Snow Research Articles

Modelling air, ground surface and permafrost temperature variability across four dissimilar valleys, Yukon, Canada

Spatial maps of the air and ground thermal regime were generated for four Yukon valleys. The aim was to model air, ground surface and ground temperature (at fine spatial resolution) using locally measured inverted surface lapse rates (SLR), to better predict temperature along an elevation gradient. These local models were then compared to a regional permafrost probability model, which utilized differing inversion assumptions, as well as circumpolar and national models generated without considering inversions. Overall, permafrost probability in the regional model matched well with the local models where assumptions of treeline and inverted SLRs held true. When normal SLRs were assumed, permafrost presence was overestimated in each valley. This discrepancy was greatest at high elevations where permafrost was predicted to be coldest and most widespread. However, the difference between valleys was dependent on surface and subsurface characteristics such as higher snow cover, mature forest or thick organic layers showed a greater disassociation from the air temperature overall. Appropriate characterization of the SLR is essential for accurate predictions of the ground thermal regime’s spatial distribution and permafrost presence. These models also provide a starting point for better predictions of warming in these valleys and other areas subject to inversions of similar magnitudes.

A Quantile-Conserving Ensemble Filter Framework. Part III: Data Assimilation for Mixed Distributions with Application to a Low-Order Tracer Advection Model

Abstract The uncertainty associated with many observed and modeled quantities of interest in Earth system prediction can be represented by mixed probability distributions that are neither discrete nor continuous. For instance, a forecast probability of precipitation can have a finite probability of zero precipitation, consistent with a discrete distribution. However, nonzero values are not discrete and are represented by a continuous distribution; the same is true for rainfall rate. Other examples include snow depth, sea ice concentration, amount of a tracer or the source rate of a tracer. Some Earth system model parameters may also have discrete or mixed distributions. Most ensemble data assimilation methods do not explicitly consider the possibility of mixed distributions. The Quantile Conserving Ensemble Filtering Framework (Anderson 2022, 2023) is extended to explicitly deal with discrete or mixed distributions. An example is given using bounded normal rank histogram probability distributions applied to observing system simulation experiments in a low-order tracer advection model. Analyses of tracer concentration and tracer source are shown to be improved when using the extended methods. A key feature of the resulting ensembles is that there can be ensemble members with duplicate values. An extension of the rank histogram diagnostic method to deal with potential duplicates shows that the ensemble distributions from the extended assimilation methods are more consistent with the truth.

Validation of Vertical Bifacial Agrivoltaic and Other Systems Modelling

In agrivoltaic systems combining solar photovoltaic and agricultural activities, ground albedo is mainly characterized by the crop and its seasonal variations. This study examines the effects of using fixed, satellite-derived, and hourly measured albedo on the performance of a vertical bifacial system and a 1-axis tracking system using a bifacial photovoltaic model (AgriOptiCE). The model is developed with Matlab® and partially based on the open-source package pvlib. AgriOptiCE is firstly validated by comparing estimated front and rear irradiances with on-site measurements for specific periods from a 1-axis tracker site in Golden, USA and a vertical agrivoltaic system in Västerås, Sweden. Furthermore, photovoltaic system power output estimations using AgriOptiCE are also validated for the vertical agrivoltaic system and the conventional ground-mounted fixed-tilt system at the same location. The validations demonstrate the high accuracy of the proposed model in estimating front and rear irradiances and power output, obtaining R2 > 0.85 for all the studied cases. The study results indicate that measured albedo provides the highest accuracy, while satellite-derived albedo has poorer results due to the broader spatial, temporal, and spectral resolution. Fixed albedo is not recommended for yearly assessment of bifacial PV systems because it cannot account for snow events and daily variations, resulting in lower overall accuracy.

Homoplasy rather than intergeneric hybridisation explains mammillose leaf cells in Schistidium mammillosum sp. nov. (Grimmiaceae)

We present a peculiar new species in the Grimmiaceae, Schistidium mammillosum, which is distinct in having mammillose leaf cells and pluristratose lamina. These features are rare in mosses but shared by a few Grimmia species including sympatric G. alpestris. Sporophyte characteristics of the new taxon, in contrast, indicated a species of Schistidium and suggested a possible intergeneric hybrid origin. Nuclear ITS and plastid rps4‒trnF sequence data consistently resolved the new taxon within species of Schistidium, thus contradicting the hybridogenic hypothesis. We suggest that the shared feature of mammillose cells evolved independently in the two genera in adaptation to similar environmental conditions, possibly prolonged snow cover. Supported by morphological similarity, our phylogenetic analyses indicate a sister relationship of S. mammillosum and S. marginale in the Confertum clade. Schistidium mammillosum colonises siliceous rocks in the alpine and nival zone and is to date known from the French Alps and a single locality in Switzerland.

