Snow Hydrology

The Himalayas is a serious victim of climate change. Consequences of the change will be the hardest for the people there. Residents in downstream will be no less affected. Melting ice and decreasing ice reserves indicate a disastrous future for those who rely on the Himalayas for water sources. The Himalayas are source of life for millions. Impacts are already visible in the form of flash floods, GLOF and unpredictable monsoon. Results are declining agricultural production, scarcity of water resources and deterioration of human health. Growing vehicular pollution, tourism and mega hydropower dams are some of the internal factors raising alarms in the Himalayas. Moreover, human activities in the vicinity of Bhutan not within its control are bigger influencers of climate changes.

This work provides an overview of various methods for estimating snow cover and properties in high mountains using remote sensing techniques involving microwaves. Satellite based remote sensing with its characteristics such as synoptic view, repetitive coverage and uniformity over large areas, has great potential for identifying the temporal snow cover. Many sensors have been used in the past with various algorithms and accuracies for this purpose. These methods have been improving with the use of Synthetic Aperture Radar (SAR) sensors, working in different microwave frequencies, polarization and acquisition modes. The limitations, advantages and drawbacks are illustrated while error sources and strategies on how to ease their impacts are also reviewed. An extensive list of references, with an emphasis on studies since 1990s, allows the reader to delve into specific topics.

For determining of Muskingum model coefficients, requires to an output hydrograph. Such hydrograph is not available in most rivers. In this research, the Muskingum's new coefficients are determined by the method that which is not require to output hydrograph and its accuracy is high. This coefficient is determined based on kinematic wave model with suitable scheme. The comparison between results of Muskingum model with new coefficients and dynamic wave model, showed that the new coefficients, are valid at special conditions. The new coefficients were adjusted by optimization technique for all conditions. The new adjusted coefficients are function of bed slop, bottom width and Manning's roughness coefficient for the river. The results of these coefficients were validated by dynamic wave model.

This study is motivated by the sharp contrast between physical and probabilistic models of civil engineering. The current practice focuses on physical models while probabilistic ones are relatively underdeveloped. This unbalance can even hinder advances in physical models. Moreover, this rarely acknowledged asymmetry creates the illusion that our deterministic models accurately capture all or at least the main aspects of reality. Thus, this thesis aspires to subtly adjust this imbalance by focusing on probabilistic models.

The main contribution is that it explores often neglected or oversimplified aspects of probabilistic analysis in civil engineering. These distinct but related issues are: (i ) selection of an appropriate distribution type; (ii ) effect of statistical uncertainty; (iii ) measurement uncertainty; (iv) long-term trends; and (v) dependence structure. These are demonstrated through analyzing extreme ground snow loads, although they are inevitably present for most random variables. Snow action, which has recently led to numerous structural failures in Central Europe, is treated as a vehicle of illustration to give a sharp focus to the study.

Methods developed in mathematical statistics, probability theory, information theory, and structural reliability are applied to tackle these issues. Fully statistical analysis of snow extremes is undertaken in conjunction with structural reliability analysis. The popular civil engineering approaches are compared with more advanced techniques that are able to capture the neglected effects in the former approaches. Snow water equivalent data of the Carpathian Region from more than 600 meteorological stations are analyzed, thus the results are representative for lowlands, for highlands, and for mountains as well. Furthermore, extensive parametric analyses are performed to extend the investigations to random variables other than ground snow intensity.

Based on the analysis of the above-listed issues the following main conclusions are drawn:

(i) It is demonstrated that mountains and highlands are better represented by Weibull, while lowlands by Fréchet distribution. In comparison, the currently standardized Gumbel model recommended in Eurocode often appreciably underestimates the snow maxima of lowlands and thus leads to overestimation of structural reliability.

(ii) It is shown that current practice, which neglects statistical uncertainties (parameter estimation and model selection uncertainties), can yield to even multiple orders of magnitude underestimation of failure probability. Bayesian posterior predictive distribution and Bayesian model averaging is proposed to account for parameter estimation and model selection uncertainties, respectively.

