Seasonal Snowpack Research Articles

Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate.

Snowpack affects soil microclimate throughout the year

Variations in snow depth have complex effects on soil microclimate. Snow insulates soil and thus regulates, along with air temperature, the nature, and extent of soil freezing. There is great uncertainty about the main drivers of soil freezing, which have important effects on ecosystem carbon and nitrogen cycling processes and might change as climate warms and snowfall decreases as part of climate change. Here, we utilitze sites from a variety of elevations and aspects within the northern hardwood forest at the Hubbard Brook Experimental Forest (New Hampshire, USA) to investigate relationships between seasonal snowpack, soil freezing, and soil microclimate across this gradient using 8 years of bi-weekly snowpack and soil frost-depth measurements, and continuous soil climate monitoring. We utilize a time-integrated snowpack descriptor and find that snowpacks with lower seasonal snow water equivalents result in more soil temperature variation and deeper soil frost but have no effect on variation in soil moisture. Seasonal snow water equivalent of the snowpack influences the date of rapid soil warming in the spring, which in turn influences both summer soil moisture and an index of annual cumulative soil heat. These results show that snowpack dynamics, which are highly sensitive to changes in climate, have wide-ranging effects on soil microclimate year-round and thus could have important implications for ecosystem carbon and nitrogen cycling processes.

Evaluation of temporal consistency of snow depth drivers of a Rocky Mountain watershed in southern Alberta

AbstractCollecting spatially representative data over large areas is a challenge within snow monitoring frameworks. Identifying consistent trends in snow accumulation properties enables increased sampling efficiency by minimizing field collection time and/or remote sensing costs. Seasonal snowpack depth estimations during mid‐winter and melt onset conditions were derived from airborne Lidar over the West Castle Watershed in the southern Canadian Rockies on three dates. Each dataset was divided into five sets of snow depth driver classes: elevation, aspect, topographic position index, canopy cover and slope. Datasets were quality controlled by eliminating snow depth values above the 99th percentile value, which had a negligible effect on average snow depths. Consistent trends were observed among driver classes with peak snow accumulation occurring within the treeline ecotone, north‐facing aspects, open canopies, topographic depressions and areas with low slope angle. Although mid‐winter class trends for each driver were similar and watershed‐scale snow depth distributions were significantly correlated (0.76, p < .01), depth distributions within the same driver class of the three datasets were not correlated due to recent snowfall events, redistribution and settling processes. Trends in driver classes during late season melt onset were similar to mid‐winter conditions but watershed scale distribution correlation results varied with seasonality (0.68 mid‐winter 2014 and melt onset 2016; 0.65 mid‐winter 2017 and melt onset 2016, p < .1). This is due to the differing stages of accumulation or ablation and the upward migration in the 0°C isotherm during spring, when snow depth can be declining in valley bottoms while still increasing at higher elevations. The observed consistency in depth driver controls can be used to guide future integrated snow monitoring frameworks.

Weather-Dependent Nonlinear Microwave Behavior of Seasonal High-Elevation Snowpacks

Ensemble predictions of the seasonal snowpack over the Grand Mesa, CO (~300 km2) for the hydrologic year 2016–2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time-scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8–10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of the ensemble mean backscatter with SWE (R2 > 0.95) depends on weather conditions (e.g., 5–6 cm/dB/month in January; 2–2.5 cm/dB/month in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snow at Ku-band. The emissions behave as a limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior during melt that is different for deep (winter-spring transition) and shallow snow (spring-summer), and offsets that increase with frequency. These findings suggest potential for multi-frequency active-passive remote-sensing of high-elevation SWE conditional on snowpack regime, particularly suited for data-assimilation using coupled snow hydrology-microwave models extended to include snow-soil and snow-vegetation interactions.

