A comparison of geological and statistical approaches to element selection for sediment fingerprinting
Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contribution...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Laceby, John Patrick [verfasserIn] McMahon, Joe [verfasserIn] Evrard, Olivier [verfasserIn] Olley, Jon [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of soils and sediments - Berlin : Springer, 2001, 15(2015), 10 vom: 24. März, Seite 2117-2131 |
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| Übergeordnetes Werk: |
volume:15 ; year:2015 ; number:10 ; day:24 ; month:03 ; pages:2117-2131 |
| Links: |
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| DOI / URN: |
10.1007/s11368-015-1111-9 |
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| Katalog-ID: |
SPR018956637 |
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| 245 | 1 | 2 | |a A comparison of geological and statistical approaches to element selection for sediment fingerprinting |
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| 520 | |a Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. | ||
| 650 | 4 | |a Geochemical fingerprinting |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sediment provenance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sediment tracing |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Discriminant function analyses |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a McMahon, Joe |e verfasserin |4 aut | |
| 700 | 1 | |a Evrard, Olivier |e verfasserin |4 aut | |
| 700 | 1 | |a Olley, Jon |e verfasserin |4 aut | |
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10.1007/s11368-015-1111-9 doi (DE-627)SPR018956637 (SPR)s11368-015-1111-9-e DE-627 ger DE-627 rakwb eng 550 ASE 58.52 bkl Laceby, John Patrick verfasserin aut A comparison of geological and statistical approaches to element selection for sediment fingerprinting 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 McMahon, Joe verfasserin aut Evrard, Olivier verfasserin aut Olley, Jon verfasserin aut Enthalten in Journal of soils and sediments Berlin : Springer, 2001 15(2015), 10 vom: 24. März, Seite 2117-2131 (DE-627)373325134 (DE-600)2125896-X 1614-7480 nnns volume:15 year:2015 number:10 day:24 month:03 pages:2117-2131 https://dx.doi.org/10.1007/s11368-015-1111-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.52 ASE AR 15 2015 10 24 03 2117-2131 |
| spelling |
10.1007/s11368-015-1111-9 doi (DE-627)SPR018956637 (SPR)s11368-015-1111-9-e DE-627 ger DE-627 rakwb eng 550 ASE 58.52 bkl Laceby, John Patrick verfasserin aut A comparison of geological and statistical approaches to element selection for sediment fingerprinting 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 McMahon, Joe verfasserin aut Evrard, Olivier verfasserin aut Olley, Jon verfasserin aut Enthalten in Journal of soils and sediments Berlin : Springer, 2001 15(2015), 10 vom: 24. März, Seite 2117-2131 (DE-627)373325134 (DE-600)2125896-X 1614-7480 nnns volume:15 year:2015 number:10 day:24 month:03 pages:2117-2131 https://dx.doi.org/10.1007/s11368-015-1111-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.52 ASE AR 15 2015 10 24 03 2117-2131 |
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10.1007/s11368-015-1111-9 doi (DE-627)SPR018956637 (SPR)s11368-015-1111-9-e DE-627 ger DE-627 rakwb eng 550 ASE 58.52 bkl Laceby, John Patrick verfasserin aut A comparison of geological and statistical approaches to element selection for sediment fingerprinting 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 McMahon, Joe verfasserin aut Evrard, Olivier verfasserin aut Olley, Jon verfasserin aut Enthalten in Journal of soils and sediments Berlin : Springer, 2001 15(2015), 10 vom: 24. März, Seite 2117-2131 (DE-627)373325134 (DE-600)2125896-X 1614-7480 nnns volume:15 year:2015 number:10 day:24 month:03 pages:2117-2131 https://dx.doi.org/10.1007/s11368-015-1111-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.52 ASE AR 15 2015 10 24 03 2117-2131 |
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10.1007/s11368-015-1111-9 doi (DE-627)SPR018956637 (SPR)s11368-015-1111-9-e DE-627 ger DE-627 rakwb eng 550 ASE 58.52 bkl Laceby, John Patrick verfasserin aut A comparison of geological and statistical approaches to element selection for sediment fingerprinting 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 McMahon, Joe verfasserin aut Evrard, Olivier verfasserin aut Olley, Jon verfasserin aut Enthalten in Journal of soils and sediments Berlin : Springer, 2001 15(2015), 10 vom: 24. März, Seite 2117-2131 (DE-627)373325134 (DE-600)2125896-X 1614-7480 nnns volume:15 year:2015 number:10 day:24 month:03 pages:2117-2131 https://dx.doi.org/10.1007/s11368-015-1111-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.52 ASE AR 15 2015 10 24 03 2117-2131 |
