An overview of methods to identify and manage uncertainty for modelling problems in the