Stormwater Runoff Model Research Articles

Integration of Building Information Modeling and Stormwater Runoff Modeling: Enhancing Design Tools for Nature-Based Solutions in Sustainable Landscapes

Building information modeling (BIM) has been used by the architectural and engineering disciplines to streamline the building design, construction, and management process, but there has been much more limited experience in extending the application to landscape design and implementation. This study integrated BIM software (Autodesk InfraWorks 2024.1) with a dynamic, process-oriented, conceptual hydrologic/hydraulic model (PCSWMM 2023, version 7.6.3665) to enhance the analytical tools for sustainable landscape design. We illustrate the model integration through a case study that links an existing nature-based solution (NbS) development, the PTT Metro Forest Park, Bangkok, Thailand, with theoretical new-build NbS for an adjacent property. A BIM school building was virtually situated on an empty lot beside the Metro Forest Park and seven NbS scenarios were run with design storms having 2-year, 5-year, and 100-year return intervals. The combination of a rain garden, permeable pavement, a retention pond, and a green roof was effective in sustainably managing runoff from the theoretical new-build site discharging to the Metro Forest. NbS design characteristics such as rain garden substrate depth and green roof area were optimized using the hydrologic/hydraulic model. Model results showed that even with the 100-year rainfall event, the existing Metro Forest pond storage capacity was sufficient so that flooding on the property would not occur. The consideration of connectivity between NbS features is facilitated by the modeling approach, which is important for NbS planning and assessment at a regional scale.

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.

An integrated urban stormwater model system supporting the whole life cycle of sponge city construction programs in China

Abstract The nationwide Sponge City Construction (SCC) program was launched by the Chinese government in 2014. There is a lack of an integrated model system to assist the efforts throughout all the SCC phases, including planning, design, construction, evaluation and maintenance stages. In this study, an integrated stormwater system named Uwater was developed based on a Geographic Information System (GIS) platform, in which a comprehensive open-source urban stormwater runoff model called Storm Water Management Model (SWMM) was embedded. The platform utilized the spatial data management tools in GIS to extract the discrete spatial information such as land use and vegetation cover condition to obtain the composite hydrologic parameters required in the SWMM simulations. The system also fully uses the visualization capabilities of GIS to assist visual design of the Low Impact Development (LID) facilities and Capital Improvement Projects frequently used in the SCC programs. Furthermore, it could be used to assess the drainage capacity of the stormwater system and the corresponding inundation limits for further optimization of the design plans. Finally, a study case using the platform was conducted to test and verify the reliability and applicability of the Uwater system. Results show that the Uwater platform has great strengths and potential to assist the whole life cycle in the SCC program.

Probabilistic Stormwater Runoff and Water Quality Modeling of a Highway in Suburban Maryland

AbstractThe U.S. EPA Storm Water Management Model (SWMM) was used to simulate upland runoff production and the subsequent performance of a downstream, ponded infiltration basin installed adjacent t...

Measuring and Modeling Flow Rates in Tidal Creeks: A Case Study from the Central Coast of South Carolina