(iii) Statistical and interval based approaches are used to explore the effect of prevalently neglected measurement uncertainty on structural reliability. It is demonstrated that such simplification can lead to an order of magnitude underestimation of failure probability. If sufficient data are available to infer the probabilistic model of measurement uncertainty, then the statistical approach is recommended. Otherwise, the interval approach is advocated.

(iv) Using non-stationary extreme value analysis, statistically significant decreasing trend is found in annual ground snow extremes for most of the Carpathian Region. For some locations the effect of the trend on structural reliability is practically significant. This change is favorable from safety point of view as it increases reliability. Hence, revision of current regulations due to long-term trends is not needed from safety reasons, though it might be desirable from economic considerations. However, record lengths are insufficient to draw strong conclusions and to include trends in predicting extreme values with return periods of hundreds of years.

(v) Study of the widely used Gauss (normal, or Gaussian) copula assumption of time-continuous stochastic processes is performed. It reveals that Gauss copula can four times underestimate or even ten times overestimate time-variant failure probabilities obtained by other adopted copulas (t, Gumbel, rotated Gumbel, and rotated Clayton). Model averaging is proposed as a viable approach to rigorously account for copula function uncertainty.

Based on these findings, practical recommendations are made for normal and safety critical structures. The effects and proposed approaches are illustrated through real life examples. Reliability of the more than 130 years old wrought iron structure of Eiffel-hall and a steel hall of Paks Nuclear Power Plant is analyzed. The tackled challenges are general and relevant for most random variables such as wind, traffic, and earthquake actions. Therefore, the findings can be applied for these as well and they can help to draft more consistent standards and to build safer structures.

This article contributes to better understanding of the precipitation data, analyzing several measurement errors in Romania. Based on the influence of wind speed, solid precipitation rate, and wetting losses, we adjusted the monthly amounts registered at 159 weather stations through 1961–2006. The results emphasize distinct temporal and spatial distributions of the adjustment magnitude. In general, the correction factors increase with altitude and they have high values in the cold season, as they highly depend on wind speed and solid precipitation percentage. In Romania, bias corrections increase monthly precipitation by less than 10% from June to September, by 10–20% in the transition months, and by higher values during the winter. Copyright © 2010 Royal Meteorological Society

The quantity of liquid water in the snowpack defines its wetness. The temporal evolution of snow wetness's plays a significant role in wet-snow avalanche prediction, meltwater release, and water availability estimations and assessments within a river basin. However, it remains a difficult task and a demanding issue to measure the snowpack's liquid water content (LWC) and its temporal evolution with conventional in situ techniques. We propose an approach based on the use of time-domain reflectometry (TDR) and CS650 soil water content reflectometers to measure the snowpack's LWC and temperature profiles. For this purpose, we created an easily-applicable, low-cost, automated, and continuous LWC profiling instrument using reflectometers at the Cooperative Remote Sensing Science and Technology Center-Snow Analysis and Field Experiment (CREST-SAFE) in Caribou, ME, USA, and tested it during the snow melt period (February–April) immediately after installation in 2014. Snow Thermal Model (SNTHERM) LWC simulations forced with CREST-SAFE meteorological data were used to evaluate the accuracy of the instrument. Results showed overall good agreement, but clearly indicated inaccuracy under wet snow conditions. For this reason, we present two (for dry and wet snow) statistical relationships between snow LWC and dielectric permittivity similar to Topp's equation for the LWC of mineral soils. These equations were validated using CREST-SAFE in situ data from winter 2015. Results displayed high agreement when compared to LWC estimates obtained using empirical formulas developed in previous studies, and minor improvement over wet snow LWC estimates. Additionally, the equations seemed to be able to capture the snowpack state (i.e., onset of melt, medium, and maximum saturation). Lastly, field test results show advantages, such as: automated, continuous measurements, the temperature profiling of the snowpack, and the possible categorization of its state. However, future work should focus on improving the instrument's capability to measure the snowpack's LWC profile by properly calibrating it with in situ LWC measurements. Acceptable validation agreement indicates that the developed snow LWC, temperature, and wetness profiler offers a promising new tool for snow hydrology research.

