Automated calibration of the EPA-SWMM model for a small suburban catchment using PEST: a case study

An improved Back Propagation Neural Network framework and its application in the automatic calibration of Storm Water Management Model for an urban river watershed

The use of the Storm Water Management Model (SWMM) to simulate flows in urban river watersheds necessitates the proper calibration of the various parameters involved in the process. Back Propagation Neural Network (BPNN) is often used to establish relationship between two sets of multivariate variables, such as parameters and simulation results of SWMM. The aim of this study is to establish an improved BPNN to calibrate SWMM. It was found that when using gauged flow data obtained from the urban river management system as calibration data, only using BPNN was not sufficient. An improved BPNN framework was proposed with integrating Principal Component Analysis (PCA) and Genetic Algorithm (GA) process, abbreviated as PCA-GA-BPNN. It was proved to be effective for calibration. The BPNN combined with GA process made 90 % of the predicted parameters within reasonable range, which was only 8 % using BPNN alone. The PCA process reduced the training time up to 64 %. Using a hydrograph of 196 h, compared with the nondominated sorting genetic algorithm (NSGA), PCA-GA-BPNN training time can be reduced from 18,142 s down to 4.5 s. Nash efficiency coefficients (NSE) of hydrographs fitting was 0.75. Including more rainfall events data in calibration achieved better fitting than including more gauging station data.

How to simulate future scenarios of urban stormwater management? A novel framework coupling climate change, urbanization, and green stormwater infrastructure development

Climate change, urbanization, and green stormwater infrastructure (GSI) planning policies lead to uncertainties in future urban sustainability. Coupling multiple influencing factors such as climate change, urbanization, and GSI development, this study proposes a novel framework for simulating future scenarios of urban stormwater. Subsequently, the changes in annual surface runoff and runoff pollutants in Shanghai's new and old urban areas were compared and analyzed based on 35 typical future and seven baseline scenarios. The following results were obtained: 1) The runoff control rate of the new urban area was significantly higher than that of the old urban area before GSI construction. After GSI construction, both areas could control stormwater runoff and pollutants, while the decline in efficiency in GSI facilities enormously impacted the old area. 2) Surface runoff in the new urban area was mainly affected by urbanization, while climate change was a major factor in the old urban area; runoff pollutants in new and old urban areas were mainly affected by urbanization, and the change in pollutants in new areas was more pronounced. 3) GSI facilities were unlikely to guarantee the quantity and quality of water resources, especially in scenarios where the efficiency of GSI facilities decreases. In old urban areas, the more extreme climate change and urbanization were, the more significant the effect of improving stormwater management facilities. Our findings showed that future studies on stormwater management should specifically consider the different characteristics of new and old urban regions, pay attention to the maintenance and management of GSI facilities, and build adaptive strategies to cope with climate change, urbanization, and GSI facility destruction.

Development of a Multiobjective Automatic Parameter-Calibration Framework for Urban Drainage Systems

Urban drainage systems (UDSs) continue to face challenges, despite numerous efforts to improve their sustainability through design, planning, and management. The goal of such initiatives is to avoid and minimize flooding as well as maintain the UDS’s sustainable functionality, which can be analyzed using a stormwater management model (SWMM). In this study, a multiobjective automatic parameter-calibration (MAPC) framework was developed based on the SWMM. It consisted of three steps: sensitivity analysis (Step I), objective selection (Step II), and SWMM parameter calibration (Step III). The proposed MAPC framework was verified using the Yongdap drainage network located in Seoul, South Korea. The resultant MAPC framework demonstrated that the system characteristics (such as percent of impervious area and hillslope) and problems in UDS design, planning, and management can be well reflected by the corresponding model. The MAPC framework proposed in this study can contribute to UDS modeling sustainability.

Linking Urban Floods to Citizen Science and Low Impact Development in Poorly Gauged Basins under Climate Changes for Dynamic Resilience Evaluation

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario.

Investigation of the low impact development strategies for highly urbanized area via auto-calibrated Storm Water Management Model (SWMM)

This study aims to investigate the effectiveness of the low impact development (LID) practices on sustainable urban flood storm water management. We applied three LID techniques, i.e. green roof, permeable pavements and bioretention cells, on a highly urbanized watershed in Istanbul, Turkey. The EPA-SWMM was used as a hydrologic-hydraulic model and the model calibration was performed by the well-known Parameter ESTimation (PEST) tool. The rainfall-runoff events occurred between 2012 and 2020. A sensitivity analysis on the parameter selection was applied to reduce the computational cost. The Nash-Sutcliffe efficiency coefficient (NSE) was used as the objective function and it was calculated as 0.809 in the model calibration. The simulations were conducted for six different return periods of a storm event, i.e. 2, 5, 10, 25, 50 and 100 years, in which the synthetic storm event hyetographs were produced by means of the alternating block method. The results revealed that the combination of green roof and permeable pavements have the major impact on both the peak flood reduction and runoff volume reduction compared to the single LIDs. The maximum runoff reduction percentage was obtained as 56.02% for a 10 years return period of a storm event in the combination scenario.

