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

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

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

CSO monitoring

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 ?

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) a 1 X 1 + a 2 X 2 + … + a n X n ~ b 1 Y 1 + b 2 Y 2 + … + b n Y n Evaluation of correlation with canonical loadings: –linear correlations between each characteristic and CV  Canonical loading X i = corr (X i, CVx)  Canonical loading Y i = corr (Y i, CVy) Canonical variate x CVx Canonical variate y CVy

Canonical loadings Rainfall X CV5 Duration 0.24 Max intensity Depth 0.07 Mean intensity DW duration Canonical loadings CSO Y CV5 Duration0.00 Max. Flow-0.54 Volume-0.40 Mean Flow-0.64 M_COD-0.63 M_TSS-0.51 M_CODd-0.72 mean_TSS-0.24 mean_COD-0.41 mean_CODf-0.47 mean_EC-0.23 Waste ratio (V)-0.29 Waste ratio (M)0.11 Canonical loadings Rainfall X CV6 Duration0.58 Max intensity0.35 Depth0.84 Mean intensity-0.17 DW duration0.22 Canonical loadings CSO Y CV6 Duration0.64 Max. Flow0.39 Volume0.72 Mean Flow0.09 M_COD0.12 M_TSS0.23 M_CODd0.13 mean_TSS-0.55 mean_COD-0.57 mean_CODf-0.62 mean_EC-0.71 Waste ratio (V)-0.71 Waste ratio (M)-0.60 Max intensity Mean intensity Max flow Mean flow Pollutant loads Duration Depth Duration Volume Mean concentrations Canonical Variate 1Canonical Variate 2

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 a i is found by maximizing the covariance between X and Y Y = a 1 X 1 + a 2 X 2 + … + a n X c Identification of most important rain characteristics (high coefficients)

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

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

Max intensity Mean intensity Duration Depth Max flow Mean flow Duration Volume DW duration Max intensity Pollutant loads Duration Depth Mean concentrations Conclusion PLS and CCA highlight the influence of rainfall on CSO characteristics 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

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