Sampling Issues in Urban Runoff Monitoring Programs: Composite versus Grab

Ma, Jiun-Shiu; Kang, Joo‐Hyon; Kayhanian, Masoud; Stenstrom, Michael K.

doi:10.1061/(asce)0733-9372(2009)135:3(118)

Cited by 32 publications

(21 citation statements)

References 9 publications

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“…Furthermore, Leecaster et al (2002) found that 12 samples per storm were preferable over 4 or 8 samples to avoid excessive variability in estimation results. Ma et al (2009) came to a similar conclusion based on a statistical simulation of various sampling strategies to estimate event mean concentration (EMC) of chemical oxygen demand. They concluded that volume-paced sampling was superior to time-paced sampling and that approximately 20 samples are required to estimate the EMCs within 20% error for volume-paced sampling.…”

Section: Introductionmentioning

confidence: 78%

“…The common features of both these approaches are (1) use of volume pacing, as opposed to time pacing; (2) ability to capture a range of different storm types (i.e., sizes and timing); and (3) inclusion of multiple discrete samples. Numerous authors have previously documented that volume-based sampling is more accurate than time-based because it provides better representation of the overall storm (Leecaster et al 2002;King et al 2005;Ma et al 2009). By targeting the volumetric pacing based on anticipated storm size, volume-based sampling is better able to capture a representative portion of the storm.…”

Section: Discussionmentioning

confidence: 97%

“…Previous studies have evaluated the effect of various sampling strategies on estimates of pollutant concentration and load (Izuno et al 1998;Robertson and Roerish 1999;Stone et al 2000;Ma et al 2009). Leecaster et al (2002) evaluated sampling approaches based on over 1,700 total suspended solid samples collected at 15-min intervals from the Santa Ana River, California.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of using "true" EMCs, previous studies have relied on concentration data from multiple independent watersheds (Robertson and Roerish 1999), derived from numerical approaches (Shih et al 1994), Monte Carlo simulations (Richards and Holloway 1987), or statistical approaches (King and Harmel 2004;King et al 2005;Ma et al 2009). Because the EMC was not known in each of these studies there exists a great deal of uncertainty when estimating the accuracy of the sampling methods.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Ackerman

Stein

Ritter

2010

Environ Monit Assess

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Accurate quantification of stormwater pollutant levels is essential for estimating overall contaminant discharge to receiving waters. Numerous sampling approaches exist that attempt to balance accuracy against the costs associated with the sampling method. This study employs a novel and practical approach of evaluating the accuracy of different stormwater monitoring methodologies using stormflows and constituent concentrations produced by a fully validated continuous simulation watershed model. A major advantage of using a watershed model to simulate pollutant concentrations is that a large number of storms representing a broad range of conditions can be applied in testing the various sampling approaches. Seventy-eight distinct methodologies were evaluated by "virtual samplings" of 166 simulated storms of varying size, intensity and duration, representing 14 years of storms in Ballona Creek near Los Angeles, California. The 78 methods can be grouped into four general strategies: volume-paced compositing, time-paced compositing, pollutograph sampling, and microsampling. The performances of each sampling strategy was evaluated by comparing the (1) median relative error between the virtually sampled and the true modeled event mean concentration (EMC) of each storm (accuracy), (2) median absolute deviation about the median or "MAD" of the relative error or (precision), and (3) the percentage of storms where sampling methods were within 10% of the true EMC (combined measures of accuracy and precision). Finally, costs associated with site setup, sampling, and laboratory analysis were estimated for each method. Pollutograph sampling consistently outperformed the other three methods both in terms of accuracy and precision, but was the most costly method evaluated. Time-paced sampling consistently underestimated while volume-paced sampling over estimated the storm EMCs. Microsampling performance approached that of pollutograph sampling at a substantial cost savings. The most efficient method for routine stormwater monitoring in terms of a balance between performance and cost was volume-paced microsampling, with variable sample pacing to ensure that the entirety of the storm was captured. Pollutograph sampling is recommended if the data are to be used for detailed analysis of runoff dynamics.

