The cold rain‐on‐snow event of June 2013 in the Canadian Rockies — characteristics and diagnosis

The June 2013 flood in the Canadian Rockies featured rain‐on‐snow (ROS) runoff generation at alpine elevations that contributed to the high streamflows observed during the event. Such a mid‐summer ROS event has not been diagnosed in detail, and a diagnosis may help to understand future high discharge‐producing hydrometeorological events in mountainous cold regions. The alpine hydrology of the flood was simulated using a physically based model created with the modular cold regions hydrological modelling platform. The event was distinctive in that, although at first, relatively warm rain fell onto existing snowdrifts inducing ROS melt; the rainfall turned to snowfall as the air mass cooled and so increased snowcover and snowpacks in alpine regions, which then melted rapidly from ground heat fluxes in the latter part of the event. Melt rates of existing snowpacks were substantially lower during the ROS than during the relatively sunny periods preceding and following the event as a result of low wind speeds, cloud cover and cool temperatures. However, at the basin scale, melt volumes increased during the event as a result of increased snowcover from the fresh snowfall and consequent large ground heat contributions to melt energy, causing snowmelt to enhance rainfall–runoff by one fifth. Flow pathways also shifted during the event from relatively slow sub‐surface flow prior to the flood to an even contribution from sub‐surface and fast overland flow during and immediately after the event. This early summer, high precipitation ROS event was distinctive for the impact of decreased solar irradiance in suppressing melt rates, the contribution of ground heat flux to basin scale snowmelt after precipitation turned to snowfall, the transition from slow sub‐surface to fast overland flow runoff as the sub‐surface storage saturated and streamflow volumes that exceeded precipitation. These distinctions show that summer, mountain ROS events should be considered quite distinct from winter ROS and can be important contributors to catastrophic events. Copyright © 2016 John Wiley & Sons, Ltd.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

Uncertainty of snow water equivalent retrieved from AMSR‐E brightness temperature in northeast Asia

Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

Spatio‐temporal controls of snowmelt and runoff generation during rain‐on‐snow events in a mid‐latitude mountain catchment

A network of 30 standalone snow monitoring stations was used to investigate the snow cover distribution, snowmelt dynamics, and runoff generation during two rain‐on‐snow (ROS) events in a 40 km² montane catchment in the Black Forest region of southwestern Germany. A multiple linear regression analysis using elevation, aspect, and land cover as predictors for the snow water equivalent (SWE) distribution within the catchment was applied on an hourly basis for two significant ROS flood events that occurred in December 2012. The available snowmelt water, liquid precipitation, as well as the total retention storage of the snow cover were considered in order to estimate the amount of water potentially available for the runoff generation. The study provides a spatially and temporally distributed picture of how the two observed ROS floods developed in the catchment. It became evident that the retention capacity of the snow cover is a crucial mechanism during ROS. It took several hours before water was released from the snowpack during the first ROS event, while retention storage was exceeded within 1 h from the start of the second event. Elevation was the most important terrain feature. South‐facing terrain contributed more water for runoff than north‐facing slopes, and only slightly more runoff was generated at open compared to forested areas. The results highlight the importance of snowmelt together with liquid precipitation for the generation of flood runoff during ROS and the large temporal and spatial variability of the relevant processes. Copyright © 2015 John Wiley & Sons, Ltd.     

A new approach of dynamic monitoring of 5‐day snow cover extent and snow depth based on MODIS and AMSR‐E data from Northern Xinjiang region

Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb_18h–Tb_36h) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.     

Elevation‐dependent responses of streamflow to climate warming

Warming will affect snowline elevation, potentially altering the timing and magnitude of streamflow from mountain landscapes. Presently, the assessment of potential elevation‐dependent responses is difficult because many gauged watersheds integrate drainage areas that are both snow and rain dominated. To predict the impact of snowline rise on streamflow, we mapped the current snowline (1980 m) for the Salmon River watershed (Idaho, USA) and projected its elevation after 3 °C warming (2440 m). This increase results in a 40% reduction in snow‐covered area during winter months. We expand this analysis by collecting streamflow records from a new, elevation‐stratified gauging network of watersheds contained within high (2250–3800 m), mid (1500–2250 m) and low (300–1500 m) elevations that isolate snow, mixed and rain‐dominated precipitation regimes. Results indicate that lags between percentiles of precipitation and streamflow are much shorter in low elevations than in mid‐ and high‐elevation watersheds. Low elevation annual percentiles (Q₂₅ and Q₇₅) of streamflow occur 30–50 days earlier than in higher elevation watersheds. Extreme events in low elevations are dominated by low‐ and no‐flow events whereas mid‐ and high‐elevation extreme events are primarily large magnitude floods. Only mid‐ and high‐elevation watersheds are strongly cross correlated with catchment‐wide flow of the Salmon River, suggesting that changes in contributions from low‐elevation catchments may be poorly represented using mainstem gauges. As snowline rises, mid‐elevation watersheds will likely exhibit behaviours currently observed only at lower elevations. Streamflow monitoring networks designed for operational decision making or change detection may require modification to capture elevation‐dependent responses of streamflow to warming. Copyright © 2014 John Wiley & Sons, Ltd.     

Quantifying the role of the snowpack in generating water available for run‐off during rain‐on‐snow events from snow pillow records

Rain‐on‐snow events have generated major floods around the world, particularly in coastal, mountainous regions. Most previous studies focused on a limited number of major rain‐on‐snow events or were based primarily on model results, largely due to a lack of long‐term records from lysimeters or other instrumentation for quantifying event water balances. In this analysis, we used records from five automated snow pillow sites in south coastal British Columbia, Canada, to reconstruct event water balances for 286 rain‐on‐snow events over a 10‐year period. For large rain‐on‐snow events (event rainfall >40 mm), snowmelt enhanced the production of water available for run‐off (WAR) by approximately 25% over rainfall alone. For smaller events, a range of antecedent and meteorological factors influenced WAR generation, particularly the antecedent liquid water content of the snowpack. Most large events were associated with atmospheric rivers. Rainfall dominated WAR generation during autumn and winter events, whereas snowmelt dominated during spring and summer events. In the majority of events, the sensible heat of rain contributed less than 10% of the total energy consumed by snowmelt. This analysis illustrated the importance of understanding the amount of rainfall occurring at high elevations during rain‐on‐snow events in mountainous regions.     

Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.     

A new magnitude category disaggregation approach for temporal high‐resolution rainfall intensities

Simulation of quick runoff components such as surface runoff and associated soil erosion requires temporal high‐resolution rainfall intensities. However, these data are often not available because such measurements are costly and time consuming. Current rainfall disaggregation methods have shortcomings, especially in generating the distribution of storm events. The objectives of this study were to improve point rainfall disaggregation using a new magnitude category rainfall disaggregation approach. The procedure is introduced using a coupled disaggregation approach (Hyetos and cascade) for multisite rainfall disaggregation. The new procedure was tested with ten long‐term precipitation data sets of central Germany using summer and winter precipitation to determine seasonal variability. Results showed that dividing the rainfall amount into four daily rainfall magnitude categories (1–10, 11–25, 26–50, >50 mm) improves the simulation of high rainfall intensity (convective rainfall). The Hyetos model category approach (Hyetos_Cat) with seasonal variation performs representative to observed hourly rainfall compared with without categories on each month. The mean absolute percentage accuracy of standard deviation for hourly rainfall is 89.7% in winter and 95.6% in summer. The proposed magnitude category method applied with the coupled Hyetos_Cat–cascade approach reproduces successfully the statistical behaviour of local 10‐min rainfall intensities in terms of intermittency as well as variability. The root mean square error performance statistics for disaggregated 10‐min rainfall depth ranges from 0.20 to 2.38 mm for summer and from 0.12 to 2.82 mm for the winter season in all categories. The coupled stochastic approach preserves the statistical self‐similarity and intermittency at each magnitude category with a relatively low computational burden. Copyright © 2014 John Wiley & Sons, Ltd.     

Spatially distributed long‐term hydrologic simulation using a continuous SCS CN method‐based hybrid hydrologic model

A continuous Soil Conservation Service (SCS) curve number (CN) method that considers time‐varied SCS CN values was developed based on the original SCS CN method with a revised soil moisture accounting approach to estimate run‐off depth for long‐term discontinuous storm events. The method was applied to spatially distributed long‐term hydrologic simulation of rainfall‐run‐off flow with an underlying assumption for its spatial variability using a geographic information systems‐based spatially distributed Clark's unit hydrograph method (Distributed‐Clark; hybrid hydrologic model), which is a simple few parameter run‐off routing method for input of spatiotemporally varied run‐off depth, incorporating conditional unit hydrograph adoption for different run‐off precipitation depth‐based direct run‐off flow convolution. Case studies of spatially distributed long‐term (total of 6 years) hydrologic simulation for four river basins using daily NEXRAD quantitative precipitation estimations demonstrate overall performances of Nash–Sutcliffe efficiency (E_NS) 0.62, coefficient of determination (R²) 0.64, and percent bias 0.33% in direct run‐off and E_NS 0.71, R² 0.72, and percent bias 0.15% in total streamflow for model result comparison against observed streamflow. These results show better fit (improvement in E_NS of 42.0% and R² of 33.3% for total streamflow) than the same model using spatially averaged gauged rainfall. Incorporation of logic for conditional initial abstraction in a continuous SCS CN method, which can accommodate initial run‐off loss amounts based on previous rainfall, slightly enhances model simulation performance; both E_NS and R² increased by 1.4% for total streamflow in a 4‐year calibration period. A continuous SCS CN method‐based hybrid hydrologic model presented in this study is, therefore, potentially significant to improved implementation of long‐term hydrologic applications for spatially distributed rainfall‐run‐off generation and routing, as a relatively simple hydrologic modelling approach for the use of more reliable gridded types of quantitative precipitation estimations.     

