A Comparison of Continuous and Event-Based Rainfall–Runoff (RR) Modelling Using EPA-SWMM

New optimization strategies for SWMM modeling of stormwater quality applications in urban area

Build-up/wash-off models were originally developed for small-scale laboratory facilities with uniform properties. The effective translation of these models to catchment scale necessitates the meticulous calibration of model parameters. The present study combines the Mat-SWMM tool with a genetic algorithm (GA) to improve the calibration of build-up and wash-off parameters. For this purpose, Mat-SWMM was modified to equip it with the capacity to provide comprehensive water quality analysis outcomes. Additionally, this research also conducts a comparative examination of two distinct types of objective functions in the optimization. Rather than depending on previous literature, this study undertook a numerical campaign to ascertain an appropriate range for the relevant parameters within the case study, thereby ensuring the optimization algorithm's efficient functionality. This research also implements an integrated event calibration approach, i.e., a novel method that calibrates all rainfall events collectively, thus improving systemic interaction representation and model robustness. The findings indicate that employing this methodology significantly enhances the reliability of the outcomes, thereby establishing a more robust procedure. The first objective function (TSS instantaneous less squared difference function, OF 1), which is widely employed in the literature, was designed to minimize the difference between observed and predicted instantaneous Total Suspended Solids (TSS) concentrations. In contrast, the second function (mass and mass peak consistency function, OF 2) considers integral model outputs, i.e., the overall mass balance, the time of the peak mass flow rate, and its intensity. The analysis of the outputs revealed that both objective functions demonstrated sufficient performance. OF 1 provided slightly better performance in predicting the TSS concentrations, whereas OF 2 demonstrated superior ability in capturing global event characteristics. Notably, the optimal parameter set identified through OF 2 aligned with the physically plausible ranges traditionally recommended in technical manuals for urban catchments. In contrast, OF 1's optimal set necessitated an expansion in the acceptable parameter ranges. Finally, from a computational burden viewpoint, OF 1 demanded a significantly higher number of function evaluations, thus implying an escalating computational cost as the range expands. Conversely, OF 2 necessitated fewer evaluations to converge toward the optimal solution.

Efficiency evaluation of low impact development practices on urban flood risk

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2-22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

Assessment of hybrid machine learning algorithms using TRMM rainfall data for daily inflow forecasting in Três Marias Reservoir, eastern Brazil

This study investigates the application of the Gaussian Radial Basis Function Neural Network (GRNN), Gaussian Process Regression (GPR), and Multilayer Perceptron Optimized by Particle Swarm Optimization (MLP-PSO) models in analyzing the relationship between rainfall and runoff and in predicting runoff discharge. These models utilize autoregressive input vectors based on daily-observed TRMM rainfall and TMR inflow data. The performance evaluation of each model is conducted using statistical measures to compare their effectiveness in capturing the complex relationships between input and output variables. The results consistently demonstrate that the MLP-PSO model outperforms the GRNN and GPR models, achieving the lowest root mean square error (RMSE) across multiple input combinations. Furthermore, the study explores the application of the Empirical Mode Decomposition-Hilbert-Huang Transform (EMD-HHT) in conjunction with the GPR and MLP-PSO models. This combination yields promising results in streamflow prediction, with the MLP-PSO-EMD model exhibiting superior accuracy compared to the GPR-EMD model. The incorporation of different components into the MLP-PSO-EMD model significantly improves its accuracy. Among the presented scenarios, Model M4, which incorporates the simplest components, emerges as the most favorable choice due to its lowest RMSE values. Comparisons with other models reported in the literature further underscore the effectiveness of the MLP-PSO-EMD model in streamflow prediction. This study offers valuable insights into the selection and performance of different models for rainfall-runoff analysis and prediction.

Impact of rainfall characteristics on urban stormwater quality using data mining framework

Understanding the impact of rainfall characteristics on urban stormwater quality is important for stormwater management. Even though significant attempts have been undertaken to study the relationship between rainfall and urban stormwater quality, the knowledge developed may be difficult to apply in commercial stormwater management models. A data mining framework was proposed to study the impacts of rainfall characteristics on stormwater quality. A rainfall type-based calibration approach was developed to improve water quality model performance. Specifically, the relationship between rainfall characteristics and stormwater quality was studied using principal component analysis and correlation analysis. Rainfall events were classified using a K-means clustering method based on the selected rainfall characteristics. A rainfall type-based (RTB) model was independently calibrated for each rainfall type to obtain optimal parameter sets of stormwater quality models. The results revealed that antecedent dry days, average rainfall intensity, and rainfall duration were the most critical rainfall characteristics affecting the event mean concentrations (EMCs) of total suspended solids, total nitrogen, and total phosphorus, while total rainfall was found to be of negligible importance. The K-means method effectively clustered the rainfall events into four types that could represent the rainfall characteristics in the study areas. The rainfall type-based calibration approach can considerably improve water quality model accuracy. Compared to the traditional continuous simulation model, the relative error of the RTB model was reduced by 11.4 % to 16.4 % over the calibration period. The calibrated stormwater quality parameters can be transferred to adjacent catchments with similar characteristics.

