Modelling runoff quantity and quality in tropical urban catchments using Storm Water Management Model

Abstract

Due to differences in rainfall regimes and management practices, tropical urban catchments are expected to behave differently from temperate catchments in terms of pollutant sources and their transport mechanism. Storm Water Management Model (SWMM) was applied to simulate runoff quantity (peakflow and runoff depth) and quality (total suspended solids and total phosphorous) in residential, commercial and industrial catchments. For each catchment, the model was calibrated using 8-10 storm events and validated using seven new events. The model performance was evaluated based on the relative error, normalized objective function, Nash-Sutcliffe coefficient and 1:1 plots between the simulated and observed values. The calibration and validation results showed good agreement between simulated and measured data. Application of Storm Water Management Model for predicting runoff quantity has been improved by taking into account catchment's antecedent moisture condition. The impervious depression storages obtained for dry and wet conditions were 0.8 and 0.2 mm, respectively. The locally derived build-up and wash-off parameters were used for modelling runoff quality.

Modeling the Quality and Quantity of Runoff in a Highly Urbanized Catchment Using Storm Water Management Model

“…The initial values and range of values for Manning's roughness coefficient, the width of the overland flow path, depression storage, infiltration parameters of Horton's equation, and buildup and washoff parameters of pollutants were identified from the user's manual and previous studies before calibration [19][20][21] (Table 2).…”

“…The model performance during the sensitivity analysis, parameter optimization, and calibration and validation stages was evaluated using criteria that had been applied in other similar studies [20,26]. The Nash-Sutcliffe coefficient (R NS ), relative error (RE), and coefficient of determination (R 2 ) are commonly used to evaluate model performance and are shown in Equations 6-8, respectively.…”

Prediction of water discharge and sediment in teak forested area using artificial neural network model

“…Dalam pengelolaan DAS tersebut informasi mengenai fluktuasi hasil air dan sedimen sangat diperlukan. Hal ini dapat dijadikan sebagai dasar untuk penilaian keberhasilan pengelolaan DAS, mengingat bahwa kegiatan manusia, seperti perubahan penggunaan lahan dalam suatu DAS akan berdampak pada kondisi DAS (Chow et al, 2012;Nunes et al, 2011;Pramono dan Wahyuningrum, 2010;Shamsuddin et al, 2014). Hasil air yang berlebihan akan menimbulkan masalah banjir, sebaliknya kurangnya pasokan air berdampak pada kekeringan.…”

Characteristics of phytoplankton distribution for assessment of water quality in the Zhalong Wetland, China

“…Eutrophication generally refers to increased aquatic productivity due to increased availability of nutrients, with an array of cascading consequences within the system (Vollenweider 1989;Vetrimurugan et al 2013;Tanik et al 2013;Parmar and Bhardwai 2013;Shao et al 2013;Chow et al 2012;Rizvi et al 2012). Nutrient loading to freshwater and coastal ecosystems can be a result of fossil fuel combustion, human waste disposal, urbanization, and agricultural activity.…”