the plateau showed a significant reduction. Areas with higher average wind speeds, such as shaded slopes and semi-shaded slopes, had larger snow cover areas. These research findings provide important insights into the protection and management of the ecological environment of the Qinghai–Tibet Plateau. Full article

Land surface temperature (LST) is one of the most important variables in the physical processes of surface energy and water balance. The temporal behavior of LST was analyzed between the latitudes 32°00′ S and 34°24′ S (Valparaíso and Metropolitana regions of Chile) for three summer months (December, January, and February) in the 2000–2017 period, using the Terra MODIS image information and applying the Mann–Kendall test. The results show an increase in LST in the study area, particularly in the Andes mountain range in January (5240 km²), which mainly comprises areas devoid of vegetation and eternal snow and glaciers, and are zones that act as water reserves for the capital city of Santiago. Similarly, vegetated areas such as forests, grasslands, and shrublands also show increasing trends in LST but over smaller surfaces. Because this study is regional, it is recommended to improve the spatial and temporal resolutions of the images to obtain conclusions on more local scales. Full article

Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. Full article

The “bare runway” principle aims to ensure passenger and employee safety by making runways more usable during winter conditions, allowing for easier removal of contaminants like snow and ice. Maintaining runway operations in winter is essential, but it involves considerable cost and environmental impacts. Greater knowledge about the de-icing and anti-icing performance of runway de-icing products (RDPs) optimizes operations. The ice melting test, as per the AS6170 standard, gauges the rate at which an RDP dissolves an ice mass to determine RDP effectiveness. Here, we introduce a novel integrated methodology for assessing RDP-related ice melting. We combine laboratory-based procedures with infrared thermography and Raman spectroscopy to monitor the condition of RDPs placed on ice. The plateau of maximum efficiency, marked by the most significant Raman peak intensity, corresponds to the peak minimum temperature, indicating optimal RDP performance. Beyond this point, RDP efficacy declines, and the system temperature, including melted contaminants and RDP, approaches ambient temperature. Effective RDP performance persists when the ambient temperature exceeds the mixture’s freezing point; otherwise, a freezing risk remains. The initial phases of RDP–ice contact involve exothermic reactions that generate brine, followed by heat exchange with surrounding ice to encourage melting. The final phase is complete ice melt, leaving only brine with reduced heat exchange on the surface. By quantifying these thermal and chemical changes, we gain a deeper understanding of RDP-related ice melting, and a more robust assessment can be provided to airports using RDPs. Full article

Glacier inventories are fundamental in understanding glacier dynamics and glacier-related environmental processes. High-resolution mapping of glacier outlines is lacking, although high-resolution satellite images have become available in recent decades. Challenges in development of glacier inventories have always included accurate delineation of boundaries of debris-covered glaciers, which is particularly true for high-resolution satellite images due to their limited spectral bands. To address this issue, we introduced an automated, high-precision method in this study for mapping debris-covered glaciers based on 1 m resolution Gaofen-2 (GF-2) imagery. By integrating GF-2 reflectance, topographic features, and land surface temperature (LST), we used an attention mechanism to improve the performance of several deep learning network models (the U-Net network, a fully convolutional neural network (FCNN), and DeepLabV3+). The trained models were then applied to map the outlines of debris-covered glaciers, at 1 m resolution, in the central Karakoram regions. The results indicated that the U-Net model enhanced with the Convolutional Block Attention Module (CBAM) outperforms other deep learning models (e.g., FCNN, DeepLabV3+, and U-Net model without CBAM) in terms of precision for supraglacial debris identification. On the testing dataset, the CBAM-enhanced U-Net model achieved notable performance metrics, with its accuracy, F1 score, mean intersection over union (MIoU), and kappa coefficient reaching 0.93, 0.74, 0.79, and 0.88. When applied at the regional scale, the model even exhibits heightened precision (accuracies = 0.94, F1 = 0.94, MIoU = 0.86, kappa = 0.91) in mapping debris-covered glaciers. The experimental glacier outlines were accurately extracted, enabling the distinction of supraglacial debris, clean ice, and other features on glaciers in central Karakoram using this trained model. The results for our method revealed differences of 0.14% for bare ice and 10.36% against the manually interpreted glacier boundary for supraglacial debris. Comparison with previous glacier inventories revealed raised precisions of 8.74% and 4.78% in extracting clean ice and with supraglacial debris, respectively. Additionally, our model demonstrates exceptionally high exclusion for bare rock outside glaciers and could reduce the influence of non-glacial snow on glacier delineation, showing substantial promise in mapping debris-covered glaciers. Full article

