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Large- scale water quality modeling

Large- scale water quality modeling. Hot spots and causes of water pollution. The modeling framework Model results for Africa Progress since October 2013 Hot spots of water pollution Fecal Coliform bacteria  Risk to human health BOD  Threat to inland fishery

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Large- scale water quality modeling

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  1. Large-scalewaterqualitymodeling Hot spots and causes of water pollution

  2. The modelingframework • Model resultsforAfrica • Progress sinceOctober 2013 • Hot spotsofwaterpollution • FecalColiformbacteria Riskto human health • BOD  Threattoinlandfishery • Causesofwaterpollution • Main sectorscontributingtopollution • Conclusions & preliminaryfindings • Next steps • Outline

  3. spatial: 5’ temporal: monthly results: monthly spatial: 5’ temporal: daily results: daily/monthly • Modeling framework • discharge, runoff, • flow velocity WaterGAP3 Hydrology Model WorldQual Water Quality Model consumptive water use return flow spatial: 5‘ temporal: daily results: daily/monthly/yearly WaterGAP3 Water Use Models agriculture domestic manufacturing electricity production

  4. Progress sinceOctober 2013 point sources diffuse sources urban surface runoff manufacturing (wastewater) • domestic (urban) • sewage • domestic (rural) • sewage • hanginglatrines • domestic • septictanks • pitlatrines • inadequatesanitation agriculture (organicfertilizer) agriculture (industrialfertilizer) natural back-ground “scattered settlements” Data from Joint Monitoring Programme; country files (1980-2011)

  5. Connectivity & treatment

  6. 2010 • Loadings: Fecalcoliformbacteria Human andanimalinput

  7. Dilution capacity Climate normal period (1971-2000) Example: Modeled FC concentration at Mhlatuze River, South Africa January to December 2010

  8. February 2010 • FC in-streamconcentration

  9. February 2010 August 2010 • Comparison: FC in-streamconcentration

  10. …but noimprovementoftreatmentlevels February 2010 • Sensitivityanalysis: Assuming 100% connectivity…

  11. Total BOD loads in 2010 ~ 8.5 milliontons • BOD loadings in 2010

  12. February 2010 August 2010 • BOD in-streamconcentration

  13. Uncertaintyofmodelassumptions High: assuming 7.5% ofwashed-off manurecontributesto BOD load Low: assuming 3% ofwashed-off manurecontributesto BOD load

  14. Sensitivityanalysisfor BOD: “low rate“ assumption Total BOD loads in 2010 ~ 5 milliontons

  15. February 2010 August 2010 • Hot spotsof BOD for“low rate“ assumption

  16. High: assuming 7.5% ofwashed-off manurecontributesto BOD load • Model validation Low: assuming 3% ofwashed-off manurecontributesto BOD load

  17. Causeofwaterpollution: sourcesof BOD loads

  18. Example: Total BOD loads in theNileriverbasin contributingloads per country [%] • Transboundaryriverbasinscale

  19. First time: • Synthesizeinformationaboutpopulation, sanitationandconnectivityandmakespatially explicit for all ofAfrica • Computeloadsoforganicpollutionandbacterialcontaminationforeachriverbasin in Africa (gridcellbasis) • Geographiccomparisonof BOD andfecalcoliformloadings in Africa • Calculationof BOD andcoliformlevelsfor all rivers in Africa • Estimationofhotspotwaterpollutionareas in Africa • Conclusions

  20. Hot spotareas: 17% ofpopulationliving at bigriverswithbacterialcontamination >1000cfu/100ml in Africa • Hot spotareas: Dilution capacity + magnitudeofloadings • Magnitude of BOD loadinguncertain (manurerunoff) • Most important source of BOD: manure runoff; least important: urban surface runoff • Source profile of BOD loadings vary greatly between countries (e.g. Somalia: manure runoff; Egypt: urban domestic) • Total BOD loads steadily increased in Africa between 1990 and 2010 (increasing population, livestock, connectivity) • High potential toprovidepolicy-relevant overviewofwaterqualityissuesforAfricaandotherregions • Preliminaryfindings

  21. Further improvementofestimatesforAfrica • getting regional feedback • Extension ofestimatestoAsiaandLatinAmerica • Extension ofestimatestoinclude: • other water quality parameters (total dissolved solids, total N, total P, water temperature) • lakes • Apply water quality guidelines as thresholds • Merger of model-driven & data-drivenanalyses: threatsto human health & inlandfisheries (foodsecurity ) andpolicyresponses • Next steps

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