The aim of the study was to compare two grouping methods for regionalisation of watersheds, which are similar in respect of low flow and chosen catchments parameters (physiographic and meteorological). In the study, a residual pattern approach and cluster analysis, i.e. Ward’s method, were used. The analysis was conducted for specific low flow discharge q95 (dm3∙s–1∙km–2). In the analysis, 50 catchments, located in the area of the upper and central Vistula River basin, were taken. Daily flows used in the study were monitored from 1976 to 2016. Based on the residual pattern approach (RPA) method, the analysed catchments were classified into two groups, while using the cluster analysis method (Ward’s method) – into five. The predictive performance of the complete regional regression model checked by cross-validation R2cv was 47% and RMSEcv = 0.69 dm3∙s–1∙km–2. The cross validation procedure for the cluster analysis gives a predictive performance equal to 33% and RMSEcv = 0.81 dm3∙s–1∙km–2. Comparing both methods, based on the cross-validated coefficient of determination (R2cv), it was found that the residual pattern approach had a better fit between predicted and observed values. The analysis also showed, that in case of both methods, an overestimation of specific low flow discharge q95 was observed. For the cross-validation method and the RPA method, the PBIAS was –10%. A slightly higher value was obtained for the cross-validation method and models obtained using cluster analysis for which the PBIAS was –13.8%.

Despite many studies on the hydrological responses to forest cover changes in micro and mesoscale watersheds, the hydrological responses to forest cover alterations and associated mechanisms through the large spatial scale of the river watershed have not been comprehensively perceived. This paper thus reviews a wide range of available scientific evidence concerning the impacts exerted by the forest removal on precipitation, water yield, stream flow, and flow regimes. It is concluded that there is no statistical correlation between forest cover and precipitation and water yield at the micro and mesoscale. In contrast, there is a relative correlation coefficient ( r = 0.77, p < 0.05) between forest cover and water yield at large scales (>1000 km2). These findings help our understanding of the hydrological response to forest disturbance at large and regional scale and provide a scientific perception to future watershed management in the context of human activities and natural hazards.