A generic, process‐based model of microbial pollution in aquatic systems

Hipsey, Matthew R.; Antenucci, Jason P.; Brookes, Justin D.

doi:10.1029/2007wr006395

Cited by 119 publications

(165 citation statements)

References 155 publications

(246 reference statements)

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“…Although differences in the storm-event transport behaviour of different microorganisms once exposed to environmental pressures have been highlighted (e.g. Jamieson et al 2004;Stott et al 2011), Hipsey et al (2008 reaffirm that the general processes which influence the fate and distribution of most of these microbes are similar to that presented by Wilkinson (2001Wilkinson ( , 2008, and in the current study, with the exception that Hipsey et al (2008), included a separate treatment of sediment-bound and free microorganism transfer to and from the channel store. Other modelling studies have focussed strongly on quantifying faecal delivery from upland pastures, detailing different sources, livestock behaviours and pathways to the stream network (Collins & Rutherford 2004).…”

Section: Introductionsupporting

confidence: 59%

“…The mean values of u and k 0 from various studies as summarised by Wilkinson (2001) are 0.0285 and 0.1067, respectively, and have been used in the study presented here (see also the summary of parameter values in Hipsey et al 2008). The two die-off components are combined to give a value of overall die-off per day for average light intensity in the water column,…”

Section: Riparian Pasture Inputmentioning

confidence: 99%

“…Pommepuy et al 1992;Auer & Niehaus 1993). The equation for temperature is derived from common observations in the literature (as reviewed by Wilkinson 2001 andHipsey et al 2008).…”

Section: Riparian Pasture Inputmentioning

confidence: 99%

“…This differential transport and arrival has been observed and modelled for faecal indicators and zoonotic disease agents in New Zealand (McBride 2011) and is significant for the understanding of the fluvial dynamics of faecal contaminants. As stated by Hipsey et al (2008) a generic model architecture that can be applied in many circumstances and to different contaminant microorganisms is attractive and the model of Wilkinson (2001Wilkinson ( , 2008 as presented here offers such potential.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the faecal indicator bacteria (E. coli) is simulated in the main stem of the Motueka River and its small tributary the Sherry River, using a model based on that of Wilkinson (2001Wilkinson ( , 2008 that is broadly similar to that of Hipsey et al (2008).…”

Section: Introductionmentioning

confidence: 99%

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Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Wilkinson

McKergow

Davies-Colley

et al. 2011

New Zealand Journal of Marine and Freshwater Research

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Storm-induced Escherichia coli pulses in the Motueka River (2074 km 2 ) and the Sherry River (78.4 km 2 ) are modelled. The model focuses on the catchment outlets, representing key processes, including E. coli transfer to and from the river bed, with account taken of the hysteresis in, and non-linear, non-stationary, response of E. coli concentrations to river stormflows. The model fits the Motueka River observations well, but less well in the Sherry River. A greatly simplified description of headwater and riparian inputs is satisfactory at the larger catchment scale where near-field, in-channel processes dominate the response. Spatial heterogeneity in rainfall-run-off and faecal sources probably contribute to the poorer fit in the smaller catchment. Despite using a relatively small number of driving variables and parameters, the model has the potential to predict real-time E. coli input to Tasman Bay in river plumes causing shellfish and bathing beach contamination.

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Section: Introductionsupporting

confidence: 59%

Section: Riparian Pasture Inputmentioning

confidence: 99%

“…Pommepuy et al 1992;Auer & Niehaus 1993). The equation for temperature is derived from common observations in the literature (as reviewed by Wilkinson 2001 andHipsey et al 2008).…”

Section: Riparian Pasture Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Wilkinson

McKergow

Davies-Colley

et al. 2011

New Zealand Journal of Marine and Freshwater Research

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Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

et al. 2015

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Maintaining the health of aquatic systems is an essential component of sustainable catchment management, however, degradation of water quality and aquatic habitat continues to challenge scientists and policy-makers. To support management and restoration efforts aquatic system models are required that are able to capture the often complex trajectories that these systems display in response to multiple stressors. This paper explores the abilities and limitations of current model approaches in meeting this challenge, and outlines a strategy based on integration of flexible model libraries and data from observation networks, within a learning framework, as a means to improve the accuracy and scope of model predictions. The framework is comprised of a data assimilation component that utilizes diverse data streams from sensor networks, and a second component whereby model structural evolution can occur once the model is assessed against theoretically relevant metrics of system function. Given the scale and transdisciplinary nature of the prediction challenge, network science initiatives are identified as a means to develop and integrate diverse model libraries and workflows, and to obtain consensus on diagnostic approaches to model assessment that can guide model adaptation. We outline how such a framework can help us explore the theory of how aquatic systems respond to change by bridging bottom-up and top-down lines of enquiry, and, in doing so, also advance the role of prediction in aquatic ecosystem management. Modeling Aquatic Health: The Evolving Role of Aquatic System PredictionSustainable catchment management during the current era of global population growth and climate change is one of the most profound challenges confronting society [Bogardi et al., 2012;Gerten et al., 2013;Pahl-Wostl et al., 2013;Brookes et al., 2014]. Inland waters have changed more rapidly in the past 50 years than at any other time in human history. Water quality degradation and associated issues of water security, as well as loss of biodiversity Dudgeon, 2014], have been driven by widespread urban, agricultural and mining developments, and span both developed and emerging economies. Preserving the integrity of aquatic systems, including rivers, wetlands, lakes and estuaries, is an essential component of catchment management as these systems provide critical ecosystem services to support societal development [Zedler and Kercher, 2005;Carpenter et al., 2011]. However, the pace of contemporary environmental change is rapid and multifaceted, and hinders restoration efforts. The development of tools and approaches to quantify the function and response of aquatic systems is therefore essential to support a holistic view of catchment function [Wagener et al., 2010;Montanari et al., 2013], and guide investment in conservation and rehabilitation [Creighton et al., 2015].Substantial advances have been made toward describing changes in catchment hydrologic function and developing predictive ability to support water resource management [Bl...

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Modeling the photoinactivation and transport of somatic and F‐specific coliphages at a Great Lakes beach

et al. 2020

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Fecal indicator organisms (FIOs), such as Escherichia coli and enterococci, are often used as surrogates of contamination in the context of beach management; however, bacteriophages may be more reliable indicators than FIO due to their similarity to viral pathogens in terms of size and persistence in the environment. In the past, mechanistic modeling of environmental contamination has focused on FIOs, with virus and bacteriophage modeling efforts remaining limited. In this paper, we describe the development and application of a fate and transport model of somatic and F-specific coliphages for the Washington Park beach in Lake Michigan, which is affected by riverine outputs from the nearby Trail Creek. A three-dimensional model of coliphage transport and photoinactivation was tested and compared with a previously reported E. coli fate and transport model. The light-based inactivation of the phages was modeled using organism-specific action spectra. Results indicate that the coliphage models outperformed the E. coli model in terms of reliably predicting observed E. coli/coliphage concentrations at the beach. This is possibly due to the presence of additional E. coli sources that were not accounted for in the modeling. The coliphage models can be used to test hypotheses about potential sources and their behavior and for predictive modeling.

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A generic, process‐based model of microbial pollution in aquatic systems

Cited by 119 publications

References 155 publications

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Modelling storm-eventE.colipulses from the Motueka and Sherry Rivers in the South Island, New Zealand

Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories

Modeling the photoinactivation and transport of somatic and F‐specific coliphages at a Great Lakes beach

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