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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]

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]

Watershed-scale hydrologic models require good estimates of spatially distributed snow water equivalent (SWE) at winter's end. Snow on the ground in arctic environments is susceptible to significant wind redistribution, which results in heterogeneous snowpacks. The scarcity and quality of data collected by snow gauges provides a poor indicator of actual snowpack distribution. Snow distribution patterns are similar from year to year because they are largely controlled by the interaction of topography, vegetation, and consistent weather patterns. Consequently, shallow and deep areas of snow tend to be spatially predetermined, resulting in depth (or SWE) differences that may vary as a whole, but not relative to each other. Our aim was to identify snowpack distribution patterns and establish their stability in time and space at a watershed scale. Snow patterns were established by: (1) using numerous field surveys from end-of-winter field campaigns; and (2) differentiating snowpacks that characterize small-scale anomalies (local scale) from snowpacks that represent a large-scale area (regional scale). We concluded that basic snow survey site descriptions could be used to separate survey locations into regional and local-scale representative sites. Removing local-scale influences provides a more accurate representation of the regional snowpack, which will aid in forecasting snowmelt runoff events. [ABSTRACT FROM AUTHOR]

The research objectives are to estimate differences between the potential impact of climatic change to the Athabasca River basin (ARB) and Fraser River basin (FRB) of Canada with and without considering shifts in vegetation patterns induced by climate change and how much the difference will depend on vegetation types and climate. The hydrologic effects of vegetation shifts on ARB and FRB were estimated by applying the Mapped Atmosphere-Plant-Soil System (MAPSS) simulated results based on the Intergovernmental Panel on Climate Change's First and Second Assessment Report general circulation model (GCM) scenarios to the modified Interaction Soil-Biosphere-Atmosphere (MISBA) scheme. According to MAPSS, vegetation shifts in mountainous regions of FRB are expected to be dominated by conifer/broadleaf competition, while in ARB, climate projections of MAPSS predicted a southern expansion of the boreal forest. Because of differences in sublimation, there is a tendency for more snow to accumulate in open grassland than forests. Furthermore, changes to simulated mean annual maximum snowpack, runoff, and basin area covered by grassland are positively correlated to each other. Generally, a 4% increase in snow water equivalent (SWE) results in a 1% increase in mean annual runoff. These relationships hold true in both basins over a wide range of GCM-projected climate conditions and vegetation responses, suggesting that most changes in mean annual flow can be attributed to changes in SWE. Because of the different modeling approaches between MAPSS and MISBA, it seems that the treatment of these processes in vegetation and hydrologic models should be similar before conclusions can be drawn from various stand-alone simulations. Ideally, a land surface scheme should be coupled with a vegetation model in future studies. [ABSTRACT FROM AUTHOR]

This paper summarizes 12 years of snow water equivalent (SWE) observations collected in the data-sparse region of Arctic Alaska, United States. The In situ observations are distributed across a 200 x 300 km domain that includes the Kuparuk River watershed from the Brooks Range to the Beaufort Sea coast. Data collection methods and analyses were classified to distinguish between snow observation sites representing regional- and local-scale variability. Average SWE for the entire domain ranges from 92 mm in 2008 to 148 mm in 2011. Regional end-of-winter SWE indicates that both the extreme high SWE in 2009 and 2011 and the extreme low SWE in 2008 occurred during recent and alternating years, suggesting the limitations of 12 years of data for detecting SWE trends. By assimilating the observational datasets into SnowModel, hourly 100 m gridded SWE distributions for the central Alaska Arctic were created to provide a best-fit to the observations where and when they occur. The model simulations highlight how observed SWE data can be used as a surrogate for the more problematic winter precipitation measurements. The resulting SWE distributions are readily available to support ecological and hydrologic studies in this region. [ABSTRACT FROM AUTHOR]

