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Particular attention is given to the reliability of hydrological modelling results. The accuracy of river runoff projection depends on the selected set of hydrological model parameters, emission scenario and global climate model. The aim of this article is to estimate the uncertainty of hydrological model parameters, to perform sensitivity analysis of the runoff projections, as well as the contribution analysis of uncertainty sources (model parameters, emission scenarios and global climate models) in forecasting Lithuanian river runoff. The impact of model parameters on the runoff modelling results was estimated using a sensitivity analysis for the selected hydrological periods (spring flood, winter and autumn flash floods, and low water). During spring flood the results of runoff modelling depended on the calibration parameters that describe snowmelt and soil moisture storage, while during the low water period-the parameter that determines river underground feeding was the most important. The estimation of climate change impact on hydrological processes in the Merkys and Neris river basins was accomplished through the combination of results from A1B, A2 and B1 emission scenarios and global climate models (ECHAM5 and HadCM3). The runoff projections of the thirty-year periods (2011-2040, 2041-2070, 2071-2100) were conducted applying the HBV software. The uncertainties introduced by hydrological model parameters, emission scenarios and global climate models were presented according to the magnitude of the expected changes in Lithuanian rivers runoff. The emission scenarios had much greater influence on the runoff projection than the global climate models. The hydrological model parameters had less impact on the reliability of the modelling results. [ABSTRACT FROM AUTHOR]

Extreme floods can be caused by various combinations of hydrological and meteorological factors and river basin conditions that have not been observed for a long time. Long-term observational series permit estimation of both the frequency and variation of spring floods - the key issues of protection systems. Fortunately, Baltic States have a long-term record of hydrological data for the last 80 years. In this research, spring flood parameters (maximum discharge, height of maximum discharge and its timing) for the Baltic countries were assessed for four periods (1922-2008, 1941- 2008, 1961-2008 and 1991-2008). In total, 70 hydrological data series of spring flood parameters were used. To detect trends in time series for these periods, the Mann-Kendall test and the nonparametric Sen's method for the magnitude of the trend were used. The index flood method was used to estimate the maximum discharge in ungauged catchments. The results showed that maximum discharges and heights of spring floods decreased over a longer period. Spring flood peaks took place on earlier dates. Only some significant trends of maximum discharges and their timing were found in the last time period (1991-2008). All these changes could be caused by the increasing ambient temperature and precipitation in the later decades. [ABSTRACT FROM AUTHOR]

The increase in hydrological extremes during the last two decades has had a significant impact on natural and social environments. These hydrological extremes depend greatly on changes in the meteorological parameters. The task of this research was to evaluate the impact of meteorological factors (snow water equivalent and heavy rainfall) on the formation of spring floods in the basins of the Nemunas, Lielupė and Venta rivers. Five Lithuanian rivers (Venta, Šešuvis, Mūša, Merkys and Žeimena) from these basins were analysed in detail. These rivers fall within the Western, Central and Southeastern hydrological regions of Lithuania. Long‐time‐series data for daily discharge, precipitation and thickness of snow cover from 12 meteorological and five hydrological gauging stations were used. The evaluation of the relation between these factors was carried out for two periods: 1961–1987 and 1988–2014. The relation between the maximum discharge of the spring flood, the maximum snow water equivalent before the flood and the precipitation amount 10 days before the flood was analysed by multiple regression analysis. The high correlation co‐efficients between the observed and predicted maximum discharges of the spring flood for the created regression models fluctuated between 0.63 and 0.86. The verification of the selected regression model was performed with Hydrologiska Byråns Vattenbalansavdelning (HBV) software, which also showed a high correlation co‐efficient (0.71). The applied methodology of this research could be used for better perception of flood‐formation consequences in different hydrological regions of Europe. The multiple regression analysis of the maximum discharge of spring flood (Qmax, m3/s) conducted with the maximum snow water equivalent before the flood (SWEmax, mm) and the precipitation amount 10 days before the flood (P10, mm) was carried out for five rivers in Lithuania. The reliability of the regression models shows high correlation co‐efficients (0.63–0.86) between the observed and predicted Qmax. To verify the regression models, hydrological modelling with Hydrologiska Byråns Vattenbalansavdelning (HBV) software was performed. The analysis revealed close correlation co‐efficients (0.71–0.78) related to the observed Qmax (Obs), the modelled Qmax of the HBV (Mod), the predicted Qmax of the regression model of the reference period (RegRef), the predicted Qmax of the regression model of 1961–1987 (Reg1), the predicted Qmax of the regression model of 1988–2014 (Reg2), and their range for the verification period (1986–2005). [ABSTRACT FROM AUTHOR]

Within the SMIRES COST Action (Science and Management of Intermittent Rivers & Ephemeral Streams, www.smires.eu), examples of intermittent rivers across Europe were collected, including gauged catchments with both natural and highly influenced river flow regimes. The examples will be published in a catalogue to give a good overview of the variety of intermittent streams in Europe. Information on the gauged intermittent rivers is summarised in a two-page template: The first page of the template describes the main characteristics of the catchments (land-use, geology, climate, etc.) and the river flow regime. Two panels display hydrographs and flow durations curves and a table gives metrics specific to river flow intermittence relevant for ecology. These hydrological indices have been computed by the SMIRES R package (https://homepage.boku.ac.at/h0540352/smires/framework.html) applied to daily discharge data. The second page of the template is dedicated to the description and reasons for intermittence. A short description about the spatio-temporal pattern of zero-flow events (encompassing seasonal behaviour, observed long-term trends, locations with frequently observed zero-flow events along the river network, etc.). The monitoring network, including gauging stations and other types of observations (e.g. visual inspection of the flow states at different locations along the river) in the catchment, is also described. This presentation gives examples of the pages in the catalogue and a preliminary classification of intermittent rivers at the European scale. [ABSTRACT FROM AUTHOR]