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Radar measurements are inherently affected by various meteorological and non-meteorological factors that may lead to a degradation of their quality, and the unwanted effects are also transferred into composites, i.e., overlapping images from different radars. The paper was aimed at answering the research question whether we could create ‘cleaner’ radar composites without disturbing features, and if yes, how the operational practice could take advantage of the improved results. To achieve these goals, the qRad and qPrec software packages, based on the concept of quality indices, were used. The qRad package estimates the true quality of the C-band radar volume data using various quality indices and attempts to correct some of the adverse effects on the measurements. The qPrec package uses a probabilistic approach to estimate precipitation intensity, based on heterogeneous input data and quality-based outputs of the qRad software. The advantages of the qRad software are improved radar composites, which offer benefits, among others, for aviation meteorology. At the same time, the advantages of the qPrec software are manifested through improved quantitative precipitation estimation, which can be translated into hydrological modeling or climatological precipitation mapping. Beyond this, the developed software indirectly contributes to sustainability and environmental protection—for instance, by enabling fuel savings due to the more effective planning of flight routes or avoiding runway excursions due to information on the increased risk of aquaplaning.

Low level wind shear is a hazardous meteorological phenomena that may seriously threaten the safety of the aircraft during the phase of landing and take-off. Automated, lidar-based systems for wind shear detection and alerting offer indispensable help to pilots to avoid landing in risky weather circumstances. Due to the efforts of the company MicroStep-MIS, the Bratislava Airport is one of the few (~15) airports worldwide and the first in Central Europe that has been equipped with a Doppler lidar. This remote sensing tool monitors the surrounding air space in a circle with a radius of 6‒7 km with high temporal and spatial resolutions (~100 m horizontally, scan frequency: 2 minutes). The elevation angle of the lidar’s laser beam is set to a constant of 3°, which corresponds to the angle of the glide path of the landing aircraft. In the current paper, we focus on a selected weather event that occurred on July 5th, 2018. It was a cold front that passed over the airport area, and showed several significant and abrupt changes in the wind field during its short existence (~2.5 hours). We further analyze the situation from the perspective of the meteorological radar, installed in the neighborhood of the airport, and estimate the low level wind shear according to the requirements of the ICAO.

The Single Europe Sky Air Traffic Management Research (SESAR) program develops and implements innovative technological and operational solutions to modernize European air traffic management and to eliminate the negative environmental impacts of aviation activity. This article presents our developments within the SESAR Solution &ldquo, Safety Support Tools for Avoiding Runway Excursions&rdquo, This SESAR Solution aims to mitigate the risk of runway excursion, to optimize airport operation management by decreasing the number of runway inspections, to make chemical treatment effective with respect to the environment, and to increase resilience, efficiency and safety in adverse weather situations. The proposed approach is based on the enhancement of runway surface condition awareness by integrating data from various sources. Dangerous windy conditions based on Lidar measurements are also discussed as another relevant factor in relation to runway excursions. The paper aims to explore four different data mining methods to obtain runway conditions from the available input data sources, examines their performance and discusses their pros and cons in comparison with a rule-based algorithm approach. The output of the SESAR Solution is developed in compliance with the new Global Reporting Format of the International Civil Aviation Organization for runway condition description to be valid from 2020. This standard is expected to provide concerned stakeholders with more precise information to enhance flight safety and environmental protection.

Low-level wind shear, i.e., sudden changes in wind speed and/or wind direction up to altitudes of 1600 ft (500 m) above-ground is a hazardous meteorological phenomenon in aviation. It may radically change the aerodynamic circumstances of the flight, particularly during landing and take-off and consequently, it may threaten human lives and the health of passengers, people at the airport and its surrounding areas. The Bratislava Airport, the site of this case study, is one of the few airports worldwide and the first in Central Europe that is equipped with a Doppler lidar system, a perspective remote sensing tool for detecting low-level wind shear. The main objective of this paper was to assess the weather events collected over a period of one year with the occurrences of low-level wind shear situations, such as vertical discontinuities in the wind field, frontal passages and gust fronts to increase the level of flight safety and protect human lives and health. The lidar data were processed by a computer algorithm with the main focus on potential wind shear alerts and microburst alerts, guided by the recommendations of the International Civil Aviation Organisation. In parallel, the selected weather events were analyzed by the nearby located meteorological radar to utilize the strengths of both approaches. Additionally, an evaluation of the lidar capability to scan dynamics of aerosol content above the airport is presented.

