Cookies?
Library Header Image
LSE Research Online LSE Library Services

Modelling everything everywhere: a new approach to decision-making for water management under uncertainty

Beven, Keith J. and Alcock, Ruth E. (2012) Modelling everything everywhere: a new approach to decision-making for water management under uncertainty. Freshwater Biology, 57 (S1). pp. 124-132. ISSN 0046-5070

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (558kB) | Preview
Identification Number: 10.1111/j.1365-2427.2011.02592.x

Abstract

Summary 1. There are increasing demands to predict ecohydrological responses to future changes in catchments but such predictions will be inevitably uncertain because of natural variability and different sources of knowledge (epistemic) uncertainty. 2. Policy setting and decision-making should therefore reflect these inherent uncertainties in both model predictions and potential consequences. 3. This is the focus of a U.K. Natural Environment Research Council knowledge exchange project called the Catchment Change Network (CCN). The aim is to bring academics and practitioners together to define Guidelines for Good Practice in incorporating risk and uncertainty into assessments of the impacts of change. 4. Here, we assess the development of such Guidelines in the context of having catchment models of everywhere.

Item Type: Article
Official URL: http://onlinelibrary.wiley.com/journal/10.1111/(IS...
Additional Information: © 2011 The Authors
Divisions: Centre for Analysis of Time Series
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
T Technology > TD Environmental technology. Sanitary engineering
Sets: Research centres and groups > Centre for the Analysis of Time Series (CATS)
Date Deposited: 19 Dec 2014 14:24
Last Modified: 20 Nov 2019 10:45
URI: http://eprints.lse.ac.uk/id/eprint/60564

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics