Non‐perennial streams have a global prevalence, but quantitative knowledge of the temporal dynamics of their flowing length—namely the extent of the wet portion of the stream network—remains limited, as the monitoring of the spatiotemporal configuration of wet channels is challenging in most settings. This work combines the high spatial resolution of visual surveys and the high temporal resolution of camera‐based approaches to reconstruct the space‐time stream network dynamics in a 3.7 km ² Mediterranean catchment of central Italy. Information on the hydrological status of the stream network derived from 40 field surveys and sub‐hourly images collected with 21 stage‐cameras are combined exploiting the hierarchical principle. The latter postulates the existence of a Bayesian chain, defined from the local persistence of the nodes that dictates their wetting/drying order during expansion/retraction cycles of the flowing stream network. Our results highlight the complexity of network dynamics in the study area: while the number of wet nodes decreases during the dry season and increases during the wet season, the local persistency exhibits a highly heterogeneous non‐monotonic spatial pattern, originating a dynamically disconnected network. Despite this heterogeneity, the hierarchical model well approximates the temporal evolution of the state of the network nodes, with an accuracy that exceeds 99%. Crucially, the model allows the reconstruction of the wet portion even in cases in which part of the network was not observed. This work provides a novel conceptual approach for the reconstruction of the wet portion of the network in poorly accessible sites.

Recently, increased attention has been devoted to intermittent and ephemeral streams (IRES) due to the recognition of their importance for ecology, hydrology, and biogeochemistry. However, IRES dynamics still demand further research, and traditional monitoring approaches present several limitations in continuously and accurately capturing river network expansion/contraction. Optical-based approaches have shown promise in noninvasively estimating the water level in intermittent streams: a simple setup made up of a wildlife camera and a reference white pole led to estimations within $2\mathrm{cm}$ of accuracy in severe hydrometeorological conditions. In this work, we investigate whether the shortcomings imposed by adverse illumination can be partially mitigated by modifying this simple stage-cam setup. Namely, we estimate the image-based water level by using both the pole and a larger white bar. Further, we compare such results to those obtained with larger bars painted in the red, green, and blue primary colors. Our findings show that using larger white bars also increases reflections and, therefore, the accuracy in the estimation of the water level is not necessarily enhanced. Likewise, experimenting with colored bars does not significantly improve image-based estimations of the stage. Therefore, this work confirms that a simple stage-cam setup may be sufficient to monitor IRES dynamics, suggesting that future efforts may be rather focused on including filters and polarizers in the camera as well as on improving the performance of the image processing algorithm.

Non-perennial streams are those streams that periodically cease to flow in at least one point along their network. The research community well recognizes the importance of such watercourses for they have a global prevalence and provide diverse hydrological functions and ecosystem services. The spatiotemporal pattern of the active drainage network is anything but simple, sometimes showing a very complex pattern. Non-perennial streams, in fact, are often located in heterogeneous environments, in which the combination of climate, morphology, land cover, soil, substrate, and anthropic factors could play a role in the observed drying and wetting patterns. This work combined two techniques, with different spatiotemporal resolutions, to characterize the spatiotemporal extent of the stream network in a 3.7 km2 Mediterranean catchment of central Italy. The hydrological status of a set of nodes of the network was derived for the period 2020-2022 from sporadic visual surveys and, most importantly, through the analysis of sub-hourly images collected by 21 cameras distributed along a set of strategic nodes of the network. The latter technique is particularly promising to reconstruct the hydrological dynamics taking place in the target cross-section, as the temporal evolution of the underlying hydrological conditions (wet vs. dry), the water stage, and the corresponding discharge can be inferred from the automatic or manual analysis of the acquired images. The available experimental data was combined exploiting the hierarchical principle, that postulates the existence of a Bayesian chain based on the local persistency of the nodes that dictates their drying/wetting order during stream retraction/contraction cycles. The results highlighted the complexity of the network dynamics in the study area: while the number of wet nodes decreased during the dry season and increased during the wet season, the local persistency of the nodes showed a highly heterogeneous and non-monotonic pattern, resulting in a dynamically disconnected network. The approach allowed the reconstruction of the entire river network and represented a useful tool to estimate the extent of its wet portion, even in case part of the network could not be inspected. This work represents a novel approach to reconstruct the extension of the wet portion of the stream network in difficult-to-access environments, where traditional techniques might be inadequate.

