Maximum Snow Research Articles

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.

The benefits of homogenising snow depth series – Impacts on decadal trends and extremes for Switzerland

Abstract. Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961–2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Severity of winters in the Czech Republic during the 1961–2021 period and related environmental impacts and responses

AbstractThis paper analyses temperature and snow patterns of winters (December–February) averaged for the territory of the Czech Republic during the 1961–2021 period and their broad environmental impacts and responses. Series of mean, maximum, minimum, absolute maximum and absolute minimum temperatures show significant increasing linear trends, while decreasing trends were detected in numbers of frost, ice and extremely cold days, duration of cold waves, snowfall days, sums of heights of new snow, days with snow depths ≥1 cm, mean and maximum snow depths. The winter severity, derived from five temperature and five snow variables and expressed by temperature/snow scores, indicates decreasing severity of winters for 1961–2021, in which temperature severity is more pronounced than that of snow. Decreasing winter severity is in line with decreasing frequency of cyclonic and directional circulation types according to objective classification, while the trend in anticyclonic types was opposite. Types with maritime airflow from the Atlantic Ocean and the Mediterranean contribute to milder and types with continental airflow from the east to colder winters. The coldest winters, 1962/1963 and 1984/1985, and the mildest winters, 2006/2007 and 2019/2020, were analysed in greater detail. Concerning of different analysed environmental impacts, they are influenced not only by severe winter weather, but also by political, socioeconomic and general environmental changes in the country. In line with decreasing winter severity were only statistically significant decreasing trends in proportions of traffic accidents connected with snow and glaze ice on the roads and volumes of damaged wood due to high weights of snow and ice deposit, expressed as salvage felling. Series of other environmental impacts (e.g., fatalities attributed to weather, impacts on the economy and society) reflect rather severity of individual winters and express high interannual variability without any representative trends.

Особенности распределения снежного покрова в насаждениях Рудного Алтая

The article presents the results of examining the formation and peculiarities of snow cover in fir plantations compared to open shrubland in the conditions of Southwestern Altai obtained during 2020-2021 snow period. The forest stand affects snow distribution and characteristics in fir woods by capturing snow with tree crowns, snow accumulation around crown perimeter and, as a result, leads to the formation of near-trunk snowdrifts, in their turn facilitating higher snow accumulation among trees and at forest edges. The maximum snow cover thickness in fir woods ranged from 80 cm (under crowns) to 150 cm (among trees and along forest edges). In the open (control) zone, snow distribution appeared more homogenous – from 126 to 135 cm. During the period of maximum snow accumulation, snow density in fir plantations ranged from 0.19 to 0.26 g/cm3; and, from 0.24 to 0.29 g/cm3 in the control zone. Snow cover humidity likewise demonstrated significant differences between fir plantations and control site, and amounted to 258.3±13.47 mm and 279.7±12.80 mm, respectively. Whereas in the control (overgrown) zone the complete snow cover melting ended in the second 10-day period of April, in fir woods it finished in the second 10-day period of May. The research findings suggest that fir plantations in river catchment areas boost winter precipitation retention and slow down its loss resulting in more uniform water transition into subsurface runoff and longer period of river water replenishment.

EUMETSAT H-SAF H13 uzay tabanlı kar suyu eşdeğeri ürününün 2020-2021 kar yılı için yersel kar derinliği ölçümleriyle Türkiye üzerinde doğrulanması

Timely and consistent information on the seasonal snow cover is critical for various scientific studies and operational applications, especially for hydrological purposes. Snow water equivalent (SWE) is a significant seasonal snow parameter, which serves as a key input for many hydrological and climatological models. H13 is a SWE product supplied within the frame of EUMETSAT’s H-SAF project based on the processing of passive microwave radiometer data. The basic aim of this study is to perform a validation of H13 over Turkey for the 2020-2021 snow season by using in-situ snow depth measurements. The validation covers the period between January and March 2021, and it includes 1282 ground-based observations. According to the results, annual RMSE of the H13 SWE product is obtained as 40.00 mm, which lies within the acceptable limits of the required product compliance. The minimum and maximum snow depth measurements within the validation period are 2.80 cm and 95.34 cm, respectively. The results obtained in this validation study clearly indicate the usability of the H13 SWE product in hydrological and climatic studies.