Summary: This paper presents that a simple regional regression-based approach with readily available geographic and meteorological parameters as predictors could be a viable method for mapping snow water equivalent (SWE) climatology in the mountainous areas of the western United States. Such climatological information is potentially useful for several hydrologic applications, including estimation of real-time SWE grids and calibration and evaluation of SWE estimates obtained from remote sensing or through various snow models. Regional delineation for the mountainous regions of the western United States was done through cluster analysis in consideration of the characteristics of seasonal snow pack accumulation and ablation processes. Various geographic and meteorological parameters were further investigated through stepwise regression as potential predictors of monthly changes in climatological SWE in each delineated region. In-situ measurements of SWE, obtained from the Natural Resources Conservation Service’s Snow Telemetry network for the period from 1980 to 2004, were used to calibrate regional equations. The spatial and temporal resolution of the analysis was based on the resolution of available meteorological data – 4km and 1month, respectively. For a monthly time step, the reliability of the SWE estimates did not significantly increase when the number of regions was more than five. The performance of the developed regional equations was evaluated via a jack-knife technique. The regional equations developed using monthly resolution and large regions provided reliable estimates for the majority of regions from October to March, but not in April, especially in the North Pacific and Southwest regions. Sub-regionalization, a finer time step for the analysis and/or inclusion of additional SWE predictor variables may increase the reliability of SWE climatological estimates for warmer months. [ABSTRACT FROM AUTHOR]

Abstract: Estimating erroneous parameters in ensemble based snow data assimilation system has been given little attention in the literature. Little is known about the related methods’ effectiveness, performance, and sensitivity to other error sources such as model structural error. This research tackles these questions by running synthetic one-dimensional snow data assimilation with the ensemble Kalman filter (EnKF), in which both state and parameter are simultaneously updated. The first part of the paper investigates the effectiveness of this parameter estimation approach in a perfect-model-structure scenario, and the second part focuses on its dependence on model structure error. The results from first part research demonstrate the advantages of this parameter estimation approach in reducing the systematic error of snow water equivalent (SWE) estimates, and retrieving the correct parameter value. The second part results indicate that, at least in our experiment, there is an evident dependence of parameter search convergence on model structural error. In the imperfect-model-structure run, the parameter search diverges, although it can simulate the state variable well. This result suggest that, good data assimilation performance in estimating state variables is not a sufficient indicator of reliable parameter retrieval in the presence of model structural error. The generality of this conclusion needs to be tested by data assimilation experiments with more complex structural error configurations. [Copyright &y& Elsevier]

Summary: By taking advantage of the high spatial resolution of optical sensors and cloud penetration of a passive microwave sensor, a method is developed to generate new daily cloud-free snow cover (SC) and snow water equivalent (SWE) products, both in 500m spatial resolution, utilizing daily Terra–Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Aqua Advanced Microwave Scanning Radiometer for NASA’s Earth Observing System (AMSR-E) snow products. This method was tested in Fairbanks and Upper Susitna Valley, Alaska area for one hydrological year (October 2006–September 2007). The results confirm that daily MODIS products and Terra–Aqua MODIS combined products have similar and high classification accuracy (91–94%) in cloud-free conditions and that the daily combination can reduce cloud cover ∼10%. The results also show the snow accuracy of the new SC products is 86%, which is much higher than the 31%, 45%, and 49% of the Terra, Aqua, and Terra/Aqua combined snow cover products (in all weather conditions), respectively. The validation demonstrates that the accuracy of AMSR-E SWE products is 68.5% and they tend to overestimate SWE. Redistribution of SWE, based on sub-pixel analysis of AMSR-E pixels, not only generates the new product at higher spatial resolution, now more suitable for basin and regional monitoring and modeling, but also slightly increases the accuracy of the SWE estimations. This method can also be used in merging other optical data such as AVHRR, Landsat with passive microwave data such as SSMR, SSM/I, and for future NPP and NPOESS missions. [Copyright &y& Elsevier]

Snow water equivalent (SWE) is critical information for hydrological studies over northern latitudes. This information is needed to adequately simulate spring runoff in order to optimize hydropower generation. In this paper, a new geophysical inversion methodology for monitoring SWE evolution on a daily basis is presented. The methodology is based on a modified version of the Helsinki University of Technology (HUT) snow emission model and assimilates in situ data as ground truth. Previous studies used the mean snow grain size (ø) as an optimization parameter to retrieve SWE with the HUT model. Therefore, a priori information on ø was needed to estimate SWE, but this information is rarely measured in the field. In the HUT model, the mean snow grain size is used to compute the total extinction coefficient (κe) of the snowpack. We modified the model to eliminate the required a priori knowledge on mean snow grain size and used the total extinction coefficient as an optimization parameter instead. The methodology was applied in a subarctic territory in northern Québec (Canada) using snow measurements acquired during the 2001 to 2006 winters (January 1 to March 31). A metric based on the brightness temperature difference between the special sensor microwave/imager 19- and 37-GHz (vertical polarization) was used for SWE retrieval. In the monitoring process, the result obtained from the previous day inversion was used to initialize the current day SWE retrieval. When in situ measurements are available, they are assimilated and used to calibrate the model. The results obtained with this methodology show an improvement in the experimental context considered (high SWE values). For all winters considered in this paper, the results described in this paper show that SWE was estimated with a root-mean-square error (rmse) of less than 19% after the first process of in situ data assimilation and with an rmse of 14 % for nondense forested areas and for SWE values below 300 mm. [ABSTRACT FROM AUTHOR]

