Seasonal Snow Research Articles

Remote Sensing Data Assimilation to Improve the Seasonal Snow Cover Simulations Over the Heihe River Basin, Northwest China

ABSTRACTThe reliability of seasonal snow cover information is constrained by limitation of in situ observations and uncertainties in remote sensing data and model simulations in alpine region, thus posing important challenges to understanding the climate system and water resource management in alpine region. Here, the assimilation of daily cloud‐free Moderate Resolution Imaging Spectroradiometer (MODIS) normalised difference snow index (NDSI) product into an intermediate complexity snow mass and energy balance model—Flexible Snow Model (version FSM2_MO)—was implemented. The aim is to improve the model simulations of seasonal snow cover (snow‐covered extent; SCE, snow depth; SD, snow water equivalent; SWE, and snowmelt runoff; SMR) in the alpine region (a case of the upper‐middle reaches of the Heihe River basin, Northwest China). The results indicate comprehensive improvement in the simulation of SCE, SD, and SMR in the study area through data assimilation, with the ability to significantly reduce prior biases of the FSM2_MO. Based on the independent daily cloud‐free Advanced Very High Resolution Radiometer (AVHRR) SCE product, the updated SCE simulation (i.e., data assimilation) showed a reduction in mean absolute error (MAE) from 10.46% to 7.16%, root mean square error (RMSE) from 16.14% to 12.26%, and an increase in Pearson's correlation coefficient (CC) from 0.18 to 0.67 compared with the open loop simulation (i.e., without assimilation). The evaluation results of SD observation data showed that data assimilation improved SD simulation compared with the open loop run (OL). And utilising the monthly discharge observations at the Yingluoxia hydrological station, data assimilation slightly improved the SMR simulation. The updated SMR simulation achieved a CC of 0.91, Nash‐Sutcliffe efficiency coefficient (NSE) of 0.73, and Kling‐Gupta efficiency coefficient (KGE) of 0.76. Moreover, the Landsat 8‐derived snow cover map and Sentinel‐1‐derived SD also indicated that the updated simulation effectively filled in the missing snow cover and removed the superfluous snow cover predicted by the OL simulation in terms of spatial distribution.

Primary Modes of Northern Hemisphere Snowfall Particle Size Distributions

Abstract A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying Principal Component Analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three Empirical Orthogonal Functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2) and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability (i.e., Principal Component [PC] groups) with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low and high intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles, and deep, stratiform snowfall events with large aggregates, respectively.

Improvements in the land surface configuration to better simulate seasonal snow cover in the European Alps with the CNRM-AROME (cycle 46) convection-permitting regional climate model

Abstract. Snow cover modeling remains a major challenge in climate and numerical weather prediction (NWP) models even in recent versions of high-resolution coupled surface–atmosphere (i.e., at kilometer scale) regional models. Evaluation of recent climate simulations, carried out as part of the WCRP-CORDEX Flagship Pilot Study on Convection (FPSCONV) with the CNRM-AROME convection-permitting regional climate model at 2.5 km horizontal resolution, has highlighted significant snow cover biases, severely limiting its potential in mountain regions. These biases, which are also found in AROME numerical weather prediction (NWP) model results, have multiple causes, involving atmospheric processes and their influence on input data to the land surface models in addition to deficiencies of the land surface model itself. Here we present improved configurations of the SURFEX-ISBA land surface model used in CNRM-AROME. We thoroughly evaluated these configurations on their ability to represent seasonal snow cover across the European Alps. Our evaluation was based on coupled simulations spanning the winters of 2018–2019 and 2019–2020, which were compared against remote sensing data and in situ observations. More specifically, the study tests the influence of various changes in the land surface configuration, such as the use of multi-layer soil and snow schemes, the division of the energy balance calculation by surface type within a grid cell (multiple patches), and new physiographic databases and parameter adjustments. Our findings indicate that using only more detailed individual components in the surface model did not improve the representation of snow cover due to limitations in the approach used to account for partial snow cover within a grid cell. These limitations are addressed in further configurations that highlight the importance, even at kilometer resolution, of taking into account the main subgrid surface heterogeneities and improving representations of interactions between fractional snow cover and vegetation. Ultimately, we introduce a land surface configuration, which substantially improves the representation of seasonal snow cover in the European Alps in coupled CNRM-AROME simulations. This holds promising potential for the use of such model configurations in climate simulations and numerical weather prediction both for AROME and other high-resolution climate models.

