River networks are vital ecosystems that face increasing threats from societal pressures and global environmental changes. Effective biomonitoring of these systems is crucial for maintaining water quality and biodiversity by guiding policy and management actions. Over the past two decades, environmental DNA (eDNA) has emerged as a promising tool, offering advantages over traditional biomonitoring methods. However, the downstream transport and decay of genetic material in river networks poses a challenge for the spatial interpretation of eDNA samples. This study follows from work of Carraro et al. (2021) and aimed to find the optimal sampling density to determine taxon distributions at the catchment scale based on eDNA sampling. This was done by means of a virtual experiment, where a large number of sampling campaigns were simulated over multiple synthetic taxon distributions and river networks. This enabled the development of generalized sampling principles irrespective of system-specific confounding factors. Results showed that taking at least one sample per 10 km2 of catchment area provides a reliable estimate for taxon abundance across the catchment. Taking more samples only marginally improved the accuracy of the predictions when it concerns taxa that are commonly found in different parts of the catchment. In case of finding rare taxa, a minimum of two samples per 10 km2 is necessary to ensure that the taxon is correctly located. This study also underscores the limited capability of eDNA in capturing fine- scale spatial patterns, suggesting its complementary role alongside traditional surveys. In summary, this study provides a generalized sampling guideline for eDNA biomonitoring as a tool for broad-scale environmental assessments and biodiversity conservation.

In a Ghana project, field measurements at the Black Volta aimed to enhance hydrological predictions for the Bui power dam. Validating devices and exploring floodplain contributions, such as with GNSS reflectometry and cameras, provides crucial insights for dam management and model improvement.

In this multidisciplinary project, conducted at the Black Volta in western Ghana, the focus is on addressing challenges related to data scarcity in hydrological predictions, particularly for the Bui power dam. The dam has faced issues during flood events, leading to spillages and subsequent damages. To enhance predictions, field measurements were carried out, employing innovative methods for remote areas within the TEMBO Africa project. The project aimed to validate devices measuring river parameters and improve the understanding of floodplain contributions to river discharge. Key methods included GNSS reflectometry for water level measurement and a camera-based approach for discharge determination. The GNSS-reflectometry device was successfully tested, validated, and installed at the Black Volta, automating water level measurements. The camera-based method demonstrated success on smaller streams but faced challenges on wider rivers. Despite this, it was installed at the Black Volta for daily discharge measurements, promising a reduction in rating curve uncertainty when combined with water level measurements. Field observations were utilized to enhance an existing hydraulic model, refining the floodplain's representation. The study also delved into determining the floodplain's roughness coefficient, involving manual measurements and attempts at automation using remote sensing techniques. The roughness coefficients were implemented into the Delft3D model, showing contributions of floodplains to river discharge. Despite challenges, including equipment installation timing and location disruptions due to bridge construction, the project provides valuable insights for improving hydrological models and preventing dam spillages in the future.