At present there is a great lack of hydrological information on non-perennial rivers. In many cases, there is no knowledge of which river reaches are subject to non-flow periods, and the duration of non-flow and dry periods remains unknown. Few hydrometric stations are present along non-perennial rivers, and these stations provide point information, limiting the ability to describe the flow conditions across a river reach. For example, they do not allow to distinguish a continuous line of flow from an isolated pools condition. In contrast, approaches based on field surveys or citizen science can provide information on flow condition over entire river reaches but their temporal resolution is generally poor. Within this framework, satellite remote sensing provides significant opportunities due to the possibility of monitoring large areas with high temporal resolution. However, the use of satellite images for monitoring non-perennial river regimes has so far been limited by the availability of images with adequate spatial resolution and their accessibility in terms of cost. Multispectral satellite data freely distributed by the European Space Agency's Copernicus Sentinel-2 mission, with a spatial resolution of 10 m and an acquisition frequency of approximately five days, represent an appropriate trade-off point for monitoring non-perennial rivers with active channels not covered by vegetation and larger than about 40 m. In this study, we investigated the capability of Sentinel-2 data to differentiate among three flowing states of non-perennial rivers: "flowing" (F), "ponding" (P), and "dry" (D). The analysis was performed for 5 reaches of the streams Sciarapotamo, Mingardo and Lambro (Campania region, Italy). By analyzing the spectral signatures of land cover within river corridor, we identified the bands in which land cover classes are most differentiated. Utilizing these specific bands, we created a false-color image in which the pixels covered by water stand out from the background. The comparison between false color images and field acquired ground truth showed very good agreement. For all the archive data (since 2015) we identified one of the three possible flowing status: F, P and D. The acquired dataset was utilized to train a Random Forest model capable of predicting the daily occurrence of specific flowing statuses (F, P, D), using spatially interpolated rainfall and air temperature data as predictors. The model demonstrated strong performance in terms of accuracy (ranging from 82% to 97%) and true skill statistic (ranging from 0.65 to 0.95). In each of the five years of the observation period, all the reaches underwent no-flow condition for at least a few days and in some cases up to four months. Three of the five reaches were completely dry each year while the other two never dried completely. With its ability to monitor the presence of water in a cost-effective manner, this method has the potential to significantly improve the knowledge on non-perennial rivers regimes.

Habitat models rarely consider macroinvertebrate communities as ecological targets in rivers. Available approaches mainly focus on single macroinvertebrate species, not addressing the ecological needs and functionality of the whole community.This research aimed at providing an approach to model the habitat of the macroinvertebrate communities. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds, and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the mesohabitat scale. Using different datasets gathered from field data collection and 2D hydrodynamic simulations, the model was calibrated in the Trebbia River (2019 field campaign) and validated in the Trebbia, Taro, and Enza rivers (2020 field campaign).The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size, and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient (R2test) for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications.Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to ecological flows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Lampetra zanandreai (Vladykov, 1955) is a non-parasitic, freshwater lamprey endemic to the ancient Po basin. A few, mostly very dated studies have investigated some aspect of the biology of this lamprey, but surprisingly, despite it being considered a threatened species, information on its ecology is practically absent. Specifically, information about habitat preferences is generic and qualitative. Since most of the life cycle is spent in the fossorial larval stage, which is also the only one in which organisms feed, information about ecological requirements of ammocoetes is essential for any conservation strategy. In this study we provide the first data about physical habitat preferences for lamprey ammocoetes by analyzing their presence within sampled hydro-morphological units (HMUs), following the approach of habitat attribute description of the MesoHABSIM (MesoHABitat SImulation Model) methodology. To explore the relationship between lamprey presence and HMU characteristics, a random forest (RF) model was developed and tested using data collected in five stream reaches of the Po basin (NW Italy). The final parsimonious RF model performed well in terms of accuracy (95.2%) and true skill statistic (90.4%), allowing us to identify the most significant mesohabitat attributes for the considered species. Furthermore, in the Ghiandone River, where the highest density and number of individuals were found, a granulometric analysis of the riverbed material was carried out. Results showed that selected strains of sand and fine gravel, with low organic content, are preferred by ammocoetes. To our knowledge, this is the first study exploring the habitat preference of this endangered species, listed in Annex II of the European Habitats Directive.

