Urban Runoff Model Research Articles

Analysis of Flood Damage Mitigation Effects Based on the Presence of a Rainwater-Retaining Facility Through Flood-Depth Distribution Analysis by Grid

In recent years, localized heavy rainfall due to climate change has caused increased flood damage in urban areas. To address this issue, efforts are being made to mitigate damage through the installation of a rainwater-retaining facility and improved sewer systems. Various methods have been proposed to analyze the flood mitigation effects of these measures, with commonly used approaches including the analysis of overflow volume, peak flood discharge, and flood maps. However, these methods have limited capacity to accurately assess the reduction in flood depth, which directly affects human safety and property. Additionally, the use of flood maps requires visual analysis, which makes it difficult to evaluate the reduction effects accurately. Therefore, this study proposes a new method for flood reduction using grid-based flood-depth distribution analysis. The maximum flood depth for each grid was determined using the XP-SWMM urban runoff model, and the number of grids for each 0.1-meter flood-depth interval was analyzed using a Python program. Consequently, under various rainfall scenarios and boundary conditions, the proposed analysis method enables a more quantitative assessment of flood mitigation effects by comparing the number of reduced flood-depth grids before and after the installation of the rainwater retention facility. This approach also allows for a more precise assessment of the reconstruction of flood-depth grids, which directly affects human safety and property.

Effectiveness of urban distributed runoff model for discharge and water depth calculation in urban drainage pipe networks

Effectiveness of urban distributed runoff model for discharge and water depth calculation in urban drainage pipe networks

Mitigating urban flood Hazards: Hybrid strategy of structural measures

Effective reduction of urban flood risk can be accomplished through structural mitigation measures such as underground drainage tunnels and implementing drainage practices, such as green infrastructure (GI) techniques. In this study, we employed an urban runoff model to quantitatively evaluate the impact of these flood mitigation measures and GI technology in reducing urban flood vulnerability (UFV). Specifically, we assessed the flood control capabilities of underground drainage tunnels and detention reservoirs, among others, exploring various combinations to formulate a comprehensive flood prevention plan (CFPP) for the studied basin. Additionally, our study analyzes the effectiveness of GI technologies, such as impervious area reduction (IAR) and ground retention (GR) and introduces a method for assessing their impact on flood-prone areas. To enhance flood mitigation strategies, we propose a hybrid approach that combines CFPP with GI techniques. The effectiveness of this hybrid strategy was assessed through comparisons with a basic scenario and multiple GI technology scenarios. As a result of applying the hybrid strategy proposed in this study, the reduction rates of peak flow (PF), flood volume (FV), and flooded area (FA) in this basin were up to 37.3 %, 71.5 %, and 93.6 %, respectively. These findings offer valuable insights for designing strategies to enhance urban flood resilience in the face of a changing climate.

Development of an Integrated Urban Flood Model and Its Application in a Concave-Down Overpass Area

Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction.

Development of a cellular automata-based distributed hydrological model for simulating urban surface runoff

The present study developed a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM) to simulate two-dimensional urban surface runoff processes. CA-DUSRM uses an improved nonlinear reservoir algorithm to simulate the generation of surface runoff outflow at grid cell scale and flow interactions among cells. The CA-DUSRM simulation results (total surface runoff and peak discharge) for an asphalt road sub-catchment during different rainfall events were in good agreement with actual measurements of both surface runoff and rainfall processes. The performance of CA-DUSRM was comprehensively and systematically analyzed under different rainfall–terrain scenarios. The results showed that increasing the temporal grain resulted in uncertain simulation results in relation to the rainfall intensity, slope, and terrain undulation employed; and significant spatial grain effects were mainly caused by large terrain undulation changes. Compared with the storm water management model (SWMM), CA-DUSRM performed better overall and was more sensitive to variation in terrain undulation and fluctuations in rainfall. Based on model structure and simulation mechanism perspectives, CA-DUSRM can present the influence of spatially heterogeneous underlying surface characteristics on the runoff processes and describe the lateral water exchanges, which makes the simulated processes closer to the actual processes when compared with SWMM. CA-DUSRM is potentially suitable for application in urban areas that have complex underlying surfaces.

