Carbon dioxide (CO2) fluxes in regulated Alpine rivers are driven by multiple biogeochemical and anthropogenic processes, acting on different spatiotemporal scales. We quantified the relative importance of these drivers and their effects on the dynamics of CO2 concentration and atmospheric exchange fluxes in a representative Alpine river segment regulated by a cascading hydropower system with diversion, which includes two residual flow reaches and a reach subject to hydropeaking. We combined instantaneous and time-resolved water chemistry and hydraulic measurements at different times of the year identifying the main CO2 pathways through a one-dimensional transport-reaction model. The spatiotemporal distribution and drivers of CO2 fluxes depended on hydropower operations. Along the residual flow reaches, CO2 fluxes were directly affected by the upstream dams only in the first 2 km downstream of each dam, where the supply of supersaturated water from the reservoirs was predominant. Downstream of the hydropower diversion outlets, the magnitude and dynamics of CO2 fluxes were dominated by systematic sub-daily peaks in CO2 transport and evasion fluxes (‘carbopeaking’) driven by hydropeaking. The additional input of CO2 released locally into the river at the hydropower diversion outlet during hydropeaking matched the amount of CO2 transported, metabolised, and exchanged with the atmosphere along the whole upstream reach. Hydropower operational patterns and regulation approaches in Alpine rivers significantly affect CO2 fluxes and their response to biogeochemical drivers across different temporal scales. This work contributes to understanding and quantifying these processes to clarify the role of natural and anthropogenic drivers in global carbon cycling.

Flow regimes profoundly influence river organisms and ecosystem functions, but regulatory approaches often lack the scientific basis to support sustainable water allocation. In part, this reflects the challenge of understanding the ecological effects of flow variability over different temporal and spatial domains. Here, we use a process-based distributed hydrological model to simulate 23 years of natural flow regime in 100 target bioassessment sites across the Adige River network (NE Italy), and to identify typical nivo-glacial, nivo-pluvial, and pluvial reaches. We then applied spatial stream-network models (SSN) to investigate the relationships between hydrologic and macroinvertebrate metrics while accounting for network spatial autocorrelation and local habitat conditions. Macroinvertebrate metrics correlated most strongly with maximum, minimum and temporal variation in streamflow, but effects varied across flow regime types. For example: i) taxon richness appeared limited by high summer flows and high winter flows in nivo- glacial and pluvial streams, respectively; ii) invertebrate grazers increased proportionally with the annual coefficient of flow variation in nivo-glacial streams but tended to decline with flow variation in pluvial streams. SSN models revealed that most variation in macroinvertebrate metrics was accounted for by spatial autocorrelation, although local land use and water quality also affected benthic invertebrate communities, particularly at lower elevations. These findings highlight the importance of developing environmental flow management policies in ways that reflect specific hydro-ecological and land use contexts. Our analyses also illustrate the importance of spatially-explicit approaches that account for auto-correlation when quantifying flow-ecology relationships.

River meandering is the natural process that many lowland rivers undergo as a consequence of the alternation of bank erosion and accretion, which leads to the typical shape of the so-called meandering rivers. During the last decades, numerous modelling studies have been developed to reproduce their planform dynamics and to predict their future evolution. Most of these modelling approaches are physics-based, meaning that they solve the mathematical equations of shallow water open channel flow and fluvial sediment transport. Other types of modelling are very rare. Recent advances in artificial intelligence have led to promising results in many fields of science but their potential seems to have been so far rather unexplored in the prediction of meandering rivers morphodynamics. In this study, we have developed machine learning (hereinafter ML) models to compute the meander lateral migration rate based on training dataset: once the model has been trained with known migration rates and curvature values at two consecutive time steps, it is used to predict migration rates at the following time step. To this aim, the train and test dataset is coming from the outputs of a semi-analytical meander morphodynamic model which provides simulated evolving meandering planforms (described through the spatial curvature distribution) and migration rates computed through the excess near bank velocity. Such migration has been considered as the “Target” in the present study. The results for different models such as linear regression, feedforward neural network, SVM, and XGBoost were compared. It indicates that the “XGBoost” model with approximately 80 percent of accuracy in the prediction of the next time step, has the best result among them. This is just an opening chapter for the usage of ML in morphodynamics of meandering rivers and with advanced methods, there will be promising results ahead.