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Producción Científica, The continuous observation of flows is required to assess a river’s ecological status, to allocate irrigation withdrawals, to provide sustainable hydropower production and to plan actions as well as develop adaptive management plans. Drifters have the potential of facilitating the monitoring and modeling of river behavior at a fraction of traditional monitoring costs. They are floating objects equipped with sensors able to passively follow the movements of water. During their travel, they collect and transmit information about their movement and their surrounding environment. In this paper, we present and assess a low-cost (<150 EUR) customizable drifter developed with off-the-shelf components. The open drifter is capable of handling the majority of use cases defined in the specialized literature and in addition it offers a general river flow characterization toolkit. One of the main goals of this work is to establish an open hardware and software basis to increase the use of drifters in river studies. Results show that the proposed drifter provides reliable surface velocity estimates when compared to a commercial flow meter, offering a lower cost per data point and in contrast to traditional point measurements it can be used to identify and classify large-scale surface flow patterns. The diverse sensor payload of the open drifter presented in this work makes it a new and unique tool for autonomous river characterization., Unión Europea - (project LIFE18 NAT/ES/000121), European Union’s Horizon 2020 (Grant 101032024), Centro de Excelencia en TI de Estonia y Fondo Europeo de Desarrollo Regional (FEDER) - (grant 2014-2020.4.01.15-0018), European Economic Area (EEA) Financial Mechanism 2014–2021 - (project EMP480)

Producción Científica, The recovery of threatened and endangered fish species is among the highest priorities for biodiversity conservation in national parks and fisheries management in nearby areas. Threats to fish populations are numerous and include habitat fragmentation and degradation, proliferation of invasive and pathogen species, and climate change. Moreover, mountain areas often share the most critical threats. However, there does not exist a common strategy that integrates conservation and management plans for fish populations in mountain areas. In this regard, LIFE DIVAQUA designed a conservation strategy that integrates new knowledge gained from scientific research and long-term monitoring data, and considers the main threats to fish populations in mountain areas: (1) A long term monitoring program has been already implemented for 10 years, revealing temporal trends of fish populations in mountain streams. (2) Modeling of fish population by the use of environmental DNA allowed analyzing fish distributions in areas with scarce data and evaluating habitat suitability maps. (3) Fishways construction and removal of river barriers substantially increased the distribution area of endangered species. (4) The analysis of climate change effects in water temperature and hydrology led to the implementation of environmental flows under a climate change scenario; (5) Monitoring fish diseases, their occurrence, and temporal changes (e.g., Aeromonas spp.) can be used as an early warning signal of ecosystem unbalance. A pilot study for the implementation of this conservation and management plan in the LIFE DIVAQUA project is showing promising results. However, the success of conservation and management strategies requires a broader approach. This includes the participation of a wide range of partners and stakeholders and utilizes independent scientific oversight, assessment, and project adjustments to ensure conservation goals are met., Comisión Europea - (grant LIFE18 NAT/ES/000121)

Producción Científica, Stepped fishways are structures that allow the free movement of fish in transversal obstacles in rivers. However, the lack of or incorrect maintenance may deviate them from this objective. To handle this problem, this research work presents a novel low-cost sensor network that combines fishway hydraulics with neural networks programmed in Python (Keras + TensorFlow), generating the first autonomous obstruction/malfunction detection system for stepped fishways. The system is based on a network of custom-made ultrasonic water level nodes that transmit data and alarms remotely and in real-time. Its performance was assessed in a field study case as well as offline, considering the influence of the number of sensing nodes and obstruction dimensions. Results show that the proposed system can detect malfunctions and that allows monitoring of the hydraulic performance of the fishway. Consequently, it optimizes the timing of maintenance on fishways and, thus, has the potential of automatizing and reducing the cost of these operations as well as augmenting the service of these structures. Therefore, this novel tool is a step forward to achieve smart fishway management and to increase their operability., European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Smart fishways—grant agreement n° 101032024)