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The regulation and spatial differences of urban runoffs are of great concern in contemporary hydrological research. However, owing to a shortage of basic data sources and restrictions on urban hydrological simulation functions, simulating and investigating the regulation mechanism behind rainfall-runoff processes remain significantly challenging. In this study, the Time Variant Gain Model (TVGM), a hydrological nonlinear system model, was extrapolated to the hydrodynamic model of an urban drainage network system by integrating it with the widely used Stormwater Management Model (SWMM) to adequately simulate urban runoff events while considering various underlying surfaces and runoff routing modes, such as surface, drainage network and river runoff, in urban regions (i.e., TVGM-SWMM). Moreover, runoff events were characterized using the following four runoff regime metrics: runoff coefficient, capture ratio of annual runoff volume, standardized flood timescale, and the ratio of occurrence time differences between flow and rainfall peak to event duration (peak flow delay time). The characteristics and spatial differences of urban runoff regulations were investigated, and the key impact factors and their relative contributions were identified using multivariate statistical analyses. Four communities were selected as our study areas, consisting of communities from Beijing, Shenzhen, Wuhan, and Chongqing. Our results showed that the TVGM-SWMM performed considerably better than SWMM alone. The comprehensive simulation accuracy of 60% of the events (12/20) improved by 486%, with the bias improving the most, followed by the efficiency coefficient. Barring the runoff coefficient, significant spatial differences were identified at the patch scale for the runoff regime metrics, with differences of 0.43, 0.22, and 0.16 ( p <0.05). The key impact factors were the pipe length ( r =0.51) in the drainage network system and the forest area ratios ( r =0.56), sponge measures ( r =0.52), grassland ( r =0.48), and impervious surface ( r =0.46) in the underlying surfaces. The contributions of the drainage network system and the underlying surfaces were 4.27% and 37.83%, respectively. Regulation in the Beijing community, dominated by grassland regulation, delayed and reduced the peak flow and total runoff volume. In the Shenzhen community, sharp and thin runoff events were mainly generated by impervious surfaces and were not adequately regulated. Forest regulation was the dominant regulation type in the Wuhan community, which reduced the total runoff volume and delayed the peak flow. Waterbody regulation was the primary regulation type in the Chongqing community, which reduced the total runoff volume and peak flow. This study aims to introduce a comprehensive theoretical and technical assessment of the hydrological effects of urbanization and the performance of sponge city construction and provide a reference for urban hydrological model improvements in China.

The 1D sewer - 2D surface coupled hydrodynamic model has increasingly become an essential tool for simulating and predicting the flood process and is widely used in the study of urban rainfall-runoff simulation. The current method of using the smaller time step of the sub model in the coupled model as the synchronization time greatly limits the computational efficiency, especially in the case of the large data amount or models executed in different platforms and in various types of codes. To evaluate the impact of time synchronization on the rainfall-runoff process in a coupled hydrodynamic model, a new model that couples the 2D GPU accelerated shallow water model and the 1D SWMM is applied to two urban catchments to simulate the rainfall-runoff-drainage processes, the fixed time step (5 s, 10 s, 30 s, 60 s, 120 s, 180 s and 300 s) is adopted to ensure the calculation efficiency and precision of the model. The results show that the time computational efficiency can be improved by 7.27%–27.37% in different scenarios compared with the method applying 2D model time step as the synchronization time; the surface runoff process is hardly affected as the synchronization time changes; and the relative error of the drainage process is less than 2.5% when the synchronization time is less than 60 s. Therefore, the fixed synchronization time method is recommended in the 1D-2D coupled model to improve the computational efficiency for flood and inundation simulation. Based on the advantage that the fixed synchronization time is easy to realize in the programming of the model and the high efficiency of the fixed synchronization time method concluded above, this work is expected to provide a reference for model coupling applications.

SWAT (Soil and Water Assessment Tool) is a continuous-time, semi-distributed, river basin model widely used to evaluate the effects of alternative management decisions on water resources. This study examines the application of the SWAT model for streamflow simulation in an experimental basin with mixed-land-use characteristics (i.e., urban/peri-urban) using daily and hourly rainfall observations. The main objective of the present study was to investigate the influence of rainfall resolution on model performance to analyze the mechanisms governing surface runoff at the catchment scale. The model was calibrated for 2018 and validated for 2019 using the Sequential Uncertainty Fitting (SUFI-2) algorithm in the SWAT-CUP program. Daily surface runoff was estimated using the Curve Number method, and hourly surface runoff was estimated using the Green–Ampt and Mein–Larson method. A sensitivity analysis conducted in this study showed that the parameters related to groundwater flow were more sensitive for daily time intervals, and channel-routing parameters were more influential for hourly time intervals. Model performance statistics and graphical techniques indicated that the daily model performed better than the subdaily model (daily model, with NSE = 0.86, R2 = 0.87, and PBIAS = 4.2 %; subdaily model with NSE = 0.6, R2 = 0.63, and PBIAS = 11.7 %). The Curve Number method produced higher discharge peaks than the Green–Ampt and Mein–Larson method and better estimated the observed values. Overall, the general agreement between observations and simulations in both models suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin with high complexity and spatial distribution of input data.

Stormwater management in an urban environment is beset by uncertainties about future development. Dynamic strategies must be devised to cope with such uncertain environment. This work proposes a simulation–optimization model that minimizes the costs of low-impact development (LID) measures for mitigating impacts of future urban development on runoff. This paper’s methodology is tested in an urban watershed in Tehran, Iran, relying on the stormwater management model (SWMM) coupled with the genetic algorithm (GA) to function as a simulation–optimization method for urban–runoff control by means of LID stormwater control measures. A sensitivity analysis of the calculated optimal solution revealed the impacts the most sensitive LIDs would have on runoff considering a set of plausible future development scenarios in the urban catchment. A comparison of the results from two different scenarios of future development with the existing stormwater system’s performance shows the cost increase in redesigning the existing system to make it LID sensitive would equal 20% of the existing system’s cost. The additional cost of redesigning the existing system without LID features would be 45% of the existing system’s cost. These results demonstrate the importance of assessing the sensitivity of designed units in a stormwater management system and studying the trade-offs between possible decisions and future uncertainties concerning development in the watershed.

The characteristics of surface runoff and the infiltration properties of urban green land are important to determine the effects of runoff reduction by low-impact development (LID) facilities. In this paper, two typical types of urban green land (lawn and shrub) in Shanghai were selected to study the runoff characteristics under eight rainfall events. The sensitivity of the runoff parameters was analyzed, and then, the optimal parameters were determined using the Stormwater Management Model (SWMM). The results showed that the interception and infiltration capacities of shrub were greater than those of lawn. The rainfall intensity and rainfall pattern were the major factors that influenced the interception and infiltration of rainwater. The threshold value that generates runoff varied across the eight rainfall events ranged from 1.6 to 28.5 mm for lawn and 4.5 to 32.0 mm for shrub. The maximum reduction ratios of runoff and peak flow for shrub were 52 and 57% higher than them for lawn, respectively. The parameters for shrub were more sensitive to runoff and peak flow compared with those for lawn. Under light rainfalls with a short duration, the maximum infiltration rate and depression storage were more sensitive than those under heavy rainfalls with a long duration. Antecedent dry weather period was not found to be a sensitive parameter except for the shrub under light rainfalls. The relative errors of runoff and dynamic mean runoff (60 min) for lawn and shrub were within ± 9.5%. The errors of peak flow ranged between − 21 and 16.6%. The dynamic runoff characteristics and the parameters for lawn and shrub determined in this study can provide references for simulating urban runoff and planning LID areas.

Urban flooding intensifies with escalating urbanization. This study focuses on Xiong'er river as the study area and couples a 1D/2D urban flooding model using InfoWorks ICM (Integrated Catchment Modeling). Ten scenarios are set respectively with a rainfall return period of 5a 10a, 20a, 50a, and 100a, alongside rainfall durations of 1 and 24 h. Subsequently, the H-V (hazard–vulnerability) method was applied to evaluate urban flooding risk. Three indicators were selected for each of hazard factors and vulnerability factors. The relative weight values of each indicator factor were calculated using the AHP method. The result shows that (1) flood depth, rate, and duration escalate with longer rainfall return periods, yet decrease as the duration of rainfall increases; (2) as the rainfall return period lengthens, the proportion of node overflow rises, whereas it diminishes with longer rainfall durations, leading to an overall overloaded state in the pipeline network; and (3) the distribution in the research area is mainly low-risk areas, with very few extremely high-risk. Medium to high-risk areas are mainly distributed on both sides of the river, in densely built and low-lying urban areas. This study demonstrates that the model can accurately simulate urban flooding and provide insights for flood analyses in comparable regions.

In this paper, a new methodology is developed for urban runoff management based on global sensitivity analysis of the storm water management model (SWMM) considering uncertainties. The variogram analysis of response surface (VARS) model is utilized for sensitivity analysis of the SWMM parameters by combining the runoff simulation model of the SWMM with VARS. Three model efficiency metrics, namely Nash–Sutcliffe efficiency metric for the runoff, NSE metric for the logarithm of the runoff, and percent bias in simulating runoff are used to evaluate SWMM outputs and rank its parameters. The reliability of the obtained rankings of parameters is evaluated by developing a bootstrapping-based strategy to estimate confidence intervals for the calculated sensitivity values. A multi-objective optimization model is integrated with the calibrated SWMM, to select optimum scenarios of low impact development-best management practice (LID-BMP). To take into account the rainfall uncertainty, design storm hyetograph is stochastically derived using Monte Carlo analysis and Huff curves (Huff in Water Resour Res 3(4):1007–1019, 1967; Time distributions of heavy rainstorms in Illinois, State of Illinois Department of Energy and Natural Resources, Illinois, 1990). Finally, a socially acceptable LID-BMP scenario out of a set of non-dominated solutions is obtained using the Nash bargaining theory. The proposed method is applied to an urban watershed Iran. The resulted LID-BMPs could decrease runoff volume and pollution load by 24% and about 74%, respectively.

Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

Due to the impact of climate change, the importance of urban flood analysis is increasing. One of the biggest challenges in urban flood simulations is the complexity of storm sewer networks, which significantly affects both computational time and accuracy. This study aimed to analyze and evaluate the impact of sewer network simplification on the accuracy and computational performance of urban flood prediction by comparing different rainfall runoff methods. Using the hyper-connected solution for urban flood (HC-SURF) model, two rainfall runoff methods, the SWMM Runoff method and the Surface Runoff method, were compared. The sewer network simplification was applied based on manhole catchment areas ranging from 10 m2 to 10,000 m2. The analysis showed that the computation time could be reduced by up to 54.5% through simplification, though some accuracy loss may occur depending on the chosen runoff method. Overall, both methods produced excellent results in terms of mass balance, but the SWMM Runoff method minimized the reduction in analytical performance due to simplification. This study provides important insights into balancing computational efficiency and model accuracy in urban flood analysis.