scholarly journals Things we can do now that we could not do before: Developing and using a cross-scalar, state-wide database to support geomorphologically-informed river management

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244719
Author(s):  
Kirstie Fryirs ◽  
Fergus Hancock ◽  
Michael Healey ◽  
Simon Mould ◽  
Lucy Dobbs ◽  
...  

A fundamental premise of river management is that practitioners understand the resource they are working with. In river management this requires that baseline information is available on the structure, function, health and trajectory of rivers. Such information provides the basis to contextualise, to plan, to be proactive, to prioritise, to set visions, to set goals and to undertake objective, pragmatic, transparent and evidence-based decision making. In this paper we present the State-wide NSW River Styles database, the largest and most comprehensive dataset of geomorphic river type, condition and recovery potential available in Australia. The database is an Open Access product covering over 216,600 km of stream length in an area of 802,000 km2. The availability of the database presents unprecedented opportunities to systematically consider river management issues at local, catchment, regional and state-wide scales, and appropriately contextualise applications in relation to programs at other scales (e.g. internationally)–something that cannot be achieved independent from, or without, such a database. We present summary findings from the database and demonstrate through use of examples how the database has been used in geomorphologically-informed river management. We also provide a cautionary note on the limitations of the database and expert advice on lessons learnt during its development to aid others who are undertaking similar analyses.

1998 ◽  
Vol 12 (1) ◽  
pp. 60-65 ◽  
Author(s):  
G. Pender ◽  
D. Smart ◽  
T. B. Hoey

2018 ◽  
Vol 43 (1) ◽  
pp. 49-76
Author(s):  
Patrick G. Watson

In fields such as Sociology and Political Science, there have been, over the course of three decades, attempts to engage elected officials in “Evidence-Based Decision-Making”. Evidence is generally conceived as “expert” advice provided to politicians. A question that has gained more centrality in recent years is “why do elected officials not trust expert opinion or technical evidence?” and the answer to this question has been sought in historical or general terms (e.g. Irwin 2006; Weiss et al. 2008; Kraft et al. 2015). Here I will propose an alternative question: “when politicians exhibit a lack of trust in expert advice, how is such skepticism publicly accounted for?” I will examine this question by utilizing a case study ethnographic approach to the City of Toronto’s controversial decision to endorse the Hybrid alternative for the Gardiner expressway. By doing so, I intend to show that knowledge controversies are not inherently a form of deficiency on the part of the elected official – that they are ignorant to the implications of evidence – but rather the standard by which elected officials and appointed experts review and understand evidence can lead to very different (although both reasonably ‘correct’) conclusions.


1997 ◽  
Vol 7 (3) ◽  
pp. 8-10 ◽  
Author(s):  
Becky Sutherland-Cornett ◽  
Bernard P. Henri ◽  
Brooke Hallowell

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.


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