Snow Research Articles

Constraining future surface air temperature change on the Tibetan Plateau

Abstract The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical intermodel warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter up to 2 °C, followed by autumn with an increase of up to about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and historical periods.

Snowdrift‐Permitting Simulations of Seasonal Snowpack Processes Over Large Mountain Extents

AbstractThe melt of seasonal snowpack in mountain regions provides downstream river basins with a critical supply of freshwater. Snowdrift‐permitting models have been proposed as a way to accurately simulate snowpack heterogeneity that stems from differences in energy inputs, over winter redistribution, sublimation, melt, and variations in precipitation. However, these spatial scales can be computationally intractable for large extents. In this work, the multiscale Canadian Hydrological Model (CHM) was applied to simulate snowpacks at snowdrift‐permitting scales (≈50 m) across the Canadian Cordillera and adjacent regions (1.37 million km2) forced by downscaled atmospheric data. The use of a multiscale land surface representation resulted in a reduction of computational elements of 98% while preserving land‐surface heterogeneity. CHM includes complex terrain windflow and radiative transfer calculations, lapses temperature, humidity, and precipitation with elevation, redistributes snow by avalanching, wind transport and forest canopy interception and calculates the energetics of canopy and surface snowpacks. Model outputs were compared to a set of multiscale observations including snow‐covered area (SCA) from Sentinel and Landsat imagery, snow depth from uncrewed aerial system lidar, and point surface observations of depth and density. Including snow redistribution and sublimation processes improved the summer SCA r2 from 0.7 to 0.9. At larger scales, inclusion of snow redistribution processes delayed full snowpack ablation by an average of 33 days, demonstrating process emergence with scale. These simulations show how multiscale modeling can improve snowpack predictions to support prediction of water supply, droughts, and floods.

Western spotted skunk spatial ecology in the temperate rainforests of the Pacific Northwest

AbstractA major threat to small mammalian carnivore populations is human‐induced land use change, but conservation and management are inhibited by limited knowledge about their ecology and natural history. To fill a key knowledge gap of the western spotted skunk (Spilogale gracilis), we investigated their spatial ecology at the landscape and home range scale in the temperate rainforests of the Oregon Cascades during 2017–2019. For the landscape scale analysis, we used detections of western spotted skunks at 112 baited camera traps and fitted a dynamic occupancy model to investigate spatial distribution and drivers of inter‐seasonal and inter‐annual changes in occupancy. Concurrently, we radio‐collared 25 spotted skunks (9 female, 16 male) and collected 1583 relocations. Using continuous‐time movement models, we estimated large home range sizes for both male and female spotted skunks, relative to their body mass, and highly overlapping home ranges that indicated a lack of territoriality. Using these home ranges, we fitted a resource selection function using environmental covariates that we assigned to various hypotheses such as resources, predator avoidance, thermal tolerance, and disturbance. Overall, western spotted skunks were widely distributed across our study area (seasonal occupancy up to 63.7 ± 5.3%) and highly detectable (weekly detection probability = 41.2%). At both spatial scales, spotted skunks selected wetter areas and local valleys, which we attributed to areas with more food resources. At the home range scale, spotted skunks selected locations with lower predation risk and areas surrounded by more previously logged forests. In this montane environment, inter‐seasonal contractions in the spatial distributions of spotted skunks were strongly driven by their response to cold temperature and accumulated snow. This was especially evident when seasonal occupancy declined significantly following a severe heavy snow event in February 2019. Given that there is little information available on the natural history of the western spotted skunk, these results provide essential information about their ecology to focus future monitoring efforts and may help identify potential threats (e.g., forest management, severe snow events, or wildfires) to this species.

Numerical simulation of wind–snow flow on a roof considering time-varying snow phase boundaries

Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction of wind and snow, the changing characteristics of snow phase boundaries over time, and corrects for snow deposition and erosion flux. The effectiveness of the method was confirmed by simulating wind–snow flow on a three-centered cylindrical shell and comparing the results with wind tunnel measurements. The simulation considered different snowfall conditions and time-varying snow phase boundaries on a large-span shell-shaped roof. The study investigated the influence of snowfall conditions and dynamic changes in snow phase boundaries on snow cover distribution by comparing simulation results.

Glacier dynamic study of the Terre de Lambert glacier in Greenland based on Landsat 8 and Sentinel-1 data

With global temperatures on the rise, the trend of glacial melting is becoming increasingly evident. This study focuses on the northeastern region of Greenland as the research subject, utilizing remote sensing technology and geographic information technology to study glacial dynamics. Initially, the research processes Landsat 8 data using QGIS to classify the widespread snow and ice cover, facilitating the observation and analysis of its temporal variations. The study then narrows its focus to Terre de Lambert for further analysis. Subsequently, using satellite data from Sentinel-1 and employing SNAP, the research conducts an in-depth analysis of glacier flow speed in specific regions. Through intricate computational models and algorithms, precise glacier flow direction and speed maps are generated. By combining historical data, the study compares and analyzes the glacier flow speed between 2018 and from June 20th to July 14th, 2023. Results indicate that the glacier flow speed in 2023 during the same time frame was faster. Throughout the research process, limitations in current data acquisition and processing methods were identified. Thus, the paper suggests improvements to data acquisition and processing tools to enhance the efficiency of glacier flow speed studies. Theres a fervent hope for the emergence of more convenient, streamlined, and customizable temporal dimension tools for glacier flow speed data in the future. The research findings aim to draw global citizens attention to the issue of glacial melting and inspire collective actions to protect our planet.

North American Circum-Arctic Permafrost Degradation Observation Using Sentinel-1 InSAR Data

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake’s lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape.

Bioluminescence Inhibition Bioassay for Estimation of Snow Cover in Urbanised Areas within Boreal Forests of Krasnoyarsk City

It has been proposed that the level of air pollution in a city should be estimated based on the accumulation of pollutants in the snow cover of urban forests. This study presents a bioluminescence method for estimating the extent of snow cover pollution in the urbanised areas of boreal forests in Krasnoyarsk city. A bioluminescent assay involving NAD(P)H:FMN oxidoreductase (Red) and luciferase with luminous bacteria (BLuc) was employed to measure the concentrations of six heavy metals (As, Cd, Zn, Co, Hg, and Pb) in the snow cover. The tested snow samples demonstrated a correlation between the reduced activity of the enzyme system and variations in Cd concentration. Furthermore, the research indicated that the period of unfavourable meteorological conditions in Krasnoyarsk city resulted in a notable decline in the activity of the BLuc–Red enzyme system, which may be associated with elevated air pollution levels. This study underscores the potential of the bioluminescence method for monitoring environmental pollution in urban forested areas.

Subseasonal variability of sea level pressure and its influence on snowpack over mid-high-latitude Eurasia during boreal winter

The atmospheric circulation significantly influences the snowpack over mid-high-latitude Eurasia. This study examines the characteristics of the leading subseasonal variability mode of boreal winter sea level pressure (SLP) with 20-80-day period and its relationship with snowpack over mid-high-latitude Eurasia, using the fifth generation of European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data and different snowpack datasets. The SLP leading mode, characterized by a monopole pattern with a strong surface anomalous high centered near the Ural Mountains, exhibits a barotropic structure and extends from the surface to the tropopause. Above SLP and geopotential height anomalies propagate southeastward from the Barents-Kara Sea to East Asia. This leading SLP mode contributes to surface air temperature (SAT) and snowfall circulation anomalies over mid-high-latitude Eurasia. The latter two both directly influence on snowpack anomalies in situ. Over high latitude region, snowfall circulation anomaly is the dominant factor to control the snow depth anomaly. Over middle latitude region, both SAT and snowfall circulation anomalies lead to the snowpack anomaly. Furthermore, the response of snow depth to the leading subseaonal SLP mode occurs 2–5 days earlier than the response of snow cover to the same mode over middle latitude region. In addition, it is suggested that the Arctic Oscillation (AO), East Atlantic/West Russia (EAWR) and Polar/Eurasia (PEU) pattern may contribute to the development of the leading SLP mode and subsequently influence snowpack anomalies.

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

Seasonal response of soil microbial community structure and life history strategies to winter snow cover change in a temperate forest

Snow cover provides a thermally stable and humid soil environment and thereby regulates soil microbial communities and biogeochemical cycling. A warmer world with large reductions in snow cover and earlier spring snowmelt may disrupt this stability and associated ecosystem functioning. Yet, little is known about the response of soil microbial communities to decreased snowpack and potential carry-over effects beyond the snow cover period. Herein, we tested this response by conducting a snowpack manipulation experiment (control, addition, and removal) in a temperate forest. Our results showed that fungi were more sensitive to changes in snowpack. Thicker snowpack increased the diversity of fungi, but had weak effects on the diversity of bacteria in winter. Thickening snow cover promoted the ratio of fungi to bacteria abundance across the year, and such relative increase in fungi abundance was largely driven by Basidiomycota phyla (Agaricomycetes class). Increased snowpack decreased soil nitrate concentration, and produced carry-over biogeochemical effects evidenced by increased summer β-1,4-glucosidase and N-acetyl-β-glucosaminidase activities. On a seasonal scale, microbial biomass peaked at both winter and summer; winter microbial community was fungi dominated, while bacteria dominated in summer. The abundances of bacterial phyla had greater seasonal variation than fungal phyla. Specifically, Actinobacteria had greater dominance in winter than in summer, while Acidobacteria, Proteobacteria, and Verrucomicrobia had greater abundance in summer than in winter. Microbial high yield-resource acquisition-stress tolerance life history strategies showed significant seasonal tradeoffs, i.e., resource acquisition and stress tolerance strategies dominated in summer, while high yield strategy dominated in winter. Overall, our findings underline that climate-induced reductions in snow cover can disrupt soil biogeochemical cycling also beyond the snow cover period due to shifts in soil microbial community structure and life history strategies.