Photovoltaic-powered seasonal snow storage-assisted district cooling system: Site suitability analysis and performance assessment

District cooling (DC) plants coupled with cold thermal energy storage (TES) and photovoltaic (PV) systems are getting attention worldwide. Utilising winter snow is a successfully implemented TES technology, with little scale-up. Its site identification and cooling potential are not yet fully explored, especially for DC applications. The objective of this paper is to carry out site suitability analysis and performance assessment for PV-powered snow storage-assisted DC system. The site selection methodology is based on multi-criteria decision-making techniques and geographic information system. Performance analysis is carried out mathematically by considering ten indicators. The robustness of the study is enhanced by multiple sensitivity analysis. For the base case scenario, the dominant selection criteria are observed to be cooling load density (30 %), proximity to electric grids (17 %) and air temperature (13 %). Out of the selected land cover (vacant/crops/rangeland) in Estonia, around 34 km2 was observed to be moderately-suitable for the development of the proposed system. For a specific site, the theoretical generation potential of snow cooling facility is estimated as 1728 MWh with an annual cooling share of 80 %. With annual energy output of 1823 MWh and performance-ratio of 78 %, rooftop PV plants is capable to fully power the proposed system. A satisfactory economic and environmental performance is observed at the whole system level. This study is expected to scientifically support the effective utilisation of locally available energy sources (i.e. winter snow and solar energy) for space cooling applications in cold regions.

NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

The snow must go on: can snowmaking keep ski resorts profitable in a changing climate?

ABSTRACT Climate change threatens the economic performance of ski resorts, and therefore the economies of mountain regions. Increased snowmaking is the leading adaptation strategy, but its economic contribution remains unclear. To fill this gap, we used dynamic panel data modelling to analyse the economic performance of 56 French Alpine ski lift operators over a 15-year period (2004/05 to 2018/19). Economic performance remains strongly linked to ski lift capacity and shows a high degree of persistence over time, all other things being equal. No evidence has been found that increased investment in snowmaking improves resort sales or profits, even in the worst natural snow seasons. These results strongly challenge the idea that increased snowmaking is an effective economic adaptation to climate change.

A global–land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: formulation and evaluation at instrumented sites

Abstract. Snowpacks modulate water storage over extended land regions and at the same time play a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of the snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) land model version 4.1. This new snowpack model, named GLASS (Global LAnd–Snow Scheme), includes a refined and dynamical vertical-layering snow structure that allows us to track the temporal evolution of snow grain properties in each snow layer, while at the same time limiting the model computational expense, as is necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity, and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land–atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in situ observations allow for a comprehensive model evaluation. We find that when compared to the current GFDL snow model, GLASS improves predictions of seasonal snow water equivalent, primarily as a consequence of improved snow albedo. The simulated soil temperature under the snowpack also improves by about 1.5 K on average across the sites, while a negative bias of about 1 K in snow surface temperature is observed.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Quantifying Aspect‐Dependent Snowpack Response to High‐Elevation Wildfire in the Southern Rocky Mountains