Dynamics of seasonal snowpack over the High Atlas

Snowpack melting in the High Atlas constitutes the major source of freshwater for the semi-arid agricultural plains of central Morocco. Snow runoff fills dams during spring and recharges groundwater, thus providing the necessary water for irrigation and hydropower production. Despite its critical importance for the region, basic questions about the High Atlas snowpack remain largely unanswered. In particular, the spatial and temporal distribution of snow water equivalent, as well as sublimation losses, potentially significant in this region, have yet to be thoroughly investigated. The scarcity of ground data has been a major obstacle to investigating snow processes in the High Atlas. Here, we demonstrate the potential of remotely-sensed meteorological variables and downscaled climate reanalysis data to gain important insights into snow water balance in a semi-arid region. We apply a distributed energy balance snow model based on SNOW17, constrained by topographic data, meteorological data from satellites and high-resolution dynamically-downscaled ERA-Interim data, to simulate snowpack dynamics within the Oum-Er-Rbia watershed, at the heart of Morocco’s High Atlas. The simulations are compared to MODIS snow cover maps and observed snow depth at one field station. Results show that the spatial extent and temporal dynamics of snow cover at various elevation ranges are accurately captured. The snowpack is essentially concentrated above 2500 m, extends over 500–6000 km2 and holds 0.05–0.4 km3 at its peak in early February. Additionally, we find that losses by sublimation range from 0.06 to 0.14 km3 for an average of 0.09 km3 a year, about 10% of all snowfall. Above 3000 m elevation, sublimation removes on average 20% of the snowpack. Finally, we discuss the sensitivity of our results to uncertainties in the forcing meteorological data. This study reveals the essential components of the snow water balance in the High Atlas and paves the way for better understanding of its sensitivity to climate change.

Forest density intensifies the importance of snowpack to growth in water-limited pine forests.

Warming climate and resulting declines in seasonal snowpack have been associated with drought stress and tree mortality in seasonally snow-covered watersheds worldwide. Meanwhile, increasing forest density has further exacerbated drought stress due to intensified tree-tree competition. Using a uniquely detailed data set of population-level forest growth (n=2,495 sampled trees), we examined how inter-annual variability in growth relates to snow volume across a range of forest densities (e.g., competitive environments) in sites spanning a broad aridity gradient across the United States. Forest growth was positively related to snowpack in water-limited forests located at low latitude, and this relationship was intensified by forest density. However, forest growth was negatively related to snowpack in a higher latitude more energy-limited forest, and this relationship did not interact with forest density. Future reductions in snowpack may have contrasting consequences, as growth may respond positively in energy-limited forests and negatively in water-limited forests; however, these declines may be mitigated by reducing stand density through forest thinning.

IMPACT OF FLIGHT ALTITUDE ON UNMANNED AERIAL PHOTOGRAMMETRIC SURVEY OF THE SNOW HEIGHT ON MOUNT LEBANON

Abstract. In Lebanon, the seasonal snowpack is poorly monitored despite its importance for water resource supply. The snow accumulates on Mount Lebanon in karstic depressions named “sinkholes.” It is important to monitor the evolution of the snow height inside those “sinkholes”, because of their key role as “containers” for seasonal snow. UAV photogrammetry is a major technological breakthrough which allows an accurate monitoring of the snow height. Because the impact of flight parameters on snow height retrievals is not well documented yet, this research aims to evaluate the impact of UAV flight altitude on the resolution and accuracy of the resulting orthomosaic and DSM. The flight missions were done using the Phantom DJI which generated five DSMs. These are validated using total station measurements.The results indicate that the snow DSMs can be retrieved by adopting a resolution of 8 to 84 cm, a point density between 1.43 and 153 points/sqm and a RMSE of 13 to 41 cm. The testing was done using an elevation varying between 50 and 500 m. The results will be compared to total station observations. These results allow the user to choose the suitable flight altitude for required resolution and points density. We suggest that a flight altitude of 100 m is sufficient for the survey of the snow cover elevation.

Arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations

Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes.