Evaluation of a new teaching-learning sequence on the particulate nature of matter using crystal structures

Although there has been extensive research on students’ understanding of the particulate nature of matter (PNM), there is still a lack of research on contexts that can be used to teach this challenging topic. In a previous design-based research study, the authors developed a teaching-learning sequence (TLS) on the PNM in the context of crystal structures based on 40 student interviews using the method of probing acceptance. Data suggested that salt and snow crystals form an effective context for learning the concept of emergence and therefore gaining a better understanding of the PNM. To test whether the TLS also promotes students’ use of the PNM in a realistic classroom setting, a proof of principle study was conducted. In six eighth-grade classes in Vienna, students’ use of the PNM was assessed with a pretest before they were taught the TLS during four lessons. After the intervention, students were given a post-test. Open-ended questions were coded using evaluative qualitative content analysis so that quantitative analysis could be applied. T-tests comparing the means of students’ scores on both tests show significant improvements in students’ use of the PNM in the post-test. The context of crystal structures seems to be helpful to students, as most of them use the PNM when asked about crystal formation. In addition, in the post-test, students more often accepted the idea of empty space between particles and associated particle motion with temperature. However, when asked about phase changes, most students remained in a continuous conception of matter. Published by the American Physical Society 2024

Understanding the mechanism of gully erosion in the alpine region through an interpretable machine learning approach

In the alpine region, climate warming has led to the retreat of glaciers, snow cover, and permafrost. This has intensified water cycling, soil erosion, and increased the occurrence of natural disasters in the alpine region. This study investigated the Lhasa River Basin in the southern Tibetan Plateau, serving as a representative case study of a typical alpine basin, with a specific focus on gully erosion. Based on field investigations and interpretation using high-resolution satellite remote sensing images, the Random Forest (RF) algorithm was applied to evaluate gully erosion susceptibility on watershed level. The Shapley Additive Interpretation method was then used to interpret the RF model and gain deeper insights into the influencing variables of gully erosion. The results showed that the RF model achieved an area under the receiver operating characteristic (AUC) accuracy of 0.99 and 0.98 for the training and testing datasets, respectively, indicating an outstanding performance of the model. The resulting susceptibility map based on the RF model shows that areas with moderate and higher levels of gully erosion susceptibility are covering 50 % of the basin. The model interpretation results indicated that elevation, slope, permafrost, rainstorm, silt loam topsoil, human activity, stream power, and vegetation were the explaining variables with the highest importance for gully erosion occurrence. Different variables are characterized by specific thresholds promoting gully erosion such as: i) elevations higher than 4950 m, ii) slopes steeper than 13.5°, iii) extreme rainstorms longer than 11 days per year, iv) silt loam topsoil, v) presence of permafrost, vi) stream power index higher than 1.2, and vii) normalized difference vegetation index (NDVI) lower than 0.25. Our findings provide the scientific basis to improve soil erosion control in such highly vulnerable alpine area.

Cryosphere degradation in a changing climate

AbstractOver the last few decades, the instrumental climate record shows a progressive global warming. As one of the most sensitive elements of the Earth's climate system, the cryosphere is significantly affected by this trend. As a result, its various components are readjusting in a situation of disequilibrium with the climate through a series of dynamics. These include the thinning and retreat of glaciers that may lead to the formation of new lakes; the thawing of ground ice, leading to the deformation of terrain; the reduction of snow cover; and the occurrence of mass movements that threaten populations and infrastructures. This Special Issue contains 23 scientific papers with case studies that explore the above issues in the Arctic, Antarctic and high mountain regions.

