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The increasing availability and quality of remote sensing data are changing the methods used in fluvial geomorphology applications, allowing the observation of hydro-morpho-biodynamics processes and their spatial and temporal variations at broader and more refined scales. With the advent of cloud-based computing, it is nowadays possible to reduce data processing time and increase code sharing, facilitating the development of reproducible analyses at regional and global scales. The consolidation of Earth Observation mission data into a single repository such as Google Earth Engine (GEE) offers the opportunity to standardize various methods found in literature, in particular those related to the identification of key geomorphological parameters. This work investigates different computational techniques and timeframes (e.g., seasonal, annual) for the automatic detection of the active river channel and its multi-temporal aggregation, proposing a rational integration of remote sensing tools into river monitoring and management. In particular, we propose a quantitative analysis of different approaches to obtain a synthetic representative image of river corridors, where each pixel is computed as a percentile of the bands (or a combination of bands) of all available images in a given time span. Synthetic images have the advantage of limiting the variability of individual images, thus providing more robust results in terms of the classification of the main components of the riverine ecosystem (sediments, water, and riparian vegetation). We apply the analysis to a set of rivers with analogous bioclimatic conditions and different levels of anthropic pressure, using a combination of Landsat and Sentinel-2 data. The results show that synthetic images derived from multispectral indexes (such as NDVI and MDWI) are more accurate than synthetic images derived from single bands. In addition, different temporal reduction statistics affect the detection of the active channel, and we suggest using the 90th percentile instead of the median to improve the detection of vegetated areas. Individual representative images are then aggregated into multitemporal maps to define a systematic and easily replicable approach for extracting active river corridors and their inherent spatial and temporal dynamics. Finally, the proposed procedure has the potential to be easily implemented and automated as a tool to provide relevant data to river managers.

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, river morphology, flood-wave delineation and flood assessment. Optical-based monitoring and the characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on the time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high temporal and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. The intra-event reconstruction of inundation dynamics has led to (1) the estimation of the relationship between hydrometric level and wet area extension and (2) the assessment of bank erosion phenomena. In the first case, the behavior exhibits a change when the hydrometric level exceeds 1 m. In the second case, the erosion rate and cumulative lateral erosion were evaluated. The maximum erosion velocity was greater than 1 m/h, while the cumulative lateral erosion reached 130 m. Time series of SAR acquisitions, provided by Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, can support many types of analyses of river dynamics, including morphological changes, floods monitoring, and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

Several Alpine river ecosystem services (ES) depend on the streamflow regime, thus they might be affected by multiple stressors such as changing climate and anthropic water uses, with still poorly investigated consequences. We focused on the supply of three ES in an Alpine river, namely habitat provision, recreational activities, and hydroelectricity production from run-of-the-river (RoR) power plants. We applied an integrated hydrological, hydraulic and habitat modeling approach to quantify the effects of climate change (CC) on these services, based on the outcomes of four regional climate models. The paper investigated the effects of water use policies such as the introduction of prescriptions for environmental flow (EF) under the same CC models. We observed that CC significantly affects the river suitability for the supply of ES at the catchment scale, while the introduction of EF releases are relevant at a more local scales (several reaches). Under future scenarios, simulated increasing abstractions for hydroelectricity production from RoR power plants have a stronger effect on white-water rafting and a relatively smaller effect on fish habitat. Quantifying the potential effects of CC and of different strategies of river flow management under these scenarios is a promising approach to support the design of long-term water resources management strategies at catchment and local level.

Meandering rivers are complex geomorphic systems that play an important role in the environment. They provide habitat for a variety of plants and animals, help to filter water, and reduce flooding. However, meandering rivers are also susceptible to changes in flow, sediment transport, and erosion. These changes can be caused by natural factors such as climate change and human activities such as dam construction and agriculture. Studying meandering rivers is important for understanding their dynamics and developing effective management strategies. However, traditional methods such as numerical and analytical modeling for studying meandering rivers are time-consuming and/or expensive. Machine learning algorithms can be used to overcome these challenges and provide a more efficient and comprehensive way to study meandering rivers. In this study, we used machine learning algorithms to study the migration rate of simulated meandering rivers using semi-analytical model to investigate the feasibility of employing this new method. We then used machine learning algorithms such as multi-layer perceptron, eXtreme Gradient Boost, gradient boosting regressor, and decision tree to predict the migration rate. The results show ML algorithms can be used for prediction of migration rate, which in turn can predict the planform position.

Release of water from storage hydropower plants generates rapid flow and stage fluctuations (hydropeaking) in the receiving water bodies at a variety of sub-daily time-scales. In this paper we present an approach to quantify such variations, which is easy to apply, requires stream flow data at a readily available resolution, and allows for the comparison of hydropeaking flow alteration amongst several gauged stations. Hydropeaking flow alteration is quantified by adopting a rigorous statistical approach and using two indicators related to flow magnitude and rate of change. We utilised a comprehensive stream-flow dataset of 105 gauging stations from Italy, Switzerland and Norway to develop our method. Firstly, we used a GIS approach to objectively assign the stations to one of two groups: gauges with an upstream water release from hydropower plants (peaked group) and without upstream releases (unpeaked group). Secondly, we used the datasets of the unpeaked group to calculate one threshold for each of the two indicators. Thresholds defined three different classes: absent or low pressure, medium, and high pressure, and all stations were classified according to these pressure levels. Thirdly, we showed that the thresholds can change, depending on the country dataset, the year chosen for the analysis, the number of gauging stations, and the temporal resolution of the dataset, but the outcome of the classification remains the same. Hence, the classification method we propose can be considered very robust since it is almost insensitive to the hydropeaking thresholds variability. Therefore, the method is broadly applicable to procedures for the evaluation of flow regime alterations and classification of river hydromorphological quality, and may help to guide river restoration actions.

