Time series of the daily total precipitation, daily wastewater discharges and daily concentrations and pollution loads of BOD5, COD, SS, N-NH4, Ntot and Ptot were analyzed at the inflow to the wastewater treatment plant (WWTP) for a larger city in 2004-2009 (WWTP is loaded by pollution from 435,000 equivalent inhabitants). The time series of the outflow from a WWTP was also available for 2007. The time series of daily total precipitation, daily wastewater discharges, concentrations and pollution loads at the inflow and outflow from the WWTP were standardized year by year to exclude a long-term trend, and periodic components with a period of 7 days and 365 days (and potentially also 186.5 days) were excluded from the standardized series. However, these two operations eliminated only a small part of the variance; there was a substantial reduction in the variance only for ammonium nitrogen and total nitrogen at the inflow and outflow from a WWTP. The relationship between the inflow into a WWTP and the outflow from a WWTP for the concentrations and pollution loads was described by simple transfer functions (SISO models) and more complicated transfer functions (MISO models). A simple transfer function (SISO model) was employed to describe the relationship between the daily total precipitation and the wastewater discharge.
Understanding and modelling the processes of flood runoff generation is still a challenge in catchment hydrology. In particular, there are issues about how best to represent the effects of the antecedent state of saturation of a catchment on runoff formation and flood hydrographs. This paper reports on the experience of mapping saturated areas using measured water table by piezometers and more qualitative assessments of the state of the moisture at soil surface or immediately under it to provide information that can usefully condition model predictions. Vegetation patterns can also provide useful indicators of runoff source areas, but integrated over much longer periods of time. In this way, it might be more likely that models will get the right predictions for the right reasons.