Hydropower plants commonly impede the downstream migration of Atlantic salmon (Salmo salar) kelts. Thus, understanding the effects of hydraulic conditions on kelt behaviour and passage performance at dams is crucial for developing effective mitigation measures. In this study, we investigated the influence of hydraulic conditions on kelt passage performance and swimming behaviour at a Norwegian hydropower plant. We combined biological data from 48 kelts collected via acoustic telemetry with hydraulic data modelled using computational fluid dynamics. We assessed kelt passage performance using metrics such as time-to-pass, total number of detections, and total number of detections per day. Additionally, we analysed swimming depths and speeds in relation to the hydraulic conditions created by different dam operating conditions. We found that the dam operation schedule impacted the kelts\' ability to find a route past the dam. Though kelts could have passed the dam throughout the study period via a submerged pipe at the dam (which had seemingly sufficient discharge for the kelts to find), 98 % of the kelts instead waited for a spill gate to open partway through the study period. The swimming depth analysis indicated diel variation, with kelts swimming nearer to the water surface during the night. We found that swimming speed increased with increasing kelt body length, particularly under high turbulence kinetic energy and during the day. Furthermore, kelts swam faster as water velocity increased, but slowed down again as turbulence intensity increased. Our findings reveal the effects of hydraulic conditions and dam operations on the migration behaviour of Atlantic salmon kelts. This provides valuable insights for developing strategies to optimise dam operations and improve fish passage performance, including the need to spill enough water to increase passage success and will contribute to sustainable management of Atlantic salmon populations in regulated rivers.

Many rivers worldwide are regulated, and the altered hydrology can lead to mass development of aquatic plants. Plant invasions are often seen as a nuisance for human activities leading to costly remedial actions with uncertain implications for aquatic biodiversity and ecosystem functioning. Mechanical harvesting is often used to remove aquatic plants and knowledge of plant growth rate could improve management decisions. Here, we used a simple light-temperature theoretical model to make a priori prediction of aquatic plant photosynthesis. These predictions were assessed through an open-channel diel change in O2 mass balance approach. A Michaelis-Menten type model was fitted to observed gross primary production (GPP) standardised at 10 °C using a temperature dependence from thermodynamic theory of enzyme kinetics. The model explained 87 % of the variability in GPP of a submerged aquatic plant (Juncus bulbosus L.) throughout an annual cycle in the River Otra, Norway. The annual net plant production was about 2.4 (1.0-3.8) times the standing biomass of J. bulbosus. This suggests a high continuous mass loss due to hydraulic stress and natural mechanical breakage of stems, as the biomass of J. bulbosus remained relatively constant throughout the year. J. bulbosus was predicted to be resilient to mechanical harvesting with photosynthetic capacity recovered within two years following 50-85 % plant removal. The predicted recovery was confirmed through a field experiment where 72 % of J. bulbosus biomass was mechanically removed. We emphasise the value of using a theoretical approach, like metabolic theory, over statistical models where a posteriori results are not always easy to interpret. Finally, the ability to predict ecosystem resilience of aquatic photosynthesis in response to varying management scenarios offers a valuable tool for estimating aquatic ecosystem services, such as carbon regulation. This tool can benefit the EU Biodiversity Strategy and UN Sustainable Development Goals.

The development of benthic cyanobacteria currently raises concern worldwide because of their potential to produce toxins. As a result, understanding which measures of biotic and abiotic parameters influence the development of cyanobacterial assemblages is of great importance to guide management actions. In this study, we investigate the relative contributions of abiotic and biotic parameters that may drive the development of cyanobacterial assemblages in river biofilms. First, a 2D hydrodynamic model allowed us to retrace changes in depths and velocities according to discharge at a 4 m2 resolution. From this model, we set up three hydraulic zones in each of the 4 reaches investigated along a 50-km-long river stretch. We further used univariate, multivariate and variance partitioning analyses to assess the contribution of past and present hydraulics, present physical and chemical parameters and algae to the temporal variability of cyanobacterial assemblage composition. The cyanobacterial assemblages were generally dominated by Phormidium sp., Lyngbya sp., Planktolyngbya sp. and Oscillatoria sp., four genera known to contain potentially toxic species. The highest biovolumes of cyanobacteria were present in low velocity zones in early summer and shifted to high velocity zones in late summer, highlighting the major influence of hydraulic parameters on benthic cyanobacteria settlement and development in rivers. Considering the identified genera, biofilms present a potentially high risk of toxin production. Relations between cyanobacterial development, toxin production and environmental parameters need to be further assessed to better estimate this risk.