A comparative study of bifacial versus monofacial PV systems at the UK’s largest solar plant

Abstract This paper presents an extensive analysis of the UK’s largest bifacial photovoltaic (PV) power plant, located in North Yorkshire. Commissioned in January 2020, this trailblazing facility, with a total installed capacity of 34.7 MW, is a benchmark for the evaluation of bifacial solar technology within the region. This pioneering study provides a thorough comparative assessment of bifacial and monofacial PV systems through a methodical investigation of their energy production, degradation rates, and spectral responses over a 4-year operational period. Our findings reveal that bifacial PV modules, distributed across four segments of the power plant, demonstrate a remarkable average power gain ranging between 15.12% and 17.31% compared with monofacial modules. Despite experiencing marginally higher annual degradation rates—1.17% for bifacial compared with 0.91% for monofacial systems—bifacial modules show superior resilience and energy yield, particularly during winter months when albedo effects are pronounced due to snow coverage. The study also highlights the strategic importance of spectral response analysis in optimizing PV performance. Bifacial modules have shown greater efficiency in capturing infrared radiation—a property that could be exploited to enhance overall energy yield under specific environmental conditions. The empirical data indicate a consistent performance of bifacial modules with an average normalized energy output clustering around the expected efficiency level. Therefore, the results of this study are pivotal for understanding the practical implications of deploying bifacial PV technology on a large scale. They provide valuable data for stakeholders in the solar energy sector, guiding future installations and innovations in solar panel technology.

An improved crop calendar model for winter wheat incorporating vernalization and winter survival to project changes in phenology

Crop phenology and associated crop calendars are necessary for designing breeding goals and for developing effective management practices. Winter wheat is a representative biennial, the cultivation schedule of which is constrained by winter climate conditions, particularly the processes of vernalization and winter survival. Here, we present improvements to a rule-based crop calendar model by incorporating these factors so that it can be used to accurately estimate the phenological events of winter wheat from daily meteorological data. We tested the improved model in Hokkaido, the northernmost Japanese island, which is characterized by seasonal snow cover and a wet summer. The results confirmed that implementing these factors contributed to accurate estimates of peak occurrence dates of winter wheat phenological events. Furthermore, we applied the improved model to simulate wheat phenology under 2 K and 4 K warmer scenarios. The results showed a delayed sowing period up to approximately one month and slight advancements in both flowering and harvesting, leading to a shorter growth period. While this shortened period may be largely compensated by a decrease in the snow-covered period, the shifts in the vegetative and reproductive phases may modify sink-source balance of wheat. We also assessed the risks of pollination failure and preharvest sprouting, both of which are associated with the timing of precipitation, based on the number of rainy days around flowering, and the incidence of precipitation over two consecutive days around the time of harvesting. Our simulations suggested increased risk of pollination failure and reduced risk of preharvest sprouting, leading to an increase in the probability of crop failure. These findings underscore the importance of implementing adaptation measures to mitigate precipitation-related risk under future climates. Further, the findings provide valuable insights for winter wheat breeders and agronomists, thereby facilitating crop production adaptation.

The hydrological response of melting ephemeral snowpacks compared to winter rainfall events in a mid-mountainous Pyrenean catchment