Modeling the heat transfer of rainfall runoff in the typical catchment of Beijing

The stormwater runoff formed on the urban impervious surface in summer may carry a large amount of heat, which will have a negative impact on the urban receiving water bodies. In order to simulate the process of runoff generation, confluence, and heat transfer, evaluate the degree of thermal pollution of stormwater runoff, and better quantify the heat of stormwater runoff, a simplified urban stormwater runoff temperature model is developed according to the runoff process of pervious surface and impervious surface and pipe network during rainfall, combined with the heat transfer principle in the process, to calculate the runoff flow and temperature from the small catchment. The heat transfer model mainly considers the heat exchange between runoff and surface, runoff and atmosphere, and runoff and pipe. The proposed model was evaluated by monitoring of typical urban catchment in the summer of 2021–2022, and the calculation formula of Event Mean Temperatures (EMT) in the catchment was proposed based on the model principle. The average root mean square error (RMSE) between the predicted value and the monitoring value of the model is 0.6 ℃. Long duration rainfall and short duration high intensity rainfall could lead to large simulation errors. Runoff temperature is easily affected by air temperature, solar radiation, pre-rain temperature and type of impervious surface. The average error between the EMT calculated by the formula and the actual monitoring EMT is 0.6 ℃, and its significant error rainfall frequency is consistent with the model. The results show that the model and formula can be used to predict the runoff heat in typical catchments, providing a more convenient method for the analysis of runoff heat in small catchments.

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss, and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modeling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modeling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modeling and assessment in Japan.

Enhancing urban runoff modelling using water stable isotopes and ages in complex catchments

AbstractIncreased urbanization, coupled with the projected impacts of climatic change, mandates further evaluation of the impact of urban development on water flow paths to guide sustainable land‐use planning. Though the general urbanization impacts of increased storm runoff peaks and reduced baseflows are well known; how the complex, non‐stationary interaction of the dominant water fluxes within dynamic urban water stores sustain streamflow regimes over longer periods of time is less well quantified. In particular, there is a challenge in how hydrological modelling should integrate the juxtaposition of rapid and slower flow pathways of the urban ‘karst’ landscape and different approaches need evaluation. In this context, we utilized hydrological and water stable isotope datasets within a modelling framework that combined the commonly used Hydrologic Engineering Center Hydrological Modelling System (HEC‐HMS) urban runoff model along with a simple hydrological tracer module and transit time modelling to evaluate the spatial and temporal variation of water flow paths and ages within a heavily urbanized 217 km2 catchment in Berlin, Germany. Deeper groundwater was the primary flow component in the upper reaches of the catchment within fewer urbanized regions, while the addition of wastewater effluent in the mid‐reaches of the catchment was the dominant water supply to sustain baseflow in the lower main stem stream, with additional direct storm runoff and shallow subsurface contributions in the more urbanized lower reaches. Water ages from each modelling approach mirrored flow contributions and water age mixing potential in subsurface storage; with older average water and lower young water contributions in less urbanized sub‐catchments and younger average water and higher young water contributions in more urbanized regions. The results from the first step towards more integrated tracer‐aided hydrologic modelling tools for similar peri‐urban catchments, given the potential limitations of simpler model frameworks. The results have broader implications for assessing the uncertainty in evaluating urban impacts on hydrological function under environmental change.

Spatial Integration of Urban Runoff Modeling, Heat, and Social Vulnerability for Blue-Green Infrastructure Planning and Management

Climate change exacerbates environmental challenges caused by urban growth. This study offers a multifaceted approach to understanding socioenvironmental vulnerability in cities by considering fine-scale spatial distribution of social and physical metrics overlaid with runoff and surface temperature (ST) data. Stormwater runoff depth and surface temperature data for micro-subbasins in Philadelphia were estimated and integrated to create an environmental vulnerability index. Social variables representing poverty, the elderly population, population density, and buildings with basements were used to develop a social vulnerability index. Simple analysis based on medians of combined environmental and social vulnerability indexes identified specific subbasins of the city that are most vulnerable to heat and flooding impacts of climate change. Hotspot analysis of combined social and environmental vulnerability data identified regions of the city that would benefit most from a focus on implementation of new green-blue infrastructures. Results indicated that neighborhoods in southern Philadelphia are most vulnerable to flooding and heat. The outlined approach can be used for any city, and provides city managers with a city-scale visualization for prioritizing areas in immediate need of climate mitigation practices.

