Adaptive management for mitigating Cryptosporidium risk in source water: A case study in an agricultural catchment in South Australia

Bryan, Brett A.; Kandulu, John; Deere, Daniel; White, Monique; Frizenschaf, Jacqueline; Crossman, Neville D.

doi:10.1016/j.jenvman.2009.05.014

Cited by 32 publications

(33 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Active AM (AAM) is described as management where options are viewed as hypotheses to be tested; experimentation is key and both formalized learning and management are objectives. In contrast, passive AM (PAM) implements a single preferred course of action based on the best available modelling and planning, which is then modified as experience grows (Bryan et al 2009;Owens 2009;Benson & Garmestani 2011).…”

Section: Introductionmentioning

confidence: 99%

Adaptive management: where are we now?

2012

View full text Add to dashboard Cite

SUMMARYAdaptive management (AM) emerged in the literature in the mid-1970s in response both to a realization of the extent of uncertainty involved in management, and a frustration with attempts to use modelling to integrate knowledge and make predictions. The term has since become increasingly widely used in scientific articles, policy documents and management plans, but both understanding and application of the concept is mixed. This paper reviews recent literature from conservation and natural resource management journals to assess diversity in how the term is used, highlight ambiguities and consider how the concept might be further assessed. AM is currently being used to describe many different management contexts, scales and locations. Few authors define the term explicitly or describe how it offers a means to improve management outcomes in their specific management context. Many do not adhere to the idea as it was originally conceived, despite citing seminal work. Significant confusion exists over the distinction between active and passive approaches. Over half of the studies reporting to implement AM claimed to have done so successfully, yet none quantified specific benefits, or costs, in relation to possible alternatives. Similarly those studies reporting to assess the approach did so only in relation to specific models and their parameterizations; none assessed the benefits or costs of AM in the field. AM is regarded by some as an effective and wellestablished framework to support the management of natural resources, yet by others as a concept difficult to realize and fraught with implementation challenges; neither of these observations is wholly accurate. From a scientific and technical perspective many practical questions remain; in particular realworld assessments of the value of experimentation within a management framework, as well as of identified challenges and pathologies, are needed. Further discussion and systematic assessment of the approach is required, together with greater attention to its definition and description, enabling the assessment of new approaches to managing uncertainty, and AM itself.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive management: where are we now?

2012

View full text Add to dashboard Cite

show abstract

“…The catchment area (123 km 2 ) (Bryan et al, 2009) of the Myponga Reservoir (26,800 ML holding capacity) has high levels of organic loading (∼15.0 mg L −1 DOC) in Reservoir water. Six zero-order catchments (ZOCs), with distinct soil texture (sandy and/or clayey) covered by native vegetation, pine or grass, were selected for this study.…”

Section: Site Descriptions and Instrumentsmentioning

confidence: 99%

“…Dissolved organic matter (DOM), often the major component of NOM can be derived from two distinct sources: (1) allochthonous inputs derived from catchment sources and (2) autochthonous organic matter production from within the water body (e.g., reservoir), for example, by microbial activity (Sachse et al, 2005) and by photosynthesis. Water quality in catchment fed reservoirs is largely influenced by land management practices within the catchment (Bryan et al, 2009), and catchment properties such as vegetation type and loading (Naidu et al, 1993;Chantigny, 2003;Yang et al, 2013) (Ågren et al, 2007), climate (Chow et al, 2011;Yang et al, 2013) and soils (Nelson et al, 1992).…”

Section: Introductionmentioning

confidence: 99%

Treatability of organic matter derived from surface and subsurface waters of drinking water catchments