Adaptive management (AM) is often proposed as a means to resolve uncertainty in the management of socio-ecological systems but successful implementation of AM is rare. We report results from a 26 year, five-treatment, AM experiment designed to inform decision makers about the response of juvenile salmonids (Oncorhynchus spp.) to flow releases from a dam on the regulated Bridge River, British Columbia, Canada. Treatments consisted of a baseline (no dam release) and four different dam release regimes that followed a semi-natural hydrograph but varied in the magnitude of spring-summer freshet flows. We found total salmonid biomass was highest at the lowest flow release, and decreased with increasing flow, consistent with a priori predictions made by an expert solicitation process. Species-specific responses were observed that in some cases could be attributed to interactions between the flow regime and life history. The relationship between juvenile biomass and flow resulting from the experiment can inform decisions on water management for this river. The documentation of successful AM experiments is sorely needed to allow for reflection on the circumstances when AM is likely to deliver desirable outcomes, and to improve other decision processes that require fewer resources and less time to implement.

With the accelerated development of urbanization, rivers in urban areas have become the most closely synergized water ecosystem between human activities and natural processes. To achieve the restoration goal of using hydrological regime change-ecological response relationship to advance the sustainable development of regulated river ecosystems, this study collected ecohydrological data at four tributaries of the Wei River system (Ba, Chan, Feng, and Hei Rivers) at a total of 24 stations in October 2020 and June 2021. Taking ecological flow as hydrological parameter and zooplankton as indicator organism, combined with habitat data scored on-site, the indicator system of zooplankton index of biological integrity and comprehensive habitat quality index was established to explore the hydrological-ecological response relationship in a multi-dimensional way. The results showed that during the ebb stage, the ecological health of the Feng River was better overall, with an average ecological flow value of 267.09 ± 348.62. The ecological health of the Hei River was the worst, with an average ecological flow value of 37.80 ± 38.80. During the abundant water period, the ecological health of the Chan River was optimal with an average ecological flow value of 189.25 ± 190.10, while the ecological health of the Hei River remained unimproved, but the average ecological flow value increased by 283.12 ± 197.76. There was a clear negative correlation relationship between the comprehensive habitat quality index and ecological flow. The correlation between zooplankton index of biological integrity and ecological flows is extremely strong and threshold values exist, but there is strong heterogeneity in the interaction of disturbance factors across water systems, which may not provide a predictable response to flow changes. This study aims to provide a case reference for flow management in watersheds that also lack long-time series hydrological data and to contribute new thinking to the wide application of the hydrological-ecological response relationship.

Phytoplankton is critical to river ecosystems. These organisms are sensitive to streamflow and water quality changes and, therefore, used to determine stability of river ecosystems, especially in regulated rivers. However, exactly how such disturbances alter spatial distribution of phytoplankton remains unclear, particularly during different seasons. A thorough understanding of these mechanisms is required to better analyze impact of environmental factors on regulated rivers. Given this, phytoplankton communities, streamflow, and water quality factors were assessed in areas sampled four times from 2015 to 2016 in upper and middle Huai River Basin. Biodiversity indices, as well as cluster and rank analyses, were used to (1) determine phytoplankton composition and distribution and (2) clarify impacts of key streamflow and water quality factors on such communities. It was found phytoplankton composition deteriorated over time, with phyla number decreasing from six to three. Moreover, proportion of Bacillariophyta increased from 51.83% to 68.13%. Phytoplankton in three regions, upstream region (Shannon-Wiener index 1.39-2.95), midstream region (0.70-4.55), and downstream region (0.22 to 2.97), were spatially clustered. The most impact factors impacting variation in composition and distribution were water quality factors and then hydrological factors. Of these, the most important factors in wet seasons were total nitrogen and maximum runoff, while ammonia nitrogen and low flow discharge were the most important factors during dry seasons. Streamflow and water quality contributed the most in midstream region, which was significantly affected by numbers of high and low flow. Contributions of these factors to downstream region were the strongest during dry seasons, which were significantly affected by numbers of low flow. Collectively, these results reveal significant impact of streamflow and water quality factors on phytoplankton deterioration in upper and middle Huai River Basin. Critically, this study provides scientific and technological support for increased biomonitoring and ecohydrological studies in regulated river basins.