Climate and elevation control snow depth and snow phenology on the Tibetan Plateau

Snowmelt from the Tibetan Plateau (TP) is the water source of many major Asian rivers, with significant importance for regional water supply and ecosystem services. The spatiotemporal variations of snow cover on the TP and its response to climate change at different altitudes are still unclear because of the lack of in situ observations on the western TP, and limitations of optical remote sensing due to clouds. Using a daily passive microwave remote sensing snow depth dataset from 1979 to 2020 across the TP, this study investigates the spatiotemporal variations of snow depth and snow phenology as well as their responses to climate change. The results showed that during the past 40 years, the maximum snow depth (SDmax) and the snow cover duration days (SCD) on the TP decreased significantly at a rate of 0.6 cm/decade and 2.9 d/decade, respectively. The snow cover starting date (SCS) was delayed at a rate of 1.1 d/decade, whereas the snow cover melt date (SCM) advanced at a rate of 1.1 d/decade. The decrease of SCD was driven by the advance of SCM, which was caused by the decrease of SDmax, whereas the decrease of SDmax was driven by the increase in snow accumulation season temperature (Ta). The TP showed significant elevation-dependent warming (EDW) (0.04 ℃ decade-1 km−1) and elevation-dependent decrease of SDmax (0.12 cm decade-1 km−1) and SCD (4.1 d decade-1 km−1). The EDW explains the elevation-dependent decrease of SDmax (r = −0.72, P < 0.05) and SCD (r = −0.83, P < 0.05). It is expected that the negative feedback effect of SDmax and SCD on EDW may further exacerbate the EDW of the TP, and the earlier snowmelt due to the continuous decrease of snow depth may increase the frequency of droughts and floods. Our findings support the enhancement of EDW on the TP, depletion of solid reservoirs, and intensification of the regional water crisis in a warmer future.

Долгосрочное прогнозирование характеристик весеннего стока рек бассейна Тобола

For the first time, methods have been developed that allow obtaining a long-term forecast of the runoff layer for the second quarter and a forecast of the maximum water level for the spring flood period for seven rivers of the Tobol basin on March 25 every year. Both methods use linear empirical dependences of the predicted value on the maximum snow water equivalent averaged over the catchment area, the logarithm of the average water discharge for November of the previous year, and the average water discharge for the month preceding the forecast release date. To estimate the parameters of these dependencies and verify the forecasts obtained with their help, data of long-term hydrometeorological observations for the period from 1968 to 2021 were used. The analysis of the series of test forecasts showed that both methods give quite satisfactory results for all seven river basins. The methods can be recommended for use in the system for operational hydrological forecasting in order to scientifically substantiate measures on using water resources of the Tobol basin rivers and protecting the population and economic facilities from inundations during the spring flood. Keywords: flood, runoff layer, maximum water level, forecast, snow water equivalent, water discharge, empirical dependence, verification

Forest impacts on snow accumulation and melt in a semi-arid mountain environment

Snowmelt is complex under heterogeneous forest cover due to spatially variable snow surface energy and mass balances and snow accumulation. Forest canopies influence the under-canopy snowpack net total radiation energy balance by enhancing longwave radiation, shading the surface from shortwave radiation, in addition to intercepting snow, and protecting the snow surface from the wind. Despite the importance of predicting snowmelt timing for water resources, there are limited observations of snowmelt timing in heterogeneous forest cover across the Intermountain West. This research seeks to evaluate the processes that control snowmelt timing and magnitude at two paired forested and open sites in semi-arid southern Idaho, USA. Snow accumulation, snowmelt, and snow energy balance components were measured at a marginal snowpack and seasonal snowpack location in the forest, sparse vegetation, forest edge, and open environments. At both locations, the snow disappeared either later in the forest or relatively uniformly in the open and forest. At the upper elevation location, a later peak in maximum snow depth resulted in more variable snow disappearance timing between the open and forest sites with later snow disappearance in the forest. Snow disappearance timing at the marginal snowpack location was controlled by the magnitude and duration of a late season storm increasing snow depth variability and reducing the shortwave radiation energy input. Here, a shorter duration spring storm resulted in more uniform snowmelt in the forest and open. At both locations, the low-density forests shaded the snow surface into the melt period slowing the melt rate in the forest. However, the forest site had less cold content to overcome before melting started, partially canceling out the forest shading effect. Our results highlight the regional similarities and differences of snow surface energy balance controls on the timing and duration of snowmelt.