AbstractThe hydrological role of ephemeral snowpacks and their differences in stormflow and sediment transport characteristics compared to events triggered by winter rainfall conditions have received limited attention. This study aims to analyze the hydrological and sediment transport responses to rain-on-snow (ROS), melt, mixed, and rainfall events in the Araguás Catchment, situated in a mid-mountain site of the Central Spanish Pyrenees, with a climate strongly influenced by Mediterranean conditions. This catchment represents the transition from a winter ephemeral snow environment to a fully rainfall-dominated site. Results indicate that snowmelt has a modest yet measurable impact on the annual water balance, averaging 10% and rising to 30% during winter (December to February). ROS and melt events consistently exhibited higher mean and maximum discharge and elevated stormflow coefficients compared to mixed and rainfall events. The lowest water infiltration into the soil was observed during melt events, attributed to the potential for frequent freezing soils, specific poor edaphic conditions, and the rapid snowmelt in the area. Consequently, melting events displayed the shortest flood hydrographs among the four analyzed events. The study also underscores precipitation’s almost negligible erodibility capacity in the solid phase and emphasizes the protective role of snow cover in preventing soil erosion. It is important to note that the presented results are significantly influenced by the physiographic, lithological, and edaphic characteristics of the Araguás Catchment. This highlights the importance of conducting more detailed analyses of ephemeral snowpacks in experimental sites under a broader range of environmental conditions for a comprehensive understanding.

Digital model of hydraulic drive for solar tracker rotary control

An important area in the field of solar energy is considered, focusing on the use of a hydraulic drive in high-power solar trackers. In the context of the relevance of environmental problems and the commitment to energy efficiency, the role of solar trackers in increasing the productivity of solar power plants is investigated. An analysis of current trends in the field of hydraulic drive is presented, highlighting the prospects for its application. The analysis of the directions of improvement of the drives of the solar tracker is carried out. The hydraulic drive of the tracker is considered as an object of regulation. Disturbing loads of various types acting in the process of changing the position of the tracker are determined. The main load acting on the tracker drive is positional, caused by the gas–dynamic effect of the wind flow impinging on the tracker plane or the ambiguity of the behavior of the snow and ice cover. It is determined that the inertial load, when turning a high-power tracker, has a significant effect on the friction forces in the attachment points of the hinge support mechanisms. Options for the implementation of new circuit solutions for the tracker hydraulic drive are considered. Using information from sensors of movement of the rods of hydraulic motors of the tracker position drive, the sun position sensor, the control electronic module positions the solar tracker platform by supplying control signals to the electric hydraulic distributors of the drive. The hydromechanical regulators of the pump adjust the characteristics of the system to a random change in external loads on the tracker. A mathematical model of the hydraulic drive of the basic circuit tracker has been developed. The digital model of the tracker drive is designed to optimize the operation of the hydraulic drive, taking into account various factors such as inertia, viscous damping, hydraulic losses, system nonlinearities, thermal losses, elasticity of elements and the impact of external factors. The results obtained emphasize the efficiency and accuracy of the control system, making the hydraulic drive an important element in the development of clean and efficient energy.

Archaeal communities change responding to anthropogenic and natural treatments of freeze-thawed soils

The coverage of accumulated snow plays a significant role in inducing changes in both microbial activity and environmental factors within freeze-thaw soil systems. This study aimed to analyze the impact of snow cover on the dynamics of archeal communities in freeze-thaw soil. Furthermore, it seeks to investigate the role of fertilization in freeze-thaw soil. Four treatments were established based on snow cover and fertilization:No snow and no fertilizer (CK–N), snow cover without fertilizer (X–N), fertilizer without snow cover (T-N), and both fertilizer and snow cover (T-X). The research findings indicated that after snow cover treatment, the carbon, nitrogen, and phosphorus content in freeze-thaw soil exhibit periodic fluctuations. Snow covered effectively altered the community composition of bacteria and archaea in the soil, with a greater impact on archaeal communities than on bacterial communities. Snow covered improves the stability of archaeal communities in freeze-thaw soil. Additionally, the arrival of snow also enhanced the correlation between archaea and environmental factors, with the key archaeal phyla involved being Nanoarchaeota and Crenarchaeota. Further research showed that the application of organic fertilizers also had some impact on freeze-thaw soil, but this impact was smaller compared to snow cover. In summary, the arrival of snow could alter the archaeal community and protect nutrient elements in freeze-thaw soil, reducing their loss, and its effect is more pronounced compared to the application of organic fertilizers.