BACKGROUND: Conventional river engineering operations have a substantial influence on the fluvial ecosystem. Regulation and channelization generally reduce the physical heterogeneity of river beds and banks and the heterogeneity of habitats. They determine the character, diversity and species richness of plant communities. The effect of river regulation on vegetation has been repeatedly investigated, but few studies have been conducted within reaches of previously regulated rivers. The aim of this work is to expand and current knowledge about the impact of dredging on the vegetation of a regulated section of a lowland river.
METHODS: The study included pre-dredging (1 year before) and post-dredging surveys (results 1 and 2 years after dredging). The vegetation was analysed in terms of species composition, origin of species, life forms, distribution of Grime\'s life strategies, and selected ecological factors. The Shannon-Wiener biodiversity index (H) and evenness were also analysed in each year of the study. The impact of dredging on the vascular flora was assessed by \'before-after-control-impact\' (BACI) analysis.
RESULTS: The number of species and biodiversity as measured by the Shannon-Wiener index (H) increased in the analysed section of the river valley. However, enrichment of the flora was observed only on the floodplain, on the surface of the deposited dredging material, while the number of species in the river channel decreased, as dredging of the river bed and levelling of the banks had markedly reduced habitat diversity. Although species richness in the second year after the dredging approached the values recorded before the intervention, the absence of particularly species or phytocenoses associated with shallow river banks and sandbars was still observed. The change in habitat conditions and the destruction of the vegetation cover during the dredging enabled penetration by numerous previously unrecorded alien species of plants and apophytes. There was a perceptible increase in the role of therophytes in the flora. It is worth noting that the number of alien species and therophytes declined significantly in the second year after the dredging. Analysis of the proportions of species representing various life strategies showed that previously unrecorded species with the type R (ruderal) life strategy had appeared, representing by pioneer species occurring in frequently disturbed habitats. There was also a marked increase in the share of species representing the mixed C-R (competitive-ruderal) strategy, occurring in habitats with low levels of stress, whose competitive abilities are limited by repeated disturbances. By the second year after the dredging, however, these changes were largely no longer observed.
CONCLUSIONS: Through appropriate maintenance of the regulated river, it can be rapidly recolonized by vegetation after the procedure, but it may lead to the loss of some species and phytocoenoses.

In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and ecological risk assessment. For this purpose, the performance of the designed models was evaluated for four artificial weirs in the Nakdong River, Korea. The Nakdong River has harmful annual algal blooms that can affect health due to exposure to toxins. In contrast to conventional neural network (NN) that use backpropagation (BP) learning methods, ELMs are fast learning, feedforward neural networks that use least square estimates (LSE) for regression. The weights connecting the input layer to the hidden nodes are randomly assigned and are never updated. The dataset used in this study includes air temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a concentration, which were collected on a weekly basis from January 2013 to December 2016. Here, upstream chlorophyll-a concentration data was used in our ELM2 model to improve algal bloom prediction performance. In contrast, the ELM1 model only uses downstream chlorophyll-a concentration data. The experimental results revealed that the ELM2 model showed better performance in comparison to the ELM1 model. Furthermore, the ELM2 model showed good prediction and generalization performance compared to multiple linear regression (LR), conventional neural network with backpropagation (NN-BP), and adaptive neuro-fuzzy inference system (ANFIS).

Dam operations have altered flood and flow patterns and prevented successful cottonwood seedling recruitment along many rivers. To guide reservoir flow releases to meet cottonwood recruitment needs, we developed a spatially-distributed, GIS-based model that analyzes the hydrophysical requirements for cottonwood recruitment. These requirements are indicated by five physical parameters: (1) annual peak flow timing relative to the interval of seed dispersal, (2) shear stress, which characterizes disturbance, (3) local stage recession after seedling recruitment, (4) recruitment elevation above base flow stage, and (5) duration of winter flooding, which may contribute to seedling mortality. The model categorizes the potential for cottonwood recruitment in four classes and attributes a suitability value at each individual spatial location. The model accuracy was estimated with an error matrix analysis by comparing simulated and field-observed recruitment success. The overall accuracies of this Spatially-Distributed Cottonwood Recruitment model were 47% for a braided reach and 68% for a meander reach along the Kootenai River in Idaho, USA. Model accuracies increased to 64% and 72%, respectively, when fewer favorability classes were considered. The model predicted areas of similarly favorable recruitment potential for 1997 and 2006, two recent years with successful cottonwood recruitment. This model should provide a useful tool to quantify impacts of human activities and climatic variability on cottonwood recruitment, and to prescribe instream flow regimes for the conservation and restoration of riparian woodlands.