2008
DOI: 10.1016/j.ocecoaman.2008.07.003
|View full text |Cite
|
Sign up to set email alerts
|

Data variability and uncertainty limits the capacity to identify and predict critical changes in coastal systems – A review of key concepts

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
6
0

Year Published

2010
2010
2014
2014

Publication Types

Select...
6

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(6 citation statements)
references
References 28 publications
0
6
0
Order By: Relevance
“…The results can be used to distinguish the circulation in different parts of the basin, to identify areas that are at higher risk of accumulating substances (Cucco and Umgiesser, 2006;Luick et al, 2007;Wang et al, 2009;Rapaglia et al, 2010) and to determine the main forcing factors and/or processes conditioning residence time itself (Tartinville et al, 1997;Wijeratne and Rydberg, 2007;Plus et al, 2009;Malhadas et al, 2010;Huang et al, 2010;Cavalcante et al, 2011) Salinity can also be successfully simulated in transitional waters (Solidoro et al, 2004;Huang, 2007;Huang et al, 2002) and numerical models can be used to study the spatial and temporal variability of coastal lagoons (Obrador et al, 2008;Lopes et al, 2010;Faure et al, 2010). In addition, numerical models have been used to advance proposals for the zoning of shallow basins (Ferrarin et al, 2008(Ferrarin et al, , 2010, and to evaluate the consequences of different management strategies (Tsihrintzis et al, 2007;Gong et al, 2008;Hakanson and Duarte, 2008). The adoption as normal practice of the calibration and validation of every module of the model, together with the modelling quality assurance procedures, allows the associated error to be accurately estimated and ensures the reliability of numerical models.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…The results can be used to distinguish the circulation in different parts of the basin, to identify areas that are at higher risk of accumulating substances (Cucco and Umgiesser, 2006;Luick et al, 2007;Wang et al, 2009;Rapaglia et al, 2010) and to determine the main forcing factors and/or processes conditioning residence time itself (Tartinville et al, 1997;Wijeratne and Rydberg, 2007;Plus et al, 2009;Malhadas et al, 2010;Huang et al, 2010;Cavalcante et al, 2011) Salinity can also be successfully simulated in transitional waters (Solidoro et al, 2004;Huang, 2007;Huang et al, 2002) and numerical models can be used to study the spatial and temporal variability of coastal lagoons (Obrador et al, 2008;Lopes et al, 2010;Faure et al, 2010). In addition, numerical models have been used to advance proposals for the zoning of shallow basins (Ferrarin et al, 2008(Ferrarin et al, , 2010, and to evaluate the consequences of different management strategies (Tsihrintzis et al, 2007;Gong et al, 2008;Hakanson and Duarte, 2008). The adoption as normal practice of the calibration and validation of every module of the model, together with the modelling quality assurance procedures, allows the associated error to be accurately estimated and ensures the reliability of numerical models.…”
Section: Accepted Manuscriptmentioning
confidence: 99%
“…Many early warning indicators of ecological thresholds, such as increased variance, critical slowing down, and flickering, have been identified using modeling simulations and long-term data sets (Daskalov et al, 2007;Dakos et al, 2012). Identifying reliable indicators and quantifying thresholds in ecological systems can be challenging due to lack of appropriate data (deYoung et al, 2004;Håkanson and Duarte, 2008;Goberville et al, 2010). Many early warning indicators require long-term, highresolution data with relatively little noise, which are uncommon in ecological systems (Dakos et al, 2008(Dakos et al, , 2012Scheffer et al, 2009).…”
Section: Model Basedmentioning
confidence: 99%
“…A fundamental presumption of the simulations presented here is that the limits to precision of a model are ultimately set by the precision of the data used for model building and testing (e.g. Håkanson & Duarte 2008). One way to empirically assess how data uncertainty limits model precision is to plot replicate samples against each other and calculate the 'predictive power' of one sample in relation to the other (e.g.…”
Section: Simulating Maximum Precision and Predictive Power At Differementioning
confidence: 99%
“…The predictive performance of empirical models is affected by 3 types of uncertainty: (1) uncertainty in the estimation of the response variable, (2) uncertainty in the estimation of the predictor variable and (3) uncertainties caused by deficiencies in model formulation and structure (Håkanson 1999). The fact that all of these sources of uncertainty, including their rela tive importance, is highly dependent on spatial and temporal scales means that the performance, optimum structure and sampling design will vary according to the spatial and temporal context (Levin 1992, Schneider 2001, Håkanson & Duarte 2008, Elith & Leathwick 2009, Beale & Lennon 2012. Focusing on the spatial dimension, 2 aspects of scale are relevant: extent (i.e.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation