A Bayesian hierarchical framework for calibrating aquatic biogeochemical models
Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation technique...
Gespeichert in:
Veröffentlicht in: | Ecological modelling 2009-09, Vol.220 (18), p.2142-2161 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 2161 |
---|---|
container_issue | 18 |
container_start_page | 2142 |
container_title | Ecological modelling |
container_volume | 220 |
creator | Zhang, Weitao Arhonditsis, George B. |
description | Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management. |
doi_str_mv | 10.1016/j.ecolmodel.2009.05.023 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_34721352</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0304380009003925</els_id><sourcerecordid>34721352</sourcerecordid><originalsourceid>FETCH-LOGICAL-c473t-f9ad093e9fcf63090b900e6c650da9f2936d66aacdf61d56798d8be1964016f3</originalsourceid><addsrcrecordid>eNqFkMtOAyEUhonRxFp9BmejuxkP0GGGZW28JSZuuicUDi11ZlBoNb69tDXduuKEfP-5fIRcU6goUHG3rtCErg8Wu4oByArqChg_ISPaNqxsgIlTMgIOk5K3AOfkIqU1AFDWshF5mxb3-geT10Ox8hh1NCtvdFe4qHv8DvG9cCEW-ccvot74YVnoz20uTLHwYYnBrLDfB_YbpEty5nSX8OrvHZP548N89ly-vj29zKavpZk0fFM6qS1IjtIZJzhIWEgAFEbUYLV0THJhhdDaWCeorUUjW9sukEoxyTc7Pia3h7YfMXxuMW1U75PBrtMDhm1SfNIwymv2L8igqVtBd2BzAE0MKUV06iP6XscfRUHtRKu1OopWO9EKapVF5-TN3widsogsbjA-HeOMtowCl5mbHrisCb-ya5WMx8Gg9RHNRtng_531C3UxmLs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>20758612</pqid></control><display><type>article</type><title>A Bayesian hierarchical framework for calibrating aquatic biogeochemical models</title><source>Elsevier ScienceDirect Journals Complete</source><creator>Zhang, Weitao ; Arhonditsis, George B.</creator><creatorcontrib>Zhang, Weitao ; Arhonditsis, George B.</creatorcontrib><description>Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.</description><identifier>ISSN: 0304-3800</identifier><identifier>EISSN: 1872-7026</identifier><identifier>DOI: 10.1016/j.ecolmodel.2009.05.023</identifier><identifier>CODEN: ECMODT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Animal, plant and microbial ecology ; Aquatic biogeochemical models ; Bayesian calibration ; Biological and medical sciences ; Eutrophication ; Fundamental and applied biological sciences. Psychology ; General aspects. Techniques ; Hierarchical Bayes ; Mathematical modeling ; Methods and techniques (sampling, tagging, trapping, modelling...) ; Uncertainty analysis</subject><ispartof>Ecological modelling, 2009-09, Vol.220 (18), p.2142-2161</ispartof><rights>2009 Elsevier B.V.</rights><rights>2009 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c473t-f9ad093e9fcf63090b900e6c650da9f2936d66aacdf61d56798d8be1964016f3</citedby><cites>FETCH-LOGICAL-c473t-f9ad093e9fcf63090b900e6c650da9f2936d66aacdf61d56798d8be1964016f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ecolmodel.2009.05.023$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21821039$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Weitao</creatorcontrib><creatorcontrib>Arhonditsis, George B.</creatorcontrib><title>A Bayesian hierarchical framework for calibrating aquatic biogeochemical models</title><title>Ecological modelling</title><description>Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.</description><subject>Animal, plant and microbial ecology</subject><subject>Aquatic biogeochemical models</subject><subject>Bayesian calibration</subject><subject>Biological and medical sciences</subject><subject>Eutrophication</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects. Techniques</subject><subject>Hierarchical Bayes</subject><subject>Mathematical modeling</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><subject>Uncertainty analysis</subject><issn>0304-3800</issn><issn>1872-7026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkMtOAyEUhonRxFp9BmejuxkP0GGGZW28JSZuuicUDi11ZlBoNb69tDXduuKEfP-5fIRcU6goUHG3rtCErg8Wu4oByArqChg_ISPaNqxsgIlTMgIOk5K3AOfkIqU1AFDWshF5mxb3-geT10Ox8hh1NCtvdFe4qHv8DvG9cCEW-ccvot74YVnoz20uTLHwYYnBrLDfB_YbpEty5nSX8OrvHZP548N89ly-vj29zKavpZk0fFM6qS1IjtIZJzhIWEgAFEbUYLV0THJhhdDaWCeorUUjW9sukEoxyTc7Pia3h7YfMXxuMW1U75PBrtMDhm1SfNIwymv2L8igqVtBd2BzAE0MKUV06iP6XscfRUHtRKu1OopWO9EKapVF5-TN3widsogsbjA-HeOMtowCl5mbHrisCb-ya5WMx8Gg9RHNRtng_531C3UxmLs</recordid><startdate>20090924</startdate><enddate>20090924</enddate><creator>Zhang, Weitao</creator><creator>Arhonditsis, George B.