Design Of Water Infrastructure Research Articles

Isotope-based source assessment of water flowing from storm sewer systems to a receiving river during dry weather periods

Urban stormwater management systems, particularly storm sewers, are critical for managing runoff in urban areas. These systems are designed to function during wet weather events; however, field-based observations of these systems suggest that they may also be active flow pathways in dry weather conditions, ultimately contributing to streamflow. Unlike dry weather flow in wastewater systems, storm sewer dry weather flow has not been thoroughly explored. This research used stable isotopes of oxygen and hydrogen in water to examine the sources of dry weather flow from storm sewers in a highly urban catchment. A stable isotope mixing model was applied at the outfalls of two stormwater catchments and the receiving Black Creek, located in Toronto, Canada. Findings suggest that during dry periods, storm sewers receive non-stormwater inputs from tap water, wastewater, and groundwater, along with some precipitation, and that these sources may constitute up to 19 % of Black Creek's flow at the watershed scale. Seasonal patterns in flow and water sources were observed for the Black Creek and outfalls. At one outfall, dry weather flow was predominantly from the water distribution system (i.e., tap water and/or wastewater) throughout spring, summer, and fall. In contrast, at the second outfall, groundwater dominated in spring and summer, and groundwater and water distribution were equally proportioned in fall. Black Creek baseflow comprises a dynamic mix of water sources that at times are similar to the sources observed at the stormwater outfalls. Considering these findings, future work should incorporate strategic sampling of additional outfalls, and multiple years of data collection to explore inter-annual variability in these processes and focus on replicating a similar study in other urban watersheds with different climates and/or water infrastructure design. The study findings highlight that our understanding of dry weather flow from storm sewers is relatively limited, emphasizing the need for further exploration of this phenomenon to inform urban hydrological modelling, water quality studies, and urban water management.

The rain, it is a changing: non-stationarity of precipitation and the effect on water infrastructure design

The rain, it is a changing: non-stationarity of precipitation and the effect on water infrastructure design

Accounting for climate change in water infrastructure design: evaluating approaches and recommending a hybrid framework

Abstract A traditional hydrologic water infrastructure design assumes that the climate is stationary, and that historic data reflect future conditions. The traditional approach may no longer be applicable since the earth's climate is not stationary. Thus, there is a need for a new way of designing water infrastructure that accounts for the effects of climate change by shifting the current static design paradigm to a more dynamic paradigm. Researchers have proposed several approaches accounting for climate change. In this paper, we group the approaches into five groups (adaptive management, inverse climate change impact, machine learning, flood frequency analysis, and soft computing approaches), outline each approach's strengths and weaknesses, and assess their applicability to the water infrastructure design. We find that the flood frequency analysis approach is most applicable to the water infrastructure design as it is the least disruptive in terms of standard hydrological analysis methods, is cost-effective, and adaptable to most basins. However, adaptive management approaches are best suited for uncertainty reductions since they provide opportunities to constantly adjust decisions based on improved climate change data. Combining these two approaches could provide an optimal way of accounting for non-stationarity.

A Non-Stationarity Analysis of Annual Maximum Floods: A Case Study of Campaspe River Basin, Australia

A design flood is an essential input for water infrastructure design and flood protection. A flood frequency analysis has been traditionally performed under stationarity assumption indicating that the statistical properties of historical flooding will not change over time. Climate change and variability challenges the stationarity assumption, and a flood frequency analysis without consideration of non-stationarity can result in under- or overestimation of the design floods. In this study, non-stationarity of annual maximum floods (AMFs) was investigated through a methodology consisting of trend and change point tests, and non-stationary Generalized Extreme Value (NSGEV) models, and the methodology was applied to Campaspe River Basin as a case study. Statistically significant decreasing trends in AMFs were detected for almost all stations at the 0.01 significance level in Campaspe River Basin. NSGEV models outperformed the stationary counterparts (SGEV) for some stations based on statistical methods (i.e., Akaike information criterion (AIC) and Bayesian information criterion (BIC)) and graphical approaches (i.e., probability and quantile plots). For example, at Station 406235, AIC and BIC values were found to be 334 and 339, respectively, for the SGEV model, whereas AIC and BIC values were calculated as 330 and 334, respectively, for the NSGEV 15 model with time-varying location and scale parameters. Deriving a design flood from conventional stationary models will result in uneconomical water infrastructure design and poor water resource planning and management in the study basin.

