Evaluation of the MODIS snow cover fraction product

Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in‐situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different‐sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3‐pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error‐prone observations and more accurately snow‐classified pixels. Copyright © 2012 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Changes in thermal extremes in Poland

Changes in thermal extremes of the climate of Poland in 1951–2010 are examined. Warm extremes have become more frequent, while cold extremes have become less frequent. In the warming climate of Poland, the increase in the number of extremely warm days in a year and the decrease in the number of extremely cold days in a year have been observed. Also the increase of the maximum number of consecutive hot days in a year and the decrease of the maximum number of consecutive very cold and extremely cold days in a year have been observed. However, the trends are not of ubiquitous statistic significance, as the natural variability is strong.     

Assessment of methods for mapping snow cover from MODIS

Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from −0.16 to 0.04 while differences for MOD10A1 fractional range from −0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly.     

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.     

Mapping of Japanese areas susceptible to snow cover change

Abstract

Many of the Japanese regions subject to seasonal snow cover are characterized by low elevations and relatively high winter temperatures. A small change in winter temperatures could render many of these areas susceptible to snow cover change and consequently affect water resources management. This paper describes a climatological approach combined with an AGCM output to identify the regions and main river basins most sensitive to snow cover change in the case of climate change in Japan. It was found that a 1°C rise in temperature during the winter season could increase the snow-free area of Japan by 6%. The snow cover of Tohoku region and Mogami and Agano river basins was found to be the most sensitive to climate change. The AGCM output for a future scenario presents a reduction in total snowfall and an earlier peak in snowmelt for all regions.

Editor Z.W. Kundzewicz

Citation Chaffe, P.L.B, Takara, K, Yamashiki, Y, Apip, Luo, P., Silva, R.V., and Nakakita, E., 2013. Mapping of Japanese areas susceptible to snow cover change. Hydrological Sciences Journal, 58 (8), 1718–1728.     

An analysis of snow cover patterns in a small alpine catchment

Snow cover patterns in a 9.4 km² basin in the Austrian Alps are examined during spring and summer 1989. Digital mono-plotting from oblique aerophotographs is used for mapping. on the basis of a square grid with 25 m spacing, snow cover as mapped during nine surveys is analysed as a function of elevation and slope. During winter conditions the snow cover is found to be much better related to these terrain features than during the late ablation period.     

Seasonal snow cover decreases young water fractions in high Alpine catchments

Estimation of young water fractions (F_yw), defined as the fraction of water in a stream younger than approximately 2–3 months, provides key information for water resource management in catchments where runoff is dominated by snowmelt. Knowing the average dependence of summer flow on winter precipitation is an essential context for comparing regional drought severity and provides the hydrological template for downstream water users and ecosystems. However, F_yw estimation based on seasonal signals of stable isotopes of oxygen and hydrogen has not yet explicitly addressed how to parsimoniously include the seasonal shift of water input from snow. Using experimental data from three high-elevation, Alpine catchments (one dominated by glacier and two by snow), we propose a framework to explicitly include the delays induced by snow storage into estimates of F_yw. Scrutinizing the key methodological choices when estimating F_yw from isotope data, we find that the methods used to construct precipitation input signals from sparse isotope samples can significantly impact F_yw. Given this sensitivity, our revised procedure estimates a distribution of F_yw values that incorporates a wide range of possible methodological choices and their uncertainties; it furthermore compares the commonly used amplitude ratio approach to a direct convolution approach, which circumvents the assumption that the isotopic signals have a sine curve shape, an assumption that is generally violated in snow-dominated environments. Our new estimates confirm that high-elevation Alpine catchments have low F_yw values, spanning from 8 to 11%. Such low values have previously been interpreted as the impact of seasonal snow storage alone, but our comparison of different F_yw estimation methods suggests that these low F_yw values result from a combination of both snow cover effects and longer storage in the subsurface. In contrast, in the highest elevation, glacier dominated catchment, F_yw is 3–4 times greater compared to the other two catchments, due to the lower storage and faster drainage processes. A future challenge, capturing spatio-temporal snowmelt isotope signals during winter baseflow and the snowmelt period, remains to improve constraints on the F_yw estimation technique.     

