MWH Soft, a global provider of environmental and water resources applications software, has announced the newest release of its RDII Analyst (Rainfall-Derived Inflow and Infiltration) for InfoSWMM and H2OMAP SWMM with expanded functionality.
The new version incorporates many advanced features. It also openly invites users to further adjust dry-weather flow (DWF) and RTK parameters to achieve a better fit--and ultimately a better model--based on their experiences.
Excessive wet-weather flow resulting from RDII is a major source of sanitary sewer overflows. Control of sewer overflows is vital to reducing risks to public health and protecting the environment from water pollution. Computer modeling plays an important role in determining sound and economical remedial solutions that reduce RDII; improve system integrity, reliability and performance; and avoid overflows.
The processes that convert rainfall to RDII flow in sanitary sewer systems are very complicated. In addition to rainfall and antecedent moisture conditions, various other factors influence RDII responses, including depth to groundwater; depth to bedrock; land slope; number and size of sewer system defects; type of storm drainage system; soil characteristics; and type of sewer backfill. Given this degree of complexity, flow monitoring data must be combined with mathematical modeling to provide accurate results. The wastewater flow monitoring data gathered at sewer collection systems consists of dry-weather flow components and RDII flow components. A crucial element in successfully modeling sewer collection systems is the ability to decompose flow monitoring data into RDII flow and dry-weather flow information.
The new features in RDII Analyst enable users to more quickly perform such advanced flow decomposition. Among these enhancements are tabular comparisons between the observed and calibrated RDII data for each event, including R value; peak flow; hydrograph volume; and depth. These comparisons allow users to better evaluate simulated and monitored data and judge how well they correlate on a per-event basis.
The user can also directly edit the estimated DWF mean values to apply site-specific knowledge to the RDII Analyst DWF extraction algorithm. The altered DWF values can then be used to estimate the wet-weather flow component of the monitored flow using a combination of the DWF extraction algorithm and site-specific knowledge. The new version also allows direct edits to the 12 RTK and storage parameters and manual curve fitting to apply site-specific knowledge to the genetic algorithm parameter estimation. Manual curve fitting is valuable for timing differences between monitored and calibrated wet-weather flow components and to draw on previous experience in estimating RTK parameters.
