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The main scope of this study was to present the possibility of developing a heavy rainfall regional model, based on a probabilistic approach. Following the methods and conclusions of the scientific and technical work of the research team in the Upper and Middle Oder basin, carried out on rainfall data between 1961 and 2010, it was found that the generalized exponential distribution enables a satisfactory probabilistic description of heavy rainfall, and the results of the models constructed on its basis are obtained in an accessible and not time-consuming way. The regional model presented in the article sufficiently describes the empirical heavy rainfall at eight analyzed meteorological stations, especially in the range of rainfall amounts occurring most frequently from a statistical point of view, i.e. with the p-value from 0.3 to 1.0.

Quite recently, freely available mean daily discharge series representing the territory of Czechia were complemented by ten daily series of nine climate variables, spanning usually from 1961 to the present. Their length thus allows the analyses similar to those conducted approximately a year ago in relation to the long series of discharge. Besides this possibility, the current paper goes further and shows how these long series (including discharge) can be used in order to assess the presence of the so-called Hurst phenomenon. Using a wavelet-based approach, several important differences in the wavelet spectra and values of the Hurst exponent (as well as the uncertainty in their estimation) were found when focusing on discharge, air temperature and precipitation. Furthermore, using the stationarity and unit root tests, it was revealed that, unlike precipitation, air temperature and discharge series may be characterized by long-memory processes in many cases. Finally, as the paper is devoted mainly to students, a short R script is presented in Appendix that makes it easier to work with the online files offered by Czech climatologists.

Most hydrometeorological data from Czechia are still provided for a fee. This especially applies to time series with a finer step than monthly. The fact that the data must be paid with respect to an expert’s appraisement and that the proper licence agreement ratification has to be performed causes a considerable delay in the data transfer to potential customers, unfortunately including scientists as well. Naturally, this time-consuming process is unpleasant to the experts on both sides. Due to a substantial rise of university students’ requests for data, the Czech Hydrometeorological Institute’s hydrologists launched a website from which long mean daily discharge series representing ten selected water-gauging stations can be downloaded. Besides other assessments, the series may play an important role when studying climate change impacts on water resources in Czechia. Therefore, the objective here was to extract from these series some preliminary information on long-term changes such as abrupt and gradual trends caused either by the construction of reservoirs or by climate variability itself. The main tool used was nonparametric trend analysis. Mainly different months were of interest so as to determine if there have been recorded some changes in seasonality. The results may be easily expanded by students.

The effects of possible climate change on water resources in prescribed areas (e.g. river basins) are intensively studied in hydrology. These resources are highly dependent on precipitation totals. When focusing on long-term changes in climate variables, one has to rely on station measurements. However, hydrologists need the information on spatial distribution of precipitation over the areas. For this purpose, the spatial interpolation techniques must be employed. In Czechia, where the addition of elevation co-variables proved to be a good choice, several GIS tools exist that are able to produce time series necessary for climate change analyses. Nevertheless, these tools are exclusively based on commercial software and there is a lack of free-of-charge tools that could be used by everyone. Here, selected free-of-charge geostatistical tools were utilized in order to produce monthly precipitation time series representing six river basins in the Ore Mountains located in NW Bohemia, Czechia and SE Saxony, Germany. The produced series span from January 1961 to December 2012. Rain-gauge data from both Czechia and Germany were used. The universal kriging technique was employed where a multiple linear regression (based on elevation and coordinates) residuals were interpolated. The final time series seem to be homogeneous.

Changes in the frequency, magnitude, and seasonality of hydrological extremes are one of the expected consequences of climate change. Recent years suggested an increasing risk of drought occurrence and restricted water supply even in usually humid mountain regions. Our research is focused on the evaluation of regional climate change and its effect on hydrological drought characteristics in headwater areas in the Ore Mountains along the Czech/German border. The main aim was to evaluate and compare streamflow regime changes and trends in long‐term hydrological and climatic time series within selected sub‐catchments of the Svatava (Zwota) River, the Rolava River, and the Načetínský Brook (Natzschung). A comparative statistical analysis for the 1967–2018 period was performed using various homogeneity and trend tests. Longer runoff time series with earlier beginnings were examined for two catchments as well. The results pointed to significant, continuous warming in the region which is accompanied by decreasing trends in snow cover depths in the colder half of the year. Drought indices based on runoff data were derived in the Indicators of Hydrologic Alterations (IHA 7.1) software environment. The seasonality of hydrological drought occurrence changed markedly after 1992, when most severe drought events started to concentrate between August and October in the study area. Increasing trends in deficit volumes were found in two catchments characterized by lower elevation. Furthermore, there was a significant decrease in average and minimum flows in spring and summer months in all catchments observed between 1967 and 2018 despite relatively stable amounts of precipitation and land cover. [ABSTRACT FROM AUTHOR]

