Locally resident citizen technicians using basic equipment and Open Data Kit-enabled smartphones have collected flood-focused suspended sediment (SS) samples from 11 sites on the Tsitsa River and its tributaries, in the Eastern Cape Province of South Africa. In the highly degraded and gullied Tsitsa River catchment, existing modelled SS data were unverified and at odds with the results of studies based on dam sedimentation rates. Suspended sediment concentration (SSC), flux and yield data were required at subcatchment scale to support the prioritisation of community-based land rehabilitation initiatives in rural communal areas and to determine the relative contributions of subcatchments to SS yield at the site of the proposed Ntabelanga Dam. Approaches relying on researcher presence and/or installed instrumentation were precluded by cost, study area size, and the risk of equipment theft, vandalism and damage during high flows. Analysis of the quantitative data collected by the citizen technicians allows high-resolution SSC, flux, and yield data to be produced at subcatchment scale, which will be benchmarked by an acoustic SSC probe at a downstream Department of Water and Sanitation gauging weir. Qualitative descriptive and photographic data allows distant researchers to gain a real-time, catchment-wide overview of river and SS levels. This paper outlines the method, benefits and challenges of a direct-sampling approach that has the potential to address spatial and temporal challenges commonly experienced during SS sampling campaigns.

Soil erosion rates are high in many parts of Southern Africa, and are likely to rise because of climate change. Suspended sediment loads (SSL) and yields (SSY) are used to measure and benchmark soil erosion and/or sediment transport rates and determine trajectories of change. Some modelled SSY are available for Southern African catchments, but there is a dearth of contemporary observed data. Northern hemi-

<p>In South Africa, as in many developing countries, the suspended sediment (SS) data required to support catchment scale hillslope restoration and rehabilitation programmes are typically scarce or absent, leading to a reliance on modelled SS loads and yields that are generally not validated by measured SS data. An exception is the Tsitsa River catchment in the Eastern Cape Province, where modelled SS yields were high (21 – 50 t/ha/yr), leading to the establishment of a Citizen Technician-based monitoring programme (2015 – 2019) that has provided flood-focused, sub-catchment scale SS data at sub-daily timestep for 11 sites throughout the 4000 km<sup>2</sup> catchment.</p><p>A confluence-based SS fingerprinting and tracing exercise was undertaken in the catchment (2018). Analysis of the distinctive physicochemical properties of resuspended fine sediment sampled above and below major confluences allowed the percentage of SS contributed by each tributary to be apportioned, and compared with findings from both the SS monitoring campaign and from existing models.</p>

In many parts of South Africa, soil erosion rates are high, and likely to be exacerbated by the longer droughts and more intense rainfall that are predicted in long-term regional climate change scenarios. Suspended sediment loads (SSL) and yields (SSY) are accepted means of expressing and comparing sediment transport and soil erosion rates. Land care and water security initiatives in South Africa require these data to provide benchmarking, and trajectories of change. International researchers began in the 1970s to investigate SSL estimation approaches. These investigations typically used near-continuous turbidity data from installed probes as a surrogate for sampled SS, and auto-samplers to monitor SS concentration and develop sediment rating curves. Biophysical and socio-economic conditions in South Africa differ markedly from the northern hemisphere environments where foundational studies were conducted. SSL estimations in South Africa are associated with extreme hydrological regimes, remote study areas and lack the resources required to collect and analyse representative SS data. There is a dearth of measured SS data, and of observed SSL and SSY for South African catchments. Using measured SS data from the Tsitsa River catchment (Eastern Cape, South Africa) we found that a discharge-weighted interpolation estimator was more appropriate than regression estimators, and that SSY responses to biophysical factors were in some ways more similar to northern hemisphere norms than expected. Lack of technical, infrastructural, human and financial resources were our main constraints to monitoring and estimating SSY. Our findings highlight the challenges of, and provide some guidance for, estimating directly measured SSL in the southern Africa region and inform future research in resource scarce areas.