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The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining "the right answers for the wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current representation of key processes in Land Surface Models (LSM) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining "right answers for wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first-principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al. 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow and snow-water equivalent. These quantities are derived from readily meteorological data such as near-surface air temperature, precipitation and solar radiation, and soil physical properties. Whenever empirical formulations were required, e.g. pedotransfer functions and albedo-snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]

The current representation of key processes in Land Surface Models for estimating water and energy balances still relies heavily on empirical equations that require site-specific calibration. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results leading to a risk of obtaining "right answers for wrong reasons", compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To reduce the need for free parameters, here we present novel formulations based on first-principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH v.1.0 model (Davis et al. 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow and snow-water equivalent. These quantities are derived from readily meteorological data such as near-surface air temperature, precipitation and solar radiation, and soil physical properties. Whenever empirical formulations were required, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs. [ABSTRACT FROM AUTHOR]

Farmers and ranchers living in snow mountainous regions heavily rely on melting snow for agricultural irrigation. However, major natural disturbances, such as wildfires, have markedly reduced melt durations, disrupting the local hydrological cycle. In this study, we estimate the impacts of wildfires on snow accumulation and retention in the San Joaquin watershed of Sierra Nevada around California during the 2000 to 2016 water year through spatiotemporal visual variations in the snow water equivalent (SWE). To better explore the relationship between SWE and wildfire, machine learning methods, including regression analysis and deep analytics, were performed to examine the original remote sensing dataset of SWE in the San Joaquin watershed. The results suggested that wildfires have greatly increased the speed of snowmelt and shortened the timing of water availability, which may result in water resource shortages. We also comparatively analyzed the peak SWE values in each year, in which two water year groups (group (1): 2000–2003, group (2): 2013–2016) were selected and compared based on the frequency of major wildfires. Interestingly, higher fluctuations in SWE curves were observed from 2010 to 2016 than from 2000 to 2003, which suggested that the frequency of wildfires was much higher in recent years than in the past twenty years. Ultimately, the correlation between fire severity and SWE values was determined to be strongly positive (p

In the article the results of assessment of the changes in the air temperature, sums of precipitation, and the snow depth in the Arys River basin, which is the right tributary of the Syrdarya River are presented. In the context of global warming, noticeable changes are taking place in mountains, and the risk of hazards associated with snow, glaciers and permafrost is growing. In the upper reaches of the Arys River basin, strong snowmelt-rain floods occur against the backdrop of a thaw. It is necessary to study the climatic factors influencing the state of the mountain cryosphere. Based on the data of ground observations carried out by the Kazakh Hydrometeorological Service at the meteorological stations and according to the data of total precipitation gauges and field snow surveys in the mountains, the changes in the characteristics of the winter period for 1960-2023 are shown. The results of the analysis made it possible to estimate the increase in the average air temperature for the year and the cold period at 0.1°C/10 years. There is a slight increase in the sums of precipitation for November-March. The average snow depth, according to the field snow surveys, has increased by 5-20 cm. Over the past 30 years, the amount of snow-water equivalent has increased compared to the period of 1960-1990 at almost 80-100% of measurement points. The dates of occurrence of the snow cover have shifted towards somewhat earlier start and finish dates. The results of the analysis made it possible to identify changes in the mean multiyear meteorological characteristics over two 30-year periods. [ABSTRACT FROM AUTHOR]

Copyright of Hydrogeology Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we develop a watershed zonation approach for characterizing watershed organization and functions in a tractable manner by integrating multiple spatial data layers. We hypothesize that (1) a hillslope is an appropriate unit for capturing the watershed-scale heterogeneity of key bedrock-through-canopy properties and for quantifying the co-variability of these properties representing coupled ecohydrological and biogeochemical interactions, (2) remote sensing data layers and clustering methods can be used to identify watershed hillslope zones having the unique distributions of these properties relative to neighboring parcels, and (3) property suites associated with the identified zones can be used to understand zone-based functions, such as response to early snowmelt or drought and solute exports to the river. We demonstrate this concept using unsupervised clustering methods that synthesize airborne remote sensing data (lidar, hyperspectral, and electromagnetic surveys) along with satellite and streamflow data collected in the East River Watershed, Crested Butte, Colorado, USA. Results show that (1) we can define the scale of hillslopes at which the hillslope-averaged metrics can capture the majority of the overall variability in key properties (such as elevation, net potential annual radiation, and peak snow-water equivalent – SWE), (2) elevation and aspect are independent controls on plant and snow signatures, (3) near-surface bedrock electrical resistivity (top 20 m) and geological structures are significantly correlated with surface topography and plan species distribution, and (4) K-means, hierarchical clustering, and Gaussian mixture clustering methods generate similar zonation patterns across the watershed. Using independently collected data, we show that the identified zones provide information about zone-based watershed functions, including foresummer drought sensitivity and river nitrogen exports. The approach is expected to be applicable to other sites and generally useful for guiding the selection of hillslope-experiment locations and informing model parameterization. [ABSTRACT FROM AUTHOR]

A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecasting Model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent-past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-yr period centered on 2050. A pseudo–global warming approach is used, with the driver reanalysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-yr retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snowpack Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain; 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes, making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations, changes in SWE are predicted to not be significant in the midseason. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas and decreases by 25.8% at the base elevations. [ABSTRACT FROM AUTHOR]