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Section: Introductionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Ackerman

Stein

Ritter

2010

Environ Monit Assess

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“…Although notable progress was made in the modeling of urban runoff quantity, the progress with stormwater quality-its impacts on receiving waters and the means of mitigating such impacts-has been much slower (Marsalek & Viklander, 2011). Example of recent research includes identification of significant factors (e.g., land use, percentage imperviousness, conveyance, and watershed controls) affecting stormwater quality using the National Stormwater Quality Database (Maestre & Pitt, 2006), highway contribution to runoff quantity and pollutant loading (Lau et al, 2009), sampling issues in urban runoff monitoring programs by comparison of composite and grab samples (Ma et al, 2009), and groundwater contaminations by stormwater (Pitt et al, 1996;Foulquier, 2010).…”

Section: Introductionmentioning

confidence: 99%

Climate, Land Use and Hydrologic Sensitivities of Stormwater Quantity and Quality in Complex Coastal Urban Watersheds

Al-Amin¹

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The study analyzed hydro-climatic and land use sensitivities of stormwater runoff and quality in the complex coastal urban watershed of Miami River Basin, Florida by developing a Storm Water Management Model (EPA SWMM 5). Regression-based empirical models were also developed to explain stream water quality in relation to internal (land uses and hydrology) and external (upstream contribution, seawater) sources and drivers in six highly urbanized canal basins of Southeast Florida. Stormwater runoff and quality were most sensitive to rainfall, imperviousness, and conversion of open lands/parks to residential, commercial and industrial areas. In-stream dissolved oxygen and total phosphorus in the watersheds were dictated by internal stressors while external stressors were dominant for total nitrogen and specific conductance. The research findings and tools will be useful for proactive monitoring and management of storm runoff and urban stream water quality under the changing climate and environment in South Florida and around the world.

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Challenges in Mechanistic and Empirical Modelling of Stormwater: Review and Perspectives

Al-Amin

Abdul-Aziz²

2013

Irrigation and Drainage

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Much research has been conducted to quantify stormwater runoff and quality using both mechanistic (i.e. process-based) and empirical (i.e. data-driven) techniques. Mechanistic models generally include the mathematical representation of relevant physico-chemical processes to generate storm runoff quantity and quality. Empirical approaches analyse available data for potential response and predictor variables to trace the interactions of major processes and develop data-driven explanatory and/or predictive relationships. This paper reviews major, mostly unresolved, challenges with both mechanistic and empirical modelling of stormwater, sheds light on the scientific gaps with conventional practices, and offers important perspectives by taking the highly urbanized Miami River Basin of Florida as an analytical example. Appreciating the varying levels of process complexity in different urban river basins, we discuss the relative applicability of mechanistic and empirical methods for robust predictions of stormwater quantity and quality. RÉSUMÉBeaucoup de recherches ont été menées pour quantifier les eaux pluviales générées par les tempêtes, et leur qualité. Elles utilisent à la fois des techniques mécaniques (basées sur les processus) et empiriques (déduites des données). Les modèles mécanistes comprennent généralement la représentation mathématique des processus physico-chimiques pertinentes pour générer la quantité et la qualité des eaux de ruissellement en condition de tempête. Les approches empiriques analysent les données disponibles pour que potentiellement les variables de réponse et les variables prédictives retracent les interactions entre les processus majeurs, et développent des relations explicatives et/ou prédictives pilotées par les données. Ce document passe en revue les principaux défis, pour la plupart non résolus, avec la modélisation à la fois mécaniste et empirique des eaux pluviales; il met en lumière les lacunes scientifiques avec les pratiques conventionnelles et offre d'importantes perspectives en prenant comme exemple d'analyse le très urbanisé bassin de la rivière Miami, en Floride. En appréciant le niveau de complexité variable des processus dans des différents bassins hydrographiques urbains, nous avons discuté de l'applicabilité relative des méthodes mécanistes et empiriques pour des prédictions robustes de la quantité et de la qualité des eaux pluviales.

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Sampling Issues in Urban Runoff Monitoring Programs: Composite versus Grab

Cited by 32 publications

References 9 publications

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Evaluating performance of stormwater sampling approaches using a dynamic watershed model

Climate, Land Use and Hydrologic Sensitivities of Stormwater Quantity and Quality in Complex Coastal Urban Watersheds

Challenges in Mechanistic and Empirical Modelling of Stormwater: Review and Perspectives

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