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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]

Estimation of both the frequency and variation of spring floods is a key issue for the assessment and management of flood risks. Changes in river floods in Estonia, Latvia and Lithuania have been investigated in few national studies. However, there are no studies of the changes of flood patterns by using a common methodology for the rivers of this region. In this study flood pattern changes in the rivers of the Baltic countries were estimated applying trend and frequency analysis for the periods of 1922–2010, 1922–1960, 1961–2010 and 1991–2010, i.e. for the whole spring flood data sets, periods before and after 1960 (this year was considered as the beginning of the remarkable climate change), as well as for the two past decades. A comparative study of five probability distributions was performed in order to estimate which distribution at best represents statistical characteristics of the flood data. The results showed that maximum discharges of spring floods decreased over the whole studied period. Only some insignificant positive trends of maximum discharges were found in the last time period in continental and transitional rivers. Generalized extreme value distribution provided the best approximation to the maximum discharge data series of the rivers of Baltic countries for the whole observation period. [ABSTRACT FROM AUTHOR]

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]