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H95</scope><scope>H97</scope><scope>L.G</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20090924</creationdate><title>A Bayesian hierarchical framework for calibrating aquatic biogeochemical models</title><author>Zhang, Weitao ; Arhonditsis, George B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-f9ad093e9fcf63090b900e6c650da9f2936d66aacdf61d56798d8be1964016f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Animal, plant and microbial ecology</topic><topic>Aquatic biogeochemical models</topic><topic>Bayesian calibration</topic><topic>Biological and medical sciences</topic><topic>Eutrophication</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects. Techniques</topic><topic>Hierarchical Bayes</topic><topic>Mathematical modeling</topic><topic>Methods and techniques (sampling, tagging, trapping, modelling...)</topic><topic>Uncertainty analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Weitao</creatorcontrib><creatorcontrib>Arhonditsis, George B.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Ecological modelling</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Weitao</au><au>Arhonditsis, George B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Bayesian hierarchical framework for calibrating aquatic biogeochemical models</atitle><jtitle>Ecological modelling</jtitle><date>2009-09-24</date><risdate>2009</risdate><volume>220</volume><issue>18</issue><spage>2142</spage><epage>2161</epage><pages>2142-2161</pages><issn>0304-3800</issn><eissn>1872-7026</eissn><coden>ECMODT</coden><abstract>Model practitioners increasingly place emphasis on rigorous quantitative error analysis in aquatic biogeochemical models and the existing initiatives range from the development of alternative metrics for goodness of fit, to data assimilation into operational models, to parameter estimation techniques. However, the treatment of error in many of these efforts is arguably selective and/or ad hoc. A Bayesian hierarchical framework enables the development of robust probabilistic analysis of error and uncertainty in model predictions by explicitly accommodating measurement error, parameter uncertainty, and model structure imperfection. This paper presents a Bayesian hierarchical formulation for simultaneously calibrating aquatic biogeochemical models at multiple systems (or sites of the same system) with differences in their trophic conditions, prior precisions of model parameters, available information, measurement error or inter-annual variability. Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions). The comparison between observations and posterior predictive monthly distributions indicates that the plankton models calibrated under the Bayesian hierarchical scheme provided accurate system representations for all the scenarios examined. Our results also suggest that the Bayesian hierarchical approach allows overcoming problems of insufficient local data by “borrowing strength” from well-studied sites and this feature will be highly relevant to conservation practices of regions with a high number of freshwater resources for which complete data could never be practically collected. Finally, we discuss the prospect of extending this framework to spatially explicit biogeochemical models (e.g., more effectively connect inshore with offshore areas) along with the benefits for environmental management, such as the optimization of the sampling design of monitoring programs and the alignment with the policy practice of adaptive management.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ecolmodel.2009.05.023</doi><tpages>20</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0304-3800 |
ispartof | Ecological modelling, 2009-09, Vol.220 (18), p.2142-2161 |
issn | 0304-3800 1872-7026 |
language | eng |
recordid | cdi_proquest_miscellaneous_34721352 |
source | Elsevier ScienceDirect Journals Complete |
subjects | Animal, plant and microbial ecology Aquatic biogeochemical models Bayesian calibration Biological and medical sciences Eutrophication Fundamental and applied biological sciences. Psychology General aspects. Techniques Hierarchical Bayes Mathematical modeling Methods and techniques (sampling, tagging, trapping, modelling...) Uncertainty analysis |
title | A Bayesian hierarchical framework for calibrating aquatic biogeochemical models |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T10%3A21%3A35IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Bayesian%20hierarchical%20framework%20for%20calibrating%20aquatic%20biogeochemical%20models&rft.jtitle=Ecological%20modelling&rft.au=Zhang,%20Weitao&rft.date=2009-09-24&rft.volume=220&rft.issue=18&rft.spage=2142&rft.epage=2161&rft.pages=2142-2161&rft.issn=0304-3800&rft.eissn=1872-7026&rft.coden=ECMODT&rft_id=info:doi/10.1016/j.ecolmodel.2009.05.023&rft_dat=%3Cproquest_cross%3E34721352%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=20758612&rft_id=info:pmid/&rft_els_id=S0304380009003925&rfr_iscdi=true |