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.     

Eurasian snow cover and seasonal forecast of Indian summer monsoon rainfall

Abstract

This paper presents the relationship between Indian summer monsoon total rainfall and two parameters from Eurasian snow cover, one being the winter snow cover extent and the other the area of spring snowmelt. Satellite-derived Eurasian snow cover extent and Indian monsoon rainfall data were obtained from the NOAA/NESDIS and the India Meteorological Department (IMD) for the period 1966–1985. Seasonal cyclic variations of snow cover showed a higher swing in both the winter and the spring seasons of the cycle as compared to the remaining seasons of the year in the lower region of the cycle. The established inverse relation between winter snow cover and monsoon rainfall during June to September is further extended. Winter snow cover is very strongly correlated with spring snowmelt over Eurasia. Spring snowmelt area is obtained by subtracting the May snow cover extent from that of the previous February. The variations of spring snowmelt were also compared with Indian total monsoon rainfall. The detected correlation is stronger between snowmelt and monsoon rainfall than between the winter snow cover and the monsoon rainfall. There is also a significant multiple correlation among winter snow cover, spring snowmelt and monsoon rainfall. Lastly, a significant multiple correlation suggested a multiple regression equation which might improve the climatic prediction of monsoon rainfall over India.     

Chemical composition of snow cover in the taiga zone of the Komi Republic

Quantitative chemical analysis of snow cover were carried out in background territories of the southern, middle, and northern taiga of Komi Republic. The snow cover in the taiga zone of the northeastern European Russia (Komi Republic) shows low mineralization and acid pH. The acidity of melt water is caused by the predominance of strong mineral acids and deficiency of neutralizing compounds. A statistically reliable latitudinal differentiation was revealed in the distribution of macro- and microelements in the snow from the south to the north.     

Distribution and composition of suspended matter in water and snow cover in Kaliningrad Bay

Field data, collected by the authors during winter expedition studies in Kaliningrad Bay, are analyzed. Notwithstanding the presence of ice cover, considerable advective processes are shown to be taking place in the bay. Seawater enters the bay, spreading in the bottom horizon all over the water area. Suspended sediment concentration in under-ice period is on the average three times less than that in the ice-free period. The space and time variations of its quantitative and qualitative composition under ice are mostly governed by two factors—the effect of the sea and river runoff. The values of vertical fluxed of eolian material to the bay surface in winter vary from 0.5 to 2.9 with the average of 1.7 mg m^–2 day^–1.     

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.     

Seismic wave velocities in the sedimentary cover of Poland: Borehole data compilation

A knowledge of seismic wave velocities in the sedimentary cover is of great importance for interpreting reflection and refraction seismic data, deep seismic soundings and regional and global seismic tomography. This is particularly true for regions characterized by significant thicknesses and a complex sedimentary cover structure. This paper presents the results of an analysis of seismic P-wave velocities in the sedimentary cover of Poland, a complex area of juxtaposition of major tectonic units: the Precambrian East European Craton, the Palaeozoic Platform of Central and Western Europe, and the Alpine orogen represented by the Carpathian Mountains. Based on vertical seismic profiling data from 1188 boreholes, the dependence of velocity versus depth was determined for regional geological units and for successions from the Tertiary and Quaternary to the Cambrian. The data have been approximated by polynomials, and velocity-depth formulas are given down to 6000 m depth. The velocities in the sedimentary cover have been compared with those from other areas in Europe.     

Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia

Sublimation from thin snow cover at the edge of the Eurasian cryosphere in Mongolia was calculated using the aerodynamic profile method and verified by eddy covariance observations using multiple‐level meteorological data from three sites representing a variety of geographic and vegetative conditions in Mongolia. Data were collected in the winter and analysed from three sites. Intense sublimation events, defined by daily sublimation levels of more than 0·4 mm, were predominant in their effect on the temporal variability of sublimation. The dominant meteorological elements affecting sublimation were wind speed and air temperature, with the latter affecting sublimation indirectly through the vapour deficit. Seasonal and interannual variations in sublimation were investigated using long‐interval estimations for 19 years at a mountainous‐area meteorological station and for 24 years at a flat‐plain meteorological station. The general seasonal pattern indicated higher rates of sublimation in both the beginning and ending of the snow‐covered period, when the wind speed and vapour deficit were higher. Annual sublimation averaged 11·7 mm at the flat‐plain meteorological station, or 20·3% of the annual snowfall, and 15·7 mm at the site in the mountains, or 21·6% of snowfall. The sum of snow sublimation and snowmelt evaporation represented 17 to 20% of annual evapotranspiration in a couple observation years. Copyright © 2008 John Wiley & Sons, Ltd.     

Organic matter in snow cover in the influence zone of emissions from a pulp-and-paper mill

Qualitative and quantitative analysis of organic components of snow cover is carried out in the influence zone of emissions from a pulp-and-paper mill. The contribution of organic compounds to the pool of emissions of chemical pollutants during the period of snow cover formation is assessed. Criteria for establishing the pollution of natural complexes by organic substances in the influence zone of emissions from a pulp-and-paper mill are suggested.     

Sensitivity of the French Alps snow cover to the variation of climatic variables

In order to study the sensitivity of snow cover to changes in meteorological variables at a regional scale, a numerical snow model and an analysis system of the meteorological conditions adapted to relief were used. This approach has been successfully tested by comparing simulated and measured snow depth at 37 sites in the French Alps during a ten year data period. Then, the sensitivity of the snow cover to a variation in climatic conditions was tested by two different methods, which led to very similar results. To assess the impact of a particular “doubled CO₂” scenario, coherent perturbations were introduced in the input data of the snow model. It was found that although the impact would be very pronounced, it would also be extremely differentiated, dependent on the internal state of the snow cover. The most sensitive areas are the elevations below 2400 m, especially in the southern part of the French Alps.     

Spatio‐temporal variability of Great Lakes basin snow cover ablation events

In the Great Lakes basin of North America, annual run‐off is dominated by snowmelt. This snowmelt‐induced run‐off plays an important role within the hydrologic cycle of the basin, influencing soil moisture availability and driving the seasonal cycle of spring and summer lake levels. Despite this, relatively little is understood about the patterns and trends of snow ablation event frequency and magnitude within the Great Lakes basin. This study uses a gridded dataset of Canadian and United States surface snow depth observations to develop a regional climatology of snow ablation events from 1960 to 2009. An ablation event is defined as an interdiurnal snow depth decrease within an individual grid cell. A clear seasonal cycle in ablation event frequency exists within the basin and peak ablation event probability is latitudinally dependent. Most of the basin experiences peak ablation frequency in March, while the northern and southern regions of the basin experience respective peaks in April and February. An investigation into the interannual frequency of ablation events reveals ablation events significantly decrease within the northeastern and northwestern Lake Superior drainage basins and significantly increase within the eastern Lake Huron and Georgian Bay drainage basins. In the eastern Lake Huron and Georgian Bay drainage basins, larger ablation events are occurring more frequently, and a larger impact to the hydrology can be expected. Trends in ablation events are attributed primarily to changes in snowfall and snow depth across the region.     

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.     

Energy fluxes and melt rate of a seasonal snow cover in the Moroccan High Atlas

ABSTRACT

In this study, we used an energy balance model and two simple methods based on readily available data to identify the processes driving the point-scale energy and mass balance of the snowpack. Data were provided from an experimental site located at 3200 m. All models were evaluated by comparing observed and modelled snow water equivalents. Performances are variable from one season to the next and the energy balance model gives better results (mean of root mean square error, RMSE = 25 mm and r² = 0.90) than the two simplified approaches (mean of RMSE = 54 mm and r² = 0.70). There are significant amounts of snow sublimation but they are highly variable from season to season, depending on wind conditions (between 7 and 20% of the total). While the main source of energy for melting is net radiation, the amount of heat brought by sensible heat flux is significant for two of the most windy snow seasons.

Editor Z.W. Kundzewicz Associate editor not assigned