The work aims at analyzing the bivariate relationship between flood peaks and flood volumes, with a particular focus on the type and seasonality of flood generation processes. Instead of the usual approach that deals with an analysis of the annual maxima of flood events, the current analysis includes all independent flood events in a catchment. Flood events are considered independent when they originate from distinguishably different synoptic/meteorological situations. The target region is located in the northern part of Austria, and consists of 72 small and mid-sized catchments. On the basis of the discharge measurements with a time resolution of 1 h from the period 1976–2007, independent flood events were identified and were assigned to one of the three following flood generation type categories: synoptic floods, flash floods and snowmelt floods. These were subsequently divided into two seasons, thereby separating predominantly rainfall-fed and snowmelt-fed floods. Nine frequently-used copula types were locally fitted to the samples of the flood type and seasonal data. Their goodness-of-fit was examined locally as well as analyzed in a regional scope. It was concluded that (i) treating flood processes separately is beneficial for the statistical analysis; (ii) suitability patterns of acceptable copula types are distinguishably different for the seasons/flood types considered, (iii) the Clayton and Joe copulas shows an unacceptable performance for all the seasons/flood types in the region; (iv) the rejection rate of the other copula types depends on the season/flood type and also on the sample size; (v) given that usually more than one statistically suitable dependence model exists, an uncertainty analysis of the design values in the engineering studies resulting from the choice of model seems unavoidable; (vi) reducing uncertainty in the choice of model could be attempted by a deeper hydrological analysis of the dependence structure between flood peaks and volumes in order to give hydrological support to the decision on model's suitability in specific regions and for typical flood generation mechanisms.

This paper presents a method to identify intense warm season storms with convective character based on intensity thresholds and the presence of lightning, and analyzes their statistical properties. Long records of precipitation and lightning data at 4 stations and 10 min resolution in different climatological regions in Switzerland are used. Our premise is that thunderstorms associated with lightning generate bursts of high rainfall intensity. We divided all recorded storms into those accompanied by lightning and those without lightning and found the threshold I* that separates intense events based on peak 10 min intensity Ip ≥ I* for a chosen misclassification rate α. The performance and robustness of the selection method was tested by investigating the inter-annual variability of I* and its relation to the frequency of lightning strikes. The probability distributions of the main storm properties (rainfall depth R, event duration D, average storm intensity Ia and peak 10 min intensity Ip) for the intense storm subsets show that the event average and peak intensities are significantly different between the stations. Non-parametric correlations between the main storm properties were estimated for intense storms and all storms including stratiform rain. The differences in the correlations between storm subsets are greater than those between stations, which indicates that care must be exercised not to mix events of different origin when they are sampled for multivariate analysis, for example, copula fitting to rainfall data.

The study deals with estimates of return periods associated with the August 2010 heavy precipitation in northern Bohemia (Czech Republic), which resulted in flooding with enormous material damage and loss of lives, in the present climate and under climate change scenarios. We focus on the record-breaking 1-day and 2-day amounts at lower-elevation stations, exceeding 150 and 250 mm, respectively. The estimates of return periods are based on two methods of regional frequency analysis and they are compared with local (at-site) estimation. The regional methods consistently suggest that the August 2010 event was exceptional in view of past records, but the return levels decline substantially – by a factor of 2–4 – if parameters of the generalized extreme value distribution are allowed to vary in accordance with scenarios based on an ensemble of regional climate model projections for 2070–99. In spite of large uncertainty associated with future climate change scenarios, increased recurrence probability of such heavy precipitation events in the 21st century should be taken into account when designing and implementing flood risk prevention and mitigation measures.

