This study is focused on the snowmelt runoff simulations for the upper Hron basin using dte degree-day approach of the SRM model. Our effort was directed at the determination and selection of input data and model parameters using GIS tools. The aim of this paper was to simulate the snowmelt runoff using only regularly measured data, without their special pre-processing. Comparison between measured and computed runoff indicates that the SRM model could be used for the snowmelt runoff simulation for the upper Hron basin. Results for the smaller upper Telgart subbasin were not so acceptable, because of lack of representative input data, which is essential for the determination of the snow depletion curve. and Práca je venovaná simulácii odtoku zo snehu pre povodie horného Hrona s využitím modelu odtoku zo snehu - SRM. Hlavným cieľom práce bolo posúdiť a otestovať možnosti výberu a určenia vstupných údajov a kalibrácie parametrov do modelu SRM s využitím dát pravidelne meraných v sieti meteorologických staníc. Z výsledkov vyplýva, že SRM veľmi dobre simuluje prietok pre povodie horného Hrona ako celku. Simulácia pre povodie horného Hrona rozdeleného na dve výškové zóny, a najmä pre čiastkové povodie Hrona po Telgárt, nedosiahla akceptovateľné výsledky. Tento fakt spôsobili najmä nedostatečné vstupné údaje. Kritickým sa ukázali najmä vstupné informácie o priestorovom rozložení snehovej pokrývky.
Substantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.
Direct interpolation of daily runoff observations to ungauged sites is an alternative to hydrological model regionalisation. Such estimation is particularly important in small headwater basins characterized by sparse hydrological and climate observations, but often large spatial variability. The main objective of this study is to evaluate predictive accuracy of top-kriging interpolation driven by different number of stations (i.e. station densities) in an input dataset. The idea is to interpolate daily runoff for different station densities in Austria and to evaluate the minimum number of stations needed for accurate runoff predictions. Top-kriging efficiency is tested for ten different random samples in ten different stations densities. The predictive accuracy is evaluated by ordinary cross-validation and full-sample crossvalidations. The methodology is tested by using 555 gauges with daily observations in the period 1987-1997. The results of the cross-validation indicate that, in Austria, top-kriging interpolation is superior to hydrological model regionalisation if station density exceeds approximately 2 stations per 1000 km2 (175 stations in Austria). The average median of Nash-Sutcliffe cross-validation efficiency is larger than 0.7 for densities above 2.4 stations/1000 km2 . For such densities, the variability of runoff efficiency is very small over ten random samples. Lower runoff efficiency is found for low station densities (less than 1 station/1000 km2 ) and in some smaller headwater basins.
The objective of this study is to analyse the spatial variability of seasonal flood occurrences in the Upper Danube region for the period 1961-2010. The analysis focuses on the understanding of the factors that control the spatial variability of winter and summer floods in 88 basins with different physiographic conditions. The evaluation is based on circular statistics, which compare the changes in the mean date and in the seasonal flood concentration index within a year or predefined season. The results indicate that summer half-year and winter half-year floods are dominant in the Alps and northern Danube tributaries, respectively. A comparison of the relative magnitude of flood events indicates that summer half-year floods are on average more than 50% larger than floods in winter. The evaluation of flood occurrence showed that the values of seasonal flood concentration index (median 0.75) in comparison to the annual floods (median 0.58) shows higher temporal concentration of floods. The flood seasonality of winter events is dominant in the Alps; however, along the northern fringe (i.e. the Isar, Iller and Inn River) the timing of winter half-year floods is diverse. The seasonal concentration of summer floods tends to increase with increasing mean elevation of the basins. The occurrence of the three largest summer floods is more stable, i.e. they tend to occur around the same time for the majority of analysed basins. The results show that fixing the summer and winter seasons to specific months does not always allow a clear distinction of the main flood generation processes. Therefore, criteria to define flood typologies that are more robust are needed for regions such as the Upper Danube, with large climate and topographical variability between the lowland and high elevations, particularly for the assessment of the effect of increasing air temperature on snowmelt runoff and associated floods.
Spatial and temporal variability of snow line (SL) elevation, snow cover area (SCA) and depletion (SCD) in winters 2001-2014 is investigated in ten main Slovak river basins (the Western Carpathians). Daily satellite snow cover maps from MODIS Terra (MOD10A1, V005) and Aqua (MYD10A1, V005) with resolution 500 m are used. The results indicate three groups of basins with similar variability in the SL elevation. The first includes basins with maximum elevations above 1500 m a.s.l. (Poprad, Upper Váh, Hron, Hornád). Winter median SL is equal or close to minimum basin elevation in snow rich winters in these basins. Even in snow poor winters is SL close to the basin mean. Second group consists of mid-altitude basins with maximum elevation around 1000 m a.s.l. (Slaná, Ipeľ, Nitra, Bodrog). Median SL varies between 150 and 550 m a.s.l. in January and February, which represents approximately 40–80% snow coverage. Median SL is near the maximum basin elevation during the snow poor winters. This means that basins are in such winters snow free approximately 50% of days in January and February. The third group includes the Rudava/Myjava and Lower Váh/Danube. These basins have their maximum altitude less than 700 m a.s.l. and only a small part of these basins is covered with snow even during the snow rich winters. The evaluation of SCA shows that snow cover typically starts in December and last to February. In the highest basins (Poprad, Upper Váh), the snow season sometimes tends to start earlier (November) and lasts to March/April. The median of SCA is, however, less than 10% in these months. The median SCA of entire winter season is above 70% in the highest basins (Poprad, Upper Váh, Hron), ranges between 30-60% in the mid-altitude basins (Hornád, Slaná, Ipeľ, Nitra, Bodrog) and is less than 1% in the Myjava/Rudava and Lower Váh/Danube basins. However, there is a considerable variability in seasonal coverage between the years. Our results indicate that there is no significant trend in mean SCA in the period 2001-2014, but periods with larger and smaller SCA exist. Winters in the period 2002-2006 have noticeably larger mean SCA than those in the period 2007-2012. Snow depletion curves (SDC) do not have a simple evolution in most winters. The snowmelt tends to start between early February and the end of March. The snowmelt lasts between 8 and 15 days on average in lowland and high mountain basins, respectively. Interestingly, the variability in SDC between the winters is much larger than between the basins.