Snow algae darken the surface of snow, reducing albedo and accelerating melt. However, the impact of subsurface snow algae (e.g., when cells are covered by recent snowfall) on albedo is unknown. Here, we examined the impact of subsurface snow algae on surface energy absorption by adding up to 2 cm of clean snow to surface algal blooms and measuring reflectivity. Surprisingly, snow algae still absorb significant energy across an array of wavelengths when snow-covered. Furthermore, the scale of this effect correlates with algal cell densities and chlorophyll-a concentrations. Collectively, our results suggest that darkening by subsurface snow algae lowers albedo and thus potentially accelerates snowmelt even when the algae is snow-covered. Impacts of subsurface algae on melt await assessment. This implies that snow algae play a larger role in cryosphere melt than investigations of surface-only reflectance would suggest. IMPORTANCE This study addresses a gap in research by examining the impact of subsurface snow algae on snow albedo, which affects snowmelt rates. Previous studies have focused on visible surface blooms, leaving the effects of hidden algae unquantified. Our findings reveal that snow algae beneath the surface can still absorb energy across various wavelengths, accelerating melt even when not visible to the naked eye. This suggests that spectral remote sensing can detect these hidden algae, although their biomass might be underestimated. Understanding how subsurface snow algae influence albedo and snowmelt is crucial for accurate predictions of meltwater runoff, which impacts alpine ecosystems, glacier health, and water resources. Accurate projections are essential for managing freshwater supplies for agriculture, drinking water, and other vital uses. Thus, further investigation into subsurface snow algae is necessary to improve our understanding of their role in snow albedo reduction and water resource management.

The impact of the freeze-thaw process on the active layer is reflected in the changed subsurface flow (SSF) process in cold alpine regions. Identifying sources and pathways of SSF in the freeze-thaw process is critical but difficult, and the related dominant factors and mechanisms are still unknown. In this paper, the effective identification and analysis of SSF are promoted based on field sampling data from the thawing (June) to freezing (September) period of 2022 in the Qinghai Lake basin on the northeastern Qinghai-Tibetan Plateau. By the proposed method with a high sampling frequency and refined sampling spatial scale, the sources and pathways of SSF are clearly identified. The results are as follows: (1) The soil temperature is considered the most fundamental factor affecting the SSF pathways, it influences water infiltration to the deep layer and the effect is extended to the saprolite and weathered bedrock layers. (2) Thawing promotes water to infiltrating into deep layer. 30 cm soil water contributes the most to SSF (2 %-86 %) in the thawing period, while the contribution difference of the water from the 30 cm, 60 cm, and 90 cm layers is small (ranging from 32 %-33 %, 24 %-26 %, and 32 %-35 %, respectively) in the thawed period. (3) Meanwhile, the soil water from different slope positions contribute differently to SSF, and the SSF from deep soil layer is transit in prolong paths and depths. It is caused by the outof-sync water transit process in the hillslope. With continuing climate warming, we propose that the differences in the water sources of SSF across soil layers may decrease, while the differences in the transit processes of SSF across soil layers may increase.

Climate change has resulted in significant changes to subsurface hydrological processes in permafrost regions. Lateral subsurface flow (LSF) represents the dominant flow path in hillslope runoff generation. However, the contributions of runoff components to LSF, such as precipitation, soil water, and ground ice, remain unclear. This study aimed to characterize LSF generation processes in an alpine permafrost hillslope of Northeastern Tibetan Plateau, using stable isotopes and total dissolved solids (TDS) as tracers. Samples of precipitation and soil water [including mobile soil water and supra-permafrost groundwater (SPG)], LSF, and ground ice samples were collected from different thaw depths of the active layer in 2021. The results showed that LSF came directly from SPG in the active layer. Two-source partitioning using delta H-2 or TDS suggested that the dominant source of LSF gradually shifted from ground ice during the initial thaw period to precipitation with increasing thaw depths. The contributions of ground ice to LSF were 70 % and 30 % at thaw depths of 0-30 cm and >30 cm, respectively. The results of three-source partitioning indicated ground ice, precipitation, and SPG to be the dominant sources of LSF at thaw depths of 0-30 cm, 30-150 cm, and >150 cm, respectively. SPG largely regulates hillslope hydrologic processes at thaw depths >= 250 cm. Therefore, with continuing climate warming, SPG will play an increasing role in hydrological processes of alpine meadow permafrost hillslopes.

Permafrost and ground freezing/thawing processes are physically and eco-climatologically important factors in the terrestrial cryosphere. The model reproducibility of frozen ground affects the certainty and reliability of simulated eco-climate conditions in cold regions as well as on a global scale. This study evaluated the variations and their attributes in the model performance developed and employed in the recent decade regarding the subsurface thermal state using outputs from Japanese and international model intercomparison projects and reanalysis data. The simulated surface and subsurface physical states were compared at four Arctic sites under different frozen ground conditions (Fairbanks, Kevo, Tiksi, and Yakutsk). The results showed that despite large variations in the modeled permafrost temperature, all the models, including the reanalysis data, successfully reproduced the permafrost conditions for the continuous permafrost sites. In contrast, some models failed to reproduce the presence of permafrost for the sites in the discontinuous to isolated permafrost zones. Evaluations of near-surface ground temperature variability revealed that the overall wellness of the simulated ground thermal states relied on winter reproducibility. The importance of snowpack metamorphosis for adequate thermal insulation was confirmed and demonstrated. The results at the coastal tundra site imply the importance of snow cover redistribution and wind crust formation owing to strong winds, the lack of which resulted in overestimations of thermal insulation and overcooled near-surface ground by most models.