The purpose of this study was to collect site- and condition-specific hydrology data to better understand the water flow dynamics of tidal creeks and terrestrial runoff from surrounding watersheds. In this paper, we developed mathematical models of tidal creek flow (discharge) in relation to time during a tidal cycle and also estimated terrestrial runoff volume from design storms to compare to tidal creek volumes. Currently, limited data are available about how discharge in tidal creeks behaves as a function of stage or the time of tide (i.e., rising or falling tide) for estuaries in the southeastern United States, so this information fills an existing knowledge gap. Ultimately, findings from this study will be used to inform managers about numeric nutrient criteria (nitrogen-N and phosphorus-P) when it is combined with biological response (e.g., phytoplankton assemblages) data from a concurrent study. We studied four tidal creek sites, two in the Ashepoo-Combahee-Edisto (ACE) Basin and two in the Charleston Harbor system. We used ArcGIS to delineate two different watersheds for each study site, to classify the surrounding land cover using the NOAA Coastal Change Analysis Program (C-CAP) data, and to analyze the soils using the NRCS Soil Survey Geographic database (SSURGO). The size of the U.S. Geological Survey’s Elevation Derivatives for National Application (EDNA) watersheds varied from 778 to 2,582 ha; smaller geographic watersheds were delineated for all sites (except Wimbee) for stormwater modeling purposes. The two sites in Charleston Harbor were within the first-order Horlbeck Creek and the second-order Bulls Creek areas. The ACE Basin sites were within the third-order Big Bay Creek and the fourth-order Wimbee Creek areas. We measured the stage and discharge in each creek with an acoustic Doppler current profiler (ADCP) unit for multiple tide conditions over a 2-year period (2015–2016) with the goal of encompassing as large of a range of tide stage and discharge data measurements as possible. The Stormwater Runoff Modeling System (SWARM) was also used to estimate the potential water entering the creeks from the land surface; this volume was very small relative to the tide water volume except for the more-developed Bulls Creek watershed. The results show that the peak discharge occurred on the ebb tide and that the duration of the flood tide spanned a longer period of time; both of these observations are consistent with traits associated with an ebb-dominated tidal creek system. The tidal inflow and outflow (flood and ebb tides, respectively) showed an asymmetrical pattern with respect to stage and discharge; peak discharge during the flood (rising) tide occurred at a higher stage than for the peak discharge during the ebb (falling) tide. This is not an unexpected result, as the water on an ebb tide is moving down gradient funneled through the creek channel toward the coast. Furthermore, water moving with the rising flood tide must overcome frictional losses due to the marsh bank and vegetation; i.e., the peak discharge can only happen when the water has risen above these impediments. We infer from the flow dynamics data that faster water velocities during ebb tide imply that more erosive energy could transport a larger mass of suspended solids and associated nutrients (e.g., orthophosphate) from the estuary to the coastal ocean. However, the discharge and runoff modeling indicate that land-based flux was important in the developed Bulls Creek watershed, but not at the larger and less-developed Big Bay Creek watershed. At Big Bay Creek, the relatively large tidal discharge volume compared to the smaller potential runoff generated within the watershed indicates that the creek could potentially dilute terrestrial runoff contaminants. Smaller, more-urbanized tidal wetland systems may not benefit from such dilution effects and thus are vulnerable to increased runoff from adjacent developed landscapes.

Climate Change and Watershed Hydrology—Heavier Precipitation Influence on Stormwater Runoff

Stormwater runoff in the USA is a main driver of non-point source pollution and other major problems for urbanizing areas, and runoff effects will be exacerbated by the increased frequency and intensity of heavier storm events that are projected as climate changes. The purpose of this paper is to consider how increased rainfall from storms could influence direct stormwater runoff in urbanizing watersheds. As part of a recent research project in coastal Beaufort County, South Carolina, USA, we applied the Stormwater Runoff Modeling System (SWARM) to model various combinations of development levels and climate change scenarios. SWARM single-event output showed dramatic increases in runoff volume and rate, in some cases almost doubling under moderate climate change scenario and tripling under severe climate change scenario. In all cases, modeled impacts from climate change exceeded those of development. By quantifying stormwater runoff based on climate change scenarios within the context of development, the findings add to the recognition that they must be considered together when projecting changes in watershed hydrology and that climate change effects potentially exceed those of development.

Event-based stormwater management pond runoff temperature model

Stormwater management wet ponds are generally very shallow and hence can significantly increase (about 5.4°C on average in this study) runoff temperatures in summer months, which adversely affects receiving urban stream ecosystems. This study uses gene expression programming (GEP) and artificial neural networks (ANN) modeling techniques to advance our knowledge of the key factors governing thermal enrichment effects of stormwater ponds. The models developed in this study build upon and compliment the ANN model developed by Sabouri et al. (2013) that predicts the catchment event mean runoff temperature entering the pond as a function of event climatic and catchment characteristic parameters. The key factors that control pond outlet runoff temperature, include: (1) Upland Catchment Parameters (catchment drainage area and event mean runoff temperature inflow to the pond); (2) Climatic Parameters (rainfall depth, event mean air temperature, and pond initial water temperature); and (3) Pond Design Parameters (pond length-to-width ratio, pond surface area, pond average depth, and pond outlet depth). We used monitoring data for three summers from 2009 to 2011 in four stormwater management ponds, located in the cities of Guelph and Kitchener, Ontario, Canada to develop the models. The prediction uncertainties of the developed ANN and GEP models for the case study sites are around 0.4% and 1.7% of the median value. Sensitivity analysis of the trained models indicates that the thermal enrichment of the pond outlet runoff is inversely proportional to pond length-to-width ratio, pond outlet depth, and directly proportional to event runoff volume, event mean pond inflow runoff temperature, and pond initial water temperature.

Hydrological Modeling and Characterization of the Khanpur Watershed, Pakistan

The Khanpur watershed in Pakistan has a high spatial changeability of precipitation attributable to extremely undulating surface topography and complex interactions between the land elevation and precipitation. This study used stormwater runoff modeling software, adapted to determine peak discharge of the Haro River. Correlation analysis was used to select input precipitation data for the software. Horton's and Strahler's strategies were used for manipulating the streams’ analysis and characterization of the watershed. Morphological features of the watershed are important in terms of hydrology. The bifurcation ratio was 3.5, which showed that the watershed has suffered less structural disturbance and the drainage patterns have not been distorted. The stream density of 0.5 is low, demonstrating a lower flood risk and an average channel slope. The Khanpur watershed has a circularity ratio of 0.6, showing that it is a small drainage basin with homogeneous geologic material that preserves the geometrical similarity. It has been determined that the watershed is well drained, hilly, and slopey.

도시유역에서의 방재성능목표 적용과 침수원인 분석

본 연구의 목적은 도시유역의 2차원 침수해석 등의 정량적 분석이 가능한 XP-SWMM(Stormwater & Wastewater Management Model) 모형을 통해 도시방재성능 목표를 적용하여 도시지역의 침수피해 원인 분석 및 해소방안을 제시하는 것이다. 이를 위하여 대상지역의 유역 및 우수관거, 지형 데이터 등을 이용하여 DTM(Digital Terrain Model) 및 우수관망을 구축하여 입력자료로 활용하였으며, 대상유역의 현재 도시방재성능을 평가하였다. 대상유역의 침수피해 원인을 검토한 결과, 우수관거의 통수능 부족으로 인한 1차 침수피해가 발생하였으며, 또한 방류구와 인접한 하천의 외수위 상승에 따른 내수배제 불량에 의해 침수피해가 가중되는 것으로 분석되었다. 따라서, 이러한 침수피해를 해소하기 위해서는 통수능이 부족한 관거의 개량 및 유역 특성을 고려한 방수로 신설 등의 저감대책이 필요한 것으로 판단되었다. The purpose of this study is to analyze quantitatively the inundation causes by applying the prevention of performance objectives using the urban storm water runoff model XP-SWMM. The model was built by using DTM and storm sewer-network with the storm sewer and geo-data of the study area as input-data to assess the current performance of prevention. An analysis of the causes of the inundation by the frequency and the rainfall-duration. As a result, lack of pipe capacity due to flooding, as well as inundation heavier that the backwater rainfall occurs due to the rise of water level of outside. For solve the inundation damage, It is necessary to improvement pipe of capacity lack and installation of a flood control channel.

Optimal Spatial Design of Capacity and Quantity of Rainwater Harvesting Systems for Urban Flood Mitigation

This study adopts rainwater harvesting systems (RWHS) into a stormwater runoff management model (SWMM) for the spatial design of capacities and quantities of rain barrel for urban flood mitigation. A simulation-optimization model is proposed for effectively identifying the optimal design. First of all, we particularly classified the characteristic zonal subregions for spatial design by using fuzzy C-means clustering with the investigated data of urban roof, land use and drainage system. In the simulation method, a series of regular spatial arrangements specification are designed by using statistical quartiles analysis for rooftop area and rainfall frequency analysis; accordingly, the corresponding reduced flooding circumstances can be simulated by SWMM. Moreover, the most effective solution for the simulation method is identified from the calculated net benefit, which is equivalent to the subtraction of the facility cost from the decreased inundation loss. It serves as the initially identified solution for the optimization model. In the optimization method, backpropagation neural network (BPNN) are first applied for developing a water level simulation model of urban drainage systems to substitute for SWMM to conform to newly considered interdisciplinary multi-objective optimization model, and a tabu search-based algorithm is used with the embedded BPNN-based SWMM to optimize the planning solution. The developed method is applied to the Zhong-He District, Taiwan. Results demonstrate that the application of tabu search and the BPNN-based simulation model into the optimization model can effectively, accurately and fast search optimal design considering economic net benefit. Furthermore, the optimized spatial rain barrel design could reduce 72% of inundation losses according to the simulated flood events.