Snowmelt velocity predicts vegetation green-wave velocity in mountainous ecological systems of North America

The timing of spring initiates an important period for resource availability for large trophic webs within ecosystems, including forage for grazing animals, flowers for pollinators, and the higher trophic levels that depend on these resources. Spring timing is highly variable across space, being influenced strongly by the departure of snow cover (i.e. snowmelt timing, in locations with a seasonal snowpack), climate, weather, elevation, and latitude. When spring timing occurs along a gradient (e.g. spring arriving later in higher elevations of mountainous terrain), the organisms that rely on spring resources often migrate to maintain an optimal position for spring resources – a phenomenon known as ‘surfing the green wave.’ While this behavior has been observed by tracking animals, there have been no studies to quantify the green wave as a movement across space and time. Furthermore, considering that snowmelt timing has moderate power to explain green-up timing for a given location, we ask the question: does snowmelt velocity predict green wave velocity? Here, we introduce the first continental maps of snowmelt and green wave velocity for North America from 2001 to 2016 as derived from the MODIS MCD12Q2 phenology dataset. We show that both snowmelt and green wave velocities are influenced strongly by topography, including slope and aspect. Furthermore, we quantify the relationships between snowmelt and green wave velocities according to three variables: direction, speed, and distance traveled. We conclude that mountainous ecoregions, such as the western North American cordillera, have the highest correspondence between snowmelt and green wave velocities, compared to flatter regions such as the Great Plains and tundra. This work will be of interest to wildlife ecologists, biologists, and land managers who seek to conserve migratory animals and the ecosystems that support them.

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

Abstract. Understanding components of the total streamflow is important to assess the ecological functioning of rivers. Binary or two-component separation of streamflow into a quick and a slow (often referred to as baseflow) component are often based on arbitrary choices of separation parameters and also merge different delayed components into one baseflow component and one baseflow index (BFI). As streamflow generation during dry weather often results from drainage of multiple sources, we propose to extend the BFI by a delayed-flow index (DFI) considering the dynamics of multiple delayed contributions to streamflow. The DFI is based on characteristic delay curves (CDCs) where the identification of breakpoint (BP) estimates helps to avoid rather subjective separation parameters and allows for distinguishing four types of delayed streamflow contributions. The methodology is demonstrated using streamflow records from a set of 60 mesoscale catchments in Germany and Switzerland covering a pronounced elevation gradient of roughly 3000 m. We found that the quickflow signal often diminishes earlier than assumed by two-component BFI analyses and distinguished a variety of additional flow contributions with delays shorter than 60 d. For streamflow contributions with delays longer than 60 d, we show that the method can be used to assess catchments' water sustainability during dry spells. Colwell's predictability (PT), a measure of streamflow periodicity and sustainability, was applied to attribute the identified delay patterns to dynamic catchment storage. The smallest dynamic storages were consistently found for catchments between approx. 800 and 1800 m a.s.l. Above an elevation of 1800 m the DFI suggests that seasonal snowpack provides the primary contribution, whereas below 800 m groundwater resources are most likely the major streamflow contributions. Our analysis also indicates that dynamic storage in high alpine catchments might be large and is overall not smaller than in lowland catchments. We conclude that the DFI can be used to assess the range of sources forming catchments' storages and to judge the long-term sustainability of streamflow.

Approaches to Optimize Uzbekistan's Investment in Irrigation Technologies

For many decades, Uzbekistan has been one of the largest cotton producers in the world. The irrigation water needed for these high production levels has been delivered by the massive diversion of the Amu Darya and Syr Darya rivers, which naturally flowed into the Aral Sea. This diversion for agriculture was the main cause of the rapid decline of the Aral Sea, which is at only 10% of its original size today. The traditional method of irrigation, which relies on simple open canal systems, is highly inefficient for managing the region’s critical and limited water resource. It has been qualitatively estimated, for example, that irrigation water lost to evaporation and system inefficiencies is quite large. With the future availability of water at risk for agriculture in Central Asia, primarily due to the loss of glacial volume from global warming, along with declines in seasonal snowpack, it is clear that new approaches to water management are needed. Any serious efforts to restore the Aral Sea and its ecological services would also reduce supplies of irrigation water for Uzbekistan. While regional conflict over water is unlikely, it must be considered since Uzbekistan is a downstream country among several that rely on the Amu Darya and Syr Darya rivers for most of their water supplies. To insure against these risks to cotton production and the underlying economy, better irrigation technologies are needed across Uzbekistan. However, these technologies can be quite expensive, especially given that water is still nearly free. In this case study we explore the use of real options analysis (ROA) to look for optimal investment strategies in efficient irrigation technologies in light of variable climate and policy uncertainties.

Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

AbstractThis study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

Can Convection‐Permitting Modeling Provide Decent Precipitation for Offline High‐Resolution Snowpack Simulations Over Mountains?

AbstractAccurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.

Changes to rainfall, snowfall, and runoff events during the autumn–winter transition in the Rocky Mountains of North America

In cold conditions, early winter precipitation occurs as snowfall and contributes to the accumulating seasonal snowpack. In a warming climate, precipitation may occur as rainfall in mountainous areas. Detecting changes during seasonal transitions is difficult because these may encompass changes in timing, magnitude and phase, which may not be consistent between years. In this study, a sampling window from September to December is used to assess trends in magnitude, frequency and duration (of rainfall, snowfall and runoff events) in 127 climate stations and 128 watersheds with more than 30 years of record across the Rocky Mountains of North America. Rainfall events have increasing frequencies and durations, and magnitude trends are predominantly increasing at mid-latitude and mid-elevation. Snowfall events have the largest numbers of significant trends, with both increasing and decreasing trends in each of magnitude, frequency and duration. Snowfall events have decreasing frequencies and durations in the northern low-elevation sites and increasing frequencies and durations at the southern high-elevation sites. Trends in runoff events are less common than for rainfall and snowfall but are greater than expected by chance, and show similar frequency and duration patterns to snowfall trends. Snowfall and runoff events are decreasing in frequency and duration north of Wyoming and increasing to the south. Snowfall magnitude is generally decreasing to the north and increasing to the south, with runoff magnitude trends showing the reverse.

A Century of Forest Regrowth and Snow Loss Alters the Cooling Effect of Historical Land Use in the Upper Midwest

Anthropogenic land use affects climate by altering the energy balance of the Earth’s surface. In temperate regions, cooling from increased albedo is a common result of historical land-use change. However, this albedo cooling effect is dependent mainly on the exposure of snow cover following forest canopy removal and may change over time due to simultaneous changes in both land cover and snow cover. In this paper, we combine modern remote sensing data and historical records, incorporating over 100 years of realized land use and climatic change into an empirical assessment of centennial-scale surface forcings in the Upper Midwestern USA. We show that, although increases in surface albedo cooled through strong negative shortwave forcings, those forcings were reduced over time by a combination of forest regrowth and snow-cover loss. Deforestation cooled strongly (− 5.3 Wm−2) and mainly in winter, while composition shift cooled less strongly (− 3.03 Wm−2) and mainly in summer. Combined, changes in albedo due to deforestation, shifts in species composition, and the return of historical forest cover resulted in − 2.81 Wm−2 of regional radiative cooling, 55% less than full deforestation. Forcings due to changing vegetation were further reduced by 0.32 Wm−2 of warming from a shortened snow-covered season and a thinning of seasonal snowpack. Our findings suggest that accounting for long-term changes in land cover and snow cover reduces the estimated cooling impact of deforestation, with implications for long-term land-use planning.

