Stormwater Runoff Model Research Articles

From single-objective to multiple-objective multiple-rainfall events automatic calibration of urban storm water runoff models using genetic algorithms

The calibration of storm water runoff models is a complex task. Early attempts focused on the choice of a performance criterion function that could capture all the facets of the problem into a single-objective framework. Subsequently, the awareness that a good calibration must necessarily take into account conflicting objectives led to the adoption of more sophisticated multi-objective approaches. Only recently, the focus has shifted towards effective ways of exploiting the mounting information provided by the availability of many sets of concurrent rainfall and flow measurements. This paper revisits through a case study the transition just elucidated: the calibration of a SWMM model applied to a catchment in Singapore is tackled through a single-objective, a multi-objective and a multi-objective multiple-event (MOME) paradigm respectively. A new approach to support the latter is presented herein. It consists in formulating the problem of model calibration as a multi-objective problem with m x r objective functions, where m and r are the number of performance criteria and rainfall events respectively, that must be optimized simultaneously. Results suggest that the new MOME framework performs significantly better than the others tested on the case study presented.

Urban Stormwater Quality Control Analysis with Detention Ponds

This paper presents methodologies for the development of stormwater quality control measures based on the derived probability distribution approach. These stormwater control measures, such as the fraction of pollutant removed from storage facilities, are closed-form analytical models and can be effectively used to evaluate pollutant loads to receiving waters. In this study, a simple form of rainfall-runoff transformation with lumped parameters is first extended to take into account the spatial variations in model parameters. Second, the infiltration process is further incorporated to the rainfall-runoff transformation. This study demonstrates that analytical models can be developed with various levels of complexity based on different hydrologic considerations. The performance of the analytical models is evaluated in a case study, and the results indicate that, with an appropriately formulated rainfall-runoff transformation, analytical stormwater runoff models are capable of providing comparable results to continuous simulation models in the evaluation of the long-term performance of storage facilities.

Modeling Storm Water Mass Emissions to the Southern California Bight

Storm water runoff is perceived as a major source of pollutants that results in adverse environmental effects, but large-scale assessments are rarely conducted. The problem is particularly pronounced in southern California where 17 million people have rapidly developed coastal watersheds. The goal of this study was to make regionwide estimates of mass emissions, assess the relative contribution from urbanized watersheds, and compare pollutant flux from different land uses. A geographic information system-based storm water runoff model was used to estimate pollutant mass emissions based on land use, rainfall, runoff volume, and local water-quality information. Local monitoring data were used to derive runoff coefficients; over 1,700 storm water sampling events were used to calibrate and validate annual loadings. An average rainfall year produced 1,073×109 L of runoff, 118,000 metric tons (MT) of suspended solids, 1,940 MT of nitrate-N, 108 MT of zinc, and 15 kg of diazinon. The majority of mass emissions were from urbanized watersheds except for suspended solids, total DDT, and chlorpyrifos. Agricultural areas had the greatest fluxes for pesticides, including total DDT and chlorpyrifos while open areas typically had the smallest.

Application of genetic algorithms and fuzzy control to a combined sewer pumping station

In the present study, fuzzy logic control and genetic algorithms are applied to achieve improved pump operations in a combined sewer pumping station. Pumping rates are determined by fuzzy inference and fuzzy control rules corresponding to input variables. Genetic algorithms are used to automatically improve the fuzzy control rules through genetic operations such as selection, crossover and mutation. The effects of different fitness functions and learning conditions are investigated using a stormwater runoff model. It is found that current pump operations can be improved by adding the sewer water quality to the input variables and to the fitness function; the improved operations can reduce not only floods in the drainage area but also pollutant loads discharged to the receiving waters.

Application of genetic algorithms and fuzzy control to a combined sewer pumping station

In the present study, fuzzy logic control and genetic algorithms are applied to achieve improved pump operations in a combined sewer pumping station. Pumping rates are determined by fuzzy inference and fuzzy control rules corresponding to input variables. Genetic algorithms are used to automatically improve the fuzzy control rules through genetic operations such as selection, crossover and mutation. The effects of different fitness functions and learning conditions are investigated using a stormwater runoff model. It is found that current pump operations can be improved by adding the sewer water quality to the input variables and to the fitness function; the improved operations can reduce not only floods in the drainage area but also pollutant loads discharged to the receiving waters.

Stormwater Runoff and Pollutant Modeling in a Florida Drainage Basin

Stormwater Runoff and Pollutant Modeling in a Florida Drainage Basin

A Study on Stormwater Runoff Model of Urban Lowland Basin

Stormwater runoff models using the dynamic wave method, diffusion wave method and kinematic wave method were applied to an urbanized lowland basin which was the pump drainage area of the Shinkomatsu River. The simulation was carried out for fourteen rainfall events in 1991. The results confirmed that the simulated hydrographs of the dynamic and diffusion wave method were shown to compare favorably with the observed hydrographs rather than the kinematic wave method. Further, it was found that the diffusion wave method was a proper model for an urbanized lowland basin, because of its shorter analyzing times and simple form of equations, compared with the dynamic wave method.

Stormwat Reurn of Afn alysifs or the Urban Experimteanl Basin in Upland

In recent years, more spatial and temporal rainfall data are presented by progress of climatic observation technology, while the rapid human activities for basins like urbanization caused flooding problems.This paper describes the analysis of stormwater runoff model for the Yabata experimental basin (5.42km2) in urbanized Toshima Upland in Tokyo. And it was considered distributed rainfall data of 5 minutely from 6 autographic raingauges and distinguished infiltration capacity for land use.

Storm water pollution modelling: first-order atmospheric dustfall processes that affect runoff quality

The buildup of surface pollutants has been shown to be a controlling factor in the quality of storm water runoff. In industrial areas particularly, atmospheric fallout is an important component of surface pollutant loadings. Storm water runoff models presently in use do not consider the physics of atmospheric dustfall.Industries, vehicle exhausts, and blowing of wind over unprotected surfaces all introduce pollutants to the atmosphere. Redistribution of this material on the ground depends on local topography and prevailing meteorological conditions. The location of the industrial areas; the direction, velocity, and duration of wind; total precipitation; and source concentrations are important parameters in the prediction of atmospheric dustfall. The paper describes the physical processes of atmospheric fallout that are relevant to water quality modelling. A new model, called ATMDST, to predict dustfall on individual subcatchments in a metropolitan area using prevailing meteorological conditions is developed based on statistical methods. Results from average, one-variable and two-variable linear regression models were statistically compared with observed data. Finally, ATMDST is interfaced with the storm water management model version 3 (SWMM3) to compute runoff water quality. The model is applied to Hamilton, Ontario. Key words: atmospheric dustfall, air pollution, urban runoff, water quality, pollutant buildup, environmental modelling.