Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Estimation of winter leaf area index and sky view fraction for snow modelling in boreal coniferous forests: consequences on snow mass and energy balance

Leaf area index (LAI) and canopy coverage are important parameters when modelling snow process in coniferous forests, controlling interception and transmitting radiation. Estimates of LAI and sky view factor show large variability depending on the estimation method used, and it is not clear how this is reflected in the calculated snow processes beneath the canopy. In this study, the winter LAI and sky view fraction were estimated using different optical and biomass‐based approximations in several boreal coniferous forest stands in Fennoscandia with different stand density, age and site latitude. The biomass‐based estimate of LAI derived from forest inventory data was close to the values derived from the optical measurements at most sites, suggesting that forest inventory data can be used as input to snow hydrological modelling. Heterogeneity of tree species and site fertility, as well as edge effects between different forest compartments, caused differences in the LAI estimates at some sites. A snow energy and mass balance model (SNOWPACK) was applied to detect how the differences in the estimated values of the winter LAI and sky view fraction were reflected in simulated snow processes. In the simulations, an increase in LAI and a decrease in sky view fraction changed the snow surface energy balance by decreasing shortwave radiation input and increasing longwave radiation input. Changes in LAI and sky view fraction affected directly snow accumulation through altered throughfall fraction and indirectly snowmelt through the changed surface energy balance. Changes in LAI and sky view fraction had a greater impact on mean incoming radiation beneath the canopy than on other energy fluxes. Snowmelt was affected more than snow accumulation. The effect of canopy parameters on evaporation loss from intercepted snow was comparable with the effect of variation in governing meteorological variables such as precipitation intensity and air temperature. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed snow process modelling: An image processing approach

An approach to spatially distribute a snow process model by segmenting images of land cover, terrain and snow properties is reported. A small 1.7 ha study area with an existing database was selected for this preliminary evaluation. The methodology was carried out over a relatively flat valley bottom at Camp Grayling, Michigan. Meteorological measurements on two sides of the area showed only small differences, so uniform meteorological variables were assumed over the site. Initial snow cover conditions were reconstructed and were distributed over the area using snow maps and sparse snow pit measurements. One metre resolution terrain, soil, vegetation and snow type maps were individually processed into class maps. These layers were then combined to produce a segmented class map, where the attributes from the data layers were known for each class. A one-dimensional model of snow processes was run for each class, then the results were mapped back into images. Shallow snow conditions provided high sensitivity of ablation patterns to meteorological conditions over a 72 h period. The model performance was assessed by comparing predicted and observed ablation patterns. The error in total snow-covered area was less than 9%. However, the location errors were greater (predicted snow where no snow was observed and observed snow where no snow was predicted). Extensive error analysis was not justified because of the lack of multiple point measurements of snow properties.     

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.     

Assimilating snow observations to snow interception process simulations

Snow interception is a crucial hydrological process in cold regions needleleaf forests, but is rarely measured directly. Indirect estimates of snow interception can be made by measuring the difference in the increase in snow accumulation between the forest floor and a nearby clearing over the course of a storm. Pairs of automatic weather stations with acoustic snow depth sensors provide an opportunity to estimate this, if snow density can be estimated reliably. Three approaches for estimating fresh snow density were investigated: weighted post-storm density increments from the physically based Snobal model, fresh snow density estimated empirically from air temperature (Hedstrom, N. R., et al. [1998]. Hydrological Processes, 12, 1611–1625), and fresh snow density estimated empirically from air temperature and wind speed (Jordan, R. E., et al. [1999]. Journal of Geophysical Research, 104, 7785–7806). Automated snow depth observations from adjacent forest and clearing sites and estimated snow densities were used to determine snowstorm snow interception in a subalpine forest in the Canadian Rockies, Alberta, Canada. Then the estimated snow interception and measured interception information from a weighed, suspended tree and a time-lapse camera were assimilated into a model, which was created using the Cold Regions Hydrological Modelling platform (CRHM), using Ensemble Kalman Filter or a simple rule-based direct insertion method. Interception determined using density estimates from the Hedstrom-Pomeroy fresh snow density equation agreed best with observations. Assimilating snow interception information from automatic snow depth measurements improved modelled snow interception timing by 7% and magnitude by 13%, compared to an open loop simulation driven by a numerical weather model; its accuracy was close to that simulated using locally observed meteorological data. Assimilation of tree-measured snow interception improved the snow interception simulation timing and magnitude by 18 and 19%, respectively. Time-lapse camera snow interception information assimilation improved the snow interception simulation timing by 32% and magnitude by 7%. The benefits of assimilation were greatly influenced by assimilation frequency and quality of the forcing data.     

