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Climate warming influences highly on snow cover regime in the midlatitudes. Snow cover conditions, in turn, affect human activity very much. The aim of this study was (a) to analyse spatial and temporal variability of snow cover duration, (b) to analyse spatial and temporal variability of the start and end dates of the period with the permanent snow cover, (c) to describe spatial, temporal and seasonal variability of median and maximum snow depth in Estonia and (d) to determine the presence of long-term changes and trends in these parameters during the period 1950/51–2015/16. Time series of daily snow depth at 22 stations for that period were processed in order to obtain reliable estimates of changes in the snow regime. Snow cover data are non-normally distributed, therefore, median and quartile range were used to describe the mean state and variability of snow cover. Only these dates were included into the analysis when snow cover was observed at least on 50% of days in the time series. Trend analysis was made using the Mann-Kendall test and trend values were found using the Theil-Sen's method. A large spatio-temporal variability of snow cover duration was found. The median number of days with snow cover at the 22 stations was 112, varying between 61 and 130 days. In the coastal regions of Estonia and especially on the western coast of Saaremaa Island snow cover duration has been much lower than in the continental part. The longest snow cover period is observed on uplands in south-eastern and north-eastern Estonia. It was found that, in the average, the period with the permanent snow cover in the continental Estonia begins on 19 December and ends on 18 March. There was a negative trend in snow cover duration due to the earlier snow melting in spring at the majority of stations. The end date of the permanent snow cover has shifted earlier by 10–30 days in 66 years and its duration has decreased accordingly. The maximum snow depth has been recorded on uplands of south-eastern Estonia with the median value 38 cm. There was a decreasing multiannual trend in snow depth from January to the end of March. Changes in snow depth were largest in the end of February and in March when the trend was statistically significant. In the average, snow depth has decreased by 0.5–1.5 cm per decade, i.e. by 2–9 cm throughout the whole study period. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes represent the largest freshwater basin on Earth, containing 21% of the world's surface fresh water by volume. Water level fluctuations are an on-going concern and have received considerable attention in the area. We present a trend analysis of meteorological (air temperature, cloud cover, and wind speed) and hydrological (precipitation, runoff, and evaporation) variables for Lake Michigan, Lake Huron, and Georgian Bay. Using the non-parametric Mann-Kendall test, our analysis identified significant upward trends in daily minimum air temperature, whereas daily maximum air temperature demonstrated weakly decreasing trends in space and time. Evaporation was found to be increasing from late spring until early fall and this pattern may be explained by the shortening of the ice/snow cover period, which results in faster warming of lake surface due to the induced variations in albedo feedback. Time-series analysis of the over-lake precipitation revealed mostly non-significant statistical trends. Recent temperature increases may have led to elevated winter runoff in the Great Lakes region, given that precipitation falls mainly as rain instead of snow. We also provide clear evidence of reduced cloud cover and wind speed. Our study offers critical insights into the patterns of within- and among-year variability of hydro-meteorological variables useful in elucidating the mechanisms that modulate water levels in the Great Lakes. [ABSTRACT FROM AUTHOR]

The present investigation was conducted to analyze the temporal patterns of snow cover area% (SCA%), air temperature, snowfall and river discharge in parts of Chenab basin, western Himalayas. The relationship of mean SCA% with mean air temperature and river discharge was also tested using Pearson's product-moment correlation at 95% confidence limit and further sensitivity analysis of river discharge to SCA and SCA to air temperature was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day surface reflectance product MOD09A1 was used to delineate SCA during the period 2000-2013. Moreover, variation in the lowest elevation from where snow cover area starts (LESCA) was also analyzed and its relationship with mean air temperature was also studied. Non-parametric method, Mann-Kendall test was employed to determine the trend in the SCA%, air temperature, snowfall and river discharge. The investigation carried out for three meteorological stations i.e. Batote, Reasi and Tandi revealed significant findings. At Batote and Reasi, statistically significant decreasing trends were observed over the period 2000 to 2012, for maximum, minimum and mean air temperature. Mean minimum SCA% exhibited a significant upward trend during 2000-2013 which is corroborated by the significantly increasing trend of mean annual snowfall (Tandi station) from 2000 to 2010. Further, significant decreasing trend of river discharge for the winter season at Batote station from 2000 to 2011 and decreasing trends in the maximum, minimum and mean air temperature at Batote and Reasi stations are also consistent with the increasing trend of SCA%. At both Batote and Reasi stations, mean SCA% exhibited significant negative relationship with the mean air temperature. On the other hand, LESCA exhibited positive correlation with the mean air temperature except in a few months, where negative relationship was seen. Sensitivity analysis of river discharge to SCA exhibited very low values of sensitivity coefficient in most of the months, indicating less sensitivity of river discharge to SCA. On the other hand, sensitivity coefficient of SCA to air temperature exhibited comparatively higher values which indicate SCA is more sensitive to air temperature. [ABSTRACT FROM AUTHOR]

ABSTRACT The daily snow cover data from Interactive Multisensor Snow and Ice Mapping System ( IMS) by National Oceanic and Atmospheric Administration's National Environmental Satellite Data and Information Service ( NOAA/ NESDIS) covering the 2004-2012 time period is used in this study. In the first part of the study, daily IMS product is validated over Turkey using daily ground truth snow depth measurements from 219 climatological and synoptic sites. The categorical statistics of probability of detection ( POD) and false alarm rates ( FAR) are provided both for whole Turkey and with respect to geographical sub-regions. The overall POD and FAR amounts for Turkey are obtained as 73.55 and 25.11%, respectively. The performed analysis indicates an increase in IMS accuracies as the snowpack depth at the stations increase. In the second part of the study, the nonparametric Mann-Kendall test is performed to analyse the snow cover trend over Turkey with respect to sub-regions considering the whole year and each season separately. A negative trend in snow-covered area percentage is obtained for Turkey at the significance level of α = 0.05. From the seasonal perspective for Turkey, negative trend for spring and summer seasons, positive trend for autumn season and combination of negative and no trend for winter season are obtained at the same significance level. Almost the same results are valid for the sub-regions with some local and seasonal variations. [ABSTRACT FROM AUTHOR]

Precipitation is considered to be a very important climatic element for the plain areas in Romania. Thus, variation in precipitation data ranges is more and more studied. In this paper total amounts of precipitation and the depth of the snow cover were analyzed. Annual, seasonal and monthly data were used to identify the general trends in the amount of precipitation for a period of 41 years (1965-2005) in 5 weather stations from the central part of Romanian Plain: Alexandria, Piteşti, Ro Fiorii de Vede, Turnu Măgurele and Videle. They are located in different areas: on the high plain in the North of the region, in the central area and on the Danube River banks. For snow cover only winter months series were analyzed. To detect and estimate trends in the time series, Mann-Kendall test for trend and Sen's slope estimates were used while for fluctuations in precipitations WASP cumulated curve was analyzed. Main conclusions that can be mentioned are: decreasing trends in total amounts of precipitation, especially for summer months and for annual values, but also with fluctuations that had upward trends in the first part of the analyzed period and than decreasing trends beginning with October 1981; increasing trends of the snow cover during winter months. [ABSTRACT FROM AUTHOR]

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR's Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM). [ABSTRACT FROM AUTHOR]