Fish Injury from Movements across Hydraulic Structures: A Review

Fish migration is essential to maintain healthy aquatic ecosystems, but hydraulic structures across rivers have impeded natural fish migration worldwide. While efforts have been made to allow fish to pass some hydraulic structures, there is limited understanding of hydrodynamic effects that cause fish injury in different hydraulic systems, such as spillways and stilling basins as well as hydropower systems. This study reviewed available literature on this topic to identify the current knowledge of fish injury thresholds in laboratory- and field-based studies of hydraulic systems. Often, the hydraulic effects that lead to fish injury have been described with time-averaged simplified parameters including shear stress, pressure changes, acceleration, vortical motions, aeration, collision, and strike, while these hydrodynamic effects often occur simultaneously in the turbulent flows across hydraulic structures, making it difficult to link specific fish injuries to a particular hydrodynamic effect. Strong variations of injury may occur, depending on the type and the intensity of hydrodynamic effects, as well as the fish species and fish sizes. Modelling can provide information of stressors, but real-world tests are needed to accurately assess fish injury and mortality. Fish injury mechanisms at hydropower turbines are well understood, however, clear understanding at other sites is lacking. Future studies should aim to report holistic hydrodynamic thresholds with associated fish injury rates. Multidisciplinary systematic research is required, including laboratory and field studies, using passive tracer sensor packages and state-of-the art instrumentation in conjunction with live fish. This can quantify stressors with meaningful parameters, aiming to improve fish safety with more sustainable design of water infrastructure that reduces fish injury when passing across hydraulic structures.

Joint probability analysis of streamflow and sediment load based on hybrid copula.

Statistical analysis of streamflow and sediment is very important for integrated watershed management and the design of water infrastructure, especially in silt-rich rivers. Here, we propose a bivariate joint distribution framework based on nonparametric kernel density estimation (KDE) and a hybrid copula function to describe the complex streamflow-sediment dependent structure. In this framework, the non-parametric KDE is used to fit the marginal distribution function of streamflow and sediment variables, and then the hybrid copula function is constructed by using the linear combination of Clayton, Frank, and Gumbel copulas, and compared with five commonly used single copulas (Clayton, Frank, Gumbel, Gaussian, and t). We use the Jinsha River Basin (JRB) in the Yangtze River's (JR) upper reaches to verify the proposed method. The results show the following: (1) Compared with the gamma distribution (Gamma) and generalized extreme value (GEV) distribution of parameters, the marginal distribution function of streamflow and sediment variables can be effectively obtained based on nonparametric KDE. (2) Compared with the single copula, the hybrid copula function more fully reflects the complex dependent structure of streamflow and sediment variables. (3) Compared with the best single copula, the precision of return period based on hybrid copula can be increased by 7.41%. In addition, the synchronous probability of streamflow and sediment in JRB is 0.553, and the asynchronous probability of streamflow and sediment is 0.447. This study can not only improve the accuracy of streamflow and sediment statistical analysis in JRB, but also provide a useful framework for other bivariate joint probability analysis.

Design flood hydrographs: a regional analysis based on flood reduction functions

ABSTRACT Modern flood hazard mapping techniques and water infrastructure design require the entire flood hydrograph. However, statistical methods for flood hydrograph estimation in ungauged basins have not received the same attention as the models used to predict the peak flow value. Here the design hydrograph of an ungauged basin is reconstructed in a parsimonious way through the estimation of a non-dimensional flood reduction function (FRF). Based on data from 87 basins (763 station-years of flood hydrographs), we show that a two-parameter FRF can be efficiently estimated by multiple linear regression from the longest drainage path length and slope of the basin, the average basin elevation, the width function kurtosis and the mean value of the scaling exponent of the intensity–duration–frequency curve. Reasonable similarities between the estimated flood hydrograph and the original ones make the method suitable for extension into other areas to estimate design hydrographs in ungauged basins.

Recent precipitation trends in Peninsular Spain and implications for water infrastructure design

Study regionPeninsular Spain. Study focusThere is strong evidence that climate change has produced a decrease in precipitation combined with an increased frequency, intensity and magnitude of high-intensity storm events in Peninsular Spain. Such events have been particularly recurrent on the Spanish Mediterranean coast in recent years. This study’s first objective is to update the trends in the magnitude and seasonality of precipitation in Spain from 1951 to 2019 at different time scales. Secondly, we analyse how extreme precipitation events recorded in recent years can modify water infrastructure design flows. A QGIS plugin was programmed using Python to calculate the design flows in accordance with the methodologies legally established in Spain. New hydrological insights for the regionThe results confirm the decreasing trend in annual precipitation in most of the Spanish territory. This decrease was particularly significant during March and June. Moreover, the average increases of around 30–40 % in design flows, which reached double the values obtained in the reference period of 1951–1990 in some basins, confirm the relevance of incorporating high-intensity storm events recorded in recent years into flow calculations in Peninsular Spain. Design flows obtained without using the most recent rainfall data can be underestimated, with profound risk implications. Therefore, there is a sufficient technical basis to draw attention to this problem and recommend its formal consideration by competent authorities.