The contribution of glaciers to annual streamflow has been for a long time a major concern for water management agencies in western Argentina. This study provides the first quantitative assessment of the interannual buffer effect of glaciers and snowpatches on the interannual variability of streamflow in the Central Andes. Interannual variations in annual streamflow from seven Andean river basins were compared. Given the lack or unreliability of climate data in the upper Andes, variability of annual precipitation was estimated on the basis of annual streamflow from basins with no significant glacier contribution. A latitudinal gradient of precipitation variability is built on the basis of the behavior of the Rivers showing consistent nival features. The departure of a river’s interannual variability from the obtained gradient is presented as a measure of the buffer effect played by glacier and interannual snow.  Glaciers and snowpatches in the Mendoza river basin (33º S) moderate 54% of the interannual precipitation variability. For rivers located to the south the glacier-snowpack buffer effect decreases markedly due to the lower elevations of the basins (reduced interannual water-storage capacity) and the lower variability in annual precipitation. For basins to the north, lower elevations, reduced glacier cover, and higher variability in annual precipitation yield in a hydrologically negligible effect of glacier-snowpack on water discharge.

Adequate estimation of the spatial distribution
of snowfall is critical in hydrologic modelling. However,
this is a well-known problem in estimating basinscale
snowfall, especially in mountainous basins with data
scarcity. This study focuses on correction and estimation
of this spatial distribution, which considers topographic effects
within the basin. A method is proposed that optimises
an altitude-based snowfall correction factor (Cfsnow).
This is done through multi-objective calibration of a spatially
distributed, multilayer energy and water balance-based
snowmelt model (WEB-DHM-S) with observed discharge
and remotely sensed snow cover data from the Moderate Resolution
Imaging Spectroradiometer (MODIS). The Shuffled
Complex Evolution–University of Arizona (SCE–UA) automatic
search algorithm is used to obtain the optimal value of
Cfsnow for minimum cumulative error in discharge and snow
cover simulations. Discharge error is quantified by Nash–
Sutcliffe efficiency and relative volume deviation, and snow
cover error was estimated by pixel-by-pixel analysis. The
study region is the heavily snow-fed Yagisawa Basin of the
Upper Tone River in northeast Japan. First, the system was
applied to one snow season (2002–2003), obtaining an optimised
Cfsnow of 0.0007m−1. For validation purposes, the optimised
Cfsnow was implemented to correct snowfall in 2004,
2002 and 2001. Overall, the system was effective, implying
improvements in correlation of simulated versus observed
discharge and snow cover. The 4 yr mean of basin-average
snowfall for the corrected spatial snowfall distribution was
1160mm (780mm before correction). Execution of sensitivity
runs against other model input and parameters indicated
that Cfsnow could be affected by uncertainty in shortwave radiation
and setting of the threshold air temperature parameter.
Our approach is suitable to correct snowfall and estimate
its distribution in poorly gauged basins, where elevation
dependence of snowfall amount is strong.

The classification of the snow intensity is presented, based on the number of days with snow cover (snow intensity W) and on the sum of snow depths (snow intensity G). The 25% of winters in the meteorological station can be named slight snowy winters and the other 25% of winters can be named snowy winters. The other 50% of winters are named moderately snowy winters.
The mean number of days with snow cover in winter (in Lublin) is 72 and the mean sum or snow depths 768 cm. The highest number of days with snow cover, until 1995, was 119 days. The winter of 1995/1996 was an unusually snowy winter, with regard to the number of days with snow cover (134 days). It was, with regard to the sum of snow depths, the third winter (1174 cm). The highest value occurred in the winter of 1969/1970 (3127 cm).