This paper analyses the bivariate relationship between flood peaks and corresponding flood event volumes modelled by empirical and theoretical copulas in a regional context, with a focus on flood generation processes in general, the regional differentiation of these and the effect of the sample size on reliable discrimination among models. A total of 72 catchments in North-West of Austria are analysed for the period 1976–2007. From the hourly runoff data set, 25 697 flood events were isolated and assigned to one of three flood process types: synoptic floods (including long- and short-rain floods), flash floods or snowmelt floods (both rain-on-snow and snowmelt floods). The first step of the analysis examines whether the empirical peak-volume copulas of different flood process types are regionally statistically distinguishable, separately for each catchment and the role of the sample size on the strength of the statements. The results indicate that the empirical copulas of flash floods tend to be different from those of the synoptic and snowmelt floods. The second step examines how similar are the empirical flood peak-volume copulas between catchments for a given flood type across the region. Empirical copulas of synoptic floods are the least similar between the catchments, however with the decrease of the sample size the difference between the performances of the process types becomes small. The third step examines the goodness-of-fit of different commonly used copula types to the data samples that represent the annual maxima of flood peaks and the respective volumes both regardless of flood generating processes (the traditional engineering approach) and also considering the three process-based classes. Extreme value copulas (Galambos, Gumbel and Hüsler-Reiss) show the best performance both for synoptic and flash floods, while the Frank copula shows the best performance for snowmelt floods. It is concluded that there is merit in treating flood types separately when analysing and estimating flood peak-volume dependence copulas; however, even the enlarged dataset gained by the process-based analysis in this study does not give sufficient information for a reliable model choice for multivariate statistical analysis of flood peaks and volumes.

30 Pags., 2 Tabls., 10 Figs. The definitive version is available at: http://www.sciencedirect.com/science/journal/09218181, The study examines climate change scenarios of precipitation extremes over the western, central and part of the eastern Mediterranean region for the late 21st century (2070–99) in an ensemble of high-resolution regional climate model (RCM) simulations from the ENSEMBLES project. Precipitation extremes are considered at a wide range of time scales from hourly to multi-day amounts and in individual seasons (DJF, MAM, JJA, SON). We focus on (1) the dependence of the results on the time scale of precipitation aggregation, (2) seasonal differences, (3) uncertainties of the scenarios related to differences amongst the RCM simulations, and (iv) identification of regions and seasons in which the projected changes in precipitation extremes are particularly large and/or robust in the RCM ensemble. The examined ensemble of RCM simulations captures basic precipitation patterns for the recent climate (1961–90), including seasonal changes. Climate change scenarios for the late 21st century differ substantially for short-term (hourly) and multi-day (5-day and 15-day) precipitation extremes, mainly in the western Mediterranean. Projected increases in short-term extremes exceed those of daily and multi-day extremes, and occur even in regions and seasons in which mean precipitation is projected to decline. This change in the patterns of extreme precipitation may have important hydrological consequences, with increases in the severity of flash floods in a warmer climate in spite of the overall drying projected for the region. However, uncertainty of the scenarios of precipitation extremes related to within-ensemble variability is large. Consistency of the projected changes amongst the RCMs is highest in winter and lowest in summer, and generally it is higher for short-term than multi-day extremes., The study was supported by the Czech Science Foundation under project P209/10/2265 and by CGL2011–24185 financed by the Spanish Commission of Science and Technology (CICYT) and FEDER. Parts of the work were carried out during the stay of the first author at Estación Experimental de Aula Dei, Zaragoza, Spain, funded by Consejo Superior de Investigaciones Científicas (CSIC) and the Czech Science Foundation. The research team benefited also through interactions and support within the KLIMATEXT project (CZ.1.07/2.3.00/20.0086) funded by the European Social Fund, and Grupo de Excelencia E68 financed by the Aragón Government and FEDER. The RCM and E-OBS data were produced by the EU-FP6 project ENSEMBLES (contract number 505539).

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.

Assessing of IDF curves for hydrological design by simple scaling of 1-day precipitation totalsIn this paper the scaling properties of short term extreme rainfall in Slovakia were investigated. The simple scaling theory was applied to the intensity-duration-frequency (IDF) characteristics of a short duration rainfall. This method allows for the estimation of the design values of rainfall of selected recurrence intervals and durations shorter than a day by using only the daily data. The scaling behavior of rainfall intensities was examined, and the possibility of using simple scaling in Slovakia was verified. The methodology for the simple scaling of rainfall is demonstrated using an example of the meteorological station in Ilava.