A study on villages of the planning of water infrastructure planning: 25 villages in Shaanxi Province as an example

Abstract This thesis provides a water infrastructure planning strategy based on an analysis and evaluation of the present situation, which more effectively integrates the current scenario of rural development while adjusting to the trend of population development and resource allocation in rural China. In the north-central area of Shaanxi Province, 25 villages were chosen for field study in order to get a comprehensive grasp of the population and land-use conditions. The size and population of the area needing sewage and drainage infrastructure are estimated by the projection of rural population decline and the projection of rural building land scale and land-use adjustment. On this basis, sewage and drainage facilities are subsequently planned, including the contents of a three-compartment septic tank, sedimentation tank, sewage network, and ecological treatment tank. This type of water infrastructure design may not only enhance the living environment of the region but also efficiently address the issues of water collection and agricultural irrigation in the desert tablelands, enabling the efficient use of resources and sustainable growth of the community.

Projecting Flood Frequency Curves Under Near‐Term Climate Change

AbstractFlood‐frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate‐informed methodology for estimating flood‐frequency curves under non‐stationary future climate conditions. The methodology develops an asynchronous, semiparametric local‐likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log‐Pearson Type‐3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR‐MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR‐MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood‐frequency quantiles resulting from the ASLLR‐MM and GCM members compare well with the flood‐frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land‐surface conditions and a multi‐level model structure that includes other basins in a region could further improve the model performance in arid basins.

Nonstationary flood and low flow frequency analysis in the upper reaches of Huaihe River Basin, China, using climatic variables and reservoir index as covariates

Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design.

Assessing water and water infrastructure quality in community-managed water supply systems in northern Pakistan

Abstract This research assesses the quality of water and water supply infrastructure under the Water and Sanitation Extension Programme (WASEP) in Gilgit-Baltistan, Northern Pakistan. Water samples were collected from 26 randomly selected rural and urban WASEP projects and 12 control sites. Results show that 94% of rural and 83% of urban water samples from WASEP projects conformed with WHO standards for drinking water with no Escherichia coli colonies overall compared with 8% of rural and 0% of urban control sites. This corresponds with higher water infrastructure scores for WASEP projects based on engineering audits of water at intake, system, and network level. These findings demonstrate the importance of water infrastructure design for the delivery of clean drinking water, and the role of the WASEP model of community water management in improving the delivery of potable water.

Comparative Analysis of Developed Rainfall Intensity–Duration–Frequency Curves for Erbil with Other Iraqi Urban Areas

Rainfall Intensity–Duration–Frequency (IDF) relationships are widely used in water infrastructure design and construction. IDF curves represent the relationship between rainfall intensity, duration, and frequency, and are obtained by analyzing observed data. These relationships are critical for the safe design of flood protection structures, storm sewers, culverts, bridges, etc. In this study, the IDF curves and empirical IDF formulas for the city of Erbil were developed for the first time by employing the annual maximum rainfall data for a period of 39 years (1980–2018), which is the only available recorded data. Statistical techniques such as Gumbel and Log-Pearson Type III (LPT III) were utilized to determine the IDF curves and empirical equations from daily rainfall data for several standard durations and return periods. The correlation between the rainfall intensities obtained from IDF curves and the empirical formula presented a reliable match, with a coefficient of determination of (R2 = 1). The results were compared to previously developed IDF curves and empirical formulas in Iraqi cities to show their reliability. Moreover, the results can be an initial step for authorities to establish required guidelines in the studied area, and in the design process of the storm water infrastructure of urban basins in the future.