Understanding the distribution of snow‐water equivalent (SWE) is crucial for the prediction of water resources in the western United States. Backward running SWE reconstructions use satellite‐observed, binary snow presence imagery to reconstruct SWE mass estimates. This approach relies on the connection between snow timing and peak SWE, yet few studies have directly examined this relationship. Here, we investigate the strength and spatiotemporal variation in this relationship across the western United States and Alaska. Within the SNOTEL network (n = 611 sites), we find that most variance in peak SWE is explained (median R2 = 0.64, σ = 0.18) by, in order of explanatory skill, the timing of snow disappearance, onset, and cover duration, with variation in skill primarily related to climate conditions—like winter storm size and storm frequency—rather than topographical setting. We expand this analysis with a diagnostic model of peak SWE driven by remotely sensed snow timing indices applied to five hydrologically important regions in the western United States and Alaska. Uncertainties arising between blending point and 500 m grid‐scale observations were found to influence model SWE bias, but a robust correlation (median R = 0.88) with observations persisted across all tested thresholds. Overall, this supports the viability of snow timing information for quantifying spatial patterns of peak SWE (mean R2 = 0.76, percent bias = 3.6%) over the past two decades. These findings carry important implications for the development of SWE reanalysis products and for the evaluation of climate and hydrologic models. Plain Language Summary: Knowing how much snow is on the ground in the mountains of the US West and Alaska is important information for water resource managers. Currently, ground station networks that monitor the amount of water in the snowpack are too sparse to observe across landscapes, while most satellites can only observe the presence of snow, rather than water content. Snow models are often used to "fill in the gaps" by estimating snow water content using physical process knowledge about snow melt, and one example is the snow reconstruction. This type of model typically estimates historical snow water content by relating snow disappearance timing with energy inputs into the snow to reconstruct a quantity of snow water content. This relationship between snow timing and water content is known, but its variation has yet to be fully explored. Here, we examine this relationship in detail, showing how it varies in space and time, as well as what meteorological and topographical conditions influence it. We also use satellite‐observed snow information to estimate snow water content across landscapes with encouraging results in coastal areas, but with less potential further inland and in Alaska. These results may be valuable for future development of snow models. Key Points: Annual snow timing indices, primarily snow disappearance timing, explain most of the variance in peak SWE at a majority of SNOTEL locationsThe degree of explanatory power varies regionally and across years, and is affected more by local meteorology than by topographyA diagnostic model for peak SWE shows a mean R2 = 0.76 and percent bias = 3.6% across five mountainous US regions in a cross‐validation [ABSTRACT FROM AUTHOR]

The Wallowa Avalanche Center (WAC) had a challenging winter with heavy snowfall, making it difficult to investigate avalanches. The snowpack created hazards in more areas, including low-elevation slopes. Despite this, there were good skiing conditions in the Skyline region, but unfortunately, there were three avalanche fatalities. The WAC also continued its education programs, including the successful Know Before You Go program for K-12 students in Utah. Overall, the winter had one of the best snow-water equivalent levels for the WAC. The document also provides a summary of the winter weather conditions in a specific location, with the transition from fall to winter occurring in late October and snowfall in January and December being below average. [Extracted from the article]

This article presents an adaptive hybrid-tracking (AHT) algorithm designed to process GNSS-R signals with a sufficient coherent component. Coherent GNSS-R signals have the potential to enable high-precision and high-resolution carrier-phase measurements for altimetry, sea-level monitoring, soil-moisture monitoring, flood mapping, snow–water equivalent measurements, and so on. The AHT algorithm incorporates the model inputs typically used in the master–slave open-loop (MS-OL) architecture into a closed-phase lock loop. Raw IF data recorded by the CYGNSS satellites over in-land water, land, and open-ocean surface are used to demonstrate the performance of the AHT. The results show that the AHT algorithm achieves comparable robustness with the MS-OL implementation while maintaining centimeter-level accuracy and excellent carrier-phase continuity that can be achieved with a fine-tuned Kalman filter (KF)-based adaptive closed-loop (ACL) system. Moreover, the AHT is suitable for real-time implementation and is applicable to other radio signals-of-opportunity. [ABSTRACT FROM AUTHOR]

Climate patterns over preceding years affect seasonal water and moisture conditions. The linkage between regional climate and local hydrology is challenging due to scale differences, both spatially and temporally. In this study, variance, correlation, and singular spectrum analyses were conducted to identify multiple hydroclimatic phases during which climate teleconnection patterns were related to hydrology of a small headwater basin in Idaho, USA. Combined field observations and simulations from a physically based hydrological model were used for this purpose. Results showed statistically significant relations between climate teleconnection patterns and hydrological fluxes in the basin, and climate indices explained up to 58% of hydrological variations. Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), and Pacific North America (PNA) patterns affected mountain hydrology, in that order, by decreasing annual runoff and rain on snow (ROS) runoff by 43% and 26% during a positive phase of NAO and 25% and 9% during a positive phase of PNA. AAO showed a significant association with the rainfall‐to‐precipitation ratio and explained 49% of its interannual variation. The runoff response was affected by the phase of climate variability indices and the legacy of past atmospheric conditions. Specifically, a switch in the phase of the teleconnection patterns of NAO and PNA caused a transition from wet to dry conditions in the basin. Positive AAO showed no relation with peak snow water equivalent and ROS runoff in the same year, but AAO in the preceding year explained 24 and 25% (p < 0.05) of their variations, suggesting that the past atmospheric patterns are equally important as the present conditions in affecting local hydrology. Areas sheltered from the wind and acted as a source for snow transport showed the lowest (40% below normal) ROS runoff generation, which was associated with positive NAO that explained 33% (p < 0.01) of its variation. The findings of this research highlighted the importance of hydroclimatic phases and multiple year variations that must be considered in hydrological forecasts, climate projections, and water resources planning. [ABSTRACT FROM AUTHOR]