AbstractIncreasing wildfire frequency and severity in high‐elevation seasonal snow zones presents a considerable water resource management challenge across the western United States (U.S.). Wildfires can affect snowpack accumulation and melt patterns, altering the quantity and timing of runoff. While prior research has shown that wildfire generally increases snow melt rates and advances snow disappearance dates, uncertainties remain regarding variations across complex terrain and the energy balance between burned and unburned areas. Utilizing paired in situ data sources within the 2020 Cameron Peak burn area on the Front Range of Colorado, U.S., during the 2021–2022 winter, we found no significant difference in peak snow water equivalent (SWE) magnitude between burned and unburned areas. However, the burned south aspect reached peak SWE 22 days earlier than burned north. During the ablation period, burned south melt rates were 71% faster than unburned south melt rates, whereas burned north melt rates were 94% faster than unburned north aspects. Snow disappeared 7–11 days earlier in burned areas than unburned areas. Net energy differences at the burned and unburned weather station sites were seasonally variable, the burned area snowpack lost more net energy during the winter, but gained more net energy during the spring. Increased incoming shortwave radiation at the burned site was 6x more impactful in altering the net shortwave radiation balance than the decline in surface albedo. These findings emphasize the need for post‐wildfire water resource planning that accounts for aspect‐dependent differences in energy and mass balance to accurately predict snowpack storage and runoff timing.

Divergent Transformation of Wet to Cold Bias on the Tibetan Plateau in Climate Models During Snow Season

AbstractWet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP.

A machine learning approach for estimating snow depth across the European Alps from Sentinel-1 imagery

Seasonal snow plays a crucial role in society and understanding trends in snow depth and mass is essential for making informed decisions about water resources and adaptation to climate change. However, quantifying snow depth in remote, mountainous areas with complex topography remains a significant challenge. The increasing availability of high-resolution synthetic aperture radar (SAR) observations from active microwave satellites has prompted opportunistic use of the data to retrieve snow depth remotely across large spatial and frequent temporal scales and at a high spatial resolution. Nevertheless, these novel SAR-based snow depth retrieval methods face their own set of limitations, including challenges for shallow snowpacks, high vegetation cover, and wet snow conditions. In response, here we introduce a machine learning approach to enhance SAR-based snow depth estimation over the European Alps. By integrating Sentinel-1 SAR imagery, optical snow cover observations, and topographic, forest cover and snow class information, our machine learning retrieval method more accurately estimates snow depth at independent in-situ measurement sites than current methods. Further, our method provides estimates at 100 m horizontal resolution and is capable of better capturing local-scale topography-driven snow depth variability. Through detailed feature importance analysis, we identify optimal conditions for SAR data utilization, thereby providing insight into future use of C-band SAR for snow depth retrieval.

Photolytic Degradation of Water‐Soluble Organic Carbon in Snowmelts: Changes in Molecular Characteristics, Brown Carbon Chromophores, and Radiative Effects

AbstractWater‐soluble organic carbon (WSOC) deposited in ambient snowpack play key roles in regional carbon cycle and surface energy budget, but the impacts of photo‐induced processes on its optical and chemical properties are poorly understood yet. In this study, melted samples of the seasonal snow collected from northern Xinjiang, northwestern China, were exposed to ultraviolet (UV) radiation to investigate the photolytic transformations of WSOC. Molecular characteristics and chemical composition of WSOC and its brown carbon (BrC) constituents were investigated using high‐performance liquid chromatography interfaced with a photodiode array detector and a high‐resolution mass spectrometer. Upon illumination, formation of nitrogen‐ and sulfur‐containing species with high molecular weight was observed in snow samples influenced by soil‐ and plant‐derived organics. In contrast, the representative sample collected from remote region showed the lowest molecular diversity and photolytic reactivity among all samples, in which no identified BrC chromophores decomposed upon illumination. Approximately 65% of chromophores in urban samples endured UV irradiation. However, most of BrC composed of phenolic/lignin‐derived compounds and flavonoids disappeared in the illuminated samples containing WSOC from soil‐ and plant‐related sources. Effects of the photochemical degradation of WSOC on the potential modulation of snow albedo were estimated. Apparent half‐lives of WSOC estimated as albedo reduction in 300–400 nm indicated 0.1–0.4 atmospheric equivalent days, which are shorter than typical photolysis half‐lives of ambient biomass smoke aerosol. This study provides new insights into the roles of WSOC in snow photochemistry and snow surface energy balance.