Partitioning snowmelt and rainfall in the critical zone: effects of climate type and soil properties

Abstract. Streamflow generation and deep groundwater recharge may be vulnerable to loss of snow, making it important to quantify how snowmelt is partitioned between soil storage, deep drainage, evapotranspiration, and runoff. Based on previous findings, we hypothesize that snowmelt produces greater streamflow and deep drainage than rainfall and that this effect is greatest in dry climates. To test this hypothesis we examine how snowmelt and rainfall partitioning vary with climate and soil properties using a physically based variably saturated subsurface flow model, HYDRUS-1D. We developed model experiments using observed climate from mountain regions and artificial climate inputs that convert all precipitation to rain, and then evaluated how climate variability affects partitioning in soils with different hydraulic properties and depths. Results indicate that event-scale runoff is higher for snowmelt than for rainfall due to higher antecedent moisture and input rates in both wet and dry climates. Annual runoff also increases with snowmelt fraction, whereas deep drainage is not correlated with snowmelt fraction. Deep drainage is less affected by changes from snowmelt to rainfall because it is controlled by deep soil moisture changes over longer timescales. Soil texture modifies daily wetting and drying patterns but has limited effect on annual water budget partitioning, whereas increases in soil depth lead to lower runoff and greater deep drainage. Overall these results indicate that runoff may be substantially reduced with seasonal snowpack decline in all climates, whereas the effects of snowpack decline on deep drainage are less consistent. These mechanisms help explain recent observations of streamflow sensitivity to changing snowpack and highlight the importance of developing strategies to plan for changes in water budgets in areas most at risk for shifts from snow to rain.

Pressure-Driven Vapor Exchange With Surface Snow

Atmospheric pressure changes ranging from high-amplitude, low-frequency events caused by synoptic weather systems to smaller amplitude, high-frequency events caused by turbulence penetrate permeable snow surfaces. Fluxes driven by these pressure changes augment non-radiative processes that filter atmospheric aerosols and drive near-surface vapor flux by sublimation, condensation and deposition. We report on field experiments in which we measured the amplitude of mid-to-high frequency pressure changes as they varied with depth in a seasonal snowpack and on two empirical models that distinguish conditions that promote pressure-driven vapor exchange. We found that the standard deviation of pressure changes poorly characterizes pressure perturbation amplitudes that drive vapor exchange because many low amplitude perturbations mask the influence of less common but more consequential high amplitude perturbations. Spectral analysis of pressure perturbation energy at different snow depths revealed an empirical formula that quantifies perturbation pressure attenuation as a function of frequency and depth in snow. Model results indicated that sublimation enhancement is maximized for perturbation pressure periods between 0.2 and 10 s.

Snow climatology for the mountains in the Iberian Peninsula using satellite imagery and simulations with dynamically downscaled reanalysis data

AbstractThe presence of a seasonal snowpack determines the hydrology, geomorphology and ecology of wide parts of the Iberian Peninsula, with strong implications for the economy, transport and risk management. Thus, reliable information on snow is necessary from a scientific and operational point of view. This is the case of the Iberian Peninsula where, lack of observation has impeded proper analysis of snowpack duration, magnitude and interannual variability. In this study, we present the first snow climatology of the entire Iberian Peninsula. The scarcity of in situ observations has been overcome, using a newly developed remote sensing snow database from MODIS satellite sensors for the period 2000–2014 and a physically based snow model (Factorial Snow Model—FSM), driven by a regional atmospheric model (Weather Research and Forecast model—WRF) over the Iberian Peninsula for the period 1980–2014. The snowpack of the main mountain areas (Pyrenees, Cantabrian, Central, Iberian range and Sierra Nevada) are described, estimated from the generated databases. The information has been processed using a k‐means cluster algorithm, looking for similarities in snow indices at different elevation bands. Results show four different types of snowpack in terms of depth, duration and interannual variability, lying over different elevation bands in the different ranges, proving the variability of the snowpack over Iberia. Analyses reveal areas characterized by ephemeral snowpacks, while in some sectors snowpack lasts, on average, 198 days per year with 3.02 m of peak snow depth. The coefficient of variation of interannual peak snow depth oscillated between 35.2 and 162.4%. All the analysed indices show that at common elevations the Cantabrian range and the Pyrenees host the deepest and longest lasting snowpacks, followed by the Central and Iberian ranges. The Sierra Nevada exhibits the shortest, shallowest snowpack and more year‐to‐year variability.