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.     

Long‐term change of daily and multi‐daily precipitation in southern Sweden

Daily rain series from southern Sweden with records dating back to the 1870s have been analysed to investigate the trends of daily and multi‐day precipitation of different return periods with emphasis on the extremes. Probabilities of extreme storms were determined as continuously changing values based on 25 years of data. An extra set of data was used to investigate changes in Skåne, the southernmost peninsula of Sweden. Another 30‐year data set of more than 200 stations of a dense gauge network in Skåne was used to investigate the relation between very large daily rainfall and annual precipitation. The annual precipitation has increased significantly all over southern Sweden due to increased winter precipitation. There is a trend of increasing maximum annual daily precipitation at only one station, where the annual maximum often occurs in winter. The number of events with a short return period is increasing, but the number of more extreme events has not increased. Daily and multi‐daily design storms of long return periods determined from extreme value analysis with updating year by year are not higher today than during the last 100 years. The largest daily storms are not related to stations with annual rainfall but seem to occur randomly. Copyright © 2013 John Wiley & Sons, Ltd.     

Investigation of the snow‐cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow‐cover products and hydrometeorological data

The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 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.     

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.     

Regional variability in dust‐on‐snow processes and impacts in the Upper Colorado River Basin

Dust deposition onto mountain snow cover in the Upper Colorado River Basin frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau. Dust loading has increased since the intensive settlement in the western USA in the mid 1880s. The effects of dust‐on‐snow have been well studied at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, the first high‐altitude area of contact for predominantly southwesterly winds transporting dust from the southern Colorado Plateau. To capture variability in dust transport from the broader Colorado Plateau and dust deposition across a larger area of the Colorado River water sources, an additional study plot was established in 2009 on Grand Mesa, 150 km to the north of SBBSA in west central, CO. Here, we compare the 4‐year (2010–2013) dust source, deposition, and radiative forcing records at Grand Mesa Study Plot (GMSP) and Swamp Angel Study Plot (SASP), SBBSA's subalpine study plot. The study plots have similar site elevations/environments and differ mainly in the amount of dust deposited and ensuing impacts. At SASP, end of year dust concentrations ranged from 0.83 mg g^?1 to 4.80 mg g^?1, and daily mean spring dust radiative forcing ranged from 50–65 W m^?2, advancing melt by 24–49 days. At GMSP, which received 1.0 mg g^?1 less dust per season on average, spring radiative forcings of 32–50 W m^?2 advanced melt by 15–30 days. Remote sensing imagery showed that observed dust events were frequently associated with dust emission from the southern Colorado Plateau. Dust from these sources generally passed south of GMSP, and back trajectory footprints modelled for observed dust events were commonly more westerly and northerly for GMSP relative to SASP. These factors suggest that although the southern Colorado Plateau contains important dust sources, dust contributions from other dust sources contribute to dust loading in this region, and likely account for the majority of dust loading at GMSP. Copyright © 2015 John Wiley & Sons, Ltd.     

Snow hydrology signatures for model identification within a limits‐of‐acceptability approach

Discharge simulation from snow‐dominated catchments seems to be an easy task. Any spatially explicit precipitation–runoff model coupled to a temperature‐index snow model generally yields simulations that mimic well the observed daily discharges. The robustness of such models is, however, questionable: in the presence of strong annual discharge cycles, small model residuals do not guarantee high explanatory power of the underlying model. This paper proposes a methodology for snow hydrological model identification within a limits‐of‐acceptability framework, where acceptable model simulations are the ones that reproduce a set of signatures within an a priori specified range. The signatures proposed here namely include the relationship between the air temperature regime and the discharge regime, a new snow hydrology signature that can be readily transferred to other Alpine settings. The discriminatory power of all analysed signatures is assessed with a new measure of their discriminatory power in the model prediction domain. The value of the proposed snow hydrology signatures and of the limits‐of‐acceptability approach is demonstrated for the Dischma river in Switzerland, whose discharge shows a strong temporal variability of hydrologic forcing conditions over the last 30 years. The signature‐based model identification for this case study leads to the surprising conclusion that the observed discharge data contains a multi‐year period that cannot be reproduced with the model at hand. This model‐data mismatch might well result from a yet to be identified problem with the discharge observations, which would have been difficult to detect in a classical residual‐based model identification approach. Overall, the detailed results for this case study underline the robustness of the limits‐of‐acceptability approach in the presence of error‐prone observations if it is applied in combination with relatively robust signatures. Future work will show whether snow hydrology signatures and their limits‐of‐acceptability can be regionalized to ungauged catchments, which would make this model selection approach particularly powerful for Alpine environments. Copyright © 2016 John Wiley & Sons, Ltd.