The evaluation of rainfall influence on CSO characteristics: the Berlin case study

1. The evaluation of rainfall influence on CSO characteristics: the Berlin case study S. Sandoval*, A. Torres*, E. Pawlowsky-Reusing **, M. Riechel*** and N. Caradot*** * Pontificia Universidad Javeriana, Bogotá, Colombia** Berliner Wasserbetriebe, Berlin, Germany *** Kompetenzzentrum Wasser Berlin, Berlin, Germany

2. CSO monitoring in Berlin • Sub catchment: • 126000 inhabitants • 800 ha impervious area Separate sewer system 10 km Combined sewer system CSO monitoring station N

3. CSO monitoring in Berlin • Sub catchment: • 126000 inhabitants • 800 ha impervious area • Rainfall • Annual rain 570 mm/a • > 10 mm: 13/a Rain gauges Separate sewer system 10 km Combined sewer system CSO monitoring station N

4. CSO monitoring

7. Average contribution of wastewater to • CSO volume = 11% • CSO COD load = 16% • 84% contribution from other sources ! •  rain runoff wash-off •  resuspension of sewer sediments • Very strong variability of volume and concentrations  What is the influence of rainfall on CSO characteristics ?  Is it possible to predict CSO characteristics from rainfall ?

8. Canonical Correlation Analysis CCA Linear relationship between two multidimensional data sets: X (input rainfall characteristics) and Y (output CSO characteristics) Row: events / Columns: characteristics A couple of vectors a and b is found by maximizing correlation (a.X , b.Y) a1 X1 + a2 X2 + … + an Xn ~ b1 Y1 + b2 Y2 + … + bnYn Evaluation of correlation with canonical loadings: linear correlations between each characteristic and CV  Canonical loading Xi = corr (Xi, CVx)  Canonical loading Yi = corr (Yi, CVy) Canonical variate y CVy Canonical variate x CVx

9. Canonical Variate 1 Canonical Variate 2 Max intensity Mean intensity Max flow Mean flow Pollutant loads Duration Depth Duration Volume Mean concentrations

10. Partial Least Square regression PLS Linear relationship between a multidimensional input variable X (rainfall characteristics) and individual output Y (CSO characteristic) Row: events Columns: characteristics The PLS method projects original data onto a more compact space of latent variables A set of coefficients ai is found by maximizing the covariance between X and Y Y = a1 X1 + a2 X2 + … + an Xc Identification of most important rain characteristics (high coefficients)

11. For each CSO variable (e.g. max. flow) • Generation of 1000 sets of random rainfall and CSO values within their uncertainty interval  1000 PLS models • Quality of prediction : coefficient of determination R2 • Identification of most important X variables

12. Identification of most relevant explenatory variables Max intensity DW duration Rain duration Duration Max intensity Probability of being the most important rainfall variable DW duration

13. Conclusion • PLS and CCA highlight the influence of rainfall on CSO characteristics Max intensity Mean intensity Duration Depth Max flow Mean flow Duration Volume DW duration Max intensity Pollutant loads Duration Depth Mean concentrations • For PLS, low determination coefficients were obtained (< 0.6) • not suitable for prediction purposes, • useful for exploring the qualitative influence of rainfall on CSO • Future researches • Test of other analysis methods (e.g. Artificial Neural Networks) • Relation between rainfall, CSO and resulting river impacts

14. Thank you for your attention ! More information : nicolas.caradot@kompetenz-wasser.de

17. Integrated monitoring stations a a b c d 3 km N 30 km Area of water bodies River monitoring station Separate sewer system CSO monitoring station Combined sewer system