Watershed‐scale controls on snow accumulation in a small montane watershed,southwestern Alberta,Canada

Snow accumulation in mountain headwater basins is a major water source, particularly in semi‐arid environments such as southern Alberta where water resources are stressed and snowmelt supplies more than 80% of downstream runoff. Relationships between landscape predictor variables and snow water equivalent (SWE) were quantified by combining field and LiDar measurements with classification and regression tree analysis over two winter seasons (2010 and 2011) in a small, montane watershed. 2010 was a below average snow accumulation year, while 2011 was well above normal. In both the field and regression tree data, elevation was the dominant control on snow distribution in both years, although snow distribution was driven by melt processes in 2010 and accumulation processes in 2011. The importance of solar radiation and wind exposure was represented in the regression trees in both years. The regression trees also noted the lower importance of canopy closure, slope, and aspect, which was not observed in the field data. This technique could provide an additional method of forecasting annual water supply from melting snow. However, further research is required to address the lack of data collected above treeline, to provide a full‐basin estimate of SWE. Copyright © 2012 John Wiley & Sons, Ltd.     

A simple method to combine snow height and meteorological observations to estimate winter precipitation at sub-daily resolution

ABSTRACT

Measuring winter solid and liquid precipitation with high temporal resolution in remote or higher elevation regions is a challenging task because of undercatch and power supply issues. However, the number of micro-meteorological stations and ultrasonic height sensors in mountain regions is steadily increasing. To gain more benefit from such stations, a new simple approach for EStimating SOlid and LIquid Precipitation (ESOLIP) is presented. The method consists of three main steps: (1) definition of precipitation events using micro-meteorological data, (2) quantification of solid and liquid precipitation using wet-bulb temperature and filtered snow height and (3) calculation of fresh snow density. ESOLIP performance was validated using data from a heated rain gauge, snow pillow and daily manual observations both for single precipitation events and over three winter seasons. Results proved ESOLIP as an effective approach for precipitation quantification, where snow height observations and basic meteorological measurements (air temperature, solar radiation, wind speed, relative humidity), but no reliable rain gauges are available.     

Observations of late winter Canadian tundra snow cover properties

Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm³ was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.     

Estimating sublimation of intercepted and sub‐canopy snow using eddy covariance systems

Direct measurements of winter water loss due to sublimation were made in a sub‐alpine forest in the Rocky Mountains of Colorado. Above‐and below‐canopy eddy covariance systems indicated substantial losses of winter‐season snow accumulation in the form of snowpack (0·41 mm d^?1) and intercepted snow (0·71 mm d^?1) sublimation. The partitioning between these over and under story components of water loss was highly dependent on atmospheric conditions and near‐surface conditions at and below the snow/atmosphere interface. High above‐canopy sensible heat fluxes lead to strong temperature gradients between vegetation and the snow‐surface, driving substantial specific humidity gradients at the snow surface and high sublimation rates. Intercepted snowfall resulted in rapid response of above‐canopy latent heat fluxes, high within‐canopy sublimation rates (maximum = 3·7 mm d^?1), and diminished sub‐canopy snowpack sublimation. These results indicate that sublimation losses from the sub‐canopy snowpack are strongly dependent on the partitioning of sensible and latent heat fluxes in the canopy. This compels comprehensive studies of snow sublimation in forested regions that integrate sub‐canopy and over‐story processes. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluation of spatial variability in snow water equivalent for a high mountain catchment