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.

Quality Assessment of Small Urban Catchments Stormwater Models: A New Approach Using Old Metrics

Small urban catchments pose challenges in applying performance metrics when comparing measured and simulated hydrographs. Indeed, results are hampered by the short peak flows, due to rainfall variability and measurement synchronization errors, and it can be both difficult and inconvenient to remove base flows from the analysis, given their influence on combined sewer overflow (CSO) performance. A new approach, based on the application of metrics to peak flows for a selected set of different durations, is proposed and tested to support model quality assessment and calibration. Its advantages are: avoiding inconveniences arising from lags in peak flows and subjectivity of possible adjustments; favouring the assessment of the influence of base flow variability and flow lamination by CSOs; promoting integrated analysis for a wide range of rainfall events; facilitating bias identification and also guiding calibration. However, this new approach tends to provide results (e.g., for NSE, r2 and PBIAS) closer to optimal values than when applying metrics to compare the measured and simulated values of hydrographs, so the comparison of results with thresholds widely used in the literature should be done with caution. The various case study examples highlight the importance of using a judicious set of different metrics and graphical analyses.

Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS)

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing a previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.

Development of an Optical Method to Monitor Nitrification in Drinking Water

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH₂Cl) residual. The decay of monochloramine releases ammonia (NH₃), which is converted to nitrite (NO₂^-) and nitrate (NO₃^-) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L^-1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.

Modelling and Incorporating the Variable Demand Patterns to the Calibration of Water Distribution System Hydraulic Model

Calibration of a water distribution system (WDS) hydraulic model requires adjusting several parameters including hourly or sub-hourly demand multipliers, pipe roughness and settings of various hydraulic components. The water usage patterns or demand patterns in a 24-h cycle varies with the customer types and can be related to many factors including spatial and temporal factors. The demand patterns can also vary on a daily basis. For an extended period of hydraulic simulation, the modelling tools allows modelling of the variable demand patterns using daily multiplication factors. In this study, a linear modelling approach was used to handle the variable demand patterns. The parameters of the linear model allow modelling of the variable demand patterns with respect to the baseline values, and they were optimised to maximise the association with the observed data. This procedure was applied to calibrate the hydraulic model developed in EPANET of a large drinking water distribution system in regional South Australia. Local and global optimisation techniques were used to find the optimal values of the linear modelling parameters. The result suggests that the approach has the potential to model the variable demand patterns in a WDS hydraulic model and it improves the objective function of calibration.

An automatic calibration framework based on the InfoWorks ICM model: the effect of multiple objectives during multiple water pollutant modeling

An automatic calibration framework of water quality parameters for surface runoff during modeling with InfoWorks ICM was constructed. The framework is based on a genetic algorithm (GA) and fully considers the calibration sequence for multiple water pollutants, namely, total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP). Meanwhile, four different objective functions including the Nash-Sutcliff efficiency coefficient (NSE), coefficient of determination (R²), percentage error in the peak (PEP), and percentage bias (PBIAS) were selected as fitness evaluators for the GA. The framework was applied successfully to a specific area of Fuzhou in China, and the multi-objective results were compared with the single-objective results. The comprehensive indexes of TSS, COD, TN, and TP by multi-objective calibration were lower than that of the single-objective calibration in both scenarios. Compared with single-objective calibration, the iterations to reach the optimal value were shortened 9, 5, 13, and 15 iterations by multi-objective calibration. Therefore, the findings showed that the multi-objective function GA was more balanced and more efficient than the single-objective function GA. Then, the uncertainty of the model was evaluated by using the samples generated by automatic calibration, which provided a reliable basis for the subsequent application of the model. This framework can be applied to other programs through adjustments of the number and weight of objective functions according to the specific situation, which will make the modeling more efficient and accurate.

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

Using the General Regression Neural Network Method to Calibrate the Parameters of a Sub-Catchment

Computer software is an effective tool for simulating urban rainfall–runoff. In hydrological analyses, the storm water management model (SWMM) is widely used throughout the world. However, this model is ineffective for parameter calibration and verification owing to the complexity associated with monitoring data onsite. In the present study, the general regression neural network (GRNN) is used to predict the parameters of the catchment directly, which cannot be achieved using SWMM. Then, the runoff curve is simulated using SWMM, employing predicted parameters based on actual rainfall events. Finally, the simulated and observed runoff curves are compared. The results demonstrate that using GRNN to predict parameters is helpful for achieving simulation results with high accuracy. Thus, combining GRNN and SWMM creates an effective tool for rainfall–runoff simulation.