The article presents a study which developed a parallel-probe saturation profiler (PPSP) device for a more accurate and efficient recording of meltwater saturations in a snowpack. The device is used to utilize the absorption of energy during the rotation of the water molecules on the action of an external electric field. The heights of first peak signal (HFPS) corresponding to various liquid-water contents were obtained from several snow types. The HFPS were then observed and revealed its proportionality to the water saturation of the snow regardless of the snow type. The instrument is regarded as a useful tool for scientist involved in snow physics, hydrology, or glaciology. It can also be used with a data logger to monitor the liquid-water saturations or contents in field studies.

Flood forecast and water resource management requires reliable estimates of snow pack properties [snow depth and snow water equivalent (SWE)]. This study focuses on application of satellite microwave images to estimate the spatial distribution of snow depth and SWE over the Great Lakes area. To estimate SWE, we have proposed the algorithm which uses microwave brightness temperatures (Tb) measured by the Special Sensor Microwave Imager (SSM/I) radiometer along with information on the Normalized Difference Vegetation Index (NDVI). The algorithm was developed and tested over 19 test sites characterized by different seasonal average snow depth and land cover type. Three spectral signatures derived from SSM/I data, namely T19V-T37V (GTV), T19H-T37H (GTH), and T22V-T85V (SSI), were examined for correlation with the snow depth and SWE. To avoid melting snow conditions, we have used observations taken only during the period from December 1-February 28. It was found that GTH, and GTV exhibit similar correlation with the snow depth/SWE and are most should be used over deep snowpack. In the same time, SSI is more sensitive to snow depth variations over a shallow snow pack. To account for the effect of dense forests on the scattering signal of snow we established the slope of the regression line between GTV and the snow depth as a function of NDVI. The accuracy of the new technique was evaluated through its comparison with ground-based measurements and with results of SWE analysis prepared by the National Operational Hydrological Remote Sensing Center (NOHRSC) of the National Weather Service. The proposed algorithm was found to be superior to previously developed global microwave SWE retrieval techniques. [ABSTRACT FROM AUTHOR]

Conceptual schema illustrating the buffering role of groundwater during future summer low-flow periods in an alpine catchment. It shows the evolution of monthly mean recharge, snow water equivalent and discharge in the reference and the future period 2085 for a scenario RCP 8.5 with a strong increase in temperatures and decrease in summer precipitations. • Quaternary deposits play a critical role in seasonally storing alpine groundwater. • In future summer, the decrease in alpine discharge depends on groundwater storage. • The actual winter low flow appears as a lower bound for the future summer low flow. Alpine environments are particularly vulnerable to climatic warming, and long term observations suggest a shift of snow-influenced river discharge towards earlier periods of the year. For water resources management, the seasonal patterns of discharge in alpine areas are particularly relevant, as the shift to lower flows in summer and autumn combined with increased water demand could lead to water shortage in downstream catchments. The storage of groundwater in alpine catchments could significantly modulate how changing climatic conditions influence the annual streamflow regime. However, groundwater storage and its buffering capacity in alpine areas remain poorly understood. Moreover, studies on how climate change will impact water resources in alpine areas rarely consider the influence of geology. In this paper, catchment geology is used as a basis for the classification of future summer low flows behavior of several alpine catchments in Switzerland. Based on the analysis of the relationship between low-flow indicators and geology, the role of unconsolidated quaternary deposits is explored. We show that quaternary deposits play a critical role in the seasonal storage of groundwater, which can contribute to rivers during low-flow periods. Three climate change simulations based on extreme RCP 8.5 scenarios are fed into a conceptual hydrological model to illustrate the buffering role of groundwater. Past and future low flows normalized by mean past and future streamflows appear correlated with the percentage of unconsolidated quaternary deposits. These results highlight that catchments with high groundwater contribution to streamflow relative to precipitation will have a slower decrease in future summer discharge. Therefore, we propose two indicators that can be used to anticipate the response of future summers low flows in alpine areas to climate change: the current winter low flows and the percentage of unconsolidated quaternary deposits of the catchments. [ABSTRACT FROM AUTHOR]