Coupling of a distributed hydrological model with an urban storm water model for impact analysis of forced infiltration

Summary Only few studies have attempted to couple a storm water runoff model with a distributed hydrological model even though infiltration or exfiltration processes between pipes and canals of urban runoff systems and groundwater are widely recognised. We present a fully coupled model that allows simulation of the complete urban freshwater cycle including: runoff from paved and impervious areas, flow through the runoff network, overland flow, infiltration through the unsaturated zone, evapotranspiration (at green areas), and groundwater flow in complex, urban geology. For example, at the investigated urban area at the City of Silkeborg, Western Denmark, the coupled model show that one fourth (24%) of water input to the storm water runoff systems arrives from groundwater sources. The study furthermore quantifies groundwater feedback mechanisms of forced infiltration to surface water systems by the fully coupled hydrological and urban runoff model. Three local area recharge scenarios with forced infiltration are compared with the present situation without forced infiltration. The forced infiltration impacts the local groundwater table with an average rise of up to 69 cm resulting in significant feedback from the groundwater to the runoff system via drains, overland flow and leakage of groundwater to the pipes and canals of the urban runoff network.

Hydrologic and Water Quality Model Development Using Simulink

A stormwater runoff model based on the Soil Conservation Service (SCS) method and a finite-volume based water quality model have been developed to investigate the use of Simulink for use in teaching and research. Simulink, a MATLAB extension, is a graphically based model development environment for system modeling and simulation. Widely used for mechanical and electrical systems, Simulink has had less use for modeling of hydrologic systems. The watershed model is being considered for use in teaching graduate-level courses in hydrology and/or stormwater modeling. Simulink’s block (data process) and arrow (data transfer) object model, the copy and paste user interface, the large number of existing blocks, and the absence of computer code allows students to become model developers almost immediately. The visual depiction of systems, their component subsystems, and the flow of data through the systems are ideal attributes for hands-on teaching of hydrologic and mass balance processes to today’s computer-savvy visual learners. Model development with Simulink for research purposes is also investigated. A finite volume, multi-layer pond model using the water quality kinetics present in CE-QUAL-W2 has been developed using Simulink. The model is one of the first uses of Simulink for modeling eutrophication dynamics in stratified natural systems. The model structure and a test case are presented. One use of the model for teaching a graduate-level water quality modeling class is also described.

Influences of setting sizes and combination of green infrastructures on community’s stormwater runoff reduction

Implementation of green infrastructures is an effective option for mitigating the impacts of increasing urbanization on stormwater runoff. In this study, we evaluated the runoff reduction effectiveness under various setting sizes of green infrastructures using a process-based stormwater runoff model. The model was validated with field data from a typical community in Beijing and proved to be accurate in estimating stormwater runoff under larger rain events. The pervious area percentage and soil hydraulic properties were key parameters influencing stormwater runoff. The four types of green infrastructures, including green area expanding, concave green space, storage pond and porous brick pavement were effective in reducing stormwater runoff, but single facility except the storage pond could not fully control runoff of 5-year recurrence storm. With integrated green infrastructures, runoff of 5-year recurrence storm could be 100% reduced by expanding the pervious area percentage to over 50%, or increase storage pond volume to over 1800m3, whereas a maximum runoff reduction of 95% could be achieved when the green land was reformed to concave with a depth of 4cm, or 50% of the impervious area was replaced with porous brick pavement with a storage capacity of 8mm. The combination of green infrastructures with proper setting sizes is necessary for optimal control of stormwater runoff management.

Parameter sensitivity and uncertainty analysis of a stormwater runoff model

In this study, Generalized Likelihood Uncertainty Estimation (GLUE) methodology was used to perform uncertainty analysis of a stormwater model, which randomly generates parameter sets and identifies behavioral ones with higher likelihood. Latin Hypercube Sampling (LHS) is used to generate parameter sets. The Model for Urban Stormwater Improvement Conceptualization was chosen as an appropriate model for stormwater runoff. Prediction limits are determined by selecting the cutoff threshold for likelihood function. Study area is Goonja Drainage Basin located in the city of Seoul, Korea. From the results, maximum likelihood value for calibration is 0.78 and 0.73 for validation. The p-factors for the calibration and validation are 87 and 83%, respectively. The p-factor for all storm events is 85%. These are all acceptable values as the results are considered good when 60% or more of the observed data are bracketed by prediction limits. Overall, it was shown that, using GLUE methodology with LHS, the model calibrated well for the basin considered in this study.

