Snow Cover Time Series Research Articles

Multi-decadal observations in the Alps reveal less and wetter snow, with increasing variability

Snowpack is an important temporal water storage for downstream areas, a potential source of natural hazards (avalanches or floods) and a prerequisite for winter tourism. Here, we use thousands of manual measurements of the water equivalent of the snow cover (SWE) from almost 30 stations between 1,200 and 2,900 m a.s.l. from four long-term monitoring programs (earliest start in 1937) in the center of the European Alps to derive daily SWE based on snow depth data for each station. The inferred long-term daily SWE time series were analyzed regarding spatial differences, as well as potential temporal changes in variability and seasonal averages during the last 7 decades (1957–2022). The investigation based on important hydro-climatological SWE indicators demonstrates significant decreasing trends for mean SWE (Nov-Apr) and for maximum SWE, as well as a significantly earlier occurrence of the maximum SWE and earlier disappearance of the continuous snow cover. The anomalies of mean SWE revealed that the series of low-snow winters since the 1990s is unprecedented since the beginning of measurements. Increased melting during the accumulation period below 2000 m a.s.l is also observed–especially in the most recent years–as well as slower melt rates in spring, and higher day-to-day variability. For these trends no regional differences were found despite the climatological variability of the investigated stations. This indicates that the results are transferable to other regions of the Alps.

Reanalysis-based contextualization of real-time snow cover monitoring from space

Satellite remote sensing provides real-time information on the extent of the snow cover. However, the period of record is generally too short to build a reference climatology from these data alone, preventing their use as climatic indicators. Here we show that reanalysis data can be used to reconstruct a 30 year snow cover time series that fits well with the satellite observations. This climatology can then be used to put the current state of the snow cover into perspective. We implemented this approach to provide real-time information on the snow cover area in the Alps through a web application.

On the Direct Calculation of Snow Water Balances Using Snow Cover Information

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

MODSNOW-Tool: an operational tool for daily snow cover monitoring using MODIS data

Spatially distributed snow cover information is important for the assessment of climate-related variability of water resources and for calibration and validation of hydrological models in snow-dominated regions. Near-real-time snow cover data can be valuable for short term to seasonal streamflow prediction. Such information can be extracted using remote sensing techniques with good accuracy. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data, meanwhile available for more than 15 years, have been shown to be useful for monitoring snow cover extent in remote areas. This data can be processed and used with only 2-day delay, which is sufficient for many water resources management purposes. However, processing remote sensing data require knowledge and computational skills to handle large amounts of data. Moreover, cloud obscuration in optical remote sensing such as MODIS may lead to data gaps. These limitations impede the use of the freely available MODIS data for water resources management in developing countries, particularly in snow-dominated mountainous regions. To overcome this, we present the all-in-one software package MODSNOW-Tool. It processes raw MODIS data and eliminates cloud cover using advanced cloud removal algorithms. The ready-to-use output of the MODSNOW-Tool is a cloud-free snow cover map and a daily report, which includes spatiotemporal snow statistics for pre-defined river basins. The accuracy of cloud-eliminated MODSNOW snow cover maps was validated for 84 almost cloud-free days in the Karadarya river basin in Central Asia, and an average accuracy of 94 % was achieved. The MODSNOW-Tool can be used in operational and non-operational mode. In the operational mode, the tool is set up as a scheduled task on a local computer allowing automatic execution without user interaction and delivers snow cover maps on a daily basis. In the non-operational mode, the tool can be used to process historical time series of snow cover maps from MODIS.

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000–2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

Extracting Snow Cover Time Series Data from Open Access Web Mapping Tile Services

The probability of the presence of snow cover at a given location over time is a critical input to hydrologic simulation models in snowpack-driven watersheds. While a number of open access web mapping tile services exist for viewing images of current and historical snow cover over large regions, no equally accessible tools exist for extracting numerical time series data of snow cover probability defined at particular point locations. This article presents the design, development, and testing of a new open source script and web application for snow cover probability time series extraction from map images. The script is deployed as a web app using the Tethys framework making it accessible to novice users through a user interface. A WaterML web-API gives access to third-party applications for automation and embedding in modeling tools. The full design of the script is presented such that it can serve as a model for similar or extended tools that may be developed by others. A set of use case experiments is presented demonstrating the full functionality of the script and its limitations, and an example application for ground validation of the Moderate Resolution Imaging Spectroradiometer snow cover dataset is discussed.

