Patterns of Past and Future Droughts in Permanent Lowland Rivers

The problem of droughts is acute due to climate change. The study aims to assess the temporal and spatial drought patterns in Lithuanian lowland rivers in the past and to project these phenomena according to climate scenarios and models. Drought analysis was based on Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI) and Streamflow Drought Index (SDI). To evaluate the past patterns, the hydrometeorological data of 17 rivers were used from 1961–2020. Future drought changes were analyzed in 2021–2100 according to the selected RCPs (Representative Concentration Pathways) using the hydrological model HBV. There were different patterns of droughts in three hydrological regions of Lithuania (Western, Central and Southeastern). The Southeastern region was more prone to extreme summer hydrological droughts, and they had a shorter accumulation period compared to the other two regions. SPI and RDI indices showed that the number of dry months and the minimum value of the index increased, extending the accumulation period. The highest correlation was recorded between RDI-12/SPI-12 and SDI-12. The amplitude between extremely wet and dry values of river runoff will increase according to RCP8.5. The projections indicated that hydrological drought intensity in the Central region is expected to increase under both analyzed RCPs.

Statistical approaches to explore the linkages between physicochemical parameters and BQEs, and set river nutrient threshold concentrations in Hungary

The objective of the Water Framework Directive (WFD) is to achieve good ecological status in surface waters by 2027. To make a proper evaluation of the ecological status of watercourses, it is necessary to harmonize class boundaries for chemical and biological quality elements (BQEs). This paper aims to explore the linkages between physicochemical parameters and BQEs and set river nutrient threshold concentrations that support good ecological status. Regression and mismatch methods were applied to find the relationship between phytoplankton (PP) and phytobenthos (PB) environmental quality ratio and mean total phosphorus (TP) and total nitrogen (TN) concentrations. Nutrient thresholds have been suggested for several water types, which are varied in the case of highland rivers 1.8–6.2 mg TN/l, 180–400 μg TP/l; in the case of lowland rivers 1.4–5.0 mg TN/l, and 100–350 μg TP/l. These values are similar to what other studies found, but the relationship between biology and nutrients was weaker. Besides nutrients, additional data of measured dissolved organic carbon, 5-day biochemical oxygen demand, chemical oxygen demand with potassium permanganate method, and information about hydromorphological features were involved in the analysis. The research demonstrates that random forest can be used as a nonlinear, multiparametric model for predicting biological class from five variables with 35–81% error for PP and with 18–47% error for PB.

Flow Resistance in Lowland Rivers Impacted with Distributed Aquatic Vegetation

The study addresses the research concern that the employment of fixed value for bed roughness coefficient in lowland rivers (mostly ‌sand-bed rivers) is deemed practically questionable in the presence of a mobile bed and time-dependent changes in vegetation patches. To address this issue, we set up 45 cross-sections in four lowland streams to investigate seasonal flow resistance values within a year. The results first revealed that the significant sources of boundary resistance in lowland rivers with lower regime flow are bed forms and aquatic vegetation. Then, the study uses flow discharge as an influential variable reflecting the impacts of the above-mentioned sources of resistance to flow. The studied approach ended up with two new flow resistance predictors which simply connect dimensionless unit discharge with flow resistance factors, Darcy-Weisbach (f) and Manning (n) coefficients. A comparison between the computed and measured flow resistance values indicates that 87-89% of data sets were within the ±20% error bands. The flow resistance predictors are also verified against large independent sets of field and flume data. The obtained predictions using the developed predictors may overestimate flow resistance factors to about 40% for other lowland rivers. From a different view of this research, the findings on seasonal variation of vegetation abundance hint at the augmentation in flow resistance values, both f, and n, in low summer flows when the vegetation covers river bed and side banks. The highest amount of flow resistance was observed during the summer period, July-August.

A new fish-based index of biotic integrity for lowland rivers in Flanders (Belgium)—Corrigendum

This Corrigendum corrects an error in Table 2 that was published in Breine et al. (2021) on the pages 114–115: the line concerning the weatherfish Misgurnus fossilis (Linnaeus, 1758) on the page break was erroneously removed during publication and is added here.

Suspended sediment in lowland rivers – towards identifying the ratios of mineral and organic components and their variation during the year

Concentrations of suspended sediment transported by rivers are influenced by interactions between multiple drivers that act on a range of spatial and temporal scales. Such levels vary over the year, as well as across multi-year periods. Most conventional approaches to determining suspended load are based upon analyses of total suspended sediment concentration (SSC), i.e., the sum of mineral and organic matter. This approach makes it difficult, if not impossible, to determine the impact of multiple environmental factors on changes in suspension concentration precisely. The present paper focuses on the mineral and organic components of suspended sediment with the aim of determining how our knowledge of the share of each individual component can improve interpretations of SSC fluctuations during a hydro-logical year. The analysis conducted (personal and other researchers’ results) has shown that mineral and organic suspensions demonstrate mutually incompatible opposite trends under influence of environmental factors. This analysis of organic components identifies clear seasonal trends, which indicates that organic suspensions of autogenous origin have a strong influence on the dynamics of changes in suspension concentration; such analyses are rarely included in assessments of SSC dynamics.

A new fish-based index of biotic integrity for lowland rivers in Flanders (Belgium)

The first fish-based index to assess the ecological quality of lowland rivers in Flanders (Belgium) is based on data obtained from different fishing techniques without considering the gear specificity. As a consequence, this index could not be intercalibrated with other European indices which concentrate on one gear type only. In order to comply with the European Water Framework Directive, we developed a new fish-based index using data obtained from surveys in rivers with electric gear only. All 293 selected rivers belonged to the bream or barbel zone. An updated reference list of fish species was compiled based on previous work and recent data. Abiotic data were collected according to standard methods and habitat quality of all surveyed sites was pre-classified using pressure indicators. To develop the new index candidate metrics were selected from the literature and metric values were calculated. Linear mixed regression models selected metrics based on their response to the pre-classified habitat status. Correlation tests were performed to avoid redundancy among responsive metrics. Boundaries for metric scores were defined based on the calculated metric values. The new index of biotic integrity (IBI) was calculated by summation of the metric scores, and transformed to an ecological quality ratio (EQR), ranging between 0 and 1. Five integrity classes, ranging from bad to maximal ecological potential, were attributed and compared to the pre-classified habitat status of the site. In addition, the new index was also validated with an independent set of data. The new IBI proved to successfully assess the ecological status of the rivers.