Recent snow droughts in southwestern British Columbia (BC), Canada, have reduced seasonal streamflow during the typically dry late‐spring and summer months, leading to socio‐economic and ecological impacts that draw attention to the impending consequences of climate change. Knowledge of annual winter snowfall variability within this region is largely derived from a sparse network of short‐duration (≤50 years) snow survey stations. In this paper, we develop an annual April 1 snow water equivalent (SWE) reconstruction from living tree‐ring chronologies that offer a perspective on long‐term natural snowpack variability. The dendrohydrological model estimates the first principal component April 1 SWE for the southwestern regions of BC to 1711. Spectral analysis identified dominant multidecadal April 1 SWE variability over the pre‐instrumental period. The reconstruction successfully captures known instrumental period influences of La Niña oscillations on reconstructed SWE, suggesting that our tree‐ring based the reconstruction has the potential to provide insights on pre‐instrumental ocean–atmosphere links with southwestern BC snowpack dynamics. Runs analysis suggests pre‐instrumental snow droughts have been more than twice as long in duration and severity than during the observed period which indicates the instrumental record may not capture the full range of April 1 SWE variability. The reconstruction provides the first high‐resolution description of SWE over the past 300 years in southwestern BC and is of immediate use to regional water resource managers. [ABSTRACT FROM AUTHOR]

Lake-effect convective snowstorms frequently produce high-impact, hazardous winter weather conditions downwind of the North American Great Lakes. During lake-effect snow events, the lake surfaces can cool rapidly, and in some cases, notable development of ice cover occurs. Such rapid changes in the lake-surface conditions are not accounted for in existing operational weather forecast models, such as the National Oceanic and Atmospheric Administration's (NOAA) High-Resolution Rapid Refresh (HRRR) model, resulting in reduced performance of lake-effect snow forecasts. As a milestone to future implementations in the Great Lakes Operational Forecast System (GLOFS) and HRRR, this study examines the one-way linkage between the hydrodynamic-ice model [the Finite-Volume Community Ocean Model coupled with the unstructured grid version of the Los Alamos Sea Ice Model (FVCOM-CICE), the physical core model of GLOFS] and the atmospheric model [the Weather Research and Forecasting (WRF) Model, the physical core model of HRRR]. The realistic representation of lake-surface cooling and ice development or its fractional coverage during three lake-effect snow events was achieved by feeding the FVCOM-CICE simulated lake-surface conditions to WRF (using a regional configuration of HRRR), resulting in the improved simulation of the turbulent heat fluxes over the lakes and resulting snow water equivalent in the downwind areas. This study shows that the one-way coupling is a practical approach that is well suited to the operational environment, as it requires little to no increase in computational resources yet can result in improved forecasts of regional weather and lake conditions. [ABSTRACT FROM AUTHOR]

Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Snowpack in the Sierra Nevada Mountains accounts for around one-third of California's water supply. Melting snow provides water into dry summer months characteristic of the region's Mediterranean climate. As climate changes, understanding patterns of snowpack, snowmelt, and biological response is critical in this region of agricultural, recreational, and ecological value. Here we investigated the relationships between tree rings of montane conifer trees (Tsuga mertensiana, Abies magnifica, Abies concolor, Calocedrus decurrens, Juniperus occidentalis, and Pinus ponderosa) and regional climate indices with the goal of reconstructing April 1 snow-water equivalent (SWE) in the North Fork American River watershed of the Sierra Nevada. Chronologies were positively correlated with April 1 SWE of the year prior to ring formation. Temporal trends in correlation between tree-ring chronologies and climate indices indicate strengthening tree growth response to climate over time. We developed a skillful, nested reconstruction for April 1 SWE, 1661–2013. Variability of the reconstruction is within the envelope of 20th and 21st-century variability; however, the 2015 record low snowpack is unprecedented in the tree-ring record, as in results from previous studies. Future research should focus on integrating modern snow sensor data into paleoclimate research and understanding mechanistic linkages between snow and tree growth response. [ABSTRACT FROM AUTHOR]

Process-based models of complex environmental systems incorporate expert knowledge which is often incomplete and uncertain. With the growing amount of Earth observation data and advances in machine learning, a new paradigm is promising to synergize the advantages of deep learning in terms of data adaptiveness and performance for poorly understood processes with the advantages of process-based modeling in terms of interpretability and theoretical foundations: hybrid modeling. Here, we present such an end-to-end hybrid modeling approach that learns and predicts spatial-temporal variations of observed and unobserved (latent) hydrological variables globally. The model combines a dynamic neural network and a conceptual water balance model, constrained by the water cycle observational products of evapotranspiration, runoff, snow-water equivalent, and terrestrial water storage variations. We show that the model reproduces observed water cycle variations very well and that the emergent relations of runoff-generating processes are qualitatively consistent with our understanding. The presented model is – to our knowledge – the first of its kind and may contribute new insights about the dynamics of the global hydrological system. [ABSTRACT FROM AUTHOR]

