Most of the rivers in the Afrotropics emerge from forested uplands that are prime for human activities, including crop farming, grazing and settlement. However, streams draining these areas have a low diversity of fishes, which limits their use as indicators of water quality and ecological status. Here, we developed macroinvertebrate-and fish-based indices of biotic integrity and evaluated their responses to human activities in the Sondu-Miriu River, Kenya. Overall, water quality declined downstream because of the cumulative effects of human activities. The upper reaches had poor fish diversity with at most three species, but macroinvertebrate communities were diverse on the entire gradient of the river. Metrics that have been used to develop indices of biotic integrity in the Lake Victoria basin were selected and evaluated for responsiveness to various forms of human disturbance along the river. The poor diversity of fishes in the upper reaches limited the number of metrics that could be developed and evaluated. The fish index was only successfully applied in the middle and lower reaches where fish diversity was high. However, macroinvertebrates were diverse and sensitive to human activities on the entire gradient of the river. This study shows that for biomonitoring, macroinvertebrate communities offer a better alternative to fishes in high elevation Afromontane streams that harbour poor fish assemblages.

ABSTRACT:This study is part of the EU H2020 research Project FLOWERED (de-FLuoridation technologies for imprOving quality of WatEr and agRo-animal products along the East African Rift Valley in the context of aDaptation to climate change). FLOWERED project aims to develop technologies and methodologies at cross-boundary catchment scales to manage the risks associated with high Fluoride water supply in Africa, focusing on three representative test areas along the African Rift Valley (i.e. Ethiopia, Kenya and Tanzania), characterized by high fluoride contents in waters and soils, water scarcity, overexploitation of groundwater and high vulnerability to risks arising from climate change, as drought and desertification. It also is empowering local communities to take responsibility for the integrated-sustainability of the natural resources, growing national and international environmental priorities, enhancing transboundary cooperation and promoting local ownership based on a scientific and technological approach. Within the FLOWERED project, the transition from the land cover to the land use and water use maps is provided through the development of a mobile application (FLOWERED-GeoDBapp ). It is dedicated to the collection of local geo-information on land use, water uses, irrigation systems, household features, use of drinking water and the other information needful for the specific knowledge of water supply involving local communities through participative approach. This system is structured to be populated, through an action of crowd-generating data by local communities (students and people involved mainly by NGOs). The SHAREGEODBapp is proposed as an innovative tool for water management and agriculture institutions at regional and local level.

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Suitability of the Natural Resources Conservation Service (NRCS) curve number (CN) model of run‐off prediction was evaluated on three humid tropical forested catchments in Kimakia, Kenya. The catchments were dominated by Pinus patula (catchment A), Arundinaria alpina (catchment C) and Pennisetum clandestinum (catchment M). The study used discharge and rainfall data collected between 1958 and 1986. Seventy‐three (73) isolated storms were graphically separated into baseflow, interflow and surface run‐off. Forest cover types significantly improved catchments characteristics that influence baseflow and interflow generation in catchment C but not those that influence surface run‐off production. In its original form, the NRCS CN model resulted in direct run‐off estimates that deviated from observed ones by between 43.8% and 55.3%. These discrepancies were minimized through modification of the β and CN parameters. CN generated empirically using storm rainfall predicted the direct run‐off satisfactorily. Therefore, the modified NRCS CN model adequately estimates direct run‐off from humid tropical forested catchments.