Operational and experimental snow observation systems in the upper Rofental: data from 2017 to 2023

Abstract. This publication presents a comprehensive hydrometeorological data set for three research sites in the upper Rofental (1891–3772 m a.s.l., Ötztal Alps, Austria) and is a companion publication to a data collection published in 2018. The time series presented here comprise data from 2017 to 2023 and originate from three meteorological and snow hydrological stations at 2737, 2805, and 2919 m a.s.l. The fully equipped automatic weather stations include a specific set of sensors to continuously record snow cover properties. These are automatic measurements of snow depth, snow water equivalent, volumetric solid and liquid water contents, snow density, layered snow temperature profiles, and snow surface temperature. One station is extended by a particular arrangement of two snow depth and water equivalent recording devices to observe and quantify wind-driven snow transport. These devices are installed at nearby wind-exposed and sheltered locations and are complemented by an acoustic-based snow drift sensor. We present data for temperature, precipitation, humidity, wind speed, and radiation fluxes and explore the continuous snow measurements by combined analyses of meteorological and snow data to show typical seasonal snow cover characteristics. The potential of the snow drift observations is demonstrated with examples of measured wind speeds, snow drift rates, and redistributed snow amounts during several blowing snow events. The data complement the scientific monitoring infrastructure in the research catchment and represent a unique time series of high-altitude mountain weather and snow observations. They enable comprehensive insights into the dynamics of high-altitude meteorological and snow processes and are collected to support the scientific community, local stakeholders, and the interested public, as well as operational warning and forecasting services. The data are publicly available from the GFZ Data Services repository: https://doi.org/10.5880/fidgeo.2023.037 (Department of Geography, University of Innsbruck, 2024).

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Seasonal dynamics of small mammal populations: resource availability and cold exposure interact to govern abundance

Organisms in seasonal environments respond to both resources in the summer and environmental conditions in winter. Small mammals, in particular, respond quickly to changes in their environment, with many species reliant on the thermal refuge of the subnivium in the winter. However, there has been little research exploring how resources and cold exposure drive the seasonal dynamics of small mammal populations. We studied the populations of three subnivium-specialist small mammal species in seasonally snow-covered forests in Wisconsin, USA across 5 years during summer and winter. In summer, mast availability and canopy cover governed white-footed mouse ( Peromyscus leucopus Rafinesque, 1818) populations, coarse woody debris drove short-tailed shrew ( Blarina brevicauda Say, 1823) populations, and rainfall influenced red-backed vole ( Myodes gapperi (Vigors, 1830)) abundance. Dietary analysis via stable isotopes revealed that shrews primarily consumed arthropods, and mice predominately consumed hard mast despite interannual changes in availability. In winter, white-footed mice and red-backed vole abundances were negatively related to cold exposure. Short-tailed shrew winter population was positively related to their abundances the previous summer. These small mammals responded to species-specific drivers during the growing and snow seasons. Consequently, shorter snow seasons from climate change appear to be restructuring communities by creating a less hospitable environment for winter-adapted species, likely contributing to their regional declines.

Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021

ABSTRACT Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.

An experimental assessment of active and passive dispersal of red snow algae on the Harding Icefield, southcentral Alaska

ABSTRACT Red snow algae seasonally color glacier and alpine snow surfaces with characteristic red blooms. These blooms significantly reduce the albedo of the snow surface resulting in increased snow ablation. The global cryosphere is sensitive to the melt effect of expansive reoccurring blooms; however, the primary dispersal path by which snow algae seasonally recolonize snow surfaces is currently unresolved. Using an experimental field approach that inhibited resurfacing populations with a physical barrier, then sampled algal surface abundance the following growing season, we assessed two pathways of algal surface colonization on the Harding Icefield, Alaska. Our results provide the first experimental depiction of active resurfacing as the primary pathway of seasonal snow surface colonization by red snow algae above the equilibrium line elevation on an Alaskan glacier. Results suggest that, at the peak of the growing season, 65 percent of surface abundance was derived from actively resurfacing cells and 35 percent from passively dispersing cells.