Snowpacks and forests have complex interactions throughout the large range of altitudes where they co‐occur. However, there are no reliable data on the spatial and temporal interactions of forests with snowpacks, such as those that occur in nearby areas that have different environmental conditions and those that occur during different snow seasons. This study monitored the interactions of forests with snowpacks in four forest stands in a single valley of the central Spanish Pyrenees during three consecutive snow seasons (2015/2016, 2016/2017 and 2017/2018). Daily snow depth data from time‐lapse cameras were compared with snow data from field surveys that were performed every 10–15 days. These data thus provided information on the spatial and temporal changes of snow–water equivalent (SWE). The results indicated that forest had the same general effects on snowpack in each forest stand and during each snow season. On average, forest cover reduced the duration of snowpack by 17 days, reduced the cumulative SWE of the snowpack by about 60% and increased the spatial heterogeneity of snowpack by 190%. Overall, forest cover reduced SWE total accumulation by 40% and the rate of SWE accumulation by 25%. The forest‐mediated reduction of the accumulation rate, in combination with the occasional forest‐mediated enhancement of melting rate, explained the reduced duration of snowpacks beneath forest canopies. However, the magnitude and timing of certain forest effects on snowpack had significant spatial and temporal variations. This variability must be considered when selecting the location of an experimental site in a mountainous area, because the study site should be representative of surrounding areas. The same considerations apply when selecting a time period for study. [ABSTRACT FROM AUTHOR]

Snow densification stores water in alpine regions and transforms snow into ice on the surface of glaciers. Despite its importance in determining snow-water equivalent and glacier-induced sea level rise, we still lack a complete understanding of the physical mechanisms underlying snow compaction. In essence, compaction is a rheological process, where the rheology evolves with depth due to variation in temperature, pressure, humidity, and meltwater. The rheology of snow compaction can be determined in a few ways, for example, through empirical investigations (e.g., Herron and Langway, 1980), by microstructural considerations (e.g., Alley, 1987), or by measuring the rheology directly, which is the approach we take here. Using a "French-press" or "cafetière-à-piston" compression stage, Wang and Baker (2013) compressed numerous snow samples of different densities. Here we derive a mixture theory for compaction and airflow through the porous snow to compare against these experimental data. We find that a plastic compaction law explains experimental results. Taking standard forms for the permeability and effective pressure as functions of the porosity, we show that this compaction mode persists for a range of densities and overburden loads. These findings suggest that measuring compaction in the lab is a promising direction for determining the rheology of snow through its many stages of densification. [ABSTRACT FROM AUTHOR]

Abstract Declines in winter snowpack have increased the severity of summer droughts in western U.S. forests, with the potential to also impact soil available nitrogen (N). To understand how snowpack controls spatiotemporal N availability, we examined seasonal N dynamics across elevation, aspect, and topographic position (hollow vs. slope) in a forested watershed in the northern Rocky Mountains. As expected, peak snow‐water equivalent (SWE) was generally greater at higher elevations and on north‐facing aspects. However, the effects of topographic position and snowdrift led to variability in snow accumulation at smaller spatial scales. Spatial patterns of the snowpack, in turn, influenced soil moisture and temperature, with greater SWE leading to generally higher soil moisture levels during the summer and smaller temperature fluctuations throughout the year. Wetter conditions in spring or fall generally supported greater inorganic N pools, but at the driest locations (low‐elevation slope), pulses of N mineralization in summer may have played important roles in overall N dynamics. More importantly, soil moisture during the summer appeared to be more influenced by antecedent snowpack from the previous year than by current‐year summer rain. Subsequently, N mineralization under snowpack may be strongly influenced by soil moisture and temperature conditions from the previous fall, before snowpack accumulation. Together, our results indicate that snowpack strongly influences N dynamics beyond the current growing season in western coniferous forests through mediation of soil moisture and temperature, and suggest that further decline in winter snowpack may affect these forests through constraints in both water and N availability.

Snowfall and snow season length across Alaska control the surface hydrology and underlying soil properties and also influence near‐surface air temperature by changing the energy balance. Current projections of warming suggest that considerable change will occur to key snow parameters, possibly contributing to extensive infrastructure damage from thawing permafrost, an increased frequency of rain‐on‐snow events and reduced soil recharge in the spring due to shallow end‐of‐winter snowpack. This study investigates projected changes to mean annual snowfall, dates of snow onset and snowmelt and extreme snowfall for Alaska, using dynamically downscaled reanalysis and climate model simulations. These include the ERA‐Interim reanalysis from 1981 to 2010, and two Coupled Model Intercomparison Project Phase 5 models: Community Climate System Model version 4 (CCSM4) and Geophysical Fluid Dynamics Laboratory Climate Model version 3 (GFDL‐CM3) from 1981 to 2100. The analysis is presented in 30‐year periods (i.e., 1981–2010, 2011–2040, 2041–2070 and 2071–2100) with the future scenarios from Representative Concentration Pathway 8.5. Late‐century projections of average annual snowfall at low elevations (0–1,000 m) show decreases of 41.3 and 40.6% for CCSM4 and GFDL‐CM3, respectively. At high elevations (1,000–2,000 m), the reductions are smaller at 13.5 and 14.2%, respectively. End‐of‐winter snow‐water equivalent displays reductions at all elevations in the future periods. Snow season length is shortened due to later snow onset and earlier snowmelt; many locations in southwest Alaska no longer experience continuous winter snowpack by the late‐century period. Maximum 2‐day snowfall amounts are projected to decrease near Anchorage and Nome, while Fairbanks and Utqiaġvik (Barrow) show no significant trend. [ABSTRACT FROM AUTHOR]