Multivariate statistical analysis was used to explore relationships between catchment topography and spatial variability in snow accumulation and melt processes in a small headwater catchment in the Spanish Pyrenees. Manual surveys of snow depth and density provided information on the spatial distribution of snow water equivalent (SWE) and its depletion over the course of the 1997 and 1998 melt seasons. A number of indices expressing the topographic control on snow processes were extracted from a detailed digital elevation model of the catchment. Bivariate screening was used to assess the relative importance of these topographic indices in controlling snow accumulation at the start of the melt season, average melt rates and the timing of snow disappearance. This suggested that topographic controls on the redistribution of snow by wind are the most important influence on snow distribution at the start of the melt season. Furthermore, it appeared that spatial patterns of snow disappearance were largely determined by the distribution of snow water equivalent (SWE) at the start of the melt season, rather than by spatial variability in melt rates during the melt season. Binary regression tree models relating snow depth and disappearance date to terrain indices were then constructed. These explained 70–80% of the variance in the observed data. As well as providing insights into the influence of topography on snow processes, it is suggested that the techniques presented herein could be used in the parameterization of distributed snowmelt models, or in the design of efficient stratified snow surveys. Copyright © 2003 John Wiley & Sons, Ltd.     

Estimating the distribution of snow water equivalent and snow extent beneath cloud cover in the Salt–Verde River basin,Arizona

The temporal and spatial continuity of spatially distributed estimates of snow‐covered area (SCA) are limited by the availability of cloud‐free satellite imagery; this also affects spatial estimates of snow water equivalent (SWE), as SCA can be used to define the extent of snow telemetry (SNOTEL) point SWE interpolation. In order to extend the continuity of these estimates in time and space to areas beneath the cloud cover, gridded temperature data were used to define the spatial domain of SWE interpolation in the Salt–Verde watershed of Arizona. Gridded positive accumulated degree‐days (ADD) and binary SCA (derived from the Advanced Very High Resolution Radiometer (AVHRR)) were used to define a threshold ADD to define the area of snow cover. The optimized threshold ADD increased during snow accumulation periods, reaching a peak at maximum snow extent. The threshold then decreased dramatically during the first time period after peak snow extent owing to the low amount of energy required to melt the thin snow cover at lower elevations. The area having snow cover at this later time was then used to define the area for which SWE interpolation was done. The area simulated to have snow was compared with observed SCA from AVHRR to assess the simulated snow map accuracy. During periods without precipitation, the average commission and omission errors of the optimal technique were 7% and 11% respectively, with a map accuracy of 82%. Average map accuracy decreased to 75% during storm periods, with commission and omission errors equal to 11% and 12% respectively. The analysis shows that temperature data can be used to help estimate the snow extent beneath clouds and therefore improve the spatial and temporal continuity of SCA and SWE products. Copyright © 2004 John Wiley & Sons, Ltd.     

Modelling snowpack bulk density using snow depth,cumulative degree-days,and climatological predictor variables

Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m⁻³, a mean bias of −0.039 kg m⁻³, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

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.     

Snow accumulation and ablation response to changes in forest structure and snow surface albedo after attack by mountain pine beetle