Rolling forecast of snowmelt floods in data-scarce mountainous regions using weather forecast products to drive distributed energy balance hydrological model

Snowmelt flood forecasting plays a crucial role in water resource management and utilization in arid regions. However, the accuracy of current snowmelt flood forecasts falls short of requirements due to complex mountainous terrain and limited observational data. In this study, We conducted a snowmelt flood simulation and forecasting experiment in a mountainous watershed in northern Xinjiang. First, we used site observations, ERA5-land reanalysis data and WorldClim data to produce historical meteorological forcing, calibrated the VIC-CAS model and improved routing model using historical observed runoff data, and conducted a detailed simulation of the hourly snowmelt flood process in the basin. Then, the latest grid forecast weather product developed by the China Meteorological Administration was integrated into the system to conduct a rolling forecast test of the snowmelt floods in the next 72 h during the snowmelt period in 2023. The remotely sensed snow cover and the freezing level measured by the sounding balloon were assimilated into the model in real time to correct the deviation of runoff simulation. The results show that during the model calibration period, the KGE of the simulated monthly runoff in the Kayertes River basin (KRB) from 2006 to 2011 was 0.83 and the NSE was 0.86. In the continuous hourly runoff simulation during the 2022 snowmelt period, the model well captured the intraday fluctuations of snowmelt runoff in the KRB, with the daily peak flow error within ± 20 % and the peak occurrence time controlled within ± 4 h. In the rolling forecast test in 2023, the daily peak flow error is within ± 20 % (the peak occurrence error during the peak period is even controlled within ± 5 %), and the peak occurrence time is within ± 4 h. Real-time assimilation of freezing level measured by sounding balloons and remotely sensed snow cover can effectively correct the deviations in simulated temperature and snow cover. This study can provide insights for runoff prediction in other high-altitude mountainous areas where data are scarce.

Consistent Ground Surface Temperature Climatology Over China: 1956–2022

AbstractThe ground surface represents the land‐atmosphere interface and plays a crucial role in exchanging energy, matter, and biochemical fluxes. The ground surface temperature (Ts) is hence widely investigated as an indicator to understand the thermal state of soil in a warming world. However, regular and continuous Ts measurements are rare worldwide, and the early Ts records were derived from snow surface measurements and are not comparable with the measurements of modern automatic systems. In this study, we reconstruct the Ts records of the China Meteorological Administration (CMA) for 1956–2022 by numerical simulation. The results show that the mean annual ground surface temperature (MAGST) during 1981–2010 ranged from 0.4 to 30.8°C at 632 stations. The MAGST was mainly controlled by air temperature and refined by snow depth. The overall MAGST increased by 0.20 ± 0.02°C dec−1 across China during 1956–2022 and showed pronounced interdecadal variability corresponding to the surface air temperature (0.23 ± 0.03°C dec−1). At the snow‐covered sites, the Ts warming was amplified by increased snow depth, leading to about 0.24°C dec−1 (or 70.6%) faster warming for Ts in winter compared to that of surface air temperature. Many of the early reported ground surface temperature studies based on CMA measurements did not consider the measurement inconsistency and likely overestimated the warming trend of ground surface temperature over China.

Implementing robust outlier detection to enhance estimation accuracy of GNSS-IR based seasonal snow depth retrievals

ABSTRACT Monitoring snow depth variations aligned with the seasonal cycle is crucial for studies on climate change and its impacts due to global warming. Especially in terms of snow hydrology, the determination and continuous monitoring of snow depth to determine snow water equivalent is a priority in climate, water science, drought, flood, and inundation prediction studies, particularly in the water cycle. Recently, the so-called Global Navigation Satellite Systems – Interferometric Reflectometry (GNSS-IR) method enables to extract environmental radiometric and geometric characteristics of surface where the signals transmitted from the satellites reflect. The reflected signal follows an extra path than the direct one and causes a major error source for accurate point positioning but a useful tool for sensing the environment. However, outliers remain a challenge in long-term GNSS-IR estimations. In this study, we proposed a median-based robust outlier detection (ROD) approach for identifying outliers in long-term GNSS-IR snow depth estimations. To validate our approach, we analysed 5-year GNSS L1 SNR and L2 SNR data from 1 January 2015 to 31 December 2019 provided by AB33 and AB39 GNSS stations in Alaska, U.S.A. using the GNSS-IR method. We validated GNSS-IR estimations using snow depth measurements from the Coldfoot and Fort Yukon stations in the SNOTEL network. Applying ROD to long-term snow depth estimates increased the highest correlation from 91.58% to 94.93%, and reduced the lowest RMSE from 8.6 cm to 6.7 cm. In addition, the improvement rates calculated to assess the contribution of ROD to the results showed improvements of up to 6.9% in correlation and 26.1% in RMSE. Overall, the results demonstrate that ROD can be effectively used to detect outliers in long-term GNSS-IR snow depth time series.