To improve Greenland Ice Sheet surface mass balance (SMB) simulation, the subsurface scheme of the HIRHAM5 regional climate model was extended to include snow densification, varying hydraulic conductivity, irreducible water saturation and other effects on snow liquid water percolation and retention. Sensitivity experiments to investigate the effects of the additions and the impact of different parameterization choices are presented. Compared with 68 accumulation area ice cores, the simulated mean annual net accumulation bias is −5% (correlation coefficient of 0.90). Modeled SMB in the ablation area compares favorably with 1041 PROMICE observations with regression slope of 0.95–0.97 (depending on model configuration), correlation coefficient of 0.75–0.86 and mean bias −3%. Weighting ablation area SMB biases at low-and high-elevation with the amount of runoff from these areas, we estimate ice sheet-wide mass loss biases in the ablation area at −5 and −7% using observed (MODIS-derived) and internally calculated albedo, respectively. Comparison with observed melt day counts shows that patterns of spatial (correlation ∼0.9) and temporal (correlation coefficient of ∼0.9) variability are realistically represented in the simulations. However, the model tends to underestimate the magnitude of inter-annual variability (regression slope ∼0.7) and overestimate that of spatial variability (slope ∼1.2). In terms of subsurface temperature structure and occurrence of perennial firn aquifers and perched ice layers, the most important model choices are the albedo implementation and irreducible water saturation parameterization. At one percolation area location, for instance, the internally calculated albedo yields too high subsurface temperatures below 5 m, but when using an implementation of irreducible saturation allowing higher values, an ice layer forms in 2011, reducing the deep warm bias in subsequent years. On the other hand, prior to the formation of the ice layer, observed albedos combined with lower irreducible saturation give the smallest bias. Perennial firn aquifers and perched ice layers occur in varying thickness and area for

An estimation of the contribution of glaciers and interannual snowpack to streamflows of Blanco and Mendoza rivers during two dry years is presented. Monthly flows between 1968 and 1997 are used, as well as the monthly elevations of the 0 ºC isotherm between 1975 and 2005. A Seasonality Index was calculated, and its correlation with the 0ºC isotherm considered. On the basis of these preliminary results, a methodology to estimate the contribution from snow andice masses to streamflow was identified. For both annual cycles, the contribution of ice represented about 50%  and 26% of the annual streamflow volume for the Mendoza and Blanco rivers, respectively.. Differences between rivers are consistent with differences in mean altitude, mean annual precipitation and latitudinal position of the river basins.

This study assessed the performances of the traditional temperature-index snowmelt runoff model (SRM) and an SRM model with a finer zonation based on aspect and slope (SRM þ AS model) in a data-scarce mountain watershed in the Urumqi River Basin, in Northwest China. The proposed SRM þ AS model was used to estimate the melt rate with the degree-day factor (DDF) through the division of watershed elevation zones based on aspect and slope. The simulation results of the SRM þ AS model were compared with those of the traditional SRM model to identify the improvements of the SRM þ AS model's performance with consideration of topographic features of the watershed. The results show that the performance of the SRM þ AS model has improved slightly compared to that of the SRM model. The coefficients of determination increased from 0.73, 0.69, and 0.79 with the SRM model to 0.76, 0.76, and 0.81 with the SRM þ AS model during the simulation and validation periods in 2005, 2006, and 2007, respectively. The proposed SRM þ AS model that considers aspect and slope can improve the accuracy of snowmelt runoff simulation compared to the traditional SRM model in mountain watersheds in arid regions by proper parameterization, careful input data selection, and data preparation.