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10.1007/s11368-015-1111-9 doi (DE-627)SPR018956637 (SPR)s11368-015-1111-9-e DE-627 ger DE-627 rakwb eng 550 ASE 58.52 bkl Laceby, John Patrick verfasserin aut A comparison of geological and statistical approaches to element selection for sediment fingerprinting 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 McMahon, Joe verfasserin aut Evrard, Olivier verfasserin aut Olley, Jon verfasserin aut Enthalten in Journal of soils and sediments Berlin : Springer, 2001 15(2015), 10 vom: 24. März, Seite 2117-2131 (DE-627)373325134 (DE-600)2125896-X 1614-7480 nnns volume:15 year:2015 number:10 day:24 month:03 pages:2117-2131 https://dx.doi.org/10.1007/s11368-015-1111-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_183 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2360 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.52 ASE AR 15 2015 10 24 03 2117-2131 |
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Laceby, John Patrick @@aut@@ McMahon, Joe @@aut@@ Evrard, Olivier @@aut@@ Olley, Jon @@aut@@ |
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Laceby, John Patrick |
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Laceby, John Patrick ddc 550 bkl 58.52 misc Geochemical fingerprinting misc Sediment provenance misc Sediment tracing misc Discriminant function analyses A comparison of geological and statistical approaches to element selection for sediment fingerprinting |
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550 ASE 58.52 bkl A comparison of geological and statistical approaches to element selection for sediment fingerprinting Geochemical fingerprinting (dpeaa)DE-He213 Sediment provenance (dpeaa)DE-He213 Sediment tracing (dpeaa)DE-He213 Discriminant function analyses (dpeaa)DE-He213 |
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ddc 550 bkl 58.52 misc Geochemical fingerprinting misc Sediment provenance misc Sediment tracing misc Discriminant function analyses |
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A comparison of geological and statistical approaches to element selection for sediment fingerprinting |
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Journal of soils and sediments |
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Laceby, John Patrick McMahon, Joe Evrard, Olivier Olley, Jon |
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comparison of geological and statistical approaches to element selection for sediment fingerprinting |
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A comparison of geological and statistical approaches to element selection for sediment fingerprinting |
| abstract |
Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. |
| abstractGer |
Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. |
| abstract_unstemmed |
Purpose Elevated sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure making the management of sediment supply to reservoirs of increasing importance. Sediment fingerprinting techniques can be used to model the relative contributions of different sources of sediment accumulating in reservoirs. The goal of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Materials and methods Time-integrated samplers (n = 45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Reservoir in South East Queensland, Australia. The geochemistry of these potential sources was compared to sediment cores (n = 12) sampled in the reservoir. Elements that provided expected, observed and statistical discrimination between sediment sources were selected for modelling with the geological approach. Two statistical approaches selected elements for modelling with the Kruskal–Wallis H test and discriminatory function analysis (DFA). In particular, two approaches to the DFA were adopted to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Reservoir. Results and discussion Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge creeks). Six major elements were expected to provide discrimination. Of these six, only $ Fe_{2} %$ O_{3} $ and $ SiO_{2} $ provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated that 36 % (±9 %) of sediment sampled in the reservoir cores were from mafic-derived sources and 64 % (±9 %) were from felsic-derived sources. The geological and the first statistical approach differed by only 1 % (σ 5 %) for five out of six model groupings with only the Lexys Creek modelling results differing significantly (35 %). The statistical model with expanded elemental selection differed from the geological model by an average of 30 % for all six models. Conclusions Elemental selection for sediment fingerprinting therefore has the potential to impact modelling results. Accordingly, we believe that it is important to incorporate both robust geological and statistical approaches when selecting elements for sediment fingerprinting. For the Baroon Pocket Reservoir, management should focus on reducing the supply of sediments derived from felsic sources in each of the subcatchments. |
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A comparison of geological and statistical approaches to element selection for sediment fingerprinting |
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https://dx.doi.org/10.1007/s11368-015-1111-9 |
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McMahon, Joe Evrard, Olivier Olley, Jon |
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10.1007/s11368-015-1111-9 |
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2024-07-03T23:25:05.162Z |
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| score |
7.4014683 |