Exploring impacts of development and climate change on stormwater runoff

Stormwater runoff is a leading cause of non-point source pollution in urbanizing areas, and runoff effects will be exacerbated by climate's changing patterns of precipitation. To enhance understanding of impacts of development and climate change on stormwater runoff in small watersheds (< 6500 ha), we developed the Stormwater Runoff Modeling System (SWARM), a simple modeling system based on U.S. Department of Agriculture, Natural Resources Conservation Service curve number and unit hydrograph methods. The objective of this paper is to describe the applications possible with SWARM and to demonstrate its usefulness in exploring the impacts of development and climate change on runoff. Results encompass a range of impact scenarios. One development scenario shows that the amount of rainfall converted to runoff is 27% for an undeveloped area and 67% for a highly developed area. A climate scenario shows that the amount of rainfall converted to runoff in a medium developed area is 25% in drought conditions and 76% in wet conditions. User-friendly templates make SWARM a good tool for scientific research, for resource management and decision making, and for community science education. The modeling system also supports the investigation of social and economic impacts to communities as they adapt to increased development and climate change. Although we calibrated SWARM specifically to the southeast coastal plain, it can be applied to other regions by recalibrating parameters and modifying calculation templates. Published 2013. This article is a U.S. Government work and is in the public domain in the USA.

Quantifying and simulating stormwater runoff in watersheds

We developed the Stormwater Runoff Modeling System (SWARM) based on curve number and unit hydrograph methods of the U.S. Department of Agriculture, Natural Resources Conservation Service. SWARM models single events, targets watersheds fitting easily within hydrologic units with 12-digit codes, and has been calibrated for low-gradient topography of the Southeast coastal plain. We established protocols; made changes related to peak rate factors, travel time formulas, curve numbers, and the initial abstraction ratio; and then tested the output with multi-site validation using U.S. Geological Survey measurements of discharge and rainfall. Validation results from both undeveloped and developed watersheds support the robustness of our system in quantifying and simulating runoff: rainfall to runoff differences between measured and simulated volumes ranged from 3 to 11%; r2 for hydrograph curves ranged from 0.82 to 0.98. SWARM can be a useful tool for scientific research and for coastal resource management and decision making in the Southeast coastal plain specifically and also may be applied to other areas by recalibrating parameters and modifying calculation templates. Copyright © 2012 John Wiley & Sons, Ltd.

Application of global sensitivity analysis and uncertainty quantification in dynamic modelling of micropollutants in stormwater runoff

The need for estimating micropollutants fluxes in stormwater systems increases the role of stormwater quality models as support for urban water managers, although the application of such models is affected by high uncertainty. This study presents a procedure for identifying the major sources of uncertainty in a conceptual lumped dynamic stormwater runoff quality model that is used in a study catchment to estimate (i) copper loads, (ii) compliance with dissolved Cu concentration limits on stormwater discharge and (iii) the fraction of Cu loads potentially intercepted by a planned treatment facility. The analysis is based on the combination of variance-decomposition Global Sensitivity Analysis (GSA) with the Generalized Likelihood Uncertainty Estimation (GLUE) technique. The GSA-GLUE approach highlights the correlation between the model factors defining the mass of pollutant in the system and the importance of considering hydrological parameters as source of uncertainty when calculating Cu loads and concentrations due to their influence. The influence of hydrological parameters on simulated concentrations changes during rain events. Four informal likelihood measures are used to quantify model prediction bounds. The width of the uncertainty bounds depends on the likelihood measure, with the inverse variance based likelihood more suitable for covering measured pollutographs. Uncertainty for simulated concentration is higher than for Cu loads, which again shows lower uncertainty compared to studies neglecting the hydrological submodel as source of uncertainty. A combined likelihood measure ensuring both good predictions in flow and concentration is used to identify the parameter sets used for long time simulations. These results provide a basis for reliable application of models as support in the development of strategies aiming to reduce discharge of stormwater micropollutants to the aquatic environment.