The finding that microbial communities are active under snow has changed the estimated global rates of biogeochemical processes beneath seasonal snow packs. We used microbiological and molecular techniques to elucidate the phylogenetic composition of undersnow microbial communities in Colorado, the United States. Here, we show that tundra soil microbial biomass reaches its annual peak under snow, and that fungi account for most of the biomass. Phylogenetic analysis of tundra soil fungi revealed a high diversity of fungi and three novel clades that constitute major new groups of fungi (divergent at the subphylum or class level). An abundance of previously unknown fungi that are active beneath the snow substantially broadens our understanding of both the diversity and biogeochemical functioning of fungi in cold environments.

With climate change, modelling has suggested that increased inaccessibility of forage through snow may endanger some populations of arctic ungulates; however, contemporaneous data on snow-cover conditions, other ecological factors and ungulate responses are lacking at the landscape scale. Researchers have increasingly used remote sensing to map snow cover with higher accuracy, but such tools have not been utilized in research and management of arctic ungulate populations. We estimated field-measured percent snow-covered area (F-SCA) in wintering areas of endangered Peary caribou Rangifer tarandus pearyi in the Bathurst Island complex (BIC) and developed a threshold for a normalized difference snow index (NDSI) using Landsat data.We used our NDSI threshold and another threshold to estimate snow-covered area (SCA) in Peary caribou habitats in the BIC during 1993-2003, compared these estimates with snow data from the nearest weather station and assessed the adequacy of Landsat data for arctic ungulate research. Our calculated NDSI threshold of 0.70 reflected field observations better than the published threshold, and our estimated SCAs showed greater variation between study areas, between years and during snow melt. Estimated SCAs were not correlated with total snowfall or snow depth at the nearest weather station. We conclude that SCA using remotely-sensed data for ungulate habitats would be more useful than weather-station data. Our methods could detect winters with relatively mild snow-cover conditions, but not those with very severe conditions; therefore, we recommend development of NDSI thresholds corresponding to 75%F-SCA, instead of 50%.NDSI-derived SCAmethods should provemore useful for southerly arctic regions where sun angles would be less limiting than in the BIC. Higher resolution imagery may be more suited than Landsat for the assessment of snow cover in arctic ungulate ecology. With climate change, further development of remotely-sensed indices of snow cover, such as NDSI and SCA, should enhance our understanding of how arctic ungulates may or may not adapt.

Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The Dv and R2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2 °C, and (c) a temperature rise of +2 °C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increas...

Glaciers in the northern Pakistan are a distinctive source of freshwater for the irrigation, drinking and industrial water supplies of the people living in those regions and downstream. These glaciers are under a direct global warming impact as indicated in many previous studies. In this study, we estimated the glacier dynamics in terms of Equilibrium Line Altitude (ELA), mass balance and the snout position variation using remote sensing data between 2001 and 2018. Six glaciers, having area ≥ 20 km2 each, situated in the Chitral region (Hindukush Mountains) were investigated in this study. Digital Elevation Model (DEM) and available cloud-free continuous series of Landsat and Sentinel the entire study area was a retreat of -231 ± 140 m. No obvious relationship was found between the glacier variation trends and the available gauged climatic data possibly due to the presence of debris cover in ablation zones of all the studied glaciers which provides insulation and reduces the immediate climatic effects.