This study quantified changes in snow accumulation and ablation with forest defoliation in a young pine stand attacked by mountain pine beetle, a mature mixed species stand, and a clearcut in south‐central British Columbia. From 2006 to 2012, as trees in the pine stand turned from green to grey, average canopy transmittance increased from 27% to 49%. In the mixed stand, transmittance remained constant at 19%. In 2009, the year of greatest needle loss, average snow surface litter cover in the pine stand was 29% (range 4 – 61%), compared to ≤9% in other years and over double that in the mixed stand. By 2012, litter accumulation in the now‐grey pine stand was only a sixth of that in the mixed stand. Inter‐annual variability in the weather had the greatest effect on snow accumulation and ablation, with the greatest differences between both forested stands and the clearcut occurring in 2010, the year of lowest SWE. Differences in snow accumulation between the pine and mixed stand increased in 2010 as a result of decreased snow interception in the young stand after needlefall. Average ablation rates in the attacked stand were most different from the mixed stand in 2009 and 2012, the years with the largest and smallest over‐winter needle loss, respectively. This study shows that grey, non‐pine, and understory trees moderate snow response to changes in the main canopy. It also highlights the complex interrelationships between ecohydrological processes key to assessing watershed response to forest cover loss in snow dominated hydrologic regimes. Copyright © 2012 John Wiley & Sons, Ltd.     

Addressing snow model uncertainty for hydrologic prediction

Streamflow forecasting methods are moving towards probabilistic approaches that quantify the uncertainty associated with the various sources of error in the forecasting process. Multi-model averaging methods which try to address modeling deficiencies by considering multiple models are gaining much popularity. We have applied the Bayesian Model Averaging method to an ensemble of twelve snow models that vary in their heat and melt algorithms, parameterization, and/or albedo estimation method. Three of the models use the temperature-based heat and melt routines of the SNOW17 snow accumulation and ablation model. Nine models use heat and melt routines that are based on a simplified energy balance approach, and are varied by using three different albedo estimation schemes. Finally, different parameter sets were identified through automatic calibration with three objective functions. All models use the snow accumulation, liquid water transport, and ground surface heat exchange processes of the SNOW17. The resulting twelve snow models were combined using Bayesian Model Averaging (BMA). The individual models, BMA predictive mean, and BMA predictive variance were evaluated for six SNOTEL sites in the western U.S. The models performed best and the BMA variance was lowest at the colder sites with high winter precipitation and little mid-winter melting. An individual snow model would often outperform the BMA predictive mean. However, observed snow water equivalent (SWE) was captured within the 95% confidence intervals of the BMA variance on average 80% of the time at all sites. Results are promising that consideration of multiple snow structures would provide useful uncertainty information for probabilistic hydrologic prediction.     

Intercomparison of measurements of bulk snow density and water equivalent of snow cover with snow core samplers: Instrumental bias and variability induced by observers

Manually collected snow data are often considered as ground truth for many applications such as climatological or hydrological studies. However, there are many sources of uncertainty that are not quantified in detail. For the determination of water equivalent of snow cover (SWE), different snow core samplers and scales are used, but they are all based on the same measurement principle. We conducted two field campaigns with 9 samplers commonly used in observational measurements and research in Europe and northern America to better quantify uncertainties when measuring depth, density and SWE with core samplers. During the first campaign, as a first approach to distinguish snow variability measured at the plot and at the point scale, repeated measurements were taken along two 20 m long snow pits. The results revealed a much higher variability of SWE at the plot scale (resulting from both natural variability and instrumental bias) compared to repeated measurements at the same spot (resulting mostly from error induced by observers or very small scale variability of snow depth). The exceptionally homogeneous snowpack found in the second campaign permitted to almost neglect the natural variability of the snowpack properties and focus on the separation between instrumental bias and error induced by observers. Reported uncertainties refer to a shallow, homogeneous tundra-taiga snowpack less than 1 m deep (loose, mostly recrystallised snow and no wind impact). Under such measurement conditions, the uncertainty in bulk snow density estimation is about 5% for an individual instrument and is close to 10% among different instruments. Results confirmed that instrumental bias exceeded both the natural variability and the error induced by observers, even in the case when observers were not familiar with a given snow core sampler.     