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Snow depth observations and modeled snow density can be combined to calculate snow water equivalent (SWE). In this approach, SWE uncertainty is dominated by snow density uncertainty, which depends on meteorological data quality and process representation (e.g., compaction) in models. We test whether assimilating snow depth observations with the particle filter can improve modeled snow density, thus improving SWE estimated from intermittent depth observations. We model snowpack at Mammoth Mountain (California) over water years 2013–2016, assuming monthly snow depth data (e.g., sampling intervals relevant to lidar or manual surveys) for assimilation, and validate against observed SWE and density. The particle filter reduced density and SWE root‐mean‐square error by 27% and 28% relative to open loop simulations when using high‐quality, point location forcing. Assimilation gains were greater (35% and 51% reduction in density and SWE root‐mean‐square error) when using coarse‐resolution North American Land Data Assimilation System phase 2 meteorology. Ensembles created with both meteorological and compaction perturbations led to the greatest model improvements. Because modeled depth and density were both generally lower than observations, assimilation favored particles with higher precipitation and thus more overburden compaction. This moved depth and density (therefore SWE) closer to observations. In contrast, ensemble generation that varied only compaction parameters degraded performance. These results were supported by synthetic experiments with prescribed error sources. Thus, assimilation of snow depth data from lidar or other techniques can likely improve snow density and SWE derived at the basin scale. However, supplementary in situ observations are valuable to identify primary error sources in simulated snow depth and density. Key Points: Particle filter assimilation of monthly snow depth observations, possible with many techniques, improved modeled snow density and SWEThe particle filter yielded greater gains when using nonlocal (NLDAS) meteorological forcing than with high‐quality station dataGreatest improvements occurred when ensemble generation was designed to address sources of open loop model errors [ABSTRACT FROM AUTHOR]

We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0° S) and elevation (0 to 6993ma.s.l.) ranges, and it relies on multiple data sources (including ground data, remotesensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basinaveraged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydroclimatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r > 0:91) and lower correlation (r < 0:76) in hyperarid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. [ABSTRACT FROM AUTHOR]

Over northeastern Canada, the amount of water stored in a snowpack, estimated by its snow water equivalent (SWE) amount, is a key variable for hydrological applications. The limited number of weather stations driving snowpack models over large and remote northern areas generates great uncertainty in SWE evolution. A data assimilation (DA) scheme was developed to improve SWE estimates by updating meteorological forcing data and snowpack states with passive microwave (PMW) satellite observations and without using any surface-based data. In this DA experiment, a particle filter with a Sequential Importance Resampling algorithm (SIR) was applied and an inflation technique of the observation error matrix was developed to avoid ensemble degeneracy. Advanced Microwave Scanning Radiometer 2 (AMSR-2) brightness temperature (TB) observations were assimilated into a chain of models composed of the Crocus multilayer snowpack model and radiative transfer models. The microwave snow emission model (Dense Media Radiative Transfer - Multi-Layer model, DMRT-ML), the vegetation transmissivity model (ϖ-гopt), and atmospheric and soil radiative transfer models were calibrated to simulate the contributions from the snowpack, the vegetation, and the soil, respectively, at the top of the atmosphere. DA experiments were performed for 12 stations where daily continuous SWE measurements were acquired over 4 winters (2012-2016). Best SWE estimates are obtained with the assimilation of the TBs at 11, 19, and 37 GHz in vertical polarizations. The overall SWE bias is reduced by 68% compared to the original SWE simulations, from 23.7 kgm-2 without assimilation to 7.5 kgm-2 with the assimilation of the three frequencies. The overall SWE relative percentage of error (RPE) is 14.1% (19% without assimilation) for sites with a fraction of forest cover below 75 %, which is in the range of accuracy needed for hydrological applications. This research opens the way for global applications to improve SWE estimates over large and remote areas, even when vegetation contributions are up to 50% of the PMW signal. [ABSTRACT FROM AUTHOR]

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k -nearest neighbors ( k -NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k -NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k -NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k -NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k -NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k -NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions. [ABSTRACT FROM AUTHOR]

Abstract: A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications. [ABSTRACT FROM AUTHOR]

Abstract: Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R2 of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R2 = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R2 = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R2 = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is a key variable for various hydrological applications. It is defined as the depth of water that would result upon complete melting of a mass of snow. However, until now, continuous measurements of the SWE are either scarce, expensive, labor-intense, or lack temporal or spatial resolution especially in mountainous and remote regions. We derive the SWE for dry-snow conditions using carrier phase measurements from the Global Navigation Satellite System (GNSS) receivers. Two static GNSS receivers are used, whereby one antenna is placed below the snow and the other antenna is placed above the snow. The carrier phase measurements of both receivers are combined in double differences (DDs) to eliminate clock offsets and phase biases and to mitigate atmospheric errors. Each DD carrier phase measurement depends on the relative position between both antennas, an integer ambiguity due to the periodic nature of the carrier phase signal, and the SWE projected into the direction of incidence. The relative positions of the antennas are determined under snow-free conditions with millimeter accuracy using real-time kinematic positioning. Subsequently, the SWE and carrier phase integer ambiguities are jointly estimated with an integer least-squares estimator. We tested our method at an Alpine test site in Switzerland during the dry-snow season 2015–2016. The SWE derived solely by the GNSS shows very high correlation with conventionally measured snow pillow (root mean square error: 11 mm) and manual snow pit data. This method can be applied to dense low-cost GNSS receiver networks to improve the spatial and temporal information on snow. [ABSTRACT FROM AUTHOR]