Habitat models rarely considers macroinvertebrates as ecological targets in rivers. Available approaches mainly focus to single macroinvertebrate species, not addressing the ecological needs and functionality of the entire community. This research aimed at providing an approach to model the habitat of the macroinvertebrate community. The study was carried out in three rivers, located in Italy and characterized by a braiding morphology, gravel riverbeds and low flows during the summer period. The approach is based on the recently developed Flow-T index, together with a Random Forest (RF) regression, which is employed to apply the Flow-T index at the meso-habitat scale. Using different datasets gathered from both field data collection and 2D hydrodynamic simulations, the model has been calibrated in the Trebbia River (2019 field campaign), and then validated in the Trebbia, Taro and Enza rivers (2020 field campaign). The RF model selected 12 mesohabitat descriptors as important for the macroinvertebrate community. These descriptors belong to different frequency classes of water depth, flow velocity, substrate grain size and connectivity to the main river channel. The cross-validation R2 coefficient (R2cv) of the training dataset was 0.71, whereas the R2 coefficient for the validation dataset was 0.63. The agreement between the simulated results and the experimental data shows sufficient accuracy and reliability. The outcomes of the study reveal that the model can identify the ecological response of the macroinvertebrate community to possible flow regime alterations and river morphological modifications. Lastly, the proposed approach allowed to extend the MesoHABSIM methodology, widely used for the fish habitat assessment, to a different ecological target community. Further applications of the approach can be related to eflows design in both perennial and non-perennial rivers, including river reaches in which fish fauna is absent.

Mountain rivers are threatened by various natural and human-induced impacts, all of them potentially altering the availability of habitats for fish communities. These impacts include, among others, climate- change-associated reduction of discharge and water abstraction by humans, e.g., for hydropower production and irrigation. A quantitative assessment of future water, and subsequent fish habitat, availability is therefore pivotal to the effective and sustainable management of water resources in mountain basins.In this work, we investigated the effect of climate change on discharge and fish habitat availability in two alpine catchments in the Western Italian Alps.Historical discharge was modeled by means of a relatively simple rainfall-runoff model (TUWmodel), whereas discharge projections were computed under different state-of-the-art greenhouse gas scenarios both for the near future (2041-2060) and the far future (2080-2099). Discharge was then translated into habitat availability with the MesoHABSIM (Mesohabitat Simulation Model) methodology, an approach that allows to simulate the variations in habitat availability for the local fish population (brown and marble trout).We found significant changes in future runoff, in turn leading to marked changes in fish habitat availability, with contrasting response in glaciated vs non glaciated basins.We demonstrated that the combination of a hydrological model, climate scenarios and habitat modeling allows the depiction of future ecological scenarios for alpine rivers, thereby representing a potential support for water resources management and decision-making.

