Thesis (M.Eng.) - Asian Institute of Technology, 2018

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Water Engineering and Management

From last two decades, the proper management of wastewater has got significant attention because of the quantitative and qualitative problems in drainage network of major cities. Around the globe, these issues have ad dressed by modelers using different numerical models. The overloading of wastewater treatment plant (WWTP) during wet weather flow and deterioration of water quality in dry weather flow are the major problems of the study area. The previous research studie s on this catchment emphasized to improve model forecast for better management of urban drainage network and WWTP. The system measurements were assimilated in MIKE 1D (MIKE URBAN) model of Damhusåen catchment, Denmark using data assimilation tool under MIK E ZERO to get more reliable model forecast. The general filtering algorithm with predefined constant weighting function is used to assimilate discharge and water level measurements in drainage system. The model forecast is corrected using observation s at measurement locations to distribute model errors to the whole state s of the drainage system. The flow is forecasted using the first order auto - regressive error forecast model (AR1) which propagates model errors at measurement locations in forecast period. The model errors are estimated prior to time of forecast and used in forecast period. Furthermore, the radar - based rainfall is applied and evaluated with rain gauge rainfall. The calibration was performed using hydrodynamic and advection dispersion model. The wet weather and dry weather flow was calibrated and validated for four events. The concentration of ammonia is calibrated for one event. The discharge is assimilated at tw o locations and the volume error is significantly reduced up to 22% and 6% at verification location and inlet of WWTP, respectively. The water level measurements are bias corrected using mean filed bias adjustment (MFB) method. During water level assimilat ion, the volume error is only reduced up to 2.3% and 0.9% at FT8182 and inlet of WWTP. The update forecast skill of model is improved up to 04 and 07 hours lead time at assimilation and v erification location s as compared to without update. The average RMSE without and with update as a function of forecast lead time are 0.128 m 3 /s, 0.240 m 3 /s and 0.060 m 3 /s, 0.198 m 3 /s at assimilation and verification location s , respectively. Furthermore, the impact of discharge and water level assimilation on accumulated mass of ammonia per day at inlet of WWTP shows reduction in mass up to 209.5 kg and 9.98 k g . The discharge and water level assimilation does not have significant impact on concentration and mass of amm onia . Furthermore, t he radar data is validated with rain gauge rainfall and the correlation coefficient is found more than 0.40 for all rain gauges except one station. The radar - based runoff is compared with rain gauge and radar runoff is overestimated. Th e mean filed bias (MFB) adjustment is done to correct radar - based rainfall which shows a limited correction to radar - based runoff. Moreover, the evaluation of data assimilation on real - time control shows the minimum volume error as compared to other scenarios. This research can significantly contribute in optimization and efficient management of urban drainage network and WWTP.