We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an automatic weather station (AWS) and corroborated by data from field campaigns on the Forni Glacier in Italy. The aim of the analysis is to estimate the SWE of new snowfall and the annual SWE peak based on the average density of the new snow at the site (corresponding to the snowfall during the standard observation period of 24 h) and automated snow depth measurements. The results indicate that the daily SR50 sonic ranger measurements and the available snow pit data can be used to estimate the mean new snow density value at the site, with an error of ±6 kgm-3. Once the new snow density is known, the sonic ranger makes it possible to derive SWE values with an RMSE of 45mm water equivalent (if compared with snow pillow measurements), which turns out to be about 8% of the total SWE yearly average. Therefore, the methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total SWE using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger. [ABSTRACT FROM AUTHOR]

In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow-soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4% of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170% at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together. [ABSTRACT FROM AUTHOR]

Two hundred marine-terminating Greenland outlet glaciers deliver more than half of the annually accumulated ice into the ocean and have played an important role in the Greenland ice sheet mass loss observed since the mid-1990s. Submarine melt may play a crucial role in the mass balance and position of the grounding line of these outlet glaciers. As the ocean warms, it is expected that submarine melt will increase, potentially driving outlet glaciers retreat and contributing to sea level rise. Projections of the future contribution of outlet glaciers to sea level rise are hampered by the necessity to use models with extremely high resolution of the order of a few hundred meters. That requirement in not only demanded when modeling outlet glaciers as a stand alone model but also when coupling them with high-resolution 3-D ocean models. In addition, fjord bathymetry data are mostly missing or inaccurate (errors of several hundreds of meters), which questions the benefit of using computationally expensive 3-D models for future predictions. Here we propose an alternative approach built on the use of a computationally efficient simple model of submarine melt based on turbulent plume theory. We show that such a simple model is in reasonable agreement with several available modeling studies. We performed a suite of experiments to analyze sensitivity of these simple models to model parameters and climate characteristics. We found that the computationally cheap plume model demonstrates qualitatively similar behavior as 3-D general circulation models. To match results of the 3-D models in a quantitative manner, a scaling factor of the order of 1 is needed for the plume models. We applied this approach to model submarine melt for six representative Greenland glaciers and found that the application of a line plume can produce submarine melt compatible with observational data. Our results show that the line plume model is more appropriate than the cone plume model for simulating the average submarine melting of real glaciers in Greenland. [ABSTRACT FROM AUTHOR]

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be log-normally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses. [ABSTRACT FROM AUTHOR]

We present a novel method for the direct determination of the snowmelt coefficient of widely used degree-day models, using only cumulated temperature and precipitation over the days of snow cover. We develop a proof of concept using (1) local measurements of precipitation, temperature and snow water equivalent (SWE) at a set of well-monitored sites in the US, and (2) available time series of snow cover from satellite and gridded daily precipitation and daily average temperature for the study region of South Tyrol, in the Italian Alps. We demonstrate how the method can reproduce the snow water balance to an acceptable extent, critically depending on the accuracy of input precipitation and temperature, highlighting the importance of a reliable representation of weather forcing if the estimate has to be robust and representative. Although not always accurate at a point, our approach yields a SWE reasonably consistent with observations, and snowmelt flows compatible with measured streamflow. At the same time, the model allows an interpretation of discrepancies between observations and simulations to detect inconsistencies between snow cover and weather forcing. This method is in principle applicable for large-scale hydrological assessments thanks to the increasing global coverage of snow cover, precipitation and temperature data. As the only other type of observation available to calibrate models is often streamflow, the direct calibration of the snow component of a model using snow cover and weather forcing reduces the number of model processes and parameters to be calibrated with streamflow, and is expected to increase model robustness. [ABSTRACT FROM AUTHOR]

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the T B response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50 m) energy balance snow model over a 34 × 34 km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the T B signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data. [ABSTRACT FROM AUTHOR]

