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Introduction to Long-Term Statistics Module (LTS)

The Long-Term Statistics (LTS) module performs a time-efficient simulation of wastewater collection and urban drainage systems exposed to intermittent hydrological inputs over long, continuous historical periods and the computation of relevant statistics for the operational variables (instantaneous and accumulated) of interest.

LTS features an easy way to convert a usual simulation covering a long historical period (typically 10 - 30 years) into a discontinuous series of relevant hydrological events and to define various event-based and annual statistics for the selected variables. By these means, the long-term simulation becomes feasible even for large models, as both the simulation time and the amount of the generated output data gets significantly reduced, compared to a continuous simulation over the same historical period.

Based on the specified criteria, the system automatically selects the periods for dynamic pipe flow simulations during wet weather. This reduces the simulation time significantly, while preserving the accuracy in the simulation of relevant dynamic effects in the system - CSOs, surcharges, pollution emissions, etc.

Running LTS simulations with the existing drainage system configuration and with the planned upgrades, impacts of the planned investments (e.g. new sewers, retention tanks, control schemes) on system performance can be reliably tested and evaluated based on the computed operational statistics.

This allows the user to diagnose the current operation of the system and to develop the optimal rehabilitation/upgrade strategy, e.g. for satisfying the requirements of environmental regulations.

Statistics on maximum water levels, discharges from weirs, pollution emissions, etc. generated by simulating long continuous periods subject to impact of recorded historical rainfalls is particularly useful for the evaluation of the simulated system functionality. Statistics are used to compare the effects of various mitigation measures in the planned rehabilitation process, the performance of various control strategies, the cost-benefit efficiency of the planned investments, as well as to determine whether the functionality of the sewer system is compliant with legislative requirements.

The functionality of urban drainage systems is often subject to requirements like:

  • Flooding on terrain must not happen more frequently than once in every T-years.
  • Flooding of basements must not happen more frequently than once in every T-years.
  • Discharge from CSO weirs to receiving waters must not happen more frequently than n times per year.
  • Total volume from CSOs must not exceed a certain threshold amount per year.

LTS answers these and other similar questions and is thereby an essential simulation tool for reliable assessment of drainage system performance.

Statistics on some operational variables are only reliable if the simulated number of independent events is sufficiently large, i.e. if the simulated historical period is sufficiently long. This, in turn, may imply very long simulation times (if 'normal' computing hardware is applied) and thereby spoil the feasibility of the analysis.

However, use of dynamic simulation for the hydraulic and pollution transport analysis of drainage networks is only beneficial in periods of high-flow dynamics. Otherwise, simpler and much faster simulation tools can be used with a similar level of accuracy. Since high-flow dynamics in urban drainage networks are normally associated with rainfall events, the dynamically simulated periods can be reduced to include wet weather periods only, i.e. only a few percent of the historical period included in the analysis.

The fundamental principle behind the LTS concept is therefore the ability to reduce the simulation time without losing significant information. LTS can, based on the user-specified criteria, eliminate the irrelevant periods from the simulated series and to limit the dynamic simulations to a series of disconnected events instead of the entire simulation period.