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

Automated modelling of urban runoff based on domain knowledge and equation discovery

Modelling tools are widely used to analyse the urban drainage systems and to simulate the effects of future urban development and stormwater control measures. Usually, these tools use only one mathematical model (predetermined by the modeller) at a time to describe a single hydrological process within the urban catchment. When there are alternative mathematical models for describing the same hydrological process, their suitability needs to be investigated separately, which makes the modelling task even more complex, time consuming and open for human errors. Furthermore, models have to be calibrated to achieve a better fit between measured and simulated runoff. Calibration can be performed either manually, by using a trial-and-error approach, or by employing search techniques and parameter optimization tools. To overcome the drawbacks associated with manual selection and calibration of models, automated modelling based on equation discovery was used in this study to a) find the most suitable mathematical model among multiple alternatives for describing every (environmental) process modelled and b) to calibrate the model parameters against measured data. First, knowledge on urban runoff modelling was formalized into a new library of modelling components, compliant with the equation discovery tool ProBMoT (Process Based Modelling Tool). Next, a conceptual model of the experimental urban sub-catchment within the city of Ljubljana, Slovenia, was defined. ProBMoT was used to find the structure and parameters’ values of alternative rainfall-runoff models, according to the defined conceptual model that provide optimal fit against pipe flow measurements. Three alternative methods were used to describe infiltration: the SCS CN method, the Variable UK runoff equation, and the UK Water Industry Research equation. The proposed automated model discovery approach for finding the optimal rainfall-runoff model proved to be very efficient. Nine rainfall-runoff models were created with very good performance. The best performance was achieved by the models that used a combination of two different infiltration methods, i.e. the SCS CN infiltration method for the pervious area and one of the other two infiltration methods for the impervious area.

Developing an urban runoff management model by using satellite precipitation datasets to allocate low impact development systems under climate change conditions

Developing an urban runoff management model by using satellite precipitation datasets to allocate low impact development systems under climate change conditions

Early stopping technique using a genetic algorithm for calibration of an urban runoff model

ABSTRACT Identifying suitable parameter sets for use in catchment modelling remains a critical issue in hydrology. This paper describes an early stopping technique (EST) for use during calibration of a multi-parameter urban catchment modelling system. The proposed method takes advantage of MODE and lower confidence limit (LCL) functions in statistical analysis of spanning set of objective function values. The paper also introduces a monitoring process and regularization techniques to avoid under/overfitting during the calibration and to enhance generalisation performance. The methodology is assessed using SWMM and linked with a Genetic Algorithm for calibration of a Powells Creek catchment model in Sydney, Australia. Results demonstrate that the statistical spanning set analysis approach overcomes issues of poor interpretation and deterioration in the model’s generalisation properties. By stopping early, the calibration process avoided overfitting; this was indicated by too closely fitting to the calibration dataset and a failure to fit to the monitoring dataset.

Development of the GeoCRP for Smart River Management in the Smart City(I)

In this study, we developed the Geo City River Platform (GeoCRP), which regularly collects data on the web and provides the analyzed information by performing flood analysis based on the collected information in order to effectively perform smart city river management. GeoCRP was analyzed and tested in the Eco Delta City (EDC) area. In this platform, a watershed runoff analysis model, a river flow analysis model and an urban runoff analysis model were applied for flood analysis in EDC. GeoCRP can obtain more reliable results by taking a step-by-step approach to urban overflow that may occur in smart cities through the applied model. In addition, since all analysis processes such as data collection, input data generation and result data storage are automatically performed on the web, analysis can be performed, and results can be viewed if an environment that can access the web is established without special equipment or tools. The displayed analysis result is provided visually so that the user can intuitively confirm the information, so it is easy to understand the analysis results. Through this, smart city managers can effectively manage rivers, and it is expected that educational institutions will be able to use it as educational material on urban runoff.

A Generalized Automated Framework for Urban Runoff Modeling and Its Application at a Citywide Landscape

This research presents a fully automated framework for runoff estimation, applied to Philadelphia, Pennsylvania, a major urban area. Trends in global urbanization are exacerbating stormwater runoff, making it an increasingly critical challenge in urban areas. Understanding the fine-scale spatial distribution of local flooding is difficult due to the complexity of the urban landscape and lack of measured data, but it is critical for urban management and development. A one-meter resolution Digital Elevation Model (DEM) was used in conjunction with a model developed by using ArcGIS Pro software to create urban micro-subbasins. The DEM was manipulated to account for roof drainage and stormwater infrastructure, such as inlets. The generated micro-subbasins paired with 24-h storm data with a 10-year return period taken from the National Resources Conservation Service (NRCS) for the Philadelphia area was used to estimate runoff. One-meter land-cover and land-use data were used to estimate pervious and impervious areas and the runoff coefficients for each subbasin. Peak runoff discharge and runoff depth for each subbasin were then estimated by the rational and modified rational methods and the NRCS method. The inundation depths from the NRCS method and the modified rational method models were compared and used to generate percent agreement, maximum, and average of inundation maps of Philadelphia. The outcome of this research provides a clear picture of the spatial likelihood of experiencing negative effects of excessive precipitation, useful for stormwater management agencies, city managers, and citizen.

Urban Flood Depth Estimate With a New Calibrated Curve Number Runoff Prediction Model

The 1954 Soil Conservation Services (SCS) runoff predictive model was adopted in engineering designs throughout the world. However, its runoff prediction reliability was under scrutiny by recent studies. The conventional curve number (CN) selection methodology is often very subjective and lacks scientific justification while nested soil group catchments complicate the issue with the risk of inappropriate curve number selection which produces unreliable runoff results. The SCS CN model was statistically invalid ( $\alpha = 0.01$ level) and over predicted runoff volume as much as 21% at the Sungai Kerayong catchment in Kuala Lumpur, Malaysia. Blind adoption of the model will commit a type II error. As such, this study presented a new method to calibrate and formulate an urban runoff model with inferential statistics and residual modelling technique to correct the runoff prediction results from the SCS CN model with a corrected equation. The new model out-performed the Asymptotic runoff model and SCS CN runoff model with low predictive model bias, reduced sum of squared errors by 32% and achieved high Nash-Sutcliffe efficiency value of 0.96. The derived urban curve number is 98.0 with 99% confidence interval ranging from 97.8 to 99.5 for Sungai Kerayong catchment. Twenty-five storms generated almost 29 million $m^{3}$ runoff (11,548 Olympic size swimming pools) from the Sungai Kerayong catchment in this study. 75%-94% of the rain water became runoff from those storms and lost through the catchment, without efficient drainage infrastructure in place, the averaged flood depth reached 6.5 cm while the actual flood depth will be deeper at the flood ponding area near to the catchment outlet.

An Improved Simplified Urban Storm Inundation Model Based on Urban Terrain and Catchment Modification

Flooding caused by unpredictable high-intensity rainfall events in urban areas has become a global phenomenon due to the combined effect of urbanization and climate change. There are numerous hydrodynamic models for urban flooding simulation and management. However, it is difficult for most of these models to simplify the surface runoff process and still provide high simulation accuracy. In this study, an improved simplified urban storm inundation model (SUSIM) that integrates urban terrain, precipitation, surface runoff and inundation models was proposed to quickly and accurately simulate the different inundation conditions by modifying the urban terrain and catchments. Haining City, China, was selected as a case study in which SUSIM was tested and validated. The results were as follows: (1) Detailed locations and depths of inundation were quickly calculated with high correlation coefficient (≥75%) compared to three actual rainfall events. (2) Four scenarios under different rainfall intensities (5-, 10-, 20- and 50-year return period, respectively) were designed. The maximum inundation depths significantly increased from 403 mm to 1522 mm and the maximum inundation area increased from 2904 m2 to 7330 m2. According to the simulation results, Haining Avenue, the West Mountain Park and the old urban area in the northeast part of the city would encounter the most extensive and severe inundation. The result reveals that the SUSIM could find inundation locations and calculate inundation depth and area quickly. It provides better insights and tools for urban inundation simulation and planning strategies.