A procedure for the characterization of the temporal evolution of river morphology is presented. Wet and active river channels are obtained from the processing of imagery datasets. Information about channel widths and active channel surface subdivision in water, vegetation and gravel coverage classes are evaluated along with channel centerline lengths and sinuosity indices. The analysis is carried out on a series of optical remotely-sensed imagery acquired by different satellite missions during the time period between 1968 and 2017. Data from the CORONA, LANDSAT and Sentinel-2 missions were considered. Besides satellite imagery, a digital elevation model and aerial ortho-photos were also used. The procedure was applied to three, highly dynamic, Albanian rivers: Shkumbin, Seman and Vjosë, showing a high potential for application in contexts with limitations in ground data availability. The results of the procedure were assessed against reference data produced by means of expert interpretation of a reference set of river reaches. The results differ from reference values by just a few percentage points (<6%). The time evolution of hydromorphological parameters is well characterized, and the results support the design of future studies aimed at the understanding of the relations between climatic and anthropogenic controls and the response of river morphological trajectories. Moreover, the high spatial and temporal resolution of the Sentinel-2 mission motivates the development of an automatic monitoring system based on a rolling application of the defined procedure.

A 1:50,000-scale geomorphological map of the Adige/Etsch River valley bottom (NE Italy) is presented. The study area is 115 km long, and it extends between the villages of Merano/Meran and Calliano, including also the terminal segments of 9 major tributaries of the Adige River. Presently, the Adige shows a sinuous to straight morphology owing to massive channelization occurred during the nineteenth century. Fluvial geomorphological features have been mapped through a detailed-scale comparative multi-temporal analysis carried out on several historical maps dating since the eighteenth century, previous thematic maps, geological maps of the Italian 'CARG' project, orthophotos (2011) and high - resolution DEMs. The map shows the active river channel, dating to 1803-1805 (before channelization), to 1856-1861 (during channelization) and under present conditions, as well as several paleo-channels dating up to the thirteenth century. The analysis led to define the corridor of historical channel changes, a fundamental tool for river management purposes.

The issue of morphodynamic influence in meandering streams is investigated through a series of laboratory experiments on curved and straight flumes. Both qualitative and quantitative observations confirm the suitability of the recent theoretical developments (Zolezzi & Seminara 2001) that indicate the occurrence of two distinct regimes of morphodynamic influence, depending on the value of the width ratio of the channel $\\beta$. The threshold value $\\beta_R$ separating the upstream from the downstream influence regimes coincides with the resonant value discovered by Blondeaux & Seminara (1985). Indeed it is observed that upstream influence may occur only in relatively wide channels, while narrower streams are dominated by downstream influence. A series of experiments has been carried out in order to check the above theoretical predictions and show, for the first time, evidence of the occurrence of upstream overdeepening. Two different sets of experiments have been designed where a discontinuity in channel geometry was present such that the channel morphodynamics was influenced in the upstream direction under super-resonant conditions ($\\beta{>}\\beta_R$) and in the downstream direction under sub-resonant conditions ($\\beta{<}\\beta_R$). Experimental results give qualitative and quantitative support to the theoretical predictions and allow us to clarify the limits of the linear analysis.

Climate change affects agriculture worldwide, with stronger socio-economic impacts in low-income countries where the lack of data hinders the implementation of effective interventions to face climate change effects. The paper proposes an approach to assess local effects associated with climate change in data-scarce contexts, integrating farmers’ perceptions with available climate data. The method is tested in the Upper Huallaga basin, in the Peruvian selva. The analysis of climate trends in time series of daily data from a local weather station and ERA-5 reanalysis data is integrated with 73 structured interviews with farmers. The resulting increasing temperature trend of 0.2 C per decade is consistent with the farmers’ perception. On the other hand, farmers also highlight an increase in wind gusts and precipitation, in contrast with the available quantitative data. This is further investigated analysing trends in annual crop water deficit and surplus volumes, which can be viewed as a proxy for plant health conditions, and may influence the farmers’ perception of climate change. Results show a recent increase in the annual crop water deficit and surplus volumes, suggesting an increase in sub-daily convective rainfall events, possibly explaining farmers’ perceptions. The proposed approach effectively allows for assessing climatic alterations, their effects, and locally driven adaptation measures in data-scarce regions, as well as providing some insights into trends in sub-daily meteorological events.

Release of water from reservoirs for hydropower production generates intermittent hydropeaking and thermopeaking waves in receiving rivers that can have important ecological implications at a variety of time and spatial scales. In this paper a coupled analytical‐numerical approach is used in order to grasp the relevant processes of the propagation of the hydrodynamic and thermal waves, within the framework of a one‐dimensional mathematical model governed by the Saint Venant equations coupled with a thermal energy equation. While interacting with external forcing, the waves propagate downstream with different celerities such that it is possible to identify a first phase of mutual overlap and a second phase in which the two waves proceed separately. A simplified analytical solution for flow depth and temperature is derived in explicit terms, exploiting the typical square shape of the waves and transforming the boundary conditions into equivalent initial conditions. The numerical model, which retains the complete features of the problem, is solved using a second order finite volume method. The wave properties and the characteristic timescales are investigated by means of the analytical solution and compared with numerical results for some test cases. Overall, the present approach allows for a deeper insight into the complex dynamics that characterize the propagation of hydropeaking and thermopeaking waves.