The vast development of urban areas has resulted in the increase of stormwater peak runoff and volume. Water quality has also been adversely affected. The best management practices (BMPs) and low impact development (LID) techniques could be applied to urban areas to mitigate these effects. A quantity–quality model was developed to simulate LID practices at the catchment scale using the US Environmental Protection Agency Storm Water Management Model (US EPA SWMM). The purpose of the study was to investigate the impacts of LID techniques on hydrology and water quality. The study was performed in BUNUS catchment in Kuala Lumpur, Malaysia. This study applied vegetated swale and rain garden to assess the model performance at a catchment scale using real field data. The selected LIDs occupied 7% of each subcatchment (of which 40% was swale and 30% was rain garden). The LID removal efficiency was up to 40% and 62% for TN and TSS, respectively. The peak runoff reduction was up to 27% for the rainfall of up to 70 mm, and up to 19% for the rainfall of between 70 and 90 mm, respectively. For the longer storm events of higher than 90 mm the results were not as satisfactory as expected. The model was more effective in peak runoff reduction during the shorter rainfall events. As for the water quality, it was satisfactory in all selected rainfall scenarios.

No abstract

Successful application of one-dimensional advection–dispersion models in rivers depends on the accuracy of the longitudinal dispersion coefficient (LDC). In this regards, this study aims to introduce an appropriate approach to estimate LDC in natural rivers that is based on a hybrid method of granular computing (GRC) and an artificial neural network (ANN) model (GRC-ANN). Also, adaptive neuro-fuzzy inference system (ANFIS) and ANN models were developed to investigate the accuracy of three credible artificial intelligence (AI) models and the performance of these models in different LDC values. By comparing with empirical models developed in other studies, the results revealed the superior performance of GRC-ANN for LDC estimation. The sensitivity analysis of the three intelligent models developed in this study was done to determine the sensitivity of each model to its input parameters, especially the most important ones. The sensitivity analysis results showed that the W/H parameter (W: channel width; H: flow depth) has the most significant impact on the output of all three models in this research.

This research aims to study a novel approach for waste load allocation (WLA) to meet environmental, economical, and equity objectives, simultaneously. For this purpose, based on a simulation-optimization model developed for Haraz River in north of Iran, the waste loads are allocated according to discharge permit market. The non-dominated solutions are initially achieved through multiobjective particle swarm optimization (MOPSO). Here, the violation of environmental standards based on dissolved oxygen (DO) versus biochemical oxidation demand (BOD) removal costs is minimized to find economical total maximum daily loads (TMDLs). This can save 41% in total abatement costs in comparison with the conventional command and control policy. The BOD discharge permit market then increases the revenues to 45%. This framework ensures that the environmental limits are fulfilled but the inequity index is rather high (about 4.65). For instance, the discharge permit buyer may not be satisfied about the equity of WLA. Consequently, it is recommended that a third party or institution should be in charge of reallocating the funds. It means that the polluters which gain benefits by unfair discharges should pay taxes (or funds) to compensate the losses of other polluters. This intends to reduce the costs below the required values of the lowest inequity index condition. These compensations of equitable fund allocation (EFA) may help to reduce the dissatisfactions and develop WLA policies. It is concluded that EFA in integration with water quality trading (WQT) is a promising approach to meet the objectives.

Watershed management includes methods to create, enhance, and maintain vegetation to reduce run‐off and provide flood control in the watershed. The assignment of priority for watershed management measures requires the use of mathematical techniques to attain the most suitable strategies. In the present study, a framework was presented for assigning the optimal combinations of watershed management measures based on simulation‐based optimisation approach. For this purpose, a one‐dimensional hydrodynamic model was used to calculate the potential damages of different flood scenarios under various combinations of watershed management measures and was coupled with the NSGA‐II multi‐objective optimisation model to provide the optimal Pareto solutions between two conflicting objectives of minimising the investment costs of flood mitigation measures and the expected flood damages of the watershed. The proposed model was then applied to a small watershed in the centre of Iran as a case study, and the optimal trade‐off solutions were calculated for flood risk mitigation. Using these trade‐offs, for each level of funding, decision makers can select the optimal combination of watershed management measures considering decision criteria.