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The classification of floods may be a supporting tool for decision-makers in regard to water management, including flood protection. The main objective of this work is the classification of flood generation mechanisms in 28 catchments of the upper Vistula basin. A significant innovation in this study lies in the utilization of decision trees for flood classification. The methodology has so far been applied in the Alpine region. The analysis reveals that peak daily precipitation in the catchments mainly occurs in summer, particularly from June to August. Maximal daily snowmelt typically happens at the end of winter (March to April) and occasionally in November. Winter peaks are observed in March to April and, in some areas, in November to December, while summer peaks occur in May and, in specific catchments, in October. Higher peak flows for annual floods are noted in March to April and June to August. Most annual floods in the Upper Vistula basin are classified as Rain-on-Snow Floods (RoSFs) or Lowland River Floods (LRFs). LRFs contribute from 19% to almost 72%, while RoSFs range from 18% to 75%. In Season 1 (summer), most seasonal floods are identified as LRFs (51%–100%), with very few as RoSFs (0%–46.9%). In Season 2 (winter), the opposite pattern is observed, with most RoSFs (48.4%–97.9%) and fewer LRFs (0%–20.6%). While there are changes in flood patterns, they are not statistically significant. Conducted studies and obtained results can be useful for the preparation of flood prevention documentation and for flood management in general. • The upper Vistula basin as the most flood-prone area in Central Europe • Genetic classification of floods as the basis for flood risk management • Decision trees effectively classify floods, serving as valuable tools for analysis • High genetic diversity of flood types in the upper Vistula basin • Rain-on-snow as the most frequent type of flood in the region [ABSTRACT FROM AUTHOR]

[Display omitted] • Land use changes could more affecting on water retention than climate changes. • The negative influence of climate change can be mitigated by landscape management. • Landscape hydric potential help to adapt to changes in land use and climate. • Machine learning help to predict future effects of land use/land cover. Are we losing water mostly due to climate change? This study delves into that question. The main innovation of this research lies in developing a methodology that forecasts future shifts in water resources through the use of nondimensional indicators, such as Landscape Hydric Potential (LHP), caused by climate change scenarios, land use and land cover (LULC) projections, and a combination of both factors. The LHP method draws upon a range of indicators that shape the geosphere at the catchment scale, namely: hydrogeological conditions, soil conditions, climatic conditions, geomorphological conditions, and LULC. The analysis was carried out for 33 catchments located in the Upper Vistula River Basin in East-Central Europe. The study was conducted in the following stages. First, LHP values were calculated for the present conditions. Subsequently, an analysis of anticipated changes in LULC and in climate were conducted for the near and far future. Lastly, simulations were performed to project how LHP might evolve, considering potential changes in climate and LULC over time. The results have shown that under current climatic conditions, mountainous catchments are characterized by higher LHP values than catchments located in highlands or plains. Agricultural areas are projected to experience the largest changes in LULC. Climatic water balance indicate minimal changes, irrespective of time horizon. Our studies conclude that changes in predicted LULC could have a more significant impact on LHP values than the projected climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • Predictability described most of macroinvertebrate structure parameters variation. • There was strong positive relation between BMWP_PL and flow predictability. • The regionalization is helpful tool for the rational management of water resources. • The predictability estimates the human impact on rivers ecological status. Flow regime and its variability play a fundamental role in preserving rivers' ecosystems. Effects of flow predictability modeling on macroinvertebrate community structure parameters and taxonomic composition were investigated. The main aim of the present work was to apply flow predictability as an indicator of river ecological health and to investigate the relationship between the flow predictability index and macroinvertebrate assemblages variation. Due to the high spatial variability in the abiotic and biotic characteristics of rivers, the classification and regression trees algorithm in respect to Polish Biological Monitoring Working Party (BMWP_PL) index on regional scale was done. The study was conducted in 30 catchments representing various rivers (from 0 m a.s.l. at the Baltic Sea, up to 2500 m a.s.l. in the Tatra Mountains) of Central Europe. Principal component analysis of macroinvertebrate habitat metrics indicated the presence of two groups of habitat characteristics. One of them is related to the quantitative characteristics of the regime, whereas is the second with the seasonality-predictability of the regime. Redundancy analysis showed that predictability of the hydrological regime was the most influential variables on macroinvertebrate community parameters. Generalized linear modeling showed that predictability (positively) and watershed area (negatively) influence on BMWP_PL index referred to the ecological statement of the river. Based on the main components analysis and CART algorithm in terms of the BMWP_PL index, analyzed catchments were divided into five groups according to predictability, high pulse durations, and catchments area. The predictability index is proposed as a good method for estimating the impact of human pressure on river biota without performing an extensive hydrobiological survey. [ABSTRACT FROM AUTHOR]

[Display omitted] • Landscape's hydric potential (LHP) could be used as indicator in water management. • LHP method refers to runoff reduction in ecosystems. • The LHP values are reflected in land cover changes. • Trends of flow discharges in rivers could be explained by LHP changes. This paper presents an approach for mitigating water-related problems, that relates the retention of precipitation and use of ecosystems, as a tool for improving the quality, quantity and availability of water resources throughout the region. One approach is the determination of the landscape's hydric potential (LHP). Land cover changes can influence ecosystems in different ways depending on crucial factors that depend on water resources. Thus-far, many studies describe the influence of land cover changes on individual features of catchments and hydrological regime, but few have made an attempt to analysis how changes in land cover can holistically influence processes in catchments. In this context the innovative aspect of this work presents the consequence of land cover changes on water retention expressed by LHP — a general descriptor that is linked with many composite influence factors on water retention. A study was carried out on 33 catchments located in central Europe. Results show increased forest cover having a positive trend in the context of LHP. Conversely, increased urban and industrial areas negatively impact LHP. The presented analysis reveals that a few land cover changes had direct and clearly positive effects on LHP. The LHP had decreased in 21 catchments and increased in 12 catchments. Decreased LHP was generally observed in catchments located on the left side of the Vistula basin where there are more advantageous conditions for agriculture, rising industry and urban areas. The study showed that during the analyzed period, obtained changes of LHP values did not strongly affect hydrological regimes in studied catchments. Our results show that LHP is a very good and useful descriptor that includes many characteristics of catchment and could be used for river catchment management purposes. The landscape's hydric potential can be used in any catchment in the world. [ABSTRACT FROM AUTHOR]