On the Changing Snow Contribution to Runoff Across Turkiye

Contribution of snowfall to runoff is especially important in snow-dominated regions where hydrological processes are mostly influenced by snowmelt. In this study, the contribution of snowfall to runoff in Turkey for the last 21 years was examined in the light of the observed data and hydrological model results globally provided by NASA. The snow dominant regions of Turkey for the last twenty-one years were revealed, and the ratio of the spatially averaged maximum snow water equivalent to runoff (Rsr) values, an indicator showing snowmelt contribution to runoff, were calculated for two separate periods (2000-2010, 2012-2022). These Rsr values were compared across Turkey to see if they are decreasing in snow-dominated regions. According to the results of the analysis, it was concluded that Rsr values are decreasing in all snow-dominated regions except Aras and Çoruh basins. Rsr values decreased by up to 50 percent in the last 11-year period in regions with high snowfall, such as the upper Euphrates basin.

Temperature‐related factors are the better determinants of belowground bud density, while moisture‐related factors are the better determinants of belowground bud diversity at the regional scale

AbstractBelowground bud banks mediate several critical processes in grassland ecosystems under global climate change. However, the functions of belowground bud bank along large‐scale climatic gradients have rarely been reported. This study focused on the temperature and moisture transects at the regional scale to study the spatial pattern of belowground bud banks in grasslands of northern China, with purpose of examining: (1) whether the effects of temperature and moisture factors on belowground bud banks were similar; and (2) whether the effects of climate factors on bud density and diversity were similar. Results showed that bud banks had stronger linear relationships with factors in the zonal transect than those in the meridional transect; for bud densities, temperature‐related factors along the zonal transect were major determinants, and the maximum annual snow depth (MAS) contributed the most to the total bud density, whereas bud bank diversity was significantly related to moisture factors along the meridional transect, but showed no significant relationships with any factors along the zonal transect. Our study demonstrated that temperature factors were better determinants of bud density, while moisture factors were better determinants of bud diversity at the regional scale. The MAS was the major factor in determining bud bank density, with a higher density in the colder region. These results are crucial for understanding the mechanisms underlying population regeneration and community dynamics under global climate change.

Optimization of snow-related parameters in the Noah land surface model (v3.4.1) using a micro-genetic algorithm (v1.7a)

Abstract. Snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas LSMs evaluate the snow cover, snow albedo, and snow depth through interaction with atmospheric conditions. Most LSMs include parameters based on empirical relations, resulting in uncertainties in model solutions. When the initially developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek the optimal parameter values in the snow-related processes – snow cover, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in situ surface observations and remotely sensed satellite data. Snow data from observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the fractional snow cover, maximum snow albedo of fresh snow, and fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of fractional snow cover on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root mean squared errors by 17.0 %, 6.2 %, and 3.3 % in snow depth, snow albedo, and fractional snow cover, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 44.2 % and 31.0 %, respectively.

Simulation and statistical analysis of ground snow loads based on a multi-layer snow accumulation and melt model