Several approaches to estimating distributions of precipitation extremes are compared by means of simulation experiments, and their applications into observed data in the Czech Republic are evaluated. Regional frequency models, which take into account data in fixed or flexible ‘regions’ when fitting a distribution at any site, lead to estimates with much smaller errors compared to a single-site analysis, and efficiently reduce random and climatologically irrelevant variations in the estimates of the model parameters and high quantiles. The region-of-influence (ROI) methodology with a built-in regional homogeneity test is recognized as a useful approach, with the model based on proximity of sites outperforming the Hosking–Wallis regional frequency analysis. Comparison of estimates of the return period of a heavy precipitation event on 24 June 2009, which triggered a disastrous local flash flood, illustrates that the at-site analysis leads to unrealistic and extremely uncertain estimates that strongly depend on whether or not a single outlying observation is involved in the sample, while all regional methods yield return periods in the same order of several hundreds of years, notwithstanding whether the 2009 data are included in the sample. The ROI method may be found useful for modelling probabilities of other meteorological variables, extremes of which are strongly influenced by sampling variability, and may also represent an efficient tool for ‘smoothing’ random variations in the estimates of model parameters and high quantiles of precipitation in high-resolution regional climate model simulations. Copyright © 2010 Royal Meteorological Society

Hybrid Approach to Delineation of Homogeneous Regions for Regional Precipitation Frequency AnalysisRegional frequency analysis of heavy precipitation amounts based on the estimation of the parameters of a regional distribution function usingL-moments is adopted for the specific geographical-climatological settings of Slovakia. The paper focuses on the first step of the regionalL-moment algorithm (Hosking, Wallis, 1997), which is the delineation of homogeneous regions. Objective and process-based logical pooling techniques are used to form homogeneous pooling groups of rainfall gauging stations for regional frequency analysis ofk-day precipitation amounts (k= 1 to 5 days). Even though the delineation of homo-geneous regions by means of objective methods is generally accepted and recommended in the literature, it is concluded here that such a pooling of similar sites should not be carried out automatically in precipitation analysis. Instead, a combination of physical/geomorphological considerations and objective methods should be preferred.

In this paper, we implement the region-of-influence (ROI) approach for modelling probabilities of heavy 1-day and 5-day precipitation amounts in the Czech Republic. The pooling groups are constructed according to (i) the regional homogeneity criterion (assessed by a built-in regional homogeneity test), which requires that in a pooling group the distributions of extremes are identical after scaling by the at-site mean; and (ii) the 5T rule, which sets the minimum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period T. The similarity of sites is evaluated in terms of climatological and geographical site characteristics. We carry out a series of sensitivity analyses by means of Monte Carlo simulations in order to explore the importance of the individual site attributes, including hybrid pooling schemes that combine both types of the site attributes with different relative weights. We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20×20 km), the actual distance between the sites plays the most important role in determining the similarity of probability distributions of heavy precipitation. There are, however, differences between the optimum pooling schemes depending on the duration of the precipitation events. While in the case of 1-day precipitation amounts the pooling scheme based on the geographical proximity of sites outperforms all hybrid schemes, for multi-day amounts the inclusion of climatological site characteristics (although with much lower weights compared to the geographical distance) enhances the performance of the pooling schemes. This finding is in agreement with the climatological expectation since multi-day heavy precipitation events are more closely linked to some typical precipitation patterns over central Europe (related e.g. to the varied roles of Atlantic and Mediterranean influences) while the dependence of 1-day extremes on climatological characteristics such as mean annual precipitation is much weaker. The findings of the paper show a promising perspective for an application of the ROI methodology in evaluating outputs of regional climate models with high resolution: the pooling schemes might serve for defining weighting functions, and the large spatial variability in the grid-box estimates of high quantiles of precipitation amounts may efficiently be reduced.