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO₃) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L⁻¹.

Long-Term Modelling of an Agricultural and Urban River Catchment with SWMM Upgraded by the Evapotranspiration Model UrbanEVA

Evapotranspiration (ET) has a decisive effect on groundwater recharge and thus also affects the base flow of the receiving water. This applies above all to low-lying areas with a low depth to groundwater (GW), as is often the case in the north German lowlands. In order to analyze this relation, a coupled rainfall-runoff and hydraulic stream model was set up using the software SWMM-UrbanEVA, a version of the software SWMM that was upgraded by a detailed ET module. A corresponding model was set up for the same site but with the conventional software SWMM to compare the water balance and hydrographs. The total amount of ET calculated with the SWMM software is 7% higher than that computed with the upgraded version in the period considered. Therefore, less water is available for soil infiltration and lateral groundwater flow to the stream. This generally leads to a slight underestimation of base flows, with the exception of a notably wet summer month when the base flows were highly overestimated. Nevertheless, the base flow hydrograph shows a good adaptation to observed values (MAE = 0.014 m3s−1, R = 0.88, NSE = 0.81) but gives worse results compared to SWMM-UrbanEVA. The latter is very well able to reflect the GW-fed base flow in the sample stream in average (MAE = 0.011 m3s−1) and in its dynamics (R = 0.93, NSE = 0.85). By applying the UrbanEVA upgrade, SWMM is applicable to model the seasonal dynamics of near-natural river basins.

Addition of overland runoff and flow routing methods to SWMM-model application to Hyderabad, India

Hydrological models apply different methods to estimate runoff and route flows. Suitability of these methods is not unique, but varies with catchment conditions. This study aims to find the suitable overland runoff and flow routing methods for a catchment in Hyderabad, India, using customised Storm Water Management Model (SWMM-C). Currently, SWMM adapts only non-linear reservoir (NLR) method to estimate overland runoff. Linear reservoir (LR) and kinematic wave overland flow (KWO) have been incorporated as additional overland runoff methods. For flow routing, SWMM currently has kinematic wave (KW) and dynamic wave (DW) methods. Muskingum, Muskingum Cunge (MC) and lag methods have been included as additional methods in this customised version. SWMM-C was calibrated with four event rainfalls and tested with six event rainfalls using all possible combinations of overland runoff and flow routing methods. Efficiency of SWMM-C in simulating runoff was evaluated using performance indices. Results showed that for low magnitude event rainfalls, NLR, LR and KWO simulated runoff with a maximum deviation of 50%, 60% and 40% from observed runoff, respectively. In high magnitude event rainfalls, NLR, LR and KWO simulated runoff with maximum deviations of 20%, 40% and 20%, respectively, from the observed runoff. It was inferred from model outputs that NLR method could simulate runoff reasonably well for rainfalls that have duration greater than the time of concentration of catchment. LR method could simulate peak runoff better. KWO method was found to be suitable for chosen catchment for all rainfall durations. Flow routing methods KW, DW and MC are found to have minor influences on the runoff.

Integrated Hydrological-Hydraulic Model for Flood Simulation in Tropical Urban Catchment

In recent decades, Malaysia has become one of the world’s most urbanized nations, causing severe flash flooding. Urbanization should meet the population’s needs by increasing the development of paved areas, which has significantly changed the catchment’s hydrological and hydraulic characteristics. Therefore, the frequency of flash flooding in Malaysia’s urban areas has grown year after year. Numerous techniques have been used, including the statistical approach, modeling, and storm design methods, in flood simulation. This research integrated hydrology and hydraulic models to simulate the urban flood events in the Aur River catchment. The primary objective is to determine water level and forecast peak flow based on hydrological assessment in the drainage system using XPSWMM software. The rainfall data for 60 min was used for this study in the hydrological analysis by obtaining an intensity-duration-frequency curve and peak flow value (Q peak). XPSWMM is used to simulate the response of a catchment to rainfall events in which runoff, water depth profile, and outflow hydrograph are obtained. Peak runoff is also obtained from the modified rational method for validation purposes. The proposed method was verified by comparing the result with the standard method. This is essential to identify flash flooding, which can lead to efficient flood mitigation planning and management in the urban catchment. The increase in residential areas results in the alteration of time of concentration, water quantity, and flow rate. Thus, to mitigate present and future problems, the effects of urbanization on water resources and flood should be analyzed.