Observed changes in the climate and snow dynamics of the Third Pole

The Third Pole (TP) is the world’s largest highland and has one of the biggest reservoirs of glacier ice mass and snow cover on the Earth. Three major Asian rivers (the Indus, Ganga and Brahmaputra) are nourished by the melting of glaciers and snow in Central Himalaya, which are inevitable for the socioeconomic sustainability and water security of South Asia. Here, we investigate the long-term (1980–2020) changes in snow depth and precipitation in TP, where major precipitation occurs in the form of rainfall in summer, and snowfall in winter and spring. The seasonal mean snow depth is deep (≥1 m) in winter and shallow (≤0.2 m) in summer. The average snowmelt and snow water equivalent are higher in the central and western Himalaya and Karakoram ranges in spring, which are the regions with most glaciers in TP. There is a significant positive trend in total precipitation, about 0.01–0.03 mm d−1 yr−1 in the central and eastern TP during the South Asian Summer Monsoon for the 1980–2020 period. Snowmelt is also increasing (>0.5 × 10−3 mm yr−1) in the western Himalaya during spring, which is consistent with the temperature rise (0.04–0.06 °C yr−1) there. In addition, there is a notable increase in the annual mean glacier melt (here, the water equivalent thickness) in TP (−1 to −5 cm w.e. yr−1), with its highest values in the eastern and central Himalaya (−3 to −5 cm w.e. yr−1), as estimated for the period 2003–2020. On top of these, by the end of the 21st century, the Coupled Model Intercomparison Project Phase 6 (CMIP6) projections show that there would be a significant decrease in snow depth and an increase in temperature of TP in all shared socioeconomic pathways (SSPs). Henceforth, the increasing trend in temperature and melting of snow/glaciers in TP would be a serious threat to the regional climate, water security and livelihood of the people of South Asia.

Challenges in applying water budget framework for estimating groundwater storage changes from GRACE observations

The application of a water budget framework to isolate Gravity Recovery and Climate Experiment (GRACE) groundwater storage anomalies (GRACE-GWA) from GRACE terrestrial water storage anomalies (GRACE-TWSA) is hindered by the lack of direct observations of water budget components. In GRACE groundwater studies, water budget components are frequently applied to isolate changes in a storage component from various auxiliary methods (e.g., land surface or hydrology models, reanalysis, or remote sensing) and are used as a supplement to, or a substitute for, in-situ measurements when direct observations are sparse or unavailable. The contribution of select auxiliary datasets to isolate GRACE-GWA from GRACE-TWSA is an enduring quandary, attributed to the various assumptions applied to resemble hydrologic processes in model formulations. This study systematically allocates water budget components from assorted auxiliary sources to demonstrate bias and distortion of GRACE-GWA resulting simply from water storage component data selection. Whereas previous GRACE-GWA studies used combinations of water budget component datasets with uncertainty derived from the variance of the combined water budget components, this study applies single estimates for each component to measure bias and variability. An initial comparative analysis focused on three basins with suitable in-situ groundwater observations and complex hydrology stores (e.g., large lakes and seasonal snow cover), with correlation coefficients ranging from 0.32 to 0.89. Our systematic analysis was extended to 12 additional basins that capture a range of hydrologic characteristics to highlight the inconsistency in GRACE-GWA estimates. The variability evident in GRACE-GWA estimates highlights that the selection of water budget components carries the risk of misleading outcomes when disaggregating GRACE-TWSA into GRACE-GWA. Our water budget intercomparison provides an important assessment of the limitation in extraction of GRACE-GWA, highlighting the usefulness of comprehensive appraisal of water budget components for GRACE groundwater studies.