Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE

Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE

Ecosystem service-support tools are commonly used to guide natural resource management. Often, empirically based models are preferred due to low data requirements, simplicity and clarity. Yet, uncertainty produced by local context or parameter estimation remains poorly quantified and documented. We...

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Bibliographic Details
Main Authors: Natalia Estrada-Carmona, Elizabeth B. Harper, Fabrice DeClerck, Alexander K. Fremier
Format: Article
Language:English
Published: Taylor & Francis Group 2017-01-01
Series:International Journal of Biodiversity Science, Ecosystem Services & Management
Subjects:
Classification and regression trees
ecosystem services
global sensitivity analysis
Random Forest
RUSLE
soil loss
Online Access:http://dx.doi.org/10.1080/21513732.2016.1237383

Internet

http://dx.doi.org/10.1080/21513732.2016.1237383

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