The streamflow seasonality in mountain catchments is often influenced by snow. However, a shift from snowfall to rain is expected in the future. Consequently, a decrease in snow storage and earlier snowmelt is predicted, which will cause changes not only in seasonal runoff distribution in snow-dominated catchments, but it may also affect the total annual runoff. The objectives of this study were to quantify (1) how inter-annual variations in snow storages affect spring and summer runoff, including summer low flows, and (2) the importance of snowmelt in generating runoff compared to rainfall. The snow storage, groundwater recharge and streamflow were simulated for 59 mountain catchments in Czechia in the period from 1980 to 2014 using a bucket-type catchment model. The model output was evaluated against observed daily runoff and snow water equivalent. Hypothetical scenarios were performed, which allowed for analysing the effect of inter-annual variations in snow storage on seasonal runoff separately from other components of the water balance. The results showed that 17 %–42 % (26 % on average) of the total runoff in the study catchments originates as snowmelt, despite the fact that only 12 %–37 % (20 % on average) of the precipitation falls as snow. This means that snow is more effective in generating catchment runoff compared to liquid precipitation. This was demonstrated by modelling experiments which showed that total annual runoff and groundwater recharge decrease in the case of a precipitation shift from snow to rain. In general, snow-poor years were clearly characterized by a lower snowmelt runoff contribution compared to snow-rich years in the analysed period. Additionally, snowmelt started earlier in these snow-poor years and caused lower groundwater recharge. This also affected summer baseflow. For most of the catchments, the lowest summer baseflow was reached in years with both relatively low summer precipitation and snow storage. This showed that summer low flows (directly related to baseflow) in our study catchments are not only a function of low precipitation and high evapotranspiration, but they are significantly affected by the previous winter snowpack. This effect might intensify drought periods in the future when generally less snow is expected. [ABSTRACT FROM AUTHOR]

Mountain catchments are largely influenced by snow, but it is expected that an increasing proportion of the precipitation will fall as rain in the future. Consequently, snow storage is expected to decrease, which, together with changes in snowmelt rates and timing, will cause changes in spring and summer runoff, mainly low flows and thus water availability. The objectives of this study were 1) to simulate the effect of changing snow storage on spring and summer runoff, mainly summer low flows and 2) to relate drought sensitivity to the inter-annual variations in snow storages at different elevations. The consideration of different elevations is crucial as snow storage and its potential change due to climate changes is highly variable with elevation. A bucket-type catchment model, HBV (in its version HBV-light), was applied to 60 mountain catchments in Czechia to simulate snow storage and streamflow in the period 1980–2014. The model performance was evaluated against observed daily runoff and snow water equivalent (SWE) using three objective functions. We also performed hypothetical simulations, which allowed us to analyze the effect of inter-annual variation in snow storage on seasonal low flows and annual runoff separately from other water balance components. This was done in the HBV snow routine using the threshold temperature TT that differentiates between snow and rain and sets the air temperature of snowmelt onset. By changing the TT, we can control the amount of accumulated snow and snowmelt timing, while other variables remain unaffected.The results showed that about 30% of the total runoff in our selected study catchments originates as snowmelt, despite the fact that only 24% of the precipitation is falling as snow. This means that snow is more effective to generate catchment runoff compared to liquid precipitation. This was proved also by modelling experiment which showed that total annual runoff decreases in the case of precipitation shift from snow to rain even in the case where the total amount of precipitation and evaporation remains unchanged. In general, snow-poor years are clearly characterized by a lower ratio of snow runoff to total runoff compared to snow-rich years in the analyzed period. Additionally, snowmelt started earlier in these snow-poor years and influenced total runoff for shorter period compared to the snow-rich years. Differences between individual catchments are strongly related to catchment elevation. Inter-annual variations in snow storages also affected summer baseflow, which is, besides snow, related to summer precipitation. For most of the catchments, the lowest summer baseflow was reached in years with both relatively low summer precipitation and snow storage. This showed that summer low flows (directly related to baseflow) are not only the function of low summer precipitation, but they are strongly affected by previous winter snowpack. This effect might intensify the summer low flows in the future when generally less snow is expected. [ABSTRACT FROM AUTHOR]