Incorporating decadal climate variability information in the operation and design of water infrastructure

Abstract The high thermal and mechanical inertia of the oceans results in slow changes in sea surface temperatures (SSTs). Changes in SSTs, in turn, can impact atmospheric circulation including water vapor transport, precipitation, and temperatures throughout the world. The Pacific Decadal Oscillation (PDO), the tropical Atlantic SST gradient variability, and the West Pacific Warm Pool are patterns of natural climate variability that tend to persist over decadal time periods. There are current efforts to produce decadal climate predictions, but there is limited understanding if this information can be used in water resources management. Understanding the current state of decadal climate variability (DCV) phenomena and the probability of persisting in that state may be useful information for water managers. This information could improve forecasts that aid operations and short-term planning for reservoir management, domestic and industrial water supplies, flood risk management, energy production, recreation, inland navigation, and irrigation. If conditions indicate a higher likelihood of drought, reservoir managers could reduce flood storage space and increase storage for conservation purposes. Improved forecasts for irrigation could result in greater efficiencies by shifting crops and rotational crop patterns. The potential benefits of using a forecast must be balanced against the risk of damages if the forecast is wrong. Seasonal forecasts using DCV information could also be used to inform drought triggers. If DCV indices indicate that the climate has a higher probability of dry conditions, drought contingency plans could be triggered earlier. Understanding of DCV phenomena could also improve long-range water resources planning. DCV can manifest itself in relatively short-term hydrologic records as linear trends that complicate hydrologic frequency analysis, which has traditionally assumed that hydrologic records are stationary.

Flood Flow Probability Distribution Model Selection on Niger/Benue River Basins in Nigeria

Flood frequency analysis is a crucial component of flood risk management which seeks to establish a quantile relationship between peak discharges and their exceedance (or non-exceedance) probabilities, for planning, design and management of infrastructure in river basins. This paper evaluates the performance of five probability distribution models using the method of moments for parameter estimation, with five GoF-tests and Q-Q plots for selection of best –fit- distribution. The probability distributions models employed are; Gumbel (EV1), 2-parameter lognormal (LN2), log Pearson type III (LP3), Pearson type III(PR3), and Generalised Extreme Value( GEV). The five statistical goodness – of – fit tests, namely; modified index of agreement (Dmod), relative root mean square error (RRMSE), Nash – Sutcliffe efficiency (NSE), Percent bias (PBIAS), ratio of RMSE and standard deviation of the measurement (RSR) were used to identify the most suitable distribution models. The study was conducted using annual maximum series of nine gauge stations in both Benue and Niger River Basins in Nigeria. The study reveals that GEV was the best – fit distribution in six gauging stations, LP3 was best – fit distribution in two gauging stations, and PR3 is best- fit distribution in one gauging station. This study has provided a significant contribution to knowledge in the choice of distribution models for predicting extreme hydrological events for design of water infrastructure in Nigeria. It is recommended that GEV, PR3 and LP3 should be considered in the development of regional flood frequency using the existing hydrological map of Nigeria.

An investigation on the non-stationarity of flood frequency across the UK

Water infrastructure design and flood mitigation projects are currently based on a so-called stationary assumption. However, this assumption has been challenged in recent years due to increased extreme weather, potentially leading to the underestimation of flood quantiles and an increased risk of structural failures. In the present study, peak flow series from 158 gauging stations in the UK are analysed using both stationary and non-stationary models based on the generalised additive models for location, scale and shape (GAMLSS) framework. Nine candidate covariates are used for non-stationary models to obtain information on the significant drivers of non-stationarity in each flood series. Very few stations are thus found to show temporal trends, which suggests that time-varying non-stationary flood models may not always be suitable, and the use of physically-based covariates can provide better-fitting models. In particular, the non-stationary models using rainfall-related covariates display the best performance for the vast majority of stations, thus suggesting that the variability of the rainfall regime remains the dominant driver for changes in flooding. Moreover, two atmospheric circulation patterns, namely the North Atlantic Oscillation (NAO) and the East Atlantic Pattern, were found to be closely related to the non-stationarity of flooding at a number of stations. Unlike rainfall, however, the influence of these climate indices is not particularly dominant; hence, they are more suitably employed as components of a multiple-covariate non-stationary model. Besides, a direct linkage between the variability of flooding and climate changes has not been identified either regionally or globally. For the majority of stations (70%), the use of multiple covariates can provide a better non-stationary model than the use of a single covariate.

Development of Rainfall Intensity-Duration-Frequency Curves Based on Dynamically Downscaled Climate Data: Arizona Case Study

AbstractThe ideal framework for water infrastructure design in any region relies on rainfall characteristics of that region, which is defined through rainfall intensity-duration-frequency (IDF) cur...

