The authors discovered large differences in the characteristics of overflows by the calculation of 1) intercepting volume of overflows for sewer systems using SWMM model which takes into consideration the runoff and pollutants from rainfalls and 2) the intercepted volume in the total flow at an investigation site. The intercepting rate at the investigation point of CSOs showed higher values than the SSDs. Based on the modeling of the receiving water quality after calculating the intercepting amount of overflows by considering the characteristics of outflows for a proper management of the overflow of sewer systems with rainfalls, it is clear that the BOD decreased by 82.9%-94.0% for the discharge after intercepting a specific amount of flows compared to the discharge from unprocessed overflows. 相似文献
This study presents a remote sensing and geographic information systems-based approach for using US EPA’s Storm Water Management Model (SWMM) in urban environment. Cartosat-1 PAN + IRS-P6 LISS-IV merged product was used to map land cover in part of Surat city at 1:10,000 scale. Cartosat-1 stereo pair was used for deriving digital elevation model of the study area. Geo-informatics-based methods were developed for delineation of sub-catchment areas, assignment of sub-catchment outlets and estimation of characteristic width. It was observed that 59% of the developed area in the study region was directly or indirectly connected to the storm water drainage network. Furthermore, dynamic rainfall-runoff simulation on three-day rainfall indicated that the average runoff coefficient on the urbanized sub-catchment areas which were directly connected to the drainage network was 0.92 as against 0.88 on those urbanized sub-catchments without having direct access to storm water drainage. 相似文献
Buildings, as impervious surfaces, are an important component of total impervious surface areas that drive urban stormwater response to intense rainfall events. Most stormwater models that use percent impervious area (PIA) are spatially lumped models and do not require precise locations of building roofs, as in other applications of building maps, but do require accurate estimates of total impervious areas within the geographic units of observation (e.g. city blocks or sub-watershed units). Two-dimensional mapping of buildings from aerial imagery requires laborious efforts from image analysts or elaborate image analysis techniques using high spatial resolution imagery. Moreover, large uncertainties exist where tall, dense vegetation obscures the structures. Analyzing LiDAR point-cloud data, however, can distinguish buildings from vegetation canopy and facilitate the mapping of buildings. This paper presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes and can be used with standard GIS software to identify building roofs under tall, thick canopy. Accuracy assessment methods are presented that can optimize model performance for modeling BIA within the geographic units of observation for hydrologic applications. The Building Extraction from LiDAR Last Returns (BELLR) model, a 2.5D rule-based GIS model, uses a non-spatial, local vertical difference filter (VDF) on LiDAR point-cloud data to automatically identify and map building footprints. The model includes an absolute difference in elevation (AdE) parameter in the VDF that compares the difference between mean and modal elevations of last-returns in each cell.
The BELLR model is calibrated for an extensive inner-city, highly urbanized small watershed in Columbia, South Carolina, USA that is covered by tall, thick vegetation canopy that obscures many buildings. The calibration of BELLR used a set of building locations compiled by photo-analysts, and validation used independent building reference data. The model is applied to two residential neighborhoods, one of which is a residential area within the primary watershed and the other is a younger suburban neighborhood with a less-well developed tree canopy used as a validation site. Performance results indicate that the BELLR model is highly sensitive to concavity in the lasboundary tool of LAStools® and those settings are highly site specific. The model is also sensitive to cell size and the AdE threshold values. However, properly calibrated the BIA for the two residential sites could be estimated within 1% error for optimized experiments.
To examine results in a hydrologic application, the BELLR estimated BIAs were tested using two different types of hydrologic models to compare BELLR results with results using the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness data. The BELLR BIA values provide more accurate results than the use of the 2011 NLCD PIA data in both models. The VDF developed in this study to map buildings could be applied to LiDAR point-cloud filtering algorithms for feature extraction in machine learning or mapping other planar surfaces in more broad-based land-cover classifications. 相似文献