Ensemble modeling of plastic flows in South Africa’s rivers with a large-scale hydrological model

<p>Plastic pollution is one of the major global water quality issues. Yet the lack of consistent data and standardized monitoring leads to a wide range of estimates of plastic load that is being delivered to marine environment. At the same time, continental and global dynamic hydrological models have emerged as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE) and its global application, World-Wide HYPE (WWH, Arheimer et al., 2020). We present the first results simulating riverine plastic pollution in South Africa using a WWH submodel.</p><p>WWH was amended to include the population living within different sanitation conditions. Sanitation service type and safely managed fraction were estimated for each catchment by combining country and regional sanitation data (WHO, 2017) with human development index data (5&#8201;arc-min resolution; Kummu et al., 2020) and high-resolution (1km grid) settlement type and population (Pesaresi et al., 2019) datasets. This information was then linked to plastic waste generation, both in terms of mismanaged waste production and microplastics associated with municipal point sources.</p><p>Data on plastic flows and concentrations in various South African freshwater bodies were collected from published literature. Traditional model calibration techniques may not be appropriate in this case due to insufficient number of data points, large variability in plastic characteristics and sampling techniques, as well as large uncertainty and a lack of current knowledge of transport and transformation processes in water bodies. Thus, an ensemble of the models was developed by varying model parameters that affect generation, transformation, and transport of plastic from the various sanitation categories and in rivers. Collected data together with other global estimates were then used to evaluate the ensemble with a weight of evidence approach, highlighting sources and processes of major significance and focusing the ensemble towards a realistic set. This set will be used to further develop modeling routines at a large scale and provide guidance in developing the full global model.</p><p>References:</p><p>Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., and Pineda, L., 2020. Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation, Hydrol. Earth Syst. Sci. 24, 535&#8211;559, doi.org</p><p>Kummu, Matti; Taka, Maija; Guillaume, Joseph H. A. (2020), Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015, Dryad, Dataset, doi.org</p><p>Pesaresi, Martino; Florczyk, Aneta; Schiavina, Marcello; Melchiorri, Michele; Maffenini, Luca (2019): GHS-SMOD R2019A - GHS settlement layers, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015). European Commission, Joint Research Centre (JRC) [Dataset] doi: 10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218 PID: data.europa.eu</p><p>World Health Organization. "Progress on drinking water, sanitation and hygiene: 2017 update and SDG baselines." (2017).</p>