Observations of urban snow properties in Calgary,Canada

Urban winter hydrology has garnered very little attention owing to the general notion that high‐intensity rainfalls are the major flood‐generating events in urban areas. As a result, few efforts have been made to research urban snow and its melt characteristics. This study investigates the characteristics of urban snow that differentiate it from rural snow, and makes recommendations for incorporating these characteristics into an urban snowmelt model. A field study was conducted from the fall of 2001 to the spring of 2002 in the city of Calgary, Canada. Snow depths and densities, soil moisture, soil temperature, snow albedo, net radiation, snow evaporation, and surface temperature were measured at several locations throughout the winter period. The combination of urban snow removal practices and the physical elements that exist in urban areas were found to influence the energy balance of the snowpack profoundly. Shortwave radiation was found to be the main source of energy for urban snow; as a consequence, the albedo of urban snow is a very important factor in urban snowmelt modelling. General observations lead to the classification of snow as one of four types: snow piles, snow on road shoulders, snow on sidewalk edges, and snow in open areas. This resulted in the development of four separate functions for the changing snow albedo values. A study of the frozen ground conditions revealed that antecedent soil moisture conditions had very little impact on frozen ground, and thus frozen ground very nearly always acts as a near impervious area. Improved flood forecasting for urban catchments in cold regions can only be achieved with accurate modelling of urban winter runoff that involves the energy balance method, incorporating snow redistribution and urban snow‐cover characteristics, and using small time steps. Copyright © 2004 John Wiley & Sons, Ltd.     

Recent snow cover changes over central European low mountain ranges

In this study, we simulated the snow water equivalent (SWE), rain-on-snow (ROS) events, evapotranspiration, and run-off for the period 1961–2016 in a central European region covered by low mountain ranges (<820 m a.s.l.) using a distributed hydrological model TRAnspiration and INterception evaporation model (TRAIN). We utilized improved cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products to evaluate the modelled snow-covered area, indicating a good performance of the snow modelling. We analysed the intra- and inter-annual variations of the simulated hydrological variables and the synchronous climate variables (air temperature and precipitation). Trend detection indicates a significant SWE decline throughout the snow season, but principally at the high elevations; the most severe warming occurred in early spring (March), whereas precipitation showed a slight increase in January and February. The snowpack in February has displayed the most striking reduction during the past 56 years, which is likely related to both the highest susceptibility of snow to warming and the increased ROS occurrence in February since the early 1990s. The increased combination of high temperatures and extreme rainfalls, as well as the earlier snowmelt, has resulted in a run-off increase during the earlier winter but a decrease in March. The expected changing climate towards warmer and wetter winters will probably exacerbate winter flooding in the future.     

Information content of snow hydrological signatures based on streamflow,precipitation and air temperature

Hydrological signatures that represent snow processes are valuable to gain insights into snow accumulation and snow melt dynamics. We investigated five snow signatures. Considering inter-annual average of each calendar day, two slopes derived from the relation between streamflow and air temperature for different periods and streamflow peak maxima are used as signatures. In addition, two different approaches are used to compute inter-annual average and yearly snow storage estimates. We evaluated the ability of these signatures to characterize average (a) snow melt dynamics and (b) snow storage. They were applied in 10 Critical Zone Observatory catchments of the Southern Sierra mountains (USA) characterized by a Mediterranean climate. The relevance and information content of the signatures are evaluated using snow depth and snow water equivalent measurements as well as inter-catchment differences in elevation. The slopes quantifying the relations between streamflow and air temperature and the date of streamflow peak were found to characterize snow melt dynamics in terms of snow melt rates and snow melt affected areas. Streamflow peak dates were linked to the period of highest snow melt rates. Snow storage could be estimated both on average, considering all years, and for each year. Snow accumulation dynamics could not be characterized due to the lack of streamflow response during the snow accumulation period. The signatures were found potentially valuable to gain insights into catchment scale snow processes. In particular, when comparing catchments or observed and simulated data, they could provide insights into differences in terms of (a) snow melt rate and/or snow melt affected area over the snow melt season and (b) average or yearly snow storage. Requiring only widely available data, these hydrological signatures can be valuable for snow processes characterization, catchment comparison/classification or model development, calibration or evaluation.