The paper compares different approaches to regional frequency analysis with the main focus on the implementation of the region-of-influence (ROI) technique for the modelling of probabilities of heavy precipitation amounts in the area of the Western Carpathians. Unlike the conventional regional frequency analysis where the at-site design values are estimated within a fixed pooling group (region), the ROI approach as a specific alternative to focused pooling techniques makes use of flexible pooling groups, i.e. each target site has its own group of sufficiently similar sites. In this paper, various ROI pooling schemes are constructed as combinations of different alternatives of sites' similarity (pooling groups defined according to climatological characteristics and geographical proximity of sites, respectively) and pooled weighting factors. The performance of the ROI pooling schemes and statistical models of conventional (regional and at-site) frequency analysis is assessed by means of Monte Carlo simulation studies for precipitation annual maxima for the 1-day and 5-day durations in Slovakia. It is demonstrated that a) all the frequency models based on the ROI method yield estimates of growth curves that are superior to the standard regional and at-site estimates at most individual sites, and b) the selection of a suitable ROI pooling scheme should be adjusted to the dominant character of the formation of heavy precipitation.

Extreme precipitation is thought to increase with warming at rates similar to or greater than the water vapour holding capacity of the air at ~ 7% °C−1, the so-called Clausius–Clapeyron (CC) rate. We present an empirical study of the variability in the rates of increase in precipitation intensity with air temperature using 30 years of 10 min and 1 h data from 59 stations in Switzerland. The analysis is conducted on storm events rather than fixed interval data, and divided into storm type subsets based on the presence of lightning which is expected to indicate convection. The average rates of increase in extremes (95th percentile) of mean event intensity computed from 10 min data are 6.5% °C−1 (no-lightning events), 8.9% °C−1 (lightning events) and 10.7% °C−1 (all events combined). For peak 10 min intensities during an event the rates are 6.9% °C−1 (no-lightning events), 9.3% °C−1 (lightning events) and 13.0% °C−1 (all events combined). Mixing of the two storm types exaggerates the relations to air temperature. Doubled CC rates reported by other studies are an exception in our data set, even in convective rain. The large spatial variability in scaling rates across Switzerland suggests that both local (orographic) and regional effects limit moisture supply and availability in Alpine environments, especially in mountain valleys. The estimated number of convective events has increased across Switzerland in the last 30 years, with 30% of the stations showing statistically significant changes. The changes in intense convective storms with higher temperatures may be relevant for hydrological risk connected with those events in the future.

The aim of this paper is to understand the causal factors controlling the relationship between flood peaks and volumes in a regional context. A case study is performed based on 330 catchments in Austria ranging from 6 to 500 km2 in size. Maximum annual flood discharges are compared with the associated flood volumes, and the consistency of the peak–volume relationship is quantified by the Spearman rank correlation coefficient. The results indicate that climate-related factors are more important than catchment-related factors in controlling the consistency. Spearman rank correlation coefficients typically range from about 0.2 in the high alpine catchments to about 0.8 in the lowlands. The weak dependence in the high alpine catchments is due to the mix of flood types, including long-duration snowmelt, synoptic floods and flash floods. In the lowlands, the flood durations vary less in a given catchment which is related to the filtering of the distribution of all storms by the catchment response time t...

There has been a surprisingly large number of major floods in the last years around the world, which suggests that floods may have increased and will continue to increase in the next decades. However, the realism of such changes is still hotly discussed in the literature. This overview article examines whether floods have changed in the past and explores the driving processes of such changes in the atmosphere, the catchments and the river system based on examples from Europe. Methods are reviewed for assessing whether floods may increase in the future. Accounting for feedbacks within the human‐water system is important when assessing flood changes over lead times of decades or centuries. It is argued that an integrated flood risk management approach is needed for dealing with future flood risk with a focus on reducing the vulnerability of the societal system. WIREs Water 2015, 2:329–344. doi: 10.1002/wat2.1079 For further resources related to this article, please visit the WIREs website.