The monitoring of permafrost is important for assessing the effects of global environmental changes and maintaining and managing social infrastructure, and remote sensing is increasingly being used for this wide-area monitoring. However, the accuracy of the conventional method in terms of temperature factor and soil factor needs to be improved. To address these two issues, in this study, we propose a new model to evaluate permafrost with a higher accuracy than the conventional methods. In this model, the land surface temperature (LST) is used as the upper temperature of the active layer of permafrost, and the temperature at the top of permafrost (TTOP) is used as the lower temperature. The TTOP value is then calculated by a modified equation using precipitation-evapotranspiration (PE) factors to account for the effect of soil moisture. This model, referred to as the TTOP-LST zero-curtain (TLZ) model, allows us to analyze subsurface temperatures for each layer of the active layer, and to evaluate the presence or absence of the zero-curtain effect through a time series analysis of stratified subsurface temperatures. The model was applied to the Qinghai-Tibetan Plateau and permafrost was classified into seven classes based on aspects such as stability and seasonality. As a result, it was possible to map the recent deterioration of permafrost in this region, which is thought to be caused by global warming. A comparison with the mean annual ground temperature (MAGT) model using local subsurface temperature data showed that the average root mean square error (RMSE) value of subsurface temperatures at different depths was 0.19 degrees C, indicating the validity of the TLZ model. A similar analysis based on the TLZ model is expected to enable detailed permafrost analysis in other areas.

In deglaciating environments, rock mass weakening and potential formation of rock slope instabilities is driven by long-term and seasonal changes in thermal- and hydraulic- boundary conditions, combined with unloading due to ice melting. However, in-situ observations are rare. In this study, we present new monitoring data from three highly instrumented boreholes, and numerical simulations to investigate rock slope temperature evolution and micrometer-scale deformation during deglaciation. Our results show that the subsurface temperatures are adjusting to a new, warmer surface temperature following ice retreat. Heat conduction is identified as the dominant heat transfer process at sites with intact rock. Observed non-conductive processes are related to groundwater exchange with cold subglacial water, snowmelt infiltration, or creek water infiltration. Our strain data shows that annual surface temperature cycles cause thermoelastic deformation that dominate the strain signals in the shallow thermally active layer at our stable rock slope locations. At deeper sensors, reversible strain signals correlating with pore pressure fluctuations dominate. Irreversible deformation, which we relate with progressive rock mass damage, occurs as short-term (hours to weeks) strain events and as slower, continuous strain trends. The majority of the short-term irreversible strain events coincides with precipitation events or pore pressure changes. Longer-term trends in the strain time series and a minority of short-term strain events cannot directly be related to any of the investigated drivers. We propose that the observed increased damage accumulation close to the glacier margin can significantly contribute to the long-term formation of paraglacial rock slope instabilities during multiple glacial cycles.

Hydrologic models are widely used for projecting influences of changing climate on water resources. In this study, we compared the original Soil and Water Assessment Tool (SWAT) model and an enhanced version of SWAT model with physically based Freeze-Thaw cycle representation (SWAT-FT) for simulating future annual ET, stream flow, water yield, surface runoff, and subsurface runoff in the Upper Mississippi River Basin (UMRB). SWAT-FT projected fewer frozen days than the original SWAT model due to its better representation of snow cover insulation effects. Both models derived declining trends in annual streamflow and terrestrial water yield in the late 21st century due to increased ET under warmer climate. However, these two models exhibited contrasting mechanisms underlying the streamflow decline. For original SWAT model, the decrease in surface runoff was the major driver, while for SWAT-FT, reduced subsurface runoff was the main cause. In general, the original SWAT model predicted more surface runoff and less subsurface runoff than SWAT-FT. Further geospatial inspection shows large discrepancies between these two models, particularly in the northern colder parts of the UMRB, where the maximum differences in annual surface and subsurface runoff reached 130 mm yr(-1) and 140 mm yr(-1), respectively. Collectively, the results demonstrate the importance of accounting for Freeze-Thaw cycles for reliable projection of future water resources.

A process-based, spatially distributed hydrological model was developed to quantitatively simulate the energy and mass transfer processes and their interactions within arctic regions (arctic hydrological and thermal model, ARHYTHM). The model first determines the flow direction in each element, the channel drainage network and the drainage area based upon the digital elevation data. Then it simulates various physical processes: including snow ablation, subsurface flow, overland flow and channel flow routing, soil thawing and evapotranspiration. The kinematic wave method is used for conducting overland flow and channel flow routing. The subsurface flow is simulated using the Darcian approach. The energy balance scheme was the primary approach used in energy-related process simulations (snowmelt and evapotranspiration), although there are options to model snowmelt by the degree-day method and evapotranspiration by the Priestley-Taylor equation. This hydrological model simulates the dynamic interactions of each of these processes and can predict spatially distributed snowmelt, soil moisture and evapotranspiration over a watershed at each time step as well as discharge in any specified channel(s). The model was applied to Imnavait watershed (about 2.2 km(2)) and the Upper Kuparuk River basin (about 146 km(2)) in northern Alaska. Simulated results of spatially distributed soil moisture content, discharge at gauging stations, snowpack ablations curves and other results yield reasonable agreement, both spatially and temporally, with available data sets such as SAR imagery-generated soil moisture data and field measurements of snowpack ablation, and discharge data at selected points. The initial timing of simulated discharge does not compare well with the measured data during snowmelt periods mainly because the effect of snow damming on runoff was not considered in the model. Results from the application of this model demonstrate that spatially distributed models have the potential for improving our understanding of hydrology for certain settings. Finally, a critical component that led to the performance of this modelling is the coupling of the mass and energy processes. Copyright (C) 2000 John Wiley & Sons, Ltd.