| Sharps, Katrina, Masante, Dario, Thomas, Amy, Jackson, Bethanna, Redhead, John, May, Linda, Prosser, Havard, Cosby, Bernard, Emmett, Bridget and Jones, Laurence 2017. Comparing strengths and weaknesses of three ecosystem services modelling tools in a diverse UK river catchment. Science of the Total Environment 584-5 , pp. 118-130. 10.1016/j.scitotenv.2016.12.160 |
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Abstract
Ecosystem services modelling tools can help land managers and policy makers evaluate the impacts of alternative management options or changes in land use on the delivery of ecosystem services. As the variety and complexity of these tools increases, there is a need for comparative studies across a range of settings, allowing users to make an informed choice. Using examples of provisioning and regulating services (water supply, carbon storage and nutrient retention), we compare three spatially explicit tools – LUCI (Land Utilisation and Capability Indicator), ARIES (Artificial Intelligence for Ecosystem Services) and InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs). Models were parameterised for the UK and applied to a temperate catchment with widely varying land use in North Wales. Although each tool provides quantitative mapped output, can be applied in different contexts, and can work at local or national scale, they differ in the approaches taken and underlying assumptions made. In this study, we focus on the wide range of outputs produced for each service and discuss the differences between each modelling tool. Model outputs were validated using empirical data for river flow, carbon and nutrient levels within the catchment. The sensitivity of the models to land-use change was tested using four scenarios of varying severity, evaluating the conversion of grassland habitat to woodland (0–30% of the landscape). We show that, while the modelling tools provide broadly comparable quantitative outputs, each has its own unique features and strengths. Therefore the choice of tool depends on the study question.
| Item Type: | Article |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Biosciences |
| Publisher: | Elsevier |
| ISSN: | 0048-9697 |
| Date of First Compliant Deposit: | 18 April 2018 |
| Date of Acceptance: | 23 December 2016 |
| Last Modified: | 06 May 2023 00:13 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/102449 |
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