A Rainfall Intensity Data Rescue Initiative for Central Chile Utilizing a Pluviograph Strip Charts Reader (PSCR)

To develop intensity-duration-frequency (IDF) curves, it is necessary to calculate annual maximum rainfall intensities for different durations. Traditionally, these intensities have been calculated from the analysis of traces recorded by rain gauges on pluviograph strip charts (PSCs). For many years, these charts have been recorded and analyzed by the personnel who operate and maintain the pluviograph gauges, thus the reliability of the observational analysis depends exclusively on the professional experience of the person performing the analysis. Traditionally, the analyzed PSCs are physically stored in data repository centers. After storing rainfall data on aging paper for many years, the risk of losing rainfall records is very high. Therefore, the conversion of PSC records to digital format is crucial to preserve and improve the historical instrumental data base of these records. We conducted the first “Data Rescue Initiative” (DRI) for central Chile using a pluviograph strip charts reader (PSCR), a tool that uses a scanner-type device combined with digital image processing techniques to estimate maximum rainfall intensities for different durations for each paper band (&gt;80,000 paper bands). On the paper bands, common irregularities associated with excess ink, annotations, or blemishes can affect the scanning process; this system was designed with a semi-automatic module that allows users to edit the detected trace to improve the recognition of the data from each PSC. The PSCR’s semi-automatic characteristics were designed to read many PSCs in a short period of time. The tool also allows for the calculation of rainfall intensities in durations ranging between 15 min to 1 h. This capability improves the value of the data for water infrastructure design, since intense storms of shorter duration often have greater impacts than longer but less intense storms. In this study, the validation of the PSCR against records obtained from observational analysis showed no significant differences between maximum rainfall intensities for durations of 1 h, 6 h, and 24 h.

A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters.

We present a low-cost (≈$150) monitoring system for collecting high temporal resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically driven, positive displacement, residential water meters and can collect data at a variable time resolution interval. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah, for a period of over 1 month. Battery life for the device was estimated to be over 5 weeks with continuous data collection at a 4 s time interval. Data collected using this system, under ideal installation conditions, was within 2% of the volume recorded by the register of the meter on which they were installed. Results from field deployments are presented to demonstrate the accuracy, functionality, and applicability of the system. Results indicate that the device is capable of collecting data at a temporal resolution sufficient for identifying individual water use events and analyzing water use at coarser temporal resolutions. This system is of special interest for water end use studies, future projections of residential water use, water infrastructure design, and for advancing our understanding of water use timing and behavior. The system’s hardware design and software are open source, are available for potential reuse, and can be customized for specific research needs.

Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis

The goal of this research is identifying major sources of uncertainty of an environmentally-sustainable urban drainage infrastructure design, based on hydrologic analysis and life cycle assessment (LCA). The uncertainty analysis intends to characterize and compare relative roles of unreliability, incompleteness, technological difference, and spatial and temporal variation in life cycle impact assessment (LCIA) data, as well as natural variability in hydrologic data. Uncertainties are analyzed using a robust Monte Carlo simulation approach, performed by High Throughput Computing (HTC) and interpreted by Morse-Scale regression models. The uncertainty analysis platform is applied to a watershed-scale LCA of rainwater harvesting systems (RWH) to control combined sewer overflows (CSOs). To consider the watershed-scale implications, RWH is simulated to serve for both the water supply and CSO control in an urban watershed in Toledo, Ohio, USA. Results suggest that, among the studied parameters, rainfall depth (as a hydrologic parameter) caused more than 86% of the uncertainty, while only 7% of the uncertainty was caused by LCIA parameters. Such an emphasis on the necessity of robust hydrologic data and associated analyses increase the reliability of LCA-based urban water infrastructure design. In addition, results suggest that such a topology-inspired model is capable of rendering an optimal RWH system capacity as a function of annual rainfall depth. Specifically, if the system could capture 1/40th of annual rainfall depth in each event from rooftops, the RWH system would be optimal and, thus, lead to minimized life cycle impacts in terms of global warming potential (GWP) and aquatic eco-toxicity (ETW). This capture depth would be around 2.1 cm for Toledo (given an 85 cm/year rainfall and 200 m2 typical roof area), which could be achieved through an RWH system with 4.25 m3 capacity per household, assuming a uniform plan for the entire studied watershed. Capacities smaller than this suggested optimal value would likely result in loss of RWH potable water treatment savings and CSO control benefits, while capacities larger than the optimal would likely incur an excessive wastewater treatment burden and construction phase impacts of RWH systems.