An integrated framework for the comprehensive evaluation of low impact development strategies

The impacts of urbanization on water quality, hydrology, society, and the environment can be minimized through low impact development (LID) practices in urban areas. This study has evaluated the performances of seven different LID scenarios including stand-alone and different combinations of green roof (GR), bioretention cells (BC), permeable pavement (PP), and infiltration trench (IT) in the Ayamama watershed, which is one of the most densely urbanized areas in Istanbul. Stormwater Management Model (SWMM) was used to obtain the performances of LID scenarios in quantitative (i.e., volume reduction and peak runoff reduction) and qualitative (i.e., Total Suspended Sediment, Chemical Oxygen Demand, Total Nitrate, Total Phosphate reductions) manner. To calibrate the SWMM model, the Parameter EStimation Tool (PEST) was integrated for sensitivity analysis and parameter optimization. A focus group discussion (FGD) was performed to identify the criteria and LID scenarios applicable to the study area. 16 criteria were determined as suitable, based on three dimensions of sustainability such as social, economic, and environmental. The criteria were evaluated in compliance with the fuzzy analytical hierarchy process (AHP) method before performing technique for order preference by similarity to ideal solution (TOPSIS) for a comprehensive assessment of LID scenarios. The results showed that community resistance, operation feasibility, and quantitative benefits were the most significant criteria for LID scenario selection in social, economic, and environmental aspects, respectively. The integrated evaluation showed that the impacts of urban flooding can be reduced significantly with the combination of GR and BC. Thus, this study provides an integrated and sustainable solution to the topic based on the PEST-SWMM-fuzzy AHP-TOPSIS framework. Furthermore, the developed framework could assist decision-makers and governmental authorities to designate optimal LID scenarios.

Design and Simulation of Stormwater Control Measures Using Automated Modeling

Stormwater control measures (SCMs) are decentralized technical elements, which can prevent the negative effects of uncontrolled stormwater flow while providing co-benefits. Optimal SCMs have to be selected and designed to achieve the desired hydrological response of an urban catchment. In this study, automated modeling and domain-specific knowledge in the fields of modeling rainfall-runoff (RR) and SCMs are applied to automate the process of optimal SCM design. A new knowledge library for modeling RR and SCMs, compliant with the equation discovery tool ProBMoT (Process-Based Modeling Tool), was developed. The proposed approach was used to (a) find the optimal RR model that best fits the available pipe flow measurements, and (b) to find the optimal SCMs design that best fits the target catchment outflow. The approach was applied to an urban catchment in the city of Ljubljana, Slovenia. First, nine RR models were created that generally had »very good« performance according to the Nash–Sutcliffe efficiency criteria. Second, six SCM scenarios (i.e., detention pond, storage tank, bio-retention cell, infiltration trench, rain garden, and green roof) were automatically designed and simulated, enabling the assessment of their ability to achieve the target outflow. The proposed approach enables the effective automation of two complex calibration tasks in the field of urban drainage.

Automatic calibration of the two-dimensional hydrodynamic and water quality model using sequential uncertainty fitting approach

Reservoir hydrodynamic and water quality modeling, in conjunction with the monitoring programs, is one of the essential tools for controlling the pollution of these types of water bodies. The complexity of the model, data scarcity, and the variable nature of natural phenomena lead to uncertainty in models, which should be considered in the calibration process of these models. Uncertainty-based automatic calibration is one of the methods that can be effective in achieving a high-reliability model. In this paper, the Sequential Uncertainty Fitting (SUFI-2) algorithm was used for the automatic calibration of the two-dimensional hydrodynamic and water quality model (CE-QUAL-W2) for the reservoir under parameter uncertainty conditions. To this end, the CE-QUAL-W2 model was developed to simulate the temperature and water surface elevation of the Karkheh Dam reservoir (western Iran). The parameters affecting temperature were regarded as uncertain parameters in the calibration process, including the coefficients of longitudinal eddy viscosity, longitudinal eddy diffusivity, Chezy coefficient or Manning, wind sheltering, solar radiation absorbed in the surface layer, extinction coefficient for pure water, and the experimental coefficients of wind speed function. The developed method demonstrated a high potential for matching the simulated temperature and water surface elevation for the reservoir with the measured data. Averagely, 69% of the simulated temperature and 90% of the simulated water surface elevation were located within the 95% confidence interval. The SUFI-2 algorithm also showed better performance in terms of the convergence rate compared with the particle swarm optimization (PSO) algorithm, which indicated a lower number of calls (80 calls compared to 2000 calls) and could reduce the total root-mean-square error by 9.6%.