Snow Cover Distribution in the Aksu Catchment (Central Tien Shan) 1986–2013 Based on AVHRR and MODIS Data

Variability in snow cover strongly influences mass budgets of glaciers, permafrost distribution, and seasonal discharge of rivers. In times of a changing climate, the spatio-temporal patterns of snow cover are of high interest. In this study, snow cover time series for the Aksu catchment in Central Tien Shan have been generated from optical remote sensing imagery. The analyses span a period between 1986 and 2013 and imbed Advanced Very High Resolution Radiometer (AVHRR) level 1b scenes, which were classified using a dichotomous decision scheme, as well as the preprocessed Moderate Resolution Imaging Spectrometer (MODIS) snow cover product. High congruence of the results could be achieved in spite of different sensors involved. However, a small bias appears especially at high elevations. The results from 2000 to 2013 reveal that snow accumulation begins in October and melting starts in March. Above an elevation of around 5200 m a.s.l., permanent snow cover can be expected, which is mirrored by a zonal mean of more than 85% of snow for the whole period 1986–2013. Anomalies are very indicative and reveal a high interannual variability of snow cover in terms of quantity and spatial distribution. Change detection of snow cover probability (SCP) shows a slight decrease in lower altitudes up to 4000 m a.s.l. and an opposite trend above. However, the negative trends are not significant. Significant gradients have been found only at high elevations where the two data sources could not perfectly be harmonized. Comparisons with climatic variables show a similar temporal behavior of SCP and temperatures.

Characterization of the time dynamics of monthly satellite snow cover data on Mountain Chains in Lebanon

• MODIS-based monthly means of snow cover on Lebanon from 2000 to 2012 were analyzed. • The yearly periodicity was significantly detected by using a robust periodogram and wavelets. • The long-term trend accounting for the 33% of the total variance was revealed by using the SSA. In this study, the time dynamics of the monthly means of the snow cover have been on Lebanese Mountain Chains from 2000 to 2012, derived from the MODIS Aqua/Terra satellite snow products was analyzed. This represents the longest satellite-based snow cover time series produced for Lebanon so far. Field survey was also carried out over the last three years in order to measure the in-situ snow/water equivalent and depth in different localities. Analyzing the regime of the snow cover in Mount-Lebanon (Western Mountain Chains) region, it was found that: (i) snowmelt accounts for about 31% of the rivers and springs discharge in Lebanon; (ii) consecutive peaks in the snow cover time series, representing the change-point between accumulation phase and ablation phase are present in three different patterns (edged, non-edged and double peaked); (iii) the areal snow coverage has big diversity between different years; (iv) the annual periodicity represents the most statistically significant and predominant frequency of the series contributing for about the 40% of the total variance of the snow cover series; (v) the long-term trend, totally hidden by the more powerful yearly component and detected by using the singular spectrum analysis (SSA), accounts for about the 33% of the total variance of the series; (vi) the long-term trend shows an apparent cyclic behavior with an estimated period (interval between the two minima) of about nine years; (vii) the comparison of the long-term trend with the North Atlantic Oscillation (NAO) monthly index reveals that the minima in 2009–2010 of the SSA long-term component coincides with a persistent negative phase in the NAO Index.

Examining spatial and temporal variability in snow water equivalent using a 27 year reanalysis: Kern River watershed, Sierra Nevada

AbstractThis paper used a data assimilation framework to estimate spatially and temporally continuous snow water equivalent (SWE) from a 27 year reanalysis (from water year 1985 to 2011) of the Landsat‐5 record for the Kern River watershed in the Sierra Nevada, California. The data assimilation approach explicitly treats sources of uncertainty from model parameters, meteorological inputs, and observations. The method is comprised of two main components: (1) a coupled land surface model (LSM) and snow depletion curve (SDC) model, which is used to generate an ensemble of predictions of SWE and fractional snow cover area (FSCA) for a given set of prior (uncertain) inputs, and (2) a retrospective reanalysis step, which updates estimation variables to be consistent with the observed fractional snow cover time series. The final posterior SWE estimate is generated from the LSM‐SDC using the posterior estimation variables consistently with all postulated sources of uncertainty in the model, inputs, and observations. A reasonable agreement was found between the SWE reanalysis and in situ SWE observations and streamflow data. The data set was studied to evaluate factors controlling SWE spatial and temporal variability. Elevation was found to be the primary control on spatial patterns of peak‐SWE and day‐of‐peak. The easting coordinate had additional explanatory power, which is hypothesized to be related to rain shadow effects due to the prevailing storm track directions. The spatial patterns were found to be interannually inconsistent. However, drier years and lower elevations were found more variable than wetter years and higher elevations, respectively. Only a very small percentage of the Kern River watershed had a significant trend in peak‐SWE and day‐of‐peak. Trends deemed to be significant were found to be positive (peak‐SWE is increasing and day‐of‐peak occurs later) at higher elevations, but negative (peak‐SWE is decreasing and day‐of‐peak occurs earlier) at lower elevations. The reanalysis approach proved to be useful in terms of identifying subwatershed variability and trends, and could be extended to larger regions and areas where in situ data are sparse or unavailable.