Modeling snow redistribution by wind and avalanches in hydrological studies in alpine catchments is important, as the spatial variability of the snow cover has an impact on timing and magnitude of the snowmelt runoff. Disregarding snow redistribution in models can lead to the formation of 'snow towers,' i.e., multi-year accumulation of snow at high elevations and an incorrect water balance. The reviewed approaches to deal with snow redistribution in hydrological models were first broadly grouped by the represented physical processes: (1) the correction of the precipitation input data to account mainly for preferential deposition, (2) the description of all wind-driven processes based on wind field data, (3) the description of gravitational transports and/or wind-driven processes based on topographic information, and (4) the statistical description of the variability of the snow water equivalent ( SWE) to account for all types of snow redistribution. The review further assessed the implementation of these approaches in physically based and bucket-type hydrological models. Generally, snow redistribution consideration has improved the simulation of snow patterns and SWE and consequently the prediction of discharge in mountain catchments worldwide. Snow redistribution approaches still have some limitations and a large gap exists between the knowledge and processes in highly detailed physically based snow models and the widely used bucket-type hydrological models used for water resources and climate change studies. There is a real need to bridge this gap using the knowledge earned by snow redistribution modeling with established physically based models to develop more conceptual approaches for the application in bucket-type models. WIREs Water 2017, 4:e1232. doi: 10.1002/wat2.1232 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The aim of this work was to assess how forest ecosystems dominated by Norway spruce (Picea abies (L.) or European beech (Fagus sylvatica L.) affect snow water equivalent (SWE) in relation to aspect and elevation. The research plots were established in a small headwater watershed of the Hučava flow belonging to the Poľana Biosphere Reserve (Central Europe, Inner Western Carpathians). The SWE values in this watershed (approximately 580-1270 m a.s.l.) were monitored during the three winter seasons starting from 2012-2013 to 2014-2015. The results revealed high variability in SWE and in snow cover duration between the studied seasons. The spatial variability was significantly affected by the forest ecosystem, aspect and elevation. The seasonal mean SWE value was lower by about 50-60% in the spruce forests and by about 21-30% in the beech forests compared to the open areas (100%). Over the whole seasons, the whole watershed mean SWE value on the slopes with the northern aspect was mostly higher compared to the slopes with the southern aspect. The effect of aspect was significant mainly in the open areas and in the forests dominated by European beech during the ablation periods of every season. In the case of the sufficient snow cover, the mean SWE value always increased with elevation. The elevation gradient of SWE was steepest at the open areas of the watershed in the peaks of the winter seasons. The threeseason mean value of SWE elevation gradient (per 100 m) at the time of snow accumulation peak was equal to 16 mm in the spruce forests, 20 mm in the beech forests and 26 mm in the open areas. The research revealed that SWE is significantly affected by the forest ecosystem whilst its effect is dependent on the occurrence of dominant deciduous or coniferous tree species. However, the effect of forests is closely related to topographic characteristics (aspect and elevation) of a locality. [ABSTRACT FROM AUTHOR]

A detection and attribution analysis on the multidecadal trend in snow water equivalent (SWE) has been conducted in four river basins located in British Columbia (BC). Monthly output from a suite of 10 general circulation models (GCMs) that participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used, including 40 climate simulations with anthropogenic and natural forcing combined (ALL), 40 simulations with natural forcing alone (NAT), and approximately 4200 yr of preindustrial control simulations (CTL). This output was downscaled to ° spatial resolution and daily temporal resolution to drive the Variable Infiltration Capacity hydrologic model (VIC). Observed (manual snow survey) and VIC-reconstructed SWE, which exhibit declines across BC, are projected onto the multimodel ensemble means of the VIC-simulated SWE based on the responses to different forcings using an optimal fingerprinting approach. Results of the detection and attribution analysis shows that these declines are attributable to the anthropogenic forcing, which is dominated by the effect of increases in greenhouse gas concentration, and that they are not caused by natural forcing due to volcanic activity and solar variability combined. Anthropogenic influence is detected in three of the four basins (Fraser, Columbia, and Campbell Rivers) based on the VIC-reconstructed SWE, and in all basins based on the manual snow survey records. The simulations underestimate the observed snowpack trends in the Columbia River basin, which has the highest mean elevation. Attribution is supported by the detection of human influence on the cold-season temperatures that drive the snowpack reductions. These results are robust to the use of different observed datasets and to the treatment of low-frequency variability effects. [ABSTRACT FROM AUTHOR]

Snow is an important component of the cryosphere and it has a direct and important influence on water storage and supply in snowmelt-dominated regions. This study evaluates the temporal evolution of snow water equivalent (SWE) for the February-April spring period using the GlobSnow observation dataset for the 1980-2012 period. The analysis is performed for different regions of hemispherical to sub-continental scales for the Northern Hemisphere. The detection-attribution analysis is then performed to demonstrate anthropogenic and natural effects on spring SWE changes for different regions, by comparing observations with six CMIP5 model simulations for three different external forcings: all major anthropogenic and natural (ALL) forcings, greenhouse gas (GHG) forcing only, and natural forcing only. The observed spring SWE generally displays a decreasing trend, due to increasing spring temperatures. However, it exhibits a remarkable increasing trend for the southern parts of East Eurasia. The six CMIP5 models with ALL forcings reproduce well the observed spring SWE decreases at the hemispherical scale and continental scales, whereas important differences are noted for smaller regions such as southern and northern parts of East Eurasia and northern part of North America. The effects of ALL and GHG forcings are clearly detected for the spring SWE decline at the hemispherical scale, based on multi-model ensemble signals. The effects of ALL and GHG forcings, however, are less clear for the smaller regions or with single-model signals, indicating the large uncertainty in regional SWE changes, possibly due to stronger influence of natural climate variability. [ABSTRACT FROM AUTHOR]

Snowpack observations in the Intermountain West are sparse and short, making them difficult for use in depicting past variability and extremes. This study presents a reconstruction of April 1 snow water equivalent (SWE) for the period of 1850-1989 using increment cores collected by the U.S. Forest Service, Interior West Forest Inventory and Analysis program (FIA). In the state of Utah, SWE was reconstructed for 38 snow course locations using a combination of standardized tree-ring indices derived from both FIA increment cores and publicly available tree-ring chronologies. These individual reconstructions were then interpolated to a 4-km grid using an objective analysis with elevation correction to create an SWE product. The results showed a significant correlation with observed SWE as well as good correspondence to regional tree-ring-based drought reconstructions. Diagnostic analysis showed statewide coherent climate variability on inter-annual and inter-decadal time-scales, with added geographical details that would not be possible using courser pre-instrumental proxy datasets. This SWE reconstruction provides water resource managers and forecasters with better spatial resolution to examine past variability in snowpack, which will be important as future hydroclimatic variability is amplified by climate change. [ABSTRACT FROM AUTHOR]

For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records. [ABSTRACT FROM AUTHOR]