Ground snow loads, the study of which relies on the snowpack data from historical records of meteorological stations, are the basis of calculating the roof snow loads. In the absence of observation data, the snow accumulation and melt model, also called the snowmelt model, can be used to simulate the annual extreme samples of ground snow loads in each locality, and then the ground snow loads under a given recurrence interval can be obtained through probability analysis. In this paper, a multi-layer snowmelt model is used to input meteorological data such as precipitation, air temperature, wind speed, and relative humidity to obtain detailed information on the long-term snowpack, including snow density, snow depth, and snow water equivalent (SWE), which overcomes the problem of insufficient sample data for observed snow loads. Firstly, the precision of multi-layer snowmelt is checked through data of snow depth and SWE. Then, based on the model, meteorological stations in thirty-seven localities of six Chinese regions are selected to simulate the ground snow loads for nearly 60 years. Subsequently, the maximum snow depth and its occurrence time are compared, and the relationship between snow depth, snow density, and SWE in each region is analyzed. Finally, a variety of probabilistic models are used to calculate the ground snow loads at 50-year recurrence intervals. The goodness of fit of each probabilistic model for the snow loads sample is examined, and the calculated values of ground snow loads are compared with recommended values by the Chinese code. The results indicate that the snowmelt model can accurately reproduce the detailed process of ground snow events, which can be used to obtain the samples of ground snow load extreme. Due to gravity compaction, ground snow loads corresponding to the maximum depth in a certain year differ from the maximum ground snow loads in that year, which requires more attention. The goodness of fit is higher and the results are larger than the values of the code when generalized extreme distribution and logarithmic normal distribution are adopted to estimate extreme ground snow loads.

Development of a Machine Learning Framework to Aid Climate Model Assessment and Improvement: Case Study of Surface Soil Moisture

The development of a computationally efficient machine learning-based framework to understand the underlying causes for biases in climate model simulated fields is presented in this study. The framework consists of a two-step approach, with the first step involving the development of a Random Forest (RF) model, trained on observed data of the climate variable of interest and related predictors. The second step involves emulations of the climate variable of interest with the RF model developed in step one by replacing the observed predictors with those from the climate model one at a time. The assumption is that comparing these emulations with that of a reference emulation driven by all observed predictors can shed light on the contribution of respective predictor biases to the biases in the climate model simulation. The proposed framework is used to understand the biases in the Global Environmental Multiscale (GEM) model simulated surface soil moisture (SSM) for the April–September period, over a domain covering part of north-east Canada. The grid cell-based RF model, trained on daily SSM and related climate predictors (water availability, 2 m temperature, relative humidity, snowmelt, maximum snow water equivalent) from the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5), demonstrates great skill in emulating SSM, with root mean square error of 0.036. Comparison of the five RF emulations based on GEM predictors with that based on ERA5 predictors suggests that the biases in the mean April–September SSM can be attributed mainly to biases in three predictors: water availability, 2 m temperature and relative humidity. The regions where these predictors contribute to biases in SSM are mostly collocated with the regions where they are shown to be the among the top three influential predictors through the predictor importance analysis, i.e., 2 m temperature in the southern part of the domain, relative humidity in the northern part of the domain and water availability over rest of the domain. The framework, without having to undertake expensive simulations with the climate model, thus successfully identifies the main causes for SSM biases, albeit with slightly reduced skill for heavily perturbed simulations. Furthermore, identification of the causes for biases, by informing targeted climate model improvements, can lead to additional reductions in computational costs.

Analysis of ground snow load for greenhouse structures in Croatia

According to the commercial production greenhouses classification, corresponding design working lives are shorter than 50 years, implying the reduction of characteristic snow load in the design process. To adjust the snow load to a shorter return period, the standard EN 13031–1 provides the procedure for calculating the snow adjustment factors, which reduce the characteristic snow loads to appropriate values. On the other hand, the greenhouse structure reliability level for sustained design situations should be satisfied. This study analyses the maximum snow loads in Croatia from climatological and engineering perspectives considering the greenhouses structures. It provides a basis for determining the characteristic snow load in Croatia and for calculating adjustment factors, emphasising its impact on the greenhouse structure reliability level. General climatology of maximum snow load was performed for 73 stations covering the data period of 1968–2018. The results revealed four main climate snow regions in Croatia: mainland, mountainous, coastal hinterland, and Adriatic. Results showed that Gumbel distribution is generally a preferred member of the generalised extreme value distribution family for snow load data in Croatia. Stationary Gumbel distribution was applied since a significant temporal trend could generally not be found in the maximum snow load data. After investigating different expressions for calculating the adjustment factor available in the literature, it is suggested to use a simplified version that considers the probability of snow winter in practice. Additionally, the study points out the need to consider the large snow load coefficient of variation of diverse snow regions in Croatia in the future updates of the national annex to the standard HRN EN 1991-1-3.

