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The author describes the methods and results of current climate change mapping in the Ural region. The maps of temperature and water vapor pressure dynamics (for 1951–2010) and also precipitation amount changes (for 1966–2010) are presented. The maps of temperature and water vapor pressure are created basing upon spline interpolation of weather stations data, taking into account altitude-dependent regression. The altitude gradient of temperature and water vapor pressure was estimated using reanalysis data. The maps of precipitation amount were created basing upon spline interpolation combined with multiple linear regression model. Altitude and large-scale slope and aspect are used as independent variables. Accuracy assessment is carried out by cross-validation method. Basing upon the created maps, it is shown that the average annual temperature increased by 0,7–1,2° between 1951–1980 and 1981–2010 throughout the Ural region. In the North-Western Ural (i. e. in Komi Republic) the rise of average annual temperature is less significant, than in other parts of the mentioned area. The increase of water vapor pressure is also observed across the Urals. The changes of precipitation amount between 1966– 1995 and 1981–2010 are multidirectional. In general, the precipitation amount increased in the western part of Ural. At the same time, the precipitation amount decreased in some regions of Eastern Urals.

The paper presents a series of maps of extreme climatic characteristics for the Ural region and their changes under climate warming observed in last decades. We calculate threshold, absolute and percentile-based indices with the use of daily temperature and precipitation dataset of 99 weather stations of Roshydromet. Extreme climatic characteristics were averaged by moving 30-year periods from 1951 to 2010 for temperature and from 1966 to 2015 for precipitation. The regression-based interpolation was used for mapping climatic extremes taking into consideration the influence of topography. Elevation and general curvature of the terrain are considered as independent variables. In addition, the changes of extreme characteristics between the 30-year periods were estimated. As a result, a series of maps of temperature and precipitation extremes for the Ural region has been created. The maps present not only spatial distribution of the climatic extremes, but also regional features of their changes under climate warming. In general, the revealed changes in extremes in the Ural region correspond to the trends observed on the most of the territory of Russia. There is a substantial decrease of the number of extremely cold days in winter, and the minimum winter temperature has a strong positive trend (up to 1-5°C/30 years). The maximum temperature in summer has a positive trend in most of the territory, but the increase rate does not exceed 2°C between 1951–1980 and 1981–2010. The precipitation extremes also increased up to 0.5-1.5 mm when comparing 1966–1995 and 1985–2015 periods.

The article describes a method of mapping climatic parameters (i. e. frequency and intensity) of severe weather events, based on the interpolation of weather station data. We applied regression-based interpolation, using some geographical variables (altitude, slope angle and curvature) for modeling their spatial distribution. Statistically significant correlations between geographical and climatic variables were selected based on regression analysis. They have been used to create a maps of frequency, average and maximum intensity of severe weather events for Perm region (160 600 km2). The proposed method allows to create more accurate and detailed maps of spatial distribution of severe weather events than traditional deterministic and geostatistical interpolation methods. The reliability of the results confirmed by a weather station observations and comparisons with MODIS satellite images thematic products. Significant advantage of proposed method is also its applicability to the risk assessment of severe weather events in ungauged site, or in ungauged areas.

The paper presents the current state of the atlas mapping of climate change. It is shown, that the atlases of climate change are absent in Russia, but the modern web-GIS technologies provide the possibility of its creation and availability for many users. We present the structure and content 1 Пермский государственный национальный исследовательский университет, ул. Букирева, д.

В статье рассматриваются возможности применения многолетних данных космической съёмки Landsat и Sentinel-2 для мониторинга экологической ситуации в угледобывающих районах. Исследования проведены на примере территории ликвидированного Кизеловского угольного бассейна (Пермский край), где в течение последних 15-20 лет наблюдается экстремальное загрязнение рек водами самоизливов шахт и стоками с отвалов с высокой концентрацией железа, алюминия и ряда тяжёлых металлов. В качестве исходной информации использован многолетний ряд снимков Landsat и Sentinel-2 за 1987-2017 гг., а также результаты гидрохимического мониторинга за поверхностными водными объектами, полученные в 2006-2013 гг. Для оценки степени загрязнения кислыми водами предложен нормализованный разностный индекс, основанный на значениях яркости в синем и красном диапазонах спектра. Проведено сопоставление значений предложенного индекса с данными о концентрации железа в воде и выявлена статистически значимая корреляция для одной из двух рассмотренных рек (р. Яйвы). Проанализирована динамика предложенного индекса за 30-летний период. Показано, что значения индекса для рек Яйвы и Косьвы в ряде случаев находятся в противофазе, что связано с различием в режиме излива шахтных вод в эти реки. Также на исследуемой территории выявлен ряд участков деградации почвенно-растительного покрова (на общей площади около 20 га) вследствие загрязнения кислыми водами. Установлено, что наиболее крупные из этих участков появились в период 2006-2010 гг., после чего процесс деградации приостановился.

It is performed an assessment of the use of daily precipitation forecasts of global numerical weather prediction (NWP) models GFS (U.S.), GEM (Canada), SLAV (Russia) and ICON (Germany) as input data for snow accumulation and melt modelling in the Kama river basin for two cold seasons. It is shown, that maximum snow water equivalent (SWE) calculated on the basis of NWP models output has an error less than 27% of the measured values, in the conditions of 2017-2018 snow accumulation season. However, this is preliminary assessment, which requires verification by several seasons. It is rather difficult to conclude which model provides highest accuracy of SWE calculation, because each of them has its specific limitations. In 2018-2019 cold season, we additionally obtained ICON model data, which provides the most accurate forecast of precipitation. The simulated SWE and meltwater outflow data are published on the online web map service.

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.