Regional beryllium-10 production rate for the mid-elevation mountainous regions in central Europe, deduced from a multi-method study of moraines and lake sediments in the Black Forest

Abstract. Beryllium-10 cosmic-ray exposure (CRE) dating has revolutionized our understanding of glacier fluctuations around the globe. A key prerequisite for the successful application of this dating method is the determination of regional production rates of in situ accumulated 10Be, usually inferred at independently dated calibration sites. Until now, no calibration site has been available for the mid-elevation mountain ranges of central Europe. We fill this gap by determining in situ 10Be concentrations in large boulders on moraines and by applying radiocarbon and infrared-stimulated luminescence (IRSL) dating to stratigraphically younger lake sediments in the southern Black Forest, SW Germany. The dating methods yielded concordant results, and, based on age–depth modelling with 14C ages, the age of a cryptotephra, and IRSL ages, we deduced a regional 10Be production rate in quartz. Calibrating the Black Forest production rate (BFPR) in the Cosmic-Ray Exposure program (CREp) resulted in a spallogenic sea-level and high-latitude (SLHL) production rate of 3.64±0.11 atoms 10Be g−1 quartz a−1 when referring to time-dependent Lal–Stone scaling, the European Reanalysis (ERA)-40 atmosphere model, and the atmospheric 10Be-based geomagnetic database in CREp. The BFPR turned out to be ∼11 % lower than both those at the nearest calibration site in the Alps (4.10±0.10 atoms 10Be g−1 quartz a−1 at SLHL) and the canonical global 10Be production rate (4.11±0.19 atoms 10Be g−1 quartz a−1 at SLHL) in CREp. A stronger weathering and snow cover bias and a higher impact of forest, soil, moss, and shrub cover at the study site likely explain this discrepancy.

Manure temperature prediction for slurry storage in Sweden: Model validation including effects of shading, snow cover and mixing

Measuring and modelling manure temperatures are crucial for estimating greenhouse gas emissions from liquid manure storage. The manure temperature was recorded at various depths in two swine slurry storage tanks situated in Vallentuna (VA) and Örsundsbro (OR) in Sweden. These data were used to assess the effectiveness of a revised mechanistic model for estimating manure temperatures, which incorporates the effects of wall shading, snow cover, and manure input mixing. The average manure temperatures were higher than air temperatures in the summer and fall. This indicated that using air temperature would result in an underestimation of methane emissions when applying the 2019 IPCC Refinement methodology. The revised model estimated manure temperatures for spring, summer, fall, and winter as 4.8, 16.1, 7.8, and 2.6 °C at the VA tank and 11.6, 17.1, 9.5, and 3.6 °C at the OR tank. The root mean square errors between daily simulated and observed temperatures in the summer decreased in both tanks due to incorporating shadow effect into the revised model. Fall estimates did not improve, possibly because of uncertainties from slurry removal and higher precipitation inputs. Sensitivity analysis indicated that solar radiative heat input was reduced with higher tank walls and smaller tank diameters when applying the revised model. Wall shading may influence manure temperatures in tanks with small diameters at high-latitude locations. This study offers insights into understanding the relationship between manure temperatures and its thermal balance influenced by latitude, storage design, snow cover and mixing, and its implications for accurately estimating methane emissions.

Predisposing, triggering and runout processes at a permafrost‐affected rock avalanche site in the French Alps (Étache, June 2020)

AbstractAlthough numerous recent studies have explored the relationship between permafrost degradation and rock slope failure, there is still a need for in‐depth investigations to develop relevant hazard assessment approaches. We investigate the predisposing, triggering and propagation processes of a rock avalanche (c. 225,000 m3) that occurred in Vallon d'Étache (France) on 18 June 2020, whose scar was coated by ice and water. Weather records and energy balance models show that the rock avalanche occurred right after the warmest spring and winter since at least 1985, but also right after the spring with the highest water supply anomaly (snowmelt and rainfall). Measured ground surface temperature and geoelectrical surveys reveal that relatively ice‐rich permafrost could exist in the NW face (release area) while it is inexistent below the SE face, contradicting certain permafrost maps. Heat transfer simulations suggest that the rock avalanche occurred during a transition from cold to warm permafrost conditions at failure depth (30 m), with a temperature increase of up to 0.6°C per decade since 2012 (when considering potential snow cover effect), and current temperature ranging between −3 and −1°C, depending on the applied model forcing. This warming certainly contributed to predispose slope to failure. In addition, the shift towards warm permafrost and water infiltration potentially enhancing permafrost degradation along fractures through heat advection or favouring the development of high hydrostatic pressures may have played as triggering factors. Finally, propagation simulations show that the rock avalanche involved several phases with different rheological properties due to the incorporation of snow and material segregation within the deposit. These new insights at various scales highlight the complexity of the triggering and propagation processes of rock slope failure occurring in high mountains, a significant part of which can be linked to snow effects on ground temperature, water supply and rheological properties.