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables. Full article

SNOSWAB (Snow, Soil Water and Water Balance) is a unique online deterministic model built using tipping-bucket approaches that allows for the daily estimation of (i) snowpack processes; (ii) soil water content; and (iii) soil water budget. SNOSWAB is most suitable for modeling field-scale processes for vertically and horizontally homogeneous soils, and its applicability is not limited to specific climate zones or geographical areas. The model is freely available, and its streamlined online interface integrates powerful calibration, visualization and data export routines. In this study, SNOSWAB development and a conceptual model, as well as an example of its application using data collected during a 12-year (2008–2019) field study conducted at the Agriculture and Agri-Food Canada Harrington Experimental Farm (HEF) on Prince Edward Island (PEI), Canada, are presented. Input data consisting of daily air temperature, total precipitation, rainfall and evapotranspiration were used in conjunction with soil properties and daily soil water content, snowpack thickness, surface runoff and groundwater recharge to calibrate (2010–2014) and validate (2015–2019) the model. For both the calibration and validation simulations, the statistical indicators used for evaluating model performance indicated, in most cases, high model fitness (i.e., R² > 0.5, NRMSE < 50% and −25% < PBIAS < 25%) for the various time intervals and parameters analyzed. SNOSWAB fills an existing gap in the online environment and, due to its ease of use, robustness and flexibility, shows promise to be adopted as an alternative for more complex, standalone models that might require extensive resources and expertise. Full article

Ice formation on roads leads to a higher incidence of accidents and increases winter de-icing/anti-icing costs. This study analyzed 3 years (2019–2021) of Road Weather Information System (RWIS) sub-hourly measurements collected by the Montana Department of Transportation (MDT) to understand the first-order factors of road ice formation and its mechanisms. First, road ice is formed only when the road pavement surface temperature is equal to or below the freezing point (i.e., 32 °F (i.e., 0 °C)), while the corresponding 2 m air temperature could be above 32 °F. Nevertheless, when the road pavement was below 32 °F ice often did not form on the roads. Therefore, one challenge is to know under what conditions road ice forms. Second, the pavement surface temperature is critical for road ice formation. The clear road (i.e., with no ice or snow) surface pavement temperature is generally warmer than the air temperature during both day and night. This feature is different from a natural land surface, where the land skin temperature is lower than the air temperature on cloud-free nights due to radiative cooling. Third, subsurface temperature, measured using a RWIS subsurface sensor below a road surface, did not vary as much as the pavement temperature and, thus, may not be a good index for road ice formation. Fourth, urban heat island effects lead to black ice formation more frequently than roads located in other regions. Fifth, evaporative cooling from the water surface near a road segment further reduces the outlying air temperature, a mechanism that increases heat loss for bridges or lake-side roads in addition to radiative cooling. Additionally, mechanical lifting via mountains and hills is also an efficient mechanism that makes the air condense and, consequently, form ice on the roads. Forecasting road ice formation is in high demand for road safety. These observed features may help to develop a road ice physical model consisting of functions of hyper-local weather conditions, local domain knowledge, the road texture, and geographical environment. Full article

Snow and ice melting in the Upper Indus Basin (UIB) is crucial for regional water availability for mountainous communities. We analyzed glacier changes in the Astak catchment, UIB, from 2000 to 2020 using remote sensing techniques based on optical satellite images from Landsat and ASTER digital elevation models. We used a surface feature-tracking technique to estimate glacier velocity. To assess the impact of climate variations, we examined temperature and precipitation anomalies using ERA5 Land climate data. Over the past two decades, the Astak catchment experienced a slight decrease in glacier area (−1.8 km²) and the overall specific mass balance was −0.02 ± 0.1 m w.e. a⁻¹. The most negative mass balance of −0.09 ± 0.06 m w.e. a⁻¹ occurred at elevations between 2810 to 3220 m a.s.l., with a lesser rate of −0.015 ± 0.12 m w.e. a⁻¹ above 5500 m a.s.l. This variation in glacier mass balance can be attributed to temperature and precipitation gradients, as well as debris cover. Recent glacier mass loss can be linked to seasonal temperature anomalies at higher elevations during winter and autumn. Given the reliance of mountain populations on glacier melt, seasonal temperature trends can disturb water security and the well-being of dependent communities. Full article