Southern Zimbabwe is an illustrative example where (increasing) water scarcity can lead to food- and financial insecurity and hinder socio-economic development. Sand rivers are a nature-based alternative to reservoirs, hosting shallow aquifers in their sandy beds with potential for decentralized and clean water abstraction. However, their extent and storage potential remain poorly understood, limiting awareness and investment. Riparian vegetation, which depends on sand rivers and remains green during the dry season, could serve as a valuable proxy for water storage.

This study uses a remote sensing approach to estimate and map the capacity of sand rivers to store water across three sub-catchments of the Limpopo River Basin (Shashani, Mzingwane, and Shashe), with two methods. Firstly, the sand river and riparian zones were delineated. The minimum storage capacity was estimated and mapped by summing WaPOR v3 evaporation data over the dry season. Secondly, 35 depth measurements were combined with spatial analyses to empirically predict depth and geometric storage capacity for the whole study area.

The results showed that there is a significant water storage potential in the channels. The two estimates can be combined for an unconsumed water availability, showing a significant sustainable potential (totaling 83 x10^6 m3 potentially irrigating 8300ha). Dry season evaporation was consistently exceeded by geometric storage at medium to large evaporation rates, suggesting that these rivers have unconsumed storage potential. The maps suggest that most potential water storage is located in the main river stems (the largest 10% hold 45-55% of water). Most tributares seemed to evaporate all water. Using water for irrigation would directly compete with vegetation. However, some local hotspots were still observed at smaller rivers.

Although significant uncertainties remain and field validation is still needed, the models show promise for wide-scale planning and development. The findings highlight that sand rivers, with rough estimates of their decentralized, cost-effective, and sustainable water storage, can be mapped remotely with minimal effort or field data. This could open up possibilities to offer support to farmers in semi-arid regions and pave the way for farmer-led irrigation initiatives.

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.