[1] We analyze the controls on flood duration based on the concept of comparative hydrology. Rather than modeling a single catchment in detail, we compare catchments with contrasting characteristics in order to understand the controls in a holistic way. We analyze the hydrographs of 9223 maximum annual flood events in 396 Austrian catchments ranging from 5 to ∼10,000 km2 as a function of climatic controls such as storm type (synoptic and convective storms, rain-on-snow, snowmelt), and catchment controls such as soils, soil moisture, geology, and land form. The ratio of the flood volume and the flood peak is used as a measure of the flood duration or flood timescale. The results indicate that, spatially, the median flood timescales range from 16 h in the hilly catchments, where convective storms prevail, to 104 h in the lowland catchments where substantial inundation into the floodplain occurs. The range is even larger for different flood types, from 7 h for flash floods in the hilly catchments to 200 h for snowmelt floods in an Alpine area with deeply weathered rocks and deep soils. The results also indicate that the catchment area is not the most important control on the flood timescales. For the range of catchments considered here, climate is very important through storm type and antecedent soil moisture, and geology is very important through soil characteristics. The concept of comparative hydrology is also used to interpret the interplay of the processes controlling the flood duration at timescales from hours to millennia. It is argued that the flood timescale is a rich fingerprint of the hydrological processes in a catchment because it integrates a range of climate and catchment characteristics by a time parameter.

Climate change scenarios of high quantiles of 5-day precipitation amounts (proxies for flood-generating events) over the Czech Republic are evaluated in an ensemble of high-resolution Regional Climate Model (RCM) simulations from the ENSEMBLES project. The region-of-influence method of the regional frequency analysis is applied as a pooling scheme. This means that for any single gridbox, a homogeneous region (set of gridboxes) is identified and data from that region are used when fitting the Generalized Extreme Value distribution. The climate change scenarios for the late 21st century (2070–2099) show widespread increases in high quantiles of 5-day precipitation amounts in winter, consistent with projected changes in mean winter precipitation. In summer, increases in precipitation extremes occur despite an overall drying (prevailing declines in mean summer precipitation), which may have important hydrological implications. The results for summer suggest a possible substantial change in characteristics of warm-season precipitation over Central Europe, with more severe dry as well as wet extremes. The spatial pattern of projected changes in summer precipitation extremes, with larger increases in the western part of the area and smaller changes towards east, may also point to a declining role of Mediterranean cyclones in producing precipitation extremes in Central Europe in a future climate. However, uncertainties of the climate change scenarios remain large, which is partly due to biases in reproducing precipitation characteristics in climate models, partly due to large differences among the RCMs, and partly due to factors that are poorly or not at all represented in the examined ensemble. The latter are related also to uncertainties in future emission scenarios and socio-economic development in general.

summary This paper proposes a method for using major flash flood events occurred at ungauged catchments to reduce the uncertainties in estimating regional flood quantiles. The approach is based on standard regionalization methods assuming that the flood peak distribution rescaled by a site-dependent index flood is uniform within a homogeneous region. A likelihood formulation and a Bayesian Markov Chain Monte Carlo (MCMC) algorithm are used to infer the parameter values of the regional distributions. This statistical inference technique has been selected for its rigorousness – various hypotheses are explicitly formulated in the likelihood function, its flexibility as for the type of data that can be treated, and its ability to compute accurate estimates of the confidence intervals for the adjusted parameters and for the corresponding flood quantiles. The proposed method is applied to two data sets from Slovakia and the South of France that consist of series of annual peak discharges at gauged sites and estimated peak discharges of extreme flash flood events that have occurred at ungauged sites. The results suggest that the confidence intervals of the quantiles can be significantly narrowed down provided that the set of ungauged extremes is the result of a comprehensive sampling over the selected region. This remains valid, even if the uncertainties in the estimated ungauged extreme discharges are considered. The flood quantiles estimated by the proposed method are also consistent with the results of site specific flood frequency studies based on historic and paleoflood information.

The aim of this study was to test the applicability of a simple scaling methodology for a regional estimation of intensity-duration-frequency (IDF) curves in Slovakia. The analysis is based on the regionalization process of Gaal (2006), which focused on the delineation of homogeneous regions for a regional frequency analysis of precipitation maxima. In order to examine the regionally estimated IDF curves, a region covering the western parts of Slovakia was chosen. The selected region, which encompasses 19 raingauging stations, may be characterized by the dominant influence of Atlantic circulation patterns. Three of the 19 stations belonging to the target region were set aside and flagged as verification stations. The regional dimensionless growth curve of 1-day precipitation maxima in the warm season was derived for the region, and the local T-year quantiles were estimated by the index value method for the stations. At the same time, a regionally averaged scaling exponent was derived using all the stations except for the three verification ones. The local IDF curves at the verification stations were estimated by downscaling the Tyear quantiles of the 1-day precipitation maxima using the regionally averaged scaling exponent. Finally, the IDF curves for these stations were compared with those defined by Samaj, Valovic (1973). This study is the first step in assessing the applicability of a simple scaling theory for the regional estimation of IDF curves in Slovakia.