The effects of spatial discretization on performances and parameters of urban hydrological model.

Selecting a proper spatial resolution for urban rainfall runoff modeling was not a trivial issue because it could affect the model outputs. Recently, the development of remote sensing technology and increasingly available data source had enabled rainfall runoff process to be modeled at detailed and microscales. However, the models with less complexity might have equally good performance with less model establishment and computation time. This study attempted to explore the impact of model spatial resolution on model performance and parameters. Models with different discretization degree were built up on the basis of actual drainage networks, urban parcels and specific land use. The results showed that there was very little difference in the total runoff volumes while peak flows showed obvious scale effects which could be up to 30%. Generally, model calibration could compensate the scale effect. The calibrated models with different resolution showed similar performances. The consideration of effective impervious area (EIA) as a calibration parameter marginally increased performance of the calibration period but also slightly decreased performance in the validation period which indicated the importance of detailed EIA identification.

Using spatially-identified effective impervious area to target green infrastructure retrofits: A modeling study in Knoxville, TN

There is a need for enhanced guidance in siting distributed, infiltrative green infrastructure (GI) practices, especially in densely developed urban watersheds where retrofits come at a high cost. To maximize the hydrologic benefit of GI practices on urban streams, the disconnection of effective impervious areas (EIA), or those impervious areas hydraulically connected to the stormwater network, has been identified as a strategic management approach that is expected to have the greatest impact. The overall effect of full disconnection of spatially-identified EIA on watershed hydrology is uncertain because this type of full disconnection is rarely brought to full-scale implementation. In this study, spatial EIA identification is used to parametrize an urban runoff model using the United States Environmental Protection Agency’s Storm Water Management Model (SWMM). The calibrated model is used to assess runoff reductions resulting from GI practices distributed through the watershed via different placement strategies, both spatially-informed and not. Full treatment of the spatially-identified EIA using bioretention cells was compared to two scenarios treating the same area of impervious surfaces, but with random placement either among all impervious areas or placement focused in areas of higher imperviousness. Model results indicate that substantially higher runoff reduction could be realized by targeting EIA, with a median runoff reduction of nearly 30% more than other treatment scenarios across storm events ranging from 1.27 to 20.7 mm using this strategic siting. Further improvements in optimizing distributed infiltrative GI practice placement are needed and targeting of spatially-identified EIA appears to be a viable method for increasing the hydrologic improvements realized through watershed scale implementations.

Determination of Spatial and Temporal Variability of Soil Hydraulic Conductivity for Urban Runoff Modelling

Soil hydraulic conductivity has a direct influence on infiltration rate, which is of great importance for modelling and design of surface runoff and stormwater control measures. In this study, three measuring techniques for determination of soil hydraulic conductivity were compared in an urban catchment in Ljubljana, Slovenia. Double ring (DRI) and dual head infiltrometer (DHI) were applied to measure saturated hydraulic conductivity (Ks) and mini disk infiltrometer (MDI) was applied to measure unsaturated hydraulic conductivity (K), which was recalculated in Ks in order to compare the results. Results showed significant differences between investigated techniques, namely DHI showed 6.8 times higher values of Ks in comparison to DRI. On the other hand, Ks values obtained by MDI and DRI exhibited the lowest difference. MDI measurements in 12 locations of the small plot pointed to the spatial variability of K ranging between 73%–89% as well as to temporal variability within a single location of 27%–99%. Additionally, a reduction of K caused by the effect of drought-induced water repellency was observed. Moreover, results indicate that hydrological models could be enhanced using different scenarios by employing a range of K values based on soil conditions.