FEATURES OF THE DISTRIBUTION AND COMPOSITION OF SNOW COVER WITHIN THE LANDSCAPES OF CHASHNIKOVO

For landscape conditions in the upper reaches of the river Klyazma, Solnechnogorsk district, Moscow region, the height and reserves of snow cover were investigated, and the chemical composition of the snow was determined. The basis for considering the component composition of snow cover was the geochemical taxonomy of chemical elements based on the characteristics of water migration and abundance. Data from 23 snow sampling points were interpolated in SAGA GIS using the inverse distance weighting (IDW) method. On this basis, zones differing in the chemical composition of snow are identified. One of the zones is confined to the M-10 Moscow-St. Petersburg highway, while the second borders on populated areas. The area close to the highway is characterized by increased levels of calcium, sodium, aluminum, and chloride ions in the snow cover. The second zone, bordering populated areas, is characterized by a high content of calcium, copper, and manganese in the snow. For the third zone, low concentrations of components in the snow were observed which are characteristic of a superaquatic landscape due to the distance from sources of pollution. The studied composition of snow waters belongs to the bicarbonate-sodium-calcium-chloride class. It has been shown that the height and reserves of snow cover are partially controlled by two factors: the type of elementary landscape and the type of ecosystem. Against this background, the spatial distribution of concentrations of elements and anions in snow is predominantly controlled by the anthropogenic factor.

Environmental controls and phenology of sea ice algal growth in a future Arctic

The future of Arctic sea ice algae is examined using a regional ocean and sea ice biogeochemical model, with a simulation from 1980 to 2085, considering a future scenario with strong warming. To analyze the impacts of climate change, we computed key dates in the development of sympagic blooms, corresponding to the occurrence of specific growth conditions, and designed diagnostics of ice algal phenology to estimate the onset and peak of blooms. These diagnostics help understand how the timing of light and nutrient availability governs the growth of ice algae and how environmental controls will be altered by climate change across regions. With thinner ice, photosynthetically active radiation in bottom ice will reach levels sufficient for growth earlier, resulting in a better synchrony of high levels of light and nutrients. Increases in snow cover can potentially offset the effect of thinner ice, leading to shorter periods of favorable growth conditions in certain regions. The loss of sea ice cover before the late 21st century only impacts sympagic blooms at lower latitudes, as the timing of sea ice break-up shows little change relative to other key dates at higher latitudes. In response to climate change, the model simulates a modified spatial distribution of blooms, with the emergence of highly productive areas and the loss of blooms in other regions. However, the changes in the timing of growth conditions do not substantially alter the timing of blooms, and both onset and peak ice algae see little change. The simulated lack of sensitivity of bloom onset is attributed to the delay in sea ice freeze-up projected by the model, causing a reduction of overwintering ice algae. The resulting lower initial biomass at the beginning of spring then causes a delay in the development of blooms, offsetting earlier light from thinner ice.

High freezing sensitivity of legumes relative to other herbaceous species in northern temperate plant communities.

Reduced snow cover and increased air temperature variability are predicted to expose overwintering herbaceous plants to more severe freezing in some northern temperate regions. Legumes are a key functional group that may exhibit lower freezing tolerance than other species in these regions, but this trend has been observed only for non-native legumes. Our aim was to confirm if this trend is restricted to non-native legumes or whether native legumes in these regions also exhibit low freezing tolerance. First, we transplanted legumes (five non-native species and four native species) into either an old field (non-native) or a prairie (native) and used snow removal to expose the plots to increased soil freezing. Second, we grew plants in mesocosms (old field) and pots (prairie species) and exposed them in controlled environment chambers to a range of freezing treatments (control, 0, -5 or -10 °C) in winter or spring. We assessed freezing responses by comparing differences in biomass, cover and nodulation between freezing (or snow removal) treatments and controls. Among legume species, lower freezing tolerance was positively correlated with a lower proportion of nodulated plants and active nodules, and under controlled conditions, freezing-induced reductions in above-ground biomass were lower on average in native legumes than in non-native legumes. Nevertheless, both non-native and native legumes (except Desmodium canadense) exhibited greater reductions in biomass in response to increased freezing than their non-leguminous neighbours, both in controlled environments and in the field. These results demonstrate that both native and non-native legumes exhibit low freezing tolerance relative to other herbaceous species in northern temperate plant communities. By reducing legume biomass and nodulation, increased soil freezing could reduce nitrogen inputs into these systems.

Modelling impacts of climate change on snow drought, groundwater drought, and their feedback mechanism in a snow-dominated watershed in western Canada

Snow accumulation and its melt are key hydrological processes in cold watersheds, which can affect groundwater (GW). With climate change projected to alter snow processes in these regions, understanding their impacts on the development of droughts is vital. A deficit in snow precipitation or accelerated snowmelt due to warming can trigger snow drought, potentially leading to GW drought. To investigate this relationship at a watershed scale, we coupled the Soil and Water Assessment Tool (SWAT) and MODFLOW to simulate various surface and groundwater processes under historical (1980–2013) and future (2040–2073) warming scenarios in western Canada. We calibrated, validated, and verified our models using streamflow, GW heads, and snow depth data from multiple hydrometric stations, observation wells, and a grid product. Using simulated data, we analyzed snow and GW drought characteristics, their dominant physical processes, and GW response time across eco-hydro(geo)logical regions like Mountains, Foothills, and Plains. Historical data show that Mountains are experiencing more snow droughts while Plains are facing greater GW droughts. However, future scenarios suggest increased snow droughts in all regions and a shift towards more severe GW droughts in Plains. Historical response time of GW to changes in snow processes spans 4–6 months from Mountains to Plains, with projected reductions in Mountains and Foothills, and a slight increase in Plains in the future. The dominant physical processes controlling GW response across all regions are soil moisture and percolation, with curve number displaying more significance in Mountains, and water yield exerts more control in Foothills and Plains. During cold seasons, SWE and snowmelt had minimal impact on GW response in Plains, while they presented a major role in Mountains. This study lays the basis for further research on snow-groundwater interactions in cold watersheds, aiding water resource management in mid-to-high latitude regions and providing a unified framework for analyzing snow and GW drought relationships.

Decomposition in an extreme cold environment and associated microbiome-prediction model implications for the postmortem interval estimation.

The accurate estimation of postmortem interval (PMI), the time between death and discovery of the body, is crucial in forensic science investigations as it impacts legal outcomes. PMI estimation in extremely cold environments becomes susceptible to errors and misinterpretations, especially with prolonged PMIs. This study addresses the lack of data on decomposition in extreme cold by providing the first overview of decomposition in such settings. Moreover, it proposes the first postmortem microbiome prediction model for PMI estimation in cold environments, applicable even when the visual decomposition is halted. The experiment was conducted on animal models in the second-coldest region in the United States, Grand Forks, North Dakota, and covered 23 weeks, including the winter months with temperatures as low as -39°C. Random Forest analysis models were developed to estimate the PMI based either uniquely on 16s rRNA gene microbial data derived from nasal swabs or based on both microbial data and measurable environmental parameters such as snow depth and outdoor temperatures, on a total of 393 samples. Among the six developed models, the best performing one was the complex model based on both internal and external swabs. It achieved a Mean Absolute Error (MAE) of 1.36 weeks and an R2 value of 0.91. On the other hand, the worst performing model was the minimal one that relied solely on external swabs. It had an MAE of 2.89 weeks and an R2 of 0.73. Furthermore, among the six developed models, the commonly identified predictors across at least five out of six models included the following genera: Psychrobacter (ASV1925 and ASV1929), Carnobacterium (ASV2872) and Pseudomonas (ASV1863). The outcome of this research provides the first microbial model able to predict PMI with an accuracy of 9.52 days over a six-month period of extreme winter conditions.