The issue of climate change has been presented mostly as a change in long-term means, mainly in terms of air temperature and precipitation totals. In the IPCC (2001) report there was for the first time a discussion on a possible change in the shape of distribution curves of individual climatic phenomena, specifically the change in probability densities in the time series from different periods. The basic alternatives of shift of distribution curves are presented in Fig. 1.

The paper deals with at-site flood frequency estimation in the case when also information on hydrological events from the past with extraordinary magnitude are available. For the joint frequency analysis of systematic observations and historical data, respectively, the Bayesian framework is chosen, which, through adequately defined likeli- hood functions, allows for incorporation of different sources of hydrological information, e.g., maximum annual flood peaks, historical events as well as measurement errors. The distribution of the parameters of the fitted distribution function and the confidence in- tervals of the flood quantiles are derived by means of the Markov chain Monte Carlo simulation (MCMC) technique. The paper presents a sensitivity analysis related to the choice of the most influential parameters of the statistical model, which are the length of the historical period h and the perception threshold X0. These are involved in the statistical model under the assumption that except for the events termed as 'historical' ones, none of the (unknown) peak dis- charges from the historical period h should have exceeded the threshold X0. Both higher values of h and lower values of X0 lead to narrower confidence intervals of the estimated flood quantiles; however, it is emphasized that one should be prudent of selecting those parameters, in order to avoid making inferences with wrong assumptions on the unknown hydrological events having occurred in the past. The Bayesian MCMC methodology is presented on the example of the maximum dis- charges observed during the warm half year at the station Vltava-Kamyk (Czech Republic) in the period 1877-2002. Although the 2002 flood peak, which is related to the vast flood- ing that affected a large part of Central Europe at that time, occurred in the near past, in

Scaling properties of the short term extreme rainfall has not been investigated so far in Slovakia. In this study the simple scaling theory is applied to the intensity- duration-frequency (IDF) characteristics of short duration rainfall. The rainfall data for the analysis consists of rainfall intensities of durations ranging from 5 minutes to 180 minutes and daily rainfall amounts for 55 stations from the whole territory of Slovakia, respectively, taken from the historical dataset of Šamaj and Valovič (1973) and from the database of the Slovak Hydrometeorological Institute (SHMI). The scaling behaviour of rainfall intensities was examined and it was shown that for time scaling, the statistical properties of rainfall follow the hypothesis of simple scaling. The scaling exponents were derived in all the analysed stations. In the selected test stations, the influence of excluding the 5 min duration on the derived IDF curves was also examined. The possibility of using wide sense simple scaling in Slovakia was demonstrated.

International audience; The paper compares different approaches to regional frequency analysis with the main focus on the implementation of the region-of-influence (ROI) technique for the modelling of probabilities of heavy precipitation amounts in the area of the Western Carpathians. Unlike the conventional regional frequency analysis where the at-site design values are estimated within a fixed pooling group (region), the ROI approach as a specific alternative to focused pooling techniques makes use of flexible pooling groups, i.e. each target site has its own group of sufficiently similar sites. In this paper, various ROI pooling schemes are constructed as combinations of different alternatives of sites' similarity (pooling groups defined according to climatological characteristics and geographical proximity of sites, respectively) and pooled weighting factors. The performance of the ROI pooling schemes and statistical models of conventional (regional and at-site) frequency analysis is assessed by means of Monte Carlo simulation studies for precipitation annual maxima for the 1-day and 5-day durations in Slovakia. It is demonstrated that a) all the frequency models based on the ROI method yield estimates of growth curves that are superior to the standard regional and at-site estimates at most individual sites, and b) the selection of a suitable ROI pooling scheme should be adjusted to the dominant character of the formation of heavy precipitation.