Evaluation of MODIS snow cover and cloud mask and its application in Northern Xinjiang, China

Using five-year (2001–2005) ground-observed snow depth and cloud cover data at 20 climatic stations in Northern Xinjiang, China, this study: 1) evaluates the accuracy of the 8-day snow cover product (MOD10A2) from the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite, 2) generates a new snow cover time series by separating the MODIS cloud masked pixels as snow and land, and 3) examines the temporal variability of snow area extent (SAE) and correlations of air temperature and elevation with SAE. Results show that, under clear sky conditions, the MOD10A2 has high accuracies when mapping snow (94%) and land (99%) at snow depth ≥ 4 cm, but a very low accuracy (< 39%) for patchy snow or thin snow depth (< 4 cm). Most of the patchy snow is misclassified as land. The mean accuracy of the cloud mask used in MOD10A2 for December, January and February is very low (19%). Based on the ratio of snow to land of ground observations in each month, the new snow cover time series generated in this study provides a better representation of actual snow cover for the study area. The SAE (%) time series exhibits similar patterns during six hydrologic years (2001–2006), even though the accumulation and melt periods do not exactly coincide. The variation of SAE is negatively associated with air temperature over the range of − 10 °C to 5 °C. An increase in elevation generally results in longer periods of snow cover, but the influence of elevation on SAE decreases as elevation exceeds 4 km in the Ili River Watershed (IRW). The number of days with snow cover shows either a decreasing trend or no trend in the IRW and the entire study area in the study period. This result is inconsistent with a reported increasing trend based on limited in situ observations. Long-term continuance of the MODIS snow cover product is critical to resolve this dilemma because the in situ observations appear to undersample the region.

Corrections to “Eurasian snow cover variability and northern hemisphere climate predictability”

Table 1. Eurasian snow cover and SSTs as predictors (SON) and concurrent (DJF) of hemispheric and regional climate indices. Observed climate indices are shown in rows all climate indices are for DJF. Climate indices are, ZMW, NAO, Scandinavia or Eurasian One Pattern (SCA), West Pacific Pattern (WPP), Pacific/North American Pattern (PNA). NAO and SCA are dominant modes of variability in North Atlantic and PNA and WPP are dominant modes of variability in North Pacific in winter /Barnston and Livezey, 1987]; also shown in square brackets are the autocorrelations of each DJF climate index with its own preceding SON index. Statistical significance greater than 95% confidence level in italics and greater than 99% confidence level in bold. The correlation coefficient included in parentheses corresponds to the statistic excluding E1-Nifio years 1972, 1982, 1986, 1991. We also computed the correlations between the detrended time-series of snow cover and the zonal mean wind, NAO and SCA indices and no changes were found in the statistical significance of the correlations from those presented in the table.

Statistical regression analysis of response of northern mid and upper tropospheric circulation to winter Eurasian snow cover effects

Response for anomalous circulation in relation to snow coverage is derived by use of regression coefficients in dealing with the Eurasian snow cover time series and northern mid and upper tropospheric height data. Results show that not only does the regression response pattern represent the correlation between snow coverage and circulation change but reflects the amplitude strength in correlation cores as well, with a greater amplitude of the circulation response in the mid troposphere and remarkable equivalent barotropy in the mid to upper levels, and that the response of winter—summer circulations to winter snow cover displays noticeable stationary planetary—scale wavetrain, leading to NEUP and NPNA patterns in winter, slightly changed forms in spring months and LEU and EANA in summer time. Also, the study demonstrates that the rasponse-produced wavetrain is marked by branch and propagates energy in a wave—front manner with the energy trapped at subtropical latitudes.