The characteristics of an aboveground cosmic-ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point-scale snow depth (SD) and snow-water-equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)-based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230-270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point-scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain. [ABSTRACT FROM AUTHOR]

In this study an airborne ground penetrating radar (GPR) is used to estimate spatial distribution of snow accumulation in the Samudra Tapu glacier (the Great Himalayan Range), Western Himalaya, India. An impulse radar system with 350 MHz antenna was mounted on a helicopter for the estimation of snow depth. The dielectric properties of snow were measured at a representative site (Patseo Observatory) using a snow fork to calibrate GPR data. The snow depths estimated from GPR signal were found to be in good agreement with those measured on ground with an absolute error of 0.04 m. The GPR survey was conducted over Samudra Tapu glacier in March 2009 and 2010. A kriging-based geostatistical interpolation method was used to generate a spatial snow accumulation map of the glacier with the GPR-collected data. The average accumulated snow depth and snow water equivalent (SWE) for a part of the glacier were found to be 2.23 m and 0.624 m for 2009 and 2.06 m and 0.496 m for 2010 respectively. Further, the snow accumulation data were analysed with various topographical parameters such as altitude, aspect and slope. The accumulated snow depth showed good correlation with altitude, having correlation coefficient varying between 0.57 and 0.84 for different parts of the glacier. Higher snow accumulation was observed in the north- and east-facing regions, and decrease in snow accumulation was found with an increase in the slope of the glacier. Thus, in this study we generate snow accumulation/SWE information using airborne GPR in the Himalayan terrain. [ABSTRACT FROM AUTHOR]

Rain on snow (ROS) has the potential to generate severe floods. Thus, precisely predicting the effect of an approaching ROS event on runoff formation is very important. Data analyses from past ROS events have shown that a snowpack experiencing ROS can either release runoff immediately or delay it considerably. This delay is a result of refreeze of liquid water and water transport, which in turn is dependent on snow grain properties but also on the presence of structures such as ice layers or capillary barriers. During sprinkling experiments, preferential flow was found to be a process that critically impacted the timing of snowpack runoff. However, current one-dimensional operational snowpack models are not capable of addressing this phenomenon. For this study, the detailed physics-based snowpack model SNOWPACK is extended with a water transport scheme accounting for preferential flow. The implemented Richards equation solver is modified using a dual-domain approach to simulate water transport under preferential flow conditions. To validate the presented approach, we used an extensive dataset of over 100 ROS events from several locations in the European Alps, comprising meteorological and snowpack measurements as well as snow lysimeter runoff data. The model was tested under a variety of initial snowpack conditions, including cold, ripe, stratified and homogeneous snow. Results show that the model accounting for preferential flow demonstrated an improved overall performance, where in particular the onset of snowpack runoff was captured better. While the improvements were ambiguous for experiments on isothermal wet snow, they were pronounced for experiments on cold snowpacks, where field experiments found preferential flow to be especially prevalent. [ABSTRACT FROM AUTHOR]

A procedure has been proposed for express assessment of the degree of pollution of melt and snow waters with volatile aromatic compounds using a set of chemical sensors. The number of measuring units has been minimized to 2 for the yes/no detection (alarm system) of volatile aromatic toxicants in natural waters of different salinities. An algorithm has been developed for the extraction of analytical information on the presence of volatile compounds or salts in meltwater by the sorption efficiency parameters A and their profiles (petal diagrams) or by cluster analysis. [ABSTRACT FROM AUTHOR]

Snow contributes to regional and global water budgets, and is of critical importance to water resources management and our society. Along with advancement in remote sensing tools and techniques to retrieve snowfall, verification and refinement of these estimates need to be performed using ground-validation datasets. A comprehensive evaluation of the Multi-Radar/Multi-Sensor (MRMS) snowfall products and Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) precipitation products is conducted using the Snow Telemetry (SNOTEL) daily precipitation and Snow Water Equivalent (SWE) datasets. Severe underestimations are found in both radar and satellite products. Comparisons are conducted as functions of air temperature, snowfall intensity, and radar beam height, in hopes of resolving the discrepancies between measurements by remote sensing and gauge, and finally developing better snowfall retrieval algorithms in the future. [ABSTRACT FROM AUTHOR]

The focus of this study is on the estimation of snow microphysical properties and the associated bulk parameters such as snow water content and water equivalent snowfall rate for Ku- and Ka-band dual-frequency radar. This is done by exploring a suitable scattering model and the proper particle size distribution (PSD) assumption that accurately represent, in the electromagnetic domain, the micro-/macrophysical properties of snow. The scattering databases computed from simulated aggregates for small-to-moderate particle sizes are combined with a simple scattering model for large particle sizes to characterize snow-scattering properties over the full range of particle sizes. With use of the single-scattering results, the snow retrieval lookup tables can be formed in a way that directly links the Ku- and Ka-band radar reflectivities to snow water content and equivalent snowfall rate without use of the derived PSD parameters. A sensitivity study of the retrieval results to the PSD and scattering models is performed to better understand the dual-wavelength retrieval uncertainties. To aid in the development of the Ku- and Ka-band dual-wavelength radar technique and to further evaluate its performance, self-consistency tests are conducted using measurements of the snow PSD and fall velocity acquired from the Snow Video Imager/Particle Image Probe (SVI/PIP) during the winter of 2014 at the NASA Wallops Flight Facility site in Wallops Island, Virginia. [ABSTRACT FROM AUTHOR]