A statistical approach for identifying factors governing streamflow recession behaviour

AbstractCatchment storage‐release relation has been widely studied using the parameters of streamflow recession analysis. The governing factors of the recession parameters are poorly understood, particularly in snow‐dominated regions. Here, we tailor a newly developed statistical approach, marginal contribution feature importance, to a hydrologic context, and couple it with random forests, in order to investigate the governing physical and climatic factors of catchment recession behaviour. The coupled approach can incorporate the interactions among catchment climatic and physical attributes. In a large sample hydrology study, we identify and compare the governing factors of recession parameters, in rainfall versus snowmelt‐dominated catchments, and in medium‐size versus large catchments, across more than 1000 catchments in United States and Canada. Results show that streamflow recession behaviour, particularly recession nonlinearity, strongly depend on belowground attributes and slope in rain‐dominated medium size catchments, and strongly depend on slope and annual maximum snow water equivalent in snow‐dominated medium size catchments. As catchment scale increases (&gt;1000 km2), the attributes related to the magnitude and timing of input water (e.g., water surplus, aridity index, maximum snow water equivalent) dictates the streamflow recession behaviour and the importance of belowground attributes is dropped. Furthermore, recession nonlinearity generally increases with an increase in catchment size. The findings of this study help improve our understanding of the governing factors and the interpretation of the spatial variability of recession behaviours. Such understanding could inform the development of a generalizable process‐based framework for estimating the sensitivity of catchment storage‐release relation to climate change in different environmental settings.

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).

Development of a Relation between Return Period Values of Annual Maximum Snow Load and Snow Depth and Its Implication in Structural Reliability for Northeastern China

Development of a Relation between Return Period Values of Annual Maximum Snow Load and Snow Depth and Its Implication in Structural Reliability for Northeastern China

Impact of Snowpack on the Land Surface Phenology in the Tianshan Mountains, Central Asia

The accumulation and ablation processes of seasonal snow significantly affect the land surface phenology in a mountainous ecosystem. However, the ability of snow to regulate the alpine land surface phenology in the arid regions is not well described in the context of climate change. The impact of snowpack changes on land surface phenology and its driving factors were investigated in the Tianshan Mountains using the land surface phenology metrics derived from satellited products and a snow dataset from downscaled regional climate model simulations covering the period from 1983 to 2015. The results demonstrated that the annual mean start of growing season (SOS) and length of growing season (LOS) experienced a significant (p &lt; 0.05) decrease and increase with a rate of −2.45 days/decade and 2.98 days/decade, respectively. The significantly advanced SOS and increased LOS were mainly seen in the Western Tianshan Mountains and Ili Valley regions with elevations from 2500 to 3500 m a.s.l and below 3000 m a.s.l, respectively. During the early spring, the significant decline in snow cover fraction (SCF) could advance the SOS. In contrast, snowmelt amount and annual maximum snow water equivalent (SWE) have an almost equally substantial positive correlation with annual maximum vegetation greenness. In particular, the SOS of grassland was the most sensitive to variations of snow cover fraction during early spring than that of other vegetation types, and their strong relationship was mainly located at elevations from 1500 to 2500 m a.s.l. Its greenness was significantly controlled by the annual maximum snow water equivalent in all elevation bands. Both decreased SCF and increased temperature in the early spring caused a significant advance of the SOS, consequently prolonging the LOS. Meanwhile, more SWE and snowmelt amount could significantly promote vegetation greenness by regulating the soil moisture. The results can improve the understanding of the snow ecosystem services in the alpine regions under climate change.

Pervasive alterations to snow-dominated ecosystem functions under climate change

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.

SnowClim v1.0: high-resolution snow model and data for the western United States

Abstract. Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades, largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, simulated at high spatial resolution, and cover large geographic domains. Most existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient process-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry (SNOTEL) sites across the western United States (US), achieving a site-median root-mean-squared error for daily snow water equivalent (SWE) of 64 mm, bias in peak SWE of −2.6 mm, and bias in snow duration of −4.5 d when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US (a domain covering 3.1 million square kilometers) using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics, including monthly SWE and snow depth, as well as annual maximum SWE and snow cover duration, for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset enable novel analyses of changing hydroclimate and its implications for natural and human systems.