et al. 2016

View full text Add to dashboard Cite

h i g h l i g h t s• DOM not complexed with clay is less amenable to removal by alum.• Lowest alum dose rates were for waters from native vegetation on sandy soil.• Soil-water from prominent O horizon sites show similarity in DOM treatability. The treatability of NOM present in runoff and subsurface waters from discrete zero-order catchments (ZOCs) with three land management practices (Australian native vegetation, pine plantation, grasslands) on varying soil textures of a closed drinking water reservoir-catchment was investigated. Subsurface water samples were collected by lysimeters and shallow piezometers and surface waters by installation of barriers that diverted waters to collection devices. For small sample volumes collected, a 'micro' jar testing procedure was developed to assess the treatability of organics by enhanced coagulation using alum, under standardised conditions. DOM present in water samples was quantified by measurement of DOC and UV absorbance (at 254 nm) and characterized using these and F-EEM. The mean alum dose rate (mg alum per mg DOC removed or Al/DOC) was found to be lower for DOM from sandy soil ZOCs (21.1 ± 11.0 Al/DOC) than from clayey soil ZOCs (38.6 ± 27.7 Al/DOC). ZOCs with Pinus radiata had prominent litter layers (6.3 ± 2.6 cm), and despite differences in soil textures showed similarity in DOM character in subsurface waters, and in alum dose rates (22.2 ± 5.5 Al/DOC). For sandy soil ZOCs, the lowest alum dose rates (16.5 ± 10.6 Al/DOC) were for waters from native vegetation catchment while, for clayey soil ZOCs, waters from pine vegetation had the lowest alum dose rates (23.0 ± 5.0 Al/DOC). Where ZOCs have a prominent O horizon, soil minerals had no apparent influence on the treatability of DOM. © 2015 Elsevier Ltd. All rights reserved. a r t i c l e i n f oAbbreviation: %DOC Rem @ED, percentage removal of DOC at enhanced alum dose; %DOC Rem @HD, percentage removal of DOC at high alum dose; Al/DOC, alum dose rate at enhanced dose; C, DOC removal rate co-efficient; DOC, dissolved organic

show abstract

“…Water quality in catchment-fed reservoirs is largely influenced by land management practices within the catchment [1], vegetation types and loading [2][3][4], topography [5], climate [2,6] and soil types [7][8][9]. Catchment runoff and stream waters generally have higher concentrations of organics that are more aromatic and humic in nature than of shallow and deep groundwater [10].…”

Section: Introductionmentioning

confidence: 99%

Characterization of dissolved organic matter for prediction of trihalomethane formation potential in surface and sub-surface waters

Awad

Leeuwen

Chow

et al. 2016

Journal of Hazardous Materials

View full text Add to dashboard Cite

h i g h l i g h t s• Models of THMFP based on the character of precursor organics were developed.• UV 254 , HPSEC-UV and F-EEM provided data that could be used for THMFP prediction.• High removal of humic compounds by enhanced coagulation led to lower THM formation. Dissolved organic matter (DOM) in surface waters used for drinking purposes can vary markedly in character dependent on their sources within catchments. The character of DOM further influences the formation of disinfection by products when precursor DOM present in drinking water reacts with chlorine during disinfection. Here we report the development of models that describe the formation potential of trihalomethanes (THMFP) dependent on the character of DOM in waters from discrete catchments with specific land-use and soil textures. DOM was characterized based on UV absorbance at 254 nm, apparent molecular weight and relative abundances of protein-like and humic-like compounds. DOM character and Br concentration (up to 0.5 mg/L) were used as variables in models (R 2 > 0.93) of THMFP, which ranged from 19 to 649 g/L. Chloroform concentration (12 − 594 g/L) and relative abundance (27 − 99%) were first modeled (R 2 > 0.85) and from these, the abundances of bromodichloromethane and chlorodibromomethane estimated using power and exponential functions, respectively (R 2 > 0.98). From these, the abundance of bromoform is calculated. The proposed model may be used in risk assessment of catchment factors on formation of trihalomethanes in drinking water, in context of treatment efficiency for removal of organic matter. a r t i c l e i n f oCrown Copyright © 2016 Published by Elsevier B.V. All rights reserved.Abbreviations: AromDOC, aromatic-organic compounds concentration; Br, bromide ion concentration in raw waters; CHBr2Cl, chloro-dibromomethane formation potential; CHBr3, bromoform formation potential; CHCl2Br, bromo-dichloromethane formation potential; CHCl3, chloroform formation potential; DOC, dissolved organic carbon; DOM, dissolved organic matter; FA, percentage abundance of fulvic-like; HA, percentage abundance of humic-like; MW, molecular weight; NOM, natural organics matter; NonAromDOC, non-aromatic-organic compounds concentration; P(1), area under Peak 1; P(2-3), area under peaks 2&3; P(4-5), area under peaks 4&5; P(6-7), area under peaks 6&7; P(8-9), area under peaks 8&9; PI, percentage abundance of protein1-like; PII, percentage abundance of protein2-like; R 2 , coefficient of determination; SE, standard deviation; SMP, percentage abundance of soluble microbial protein-like; SUVA, Specific UV absorbance; THMFP, trihalomethane formation potential; UV254, UV absorbance at wavelength 254 nm.

show abstract

Adaptive management for mitigating Cryptosporidium risk in source water: A case study in an agricultural catchment in South Australia

Cited by 32 publications

References 48 publications

Adaptive management: where are we now?

Adaptive management: where are we now?

Treatability of organic matter derived from surface and subsurface waters of drinking water catchments

Characterization of dissolved organic matter for prediction of trihalomethane formation potential in surface and sub-surface waters

Contact Info

Product

Resources

About