Non-perennial rivers are characterized by periods with dry bed or chains of isolated ponds. Given the extremely high biodiversity and various ecosystem services, these environments require careful management. The main obstacle to the implementation of correct management practices is related to the lack of information about the duration and frequency of zero-flow periods, that are the primary determinants of ecosystem processes in this kind of streams. In many cases, the presence of non-perennial reaches within the river network is unknown. Given the high extension of the network of non-perennial rivers and their strong spatial inhomogeneity, traditional gauging systems are not adequate to provide measures with adequate spatial coverage. Moreover, point measures cannot capture the space-pattern of presence/absence of water. On the other hand, field surveys of water patterns have a limited temporal resolution and therefore lack in capturing the regime’s time-patterns. In this context, satellite data can make a key contribution thanks to the possibility of monitoring large areas with high temporal resolutions. Their use for monitoring the regime of non-perennial rivers has so far been limited by the availability of images with adequate resolution and accessible costs. In this work, we explored the potential of medium-resolution multispectral Sentinel-2 data to identify non-perennial rivers and to assess their degree of intermittency. Examining the spectral signatures of water, sediment and vegetation covers, the bands in which these classes are most differentiated were identified. Exploiting these bands, we generated false-color image in which the pixels covered by water stand out from the background. From the false-color composite images, it was possible to identify the three distinct flowing status of non-perennial rivers: “flowing”, “ponding” and “dry” . The classification of flowing status was checked against ground truth, showing very good agreement. To enable a wider audience to identify flowing status along non-perennial rivers, we have developed and made freely available a code on the Google Earth Engine platform. For all the archive images (since 2015) we identified one of the three possible flowing status: flowing, ponding and dry bed. The obtained dataset allowed to train a random forest (RF) model able to predict the daily occurrence of a specific flowing status using as predictors spatially interpolated rainfall and air temperature data. The analysis was performed for 5 reaches of the streams Sciarapotamo, Mingardo and Lambro (Campania region, Italy), for which a RF model was calibrated. Classification RF models performed well in terms of accuracy (ranging from 82% to 97%) and true skill statistic (ranging from 0.65 to 0.95). All the studied reaches showed a no-flow condition during the observation period. Three of the five reaches resulted to have a dry bed condition each year while the other two reaches never dry up completely. With its ability to monitor the presence and absence of water in a cost-effective manner, this method has the potential to significantly improve the management and the conversation of non-perennial rivers, enabling a better understanding of their ecological status, as required by the European Water Framework Directive 2000/60/EC.

Abstract. Extracting accurate bathymetric information from clear, shallow waters in complex riverine environments can be challenging, but it is crucial for many applications, such as hydraulic modeling, ecological monitoring, and sediment transport analysis. Multi-view stereo photogrammetry (MVS-SfM) has emerged as a promising technique for acquiring high-resolution bathymetric data from aerial imagery. However, the accuracy of MVS-SfM can be affected by various factors, including water refraction, which can distort the depth measurements. In this study, iterative Dietrich’s refraction-correction method is tested for extracting bathymetry from MVS-SfM in a complex riverine environment. Moreover, we proposed a workflow for applying the method using raster data files, which can be more readily available than point clouds. It also compared the obtained results with previous applications and evaluated them using statistical indices and ratios. For this case study, the multicamera refraction method produces bathymetric datasets with accuracies of ~0.019% of the flying height and precisions of ~0.07% of the flying height. This study contributes to increased confidence in exploiting aerial imagery for bathymetric mapping in photogrammetric procedures under field conditions and can facilitate the management and conservation of clear water systems.

In the framework of water resources planning and management, the MesoHABSIM (MesoHABitat Simulation Model) approach demonstrated high potential to assess suitable environmental conditions for freshwater fish species. In the present study, the transferability capabilities of mesohabitat suitability criteria were evaluated in nine streams across Northern Italy. In particular, the Random Forest (RF) technique was used to calibrate and validate suitability criteria for adult and juvenile life stages of brown trout (Salmo trutta), marble trout (Salmo marmoratus), bullhead (Cottus gobio) Italian barbel (Barbus plebejus), and Italian vairone (Telestes muticellus). Presence/absence binary models were calibrated at the mesohabitat scale (i.e., the geomorphic unit scale) using field data collected in reference sites, selected for their natural hydro -morphological conditions and habitat characteristics. Model transferability tests were performed in streams located in different regions within the distribution area of the fish and not included in the model calibration dataset. Predictive capacities of the models were very good in terms of accuracy (ranging from 75% to 82%) and true skill statistic (ranging from 52% to 75%). The high predictive performances can be related to (i) the use of an ecologically relevant spatial resolution (mesohabitat) to predict fish presence, (H) a robust and adequate hydro-morphological characterization of the analyzed geomorphic units, and (iii) the large number of mesohabitat descriptors provided by the MesoHABSIM approach. Results showed that mesohabitat suitability criteria based on RF can be considered transferable among streams located in different regions of Northern Italy, especially when river channels are characterized by similar hydro-morphological characteristics.

In Mediterranean streams and rivers in general, aquatic organisms use a specific habitat for rearing, growing, breeding, and wintering. Multiple studies have focused on this subject, but few for the specific purpose of developing suitability models that feed hydrobiological models for the analysis of flow regimes and the design of environmental flows. Therefore, this study analyzes the habitat preferences of five freshwater species of fish and mussels in the NE of the Iberian Peninsula for that purpose. We use simple decision trees and random forest (RF), a machine learning technique based on the aggregation of multiple decision trees, to develop suitability models that relate the habitat preferences of the five species—separately adults and juveniles—to different attributes of a physical habitat at the meso-scale. Selected attributes are the surface percentage of different levels of depth (0–15 cm, 15–30 cm, …, >120 cm), velocity (0–15 cm/s, 15–30 cm/s, …, >120 cm/s) and abiotic/biotic substrate (e.g., gigalithal, megalithal, detritus, phytal), and absence/presence of refuges (boulder, canopy shading, emerging vegetation, undercut banks, woody debris, roots). The models were developed in order to predict three ranks of habitat suitability: absence, presence and abundance, depending on the mentioned attributes of the mesohabitat analysed. Our study provides quantitative results concerning the correspondence between the presence and abundance of different species and habitat characteristics, confirming qualitative observations stated in previous studies. We proved now that the adult mussels of Unio genus require a minimum of 5% of sand or silt, low velocities, and undercut banks and roots; that Barbus meridionalis habitat changes considerably among seasons; that Salaria fluviatilis needs coarse substrates (megalithal, macrolithal and mesolithal) and velocities above 15 cm/s; and that the adult Squalius laietanus prefers glides and pools with depths above 60 cm and velocities below 45 cm/s, depending on the season; and that Anguilla anguilla prefers intermediate size substrates (macrolithal, mesolithal and microlithal). These results are essential for the modeling of environmental flows in rivers where these species are present. Thus, by analyzing how their physical habitat changes according to the flow regime, one can see whether the available habitat of fish and mussels increases or decreases and predict periods of danger for the species.

Knowledge about the frequency and duration of each flowing status of non-perennial rivers is severely limited by the small number of streamflow gauges and reliable prediction of surface water presence by hydrological models. In this study, multispectral Sentinel-2 images were used to detect and monitor changes in water surface presence along three non-perennial Mediterranean rivers located in southern Italy. Examining the reflectance values of water, sediment and vegetation covers, the bands in which these classes are most differentiated were identified. It emerged that the false-color composition of the Sentinel-2 bands SWIR, NIR and RED allows water surfaces to be clearly distinguished from the other components of the river corridor. From the false-color composite images, it was possible to identify the three distinct flowing status of non-perennial rivers: “flowing” (F), “ponding” (P) and “dry” (D). The results were compared with field data and very high-resolution images. The flowing status was identified for all archive images not affected by cloud cover. The obtained dataset allowed to train Random Forest (RF) models able to fill temporal gaps between satellite images, and predict the occurrence of one of the three flowing status (F/P/D) on a daily scale. The most important predictors of the RF models were the cumulative rainfall and air temperature data before the date of satellite image acquisition. The performances of RF models were very high, with total accuracy of 0.82–0.97 and true skill statistic of 0.64–0.95. The annual non-flowing period (phases P and D) of the monitored rivers was assessed in range 5 to 192 days depending on the river reach. Due to the easy-to-use algorithm and the global, freely available satellite imagery, this innovative technique has large application potential to describe flowing status of non-perennial rivers and estimate frequency and duration of surface water presence.

Growing energy demand and targets for reducing greenhouse gas emissions are driving a significant hydropower (HP) generation expansion in the Alpine area. However, despite its advantages, HP implementation causes several impacts on the ecological status of river systems and the preservation of characteristic landscapes. The use of decision-making tools, like multi-criteria decision-making (MCDM) methods, can provide helpful support to achieve more sustainable solutions for complex water management problems. In Aosta Valley (NW Italy), an MCDM experimental approach is being applied to define the optimal environmental flow scenario to be released by HP plants, in an attempt to balance energy production needs, economic profitability and environmental safeguard. Four criteria are considered (energy, environment and fishing, landscape, economy) and key stakeholders are involved in the entire decision-making process. However, there is a need to test also other MCDM methodologies in order to understand if an alternative approach could be more suitable for future, more complex, HP management decisions. Therefore, in this paper, seven different MCDM methods are applied to an existing small run-of-the-river HP plant for the selection of the optimal flow release scenario, i.e., SAW, WPM, AHP, TOPSIS, VIKOR, ELECTRE III, and the initially adopted method, SHARE MCA. The results obtained with the application of the different MCDM methods are investigated and statistically compared. The strengths and weaknesses of the different methodological approaches are also discussed. Based on the comparative results and the consequent evaluations, SHARE MCA, WPM, and VIKOR appear to have the most interesting characteristics in terms of overall feasibility.