Quantification of stormwater runoff using a combined GIS and curve number approach: a case study for an urban watershed in the Ozark Highlands, USA

Managing stormwater runoff is crucial to preserving water quality in rapidly developing urban watersheds. The objective of this study was to develop a methodology to test existing stormwater drainage infrastructure, identify potential areas of improvement, and estimate potentially contaminated runoff by combining two widely used stormwater runoff prediction models. A watershed containing much of the University of Arkansas-Fayetteville campus was targeted for this study because stormwater from this watershed drains into a local river designated as an impaired water body due to siltation. The curve number method was used to estimate runoff for various flood-return periods and antecedent moisture conditions, while a flow-direction model integrated topography, land use, and stormwater drainage infrastructure in a GIS. The methodology developed and results generated will help stormwater planners visualise localised runoff and potentially adapt existing drainage networks to accommodate runoff, prevent flooding and erosion, and improve the stormwater quality entering nearby surfacewater bodies.

Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection

► We modeled event-based stormwater runoff quantity and quality using ANNs. ► Partial mutual information (PMI) was used to select input variables to ANNs. ► The ANN approach is superior to both linear and nonlinear regression approaches. ► We proved the applicability of ANNs combined with PMI in stormwater runoff modeling. Event-based stormwater runoff quantity and quality modeling remains a challenge since the processes of rainfall induced pollutant discharge are not completely understood. The complexity of physically-based models often limits the practical use of quality models in practice. Artificial neural networks (ANNs) are a data driven modeling approach that can avoid the necessity of fully understanding complex physical processes. In this paper, feed-forward multi-layer perceptron (MLP) network, a popular type of ANN, was applied to predict stormwater runoff quantity and quality including turbidity, specific conductance, water temperature, pH, and dissolved oxygen (DO) in storm events. A recently proposed input selection algorithm based on partial mutual information (PMI), which identifies input variables in a stepwise manner, was employed to select input variable sets for the development of ANNs. The ANNs developed via this approach could produce satisfactory prediction of event-based stormwater runoff quantity and quality. In particular, this approach demonstrated a superior performance over the approach involving ANNs fed by inputs selected using partial correlation and all potential inputs in flow modeling. This result suggests the applicability of PMI in developing ANN models. In addition, the ANN for flow outperformed conventional multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. For an ANN development of turbidity (multiplied by flow rate) and specific conductance, significant improvement was achieved by including a previous 3-week total rainfall amount into their input variable sets. This antecedent rainfall variable is considered a factor in the availability of land surface pollutants for wash-off. A sensitivity analysis demonstrated the potential role of this rainfall variable on modeling particulate solids and dissolved matters in stormwater runoff.

Impact of Residential Soil Disturbance on Infiltration Rate and Stormwater Runoff1

Woltemade, Christopher J., 2010. Impact of Residential Soil Disturbance on Infiltration Rate and Stormwater Runoff. Journal of the American Water Resources Association (JAWRA) 46(4): 700‐711. DOI: 10.1111/j.1752‐1688.2010.00442.xAbstract: Soil disturbances such as excavation and compaction in residential developments affect lawn infiltration rates and stormwater runoff. These effects were investigated via measuring saturated infiltration rates at 108 residential sites and 18 agricultural sites near Shippensburg, south‐central Pennsylvania, using a double‐ring infiltrometer. Residential sites included four neighborhoods distributed across three soil series classified as hydrologic soil group (HSG) B. Additional parcel data included date of house construction, percentage impervious area, lawn condition, and woody vegetation condition. Measured infiltration rates ranged from 0 to &gt;40 cm/hour. Analysis of variance indicated significantly different mean infiltration rates (p &lt; 0.001) for lots constructed pre‐2000 (9.0 cm/hour) and those constructed post‐2000 (2.8 cm/hour). Test results were used to determine a “field‐tested” HSG for each site, representing disturbed soil conditions. Stormwater runoff was estimated from residential lots for a range of 24‐hour design storms using the TR‐55 model and several alternative methods of determining curve numbers, including five different representations of soil conditions. Curve numbers and stormwater runoff were substantially higher when based on field‐tested HSGs for lots constructed post‐2000 compared with lots built pre‐2000 and when based on the HSG for undisturbed soils, documenting the magnitude of possible error in stormwater runoff models that neglect soil disturbance.