The Varied Role of Atmospheric Rivers in Arctic Snow Depth Variations

AbstractThe state and fate of snow on sea ice are crucial in the mass and energy balance of sea ice. The function of atmospheric rivers (ARs) on snow depth over sea ice has not been measured thus far, limiting the understanding of the mechanism of snow depth changes. Here, the effect of ARs on snow depth changes was explored. We found that increased AR frequency is responsible for winter‐autumn snow accumulation and spring‐summer snow melting. The 2 m air temperature (T2m), rainfall, snowfall, mean net longwave radiation (NLR), mean net shortwave radiation (NSR) and cloud radiative effect (CRE) during ARs explain the changes in snow depth triggered by AR occurrence. This work helps us understand how ARs affect snow depth changes through related physical processes, promotes an understanding of climate systems and provides a theoretical basis for snow treatment in sea ice models.

Interdecadal variability of winter and spring Eurasian snow depth and its impact on Eastern China precipitation

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Changes in Snow Cover and Its Surface Temperature across the Tibetan Plateau Region from 2000 to 2020

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.

Seasonal sonic patterns reveal phenological phases (sonophases) associated with climate change in subarctic Alaska

Given that ecosystems are composed of sounds created by geophysical events (e.g., wind, rain), animal behaviors (e.g., dawn songbird chorus), and human activities (e.g., tourism) that depend on seasonal climate conditions, the phenological patterns of a soundscape could be coupled with long-term weather station data as a complimentary ecological indicator of climate change. We tested whether the seasonality of the soundscape coincided with common weather variables used to monitor climate. We recorded ambient sounds hourly for five minutes (01 January–30 June) over three years (2019–2021) near a weather station in a subarctic ecosystem in south-central Alaska. We quantified sonic information using the Acoustic Complexity Index (ACItf), coupled with weather data, and used machine learning (TreeNet) to identify sonic-climate relationships. We grouped ACItf according to time periods of prominent seasonal events (e.g., days with temperatures >0°C, no snow cover, green up, dawn biophony, and road-based tourism) and identified distinct sonic phenophases (sonophases) for groups with non-overlapping 95% confidence intervals. In general, sonic activity increased dramatically as winter transitioned to spring and summer. We identified two winter sonophases, a spring sonophase, and a summer sonophase, each coinciding with hours of daylight, temperature, precipitation, snow cover, and the prevalence of animal and human activities. We discuss how sonophases and weather data combined serve as a multi-dimensional, systems-based approach to understanding the ecological effects of climate change in subarctic environments.

Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar

Abstract. Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE, but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE, to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 m s−1, altitude of 15 m, and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 m s−1, altitude of 8 m, and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments.

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Abstract. Estimating snow mass in the mountains remains a major challenge for remote-sensing methods. Airborne lidar can retrieve snow depth, and some promising results have recently been obtained from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data are available to evaluate model estimates of density in mountainous terrain. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne-lidar snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer-picking method that leverages the GPR reflection coherence and the distributed lidar–GPR-retrieved bulk density with machine learning. The root-mean-square error between the distributed estimates and in situ observations is 11 cm for depth, 27 kg m−3 for density, and 46 mm for SWE. The median relative uncertainty in distributed SWE is 13 %. Interactions between wind, terrain, and vegetation display corroborated controls on bulk density that show model and observation agreement. Knowledge of the spatial patterns and predictors of density is critical for the accurate assessment of SWE and essential snow research applications. The spatially continuous snow density and SWE estimated over approximately 16 km2 may serve as necessary calibration and validation for stepping prospective remote-sensing techniques toward broad-scale SWE retrieval.

Elevation-dependent effects of snowfall and snow cover changes on runoff variations at the source regions of the Yellow River basin

ABSTRACT Accurate simulation of the snowmelt runoff process is of great significance in understanding the evolution of water resources in high-altitude cold regions and achieving efficient utilization of water resources. This study focuses on the source regions of the Yellow River basin (SYRB) and aims to improve the snow identification and snowmelt simulation methods in the WEP-L hydrological model. The results show a significant decrease in the snowfall ratio from 2002 to 2018. The fraction of snow cover decreased at lower altitudes but increased at higher altitudes, displaying an exponential relationship with negative accumulated temperature. Snowmelt was found to be negatively correlated with snowfall and snow cover, with a stronger negative correlation at higher altitudes. The decrease in the snowfall ratio intensified with increasing elevation, while snow cover increased with elevation. However, the overall trend of snowmelt runoff was not significant. These findings highlight the dynamic relationship between snowfall, snow cover, and temperature in the SYRB. By incorporating the established response function, the accuracy of snow identification and snowmelt simulation in the WEP-L model has been enhanced. This study contributes to a better understanding of water resource evolution and the efficient utilization of water resources in high-altitude cold regions.