Aquatic ecosystems are particularly vulnerable to anthropogenic activity and climate change. The changes in flow regimes in Lithuanian lowland rivers due to the operation of hydropower plants (HPPs) and the impact of altered flow on some fish species have already been studied. The impact of climate change on future natural river runoff and the structure of fish assemblages was also investigated. However, it is still unknown how the combined effect of climate change and flow regulation related to hydropower generation may affect fish assemblages in the downstream river reaches below the Lithuanian HPPs. In this study, the physical habitat modelling system MesoHABSIM was used to simulate spatial and temporal changes in aquatic habitats availability for different fish species under the influence of HPP at different climate change scenarios. Changes in the available habitat were assessed for common fish species in four HPP-affected rivers representing different hydrological regions of Lithuania. The modelling results showed that the operation of HPP under climate change conditions in most rivers could be beneficial for small benthic fish species such as gudgeon Gobio gobio and stone loach Barbatula barbatula. Meanwhile, for larger fish species (e.g., chub Squalius cephalus and vimba Vimba vimba) the alteration in the temporal availability of suitable habitat was relatively higher.

Abstract Habitat modelling aims to predict changes in the structure of aquatic communities as a function of habitat availability. It is a primary tool to inform management actions and to search for the best compromise between biodiversity conservation and water supply. The construction of these models requires in-depth knowledge about the main hydrological and geomorphological drivers that affect local communities. However, these drivers act at different spatial scales and determining which investigation scale is the best trade-off between model accuracy and model transferability is crucial. The present work aims to evaluate the mesoscale for habitat modelling of aquatic macroinvertebrates, by testing the effect of microhabitat (flow velocity, water depth, substrate), mesohabitat (riffles, glides, backwaters, isolated ponds) and spatial position (coordinates and derived spatial variables) on macroinvertebrate community variability. Multivariate spatial analyses were used to analyse the macroinvertebrate data collected in a braided reach of the Trebbia River (N Italy). Mesohabitat was a good predictor of macroinvertebrate community composition, although both microhabitat and space also showed a significant effect. The outcome of this work highlights the transferability of the results across mesohabitats, which supports the use of mesoscale modelling for macroinvertebrate distribution in braided rivers.

The MesoHABitat SImulation Model (MesoHABSIM) is the preferred method to calculate spatio-temporal variation in the fish habitat availability in Italian rivers. With the aim of improving the applicability of the MesoHABSIM approach in the Italian territory, we carried out a systematic review of physical habitat preferences for 31 freshwater fish species and three freshwater lampreys, representing 75% of the total indigenous freshwater fish community of Italy. Information related to suitable ranges of depth, flow velocity, biotic/abiotic substrates, covers/shelters was collected and summarized for two critical life stages (adult and juvenile) and two bioperiods (rearing/growth and spawning). Overall, 250 publications were reviewed, classified as 206 peer-reviewed papers, 20 books, 7 PhD thesis, and 17 grey literature sources. Our analysis revealed substantial deficits of information about habitat requirements for more than 30% of Italian freshwater fish species. This information is particularly scarce for the most threatened endemic species, especially for their most critical bioperiod (i.e., spawning). With the aim of preserving freshwater fish biodiversity as required in the EU Biodiversity Strategy for 2030 (European Commission, 2020), accurate information on physical habitat requirements for spawning is crucial. As an example application of MesoHABSIM, the collected habitat preference information was used to define and apply mesohabitat suitability criteria for one fish species (Telestes muticellus) in a regulated river reach of Argentina Creek (Province of Imperia, Italy). This analysis demonstrates the potential for applying information from the current review to other fish species.