The monitoring of small headwater catchment represents a major issue in hydrology, especially in remote areas, where gathering real-time hydrological data is often prohibitive due to the limited availability of power and connectivity. However, recent advances in non-contact computer vision and informatic technology offer an opportunity to fill such technical gap. In this regard, we designed and developed the MagicHydroBox prototype (MHB), and all-in-one camera and processing unit system aimed at monitoring the water level in small headwater rivers. The tasks performed by MHB include image collection, image processing, storage and transmission of the processed data. Since the MHB is equipped with NIR (NearInfrared) leds and camera, the image collection can be carried out both during the day and the night period. The image processing takes place directly in the MHB, to guarantee the onsite analysis, it is based on the Otsu’s segmentation method to identify a properly placed target within the images, and results in the direct estimation of water depth. Finally, we built in the MHB the possibility to transmit the processed data both through Gprs (mobile data) and LoRaWan (a long-range, low-power system). The MHB is also equipped with a GUI that allows the user to set and calibrate the instrument. We carried out preliminary field tests to evaluate the effectiveness of the MHB in providing an accurate measure of the target and transmitting the processed data. The preliminary results highlight the potential of the MHB to estimate the water level, especially in NIR images, and to provide a real-time hydrological monitoring where Internet signal is available. The main innovation of the MHB is represented by the fact that it automated a series of tasks that were instead manually performed in previous works. The concentration of all the necessary tasks within the MHB simplify the data acquisition, the processing and the management providing an useful tool where frequent maintenance or monitoring surveys are not possible. Moreover, the MHB is promising for future implementation of algorithms to measure surface velocimetry and discharge.

Soil pollution is one of the main threats to soil ecosystem health, and the combination of microbial remediation with phytoremediation can promote contaminant degradation and contribute to the revitalization of ecosystem functions. Plant interactions with the surrounding physical and biological environments determine the formation of new ecosystems, which, similarly to natural ones, can provide several ecosystem services. Despite the increasing diffusion of phytoremediation and the large areas potentially suitable for the application of these technologies, the delivery of ecosystem services during phytoremediation interventions has rarely been investigated. In the framework of a bio-phytoremediation project, the present work explored the capacity of a new plant cover created at a site under bio-phytoremediation (area: 0.52 ha) to provide supporting, regulating, and cultural Ecosystem Services. The daily cumulative cooling effect due to plant transpiration during summer was 12.7 °C m⁻² day⁻¹, with species characterized by the highest Leaf Area Index and leaf transpiration rates (such as Chrysopogon zizanioides, evergreen shrub species, and Populus nigra) contributing the most to air cooling. Thanks to plant photosynthesis, 177 kg of C was sequestered each year, while the air pollution intercepted by the plant leaves amounted to 1.82 kg per year. After 30 months of phytoremediation, an increase in soil microbial activity indicated a progressive improvement in soil quality. Similarly, the cytometric analysis of hepatopancreatic cells from isopods indicated that the bioindicator organism actively and positively responded to the recovering environment, thus supporting the effectiveness of the ongoing remediation of soil quality. Finally, the implementation of the bio-phytoremediation intervention led to the creation of new habitats, increased ecological connectivity in the surrounding urban area, and generated an aesthetic value comparable to that of urban greenery. These analyses provided evidence that these techniques not only supported soil remediation and site securing but also contributed to improving citizen well-being in the proximity of the site and the environmental quality of the area. These significant positive externalities of phytoremediation should be considered when selecting technologies for environmental clean-up.