RESUMEN Los regímenes estacionales de los ríos más importantes del oeste y sudoeste del país responden no sólo a la precipitación sino también a la temperatura. Con el objeto de enriquecer la interpretación genética de la curva media de caudales medios mensuales se analizan las principales submodalidades de régimen según la variabilidad interanual pluvial y térmica. Se halló que respecto a la precipitación la estacionalidad del San Juan y del Mendoza muestran correlaciones débiles pero significativas, mientras el Atuel, el Colorado y el Neuquén no muestran su estacionalidad ligada significativamente. Al contrario el Limay muestra una correlación inversa débil pero significativa. Por otro lado se infiere que la estacionalidad es más sensible a la temperatura que a la precipitación, aunque con diferentes grados de asociación en cada caso. Introducción La adecuada comprensión de la estacionalidad de un río a lo largo del año, esto es en una escala temporal estacional, es fundamental para el diseño y el manejo de una presa. Los períodos largos de anomalías climáticas pueden introducir modificaciones en la estacionalidad del escurrimiento y por lo tanto la necesidad de readecuar pautas de gestión en el manejo de embalses y planes de regadío. Tales modificaciones en la estacionalidad, que no pueden suponerse estables en el tiempo, reflejan generalmente leves aumentos o decrecimientos numéricos en las variables hidrológicas clásicas. De esta manera las épocas de predominio de la restitución o del almacenamiento pueden acortarse o extenderse según las funciones de cada presa o plan de regadío en particular. En esta oportunidad haremos un breve análisis de la forma en que la estacionalidad de los ríos andinos tienen que ver con el monto anual de precipitación y con las temperaturas medias estacionales. Esta presentación pretende situarse en la misma temática desarrollada por Bruniard en dos excelentes trabajos relacionados con los regímenes fluviales de alimentación sólida (Bruniard, 1992,1994), cuya terminología y principios generales nos han servido de orientación. El desarrollo de las nacientes –cuenca activa-de estos ríos en un ambiente montañoso significa que en mayor o menor medida la nieve es uno de los elementos configuradores de la estacionalidad. En consecuencia no sólo la precipitación, sino también la temperatura incide en la generación del escurrimiento a lo largo de un año. Por el lado de los acumulados anuales de precipitación la estacionalidad puede verse afectada por el desarrollo de un manto nival de espesor mayor a la media. Aquí comenzamos a dilucidar en qué medida tal situación retrasa la culminación de las altas aguas primaverales o estivales de los ríos andinos. O dicho de otra manera se apunta a identificar en que proporción un espesor excepcional asegura una mayor persistencia del manto nival de cada año. Por el lado de la marcha anual de la temperatura la estacionalidad puede verse afectada de forma compleja y según se trate de un régimen nival puro o mixto. Lascano y Villalba (2005) observaron para el río Neuquen, de régimen nivo-pluvial, que las mayores temperaturas medias registradas entre mediados de los '70 y fines de los '90 generaron una mayor contribución de las altas aguas invernales al derrame. El pulso de fines de primavera respondió, aún conservando su primacía, con un adelanto y reducción de su contribución. En cambio los ríos nivales y glaciarios de curva simple, tales como el San Juan, el Mendoza y el Atuel, reaccionaron a mayores temperaturas sólo con un adelantamiento de las altas aguas estivales. Es decir, la variación de año a año de la temperatura con precipitaciones en torno a la media, o las fluctuaciones de la precipitación con temperaturas normales, pueden ser dos escenarios que ayuden a profundizar la comprensión de los regímenes fluviales de alimentación sólida en la República Argentina.

Under different climate change scenarios, the current study was planned to simulate runoff due to snowmelt in the Lidder River catchment in the Himalayan region. A basic degree-day model, the Snowmelt-Runoff Model (SRM), was utilized to assess the hydrological consequences of change in the climate. The performance of the SRM model during calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found to be 11.7, −10.1, −11.8, 1.96, and 8.6 in 2009–2014, respectively, while the respective R2 was 0.96, 0.92, 0.95, 0.90, and 0.94. The Dv and R2 values indicate that the simulated snowmelt runoff closely agrees with the observed values. The simulated findings were assessed under three different climate change scenarios: (a) an increase in precipitation by +20%, (b) a temperature rise of +2 °C, and (c) a temperature rise of +2 °C with a 20% increase in snow cover. In scenario (b), the simulated results showed that runoff increas...

The finding that microbial communities are active under snow has changed the estimated global rates of biogeochemical processes beneath seasonal snow packs. We used microbiological and molecular techniques to elucidate the phylogenetic composition of undersnow microbial communities in Colorado, the United States. Here, we show that tundra soil microbial biomass reaches its annual peak under snow, and that fungi account for most of the biomass. Phylogenetic analysis of tundra soil fungi revealed a high diversity of fungi and three novel clades that constitute major new groups of fungi (divergent at the subphylum or class level). An abundance of previously unknown fungi that are active beneath the snow substantially broadens our understanding of both the diversity and biogeochemical functioning of fungi in cold environments.

Many spatial processes analysed using Geographic Information Systems depend on terrain relief. Over the past few years it has become clear that reliably processing spatial data is necessarily accompanied by strict management of errors and uncertainties inherent to the data used. This paper presents a conceptual framework for reliable digital terrain modelling. Reliable digital terrain models emphasise both geometric and semantic aspects of terrain representation and provide information concerning their quality. Reliable terrain modelling involves producer as well as user of digital terrain models (DTMs) by presuming that the former is able to specify requirements for the DTM, and that the latter makes available a quality report of his / her products. Assessment of a DTM’s fitness for a specific use is dependent on the availability of such quality information. However, considering the process of digital terrain modelling, not only the quality of the input data is essential to the rel...

Glaciers in the northern Pakistan are a distinctive source of freshwater for the irrigation, drinking and industrial water supplies of the people living in those regions and downstream. These glaciers are under a direct global warming impact as indicated in many previous studies. In this study, we estimated the glacier dynamics in terms of Equilibrium Line Altitude (ELA), mass balance and the snout position variation using remote sensing data between 2001 and 2018. Six glaciers, having area ≥ 20 km2 each, situated in the Chitral region (Hindukush Mountains) were investigated in this study. Digital Elevation Model (DEM) and available cloud-free continuous series of Landsat and Sentinel the entire study area was a retreat of -231 ± 140 m. No obvious relationship was found between the glacier variation trends and the available gauged climatic data possibly due to the presence of debris cover in ablation zones of all the studied glaciers which provides insulation and reduces the immedia...

A key challenge to understanding the effects of climate change and nutrient deposition on ecosystem functioning is our lack of knowledge about nutrient limitations of heterotrophic and phototrophic microbial communities. This is especially true in high elevation ecosystems where it has been shown that earlier melt-out of snow beds and glacial retreat is allowing photosynthetic microbes and plants to move into previously unvegetated areas. We used landscape- level analyses of microbial enzyme stoichiometries combined with soil microcosm fertilization studies to determine which nutrients are limiting to microbes in plant-free or sparsely vegetated, snow bed areas of the Colorado Front Range. Both of these independent approaches indicated that the ultimate limiting nutrient in unvegetated and sparsely vegetated soils is phosphorus (P) for phototrophic microbes, with co- limitation by carbon (C) for the entire microbial community. In contrast, vegetated soils in the same watersheds showed more balanced nitrogen (N), P and C co-limitation similar to patterns seen in other plant- dominated ecosystems. In microcosm experiments, P additions resulted in increased growth rates and percent cover by phototrophs, whereas N additions decreased the relative abundances of phototrophs. Taken together, our findings indicate that the colonization of high elevation ecosystems being impacted by N deposition and climate warming will likely be constrained by P limitation of both heterotrophic and phototrophic microbes and by negative impacts of N on microbial phototrophs. These effects may in turn limit the ability of these fragile ecosystems to immobilize inputs of atmospheric N causing increased runoff of excess N to downstream ecosystems.

The finding that microbial communities are active under snow has changed the estimated global rates of biogeochemical processes beneath seasonal snowpacks. We used microbiological and molecular techniques to elucidate the phylogenetic composition of under-snow microbial communities in Colorado, the United States. Here, we show that tundra soil microbial biomass reaches its annual peak under snow, and that fungi account for most of the biomass. Phylogenetic analysis of tundra soil fungi revealed a high diversity of fungi and three novel clades that constitute major new groups of fungi (divergent at the subphylum or class level). An abundance of previously unknown fungi that are active beneath the snow substantially broadens our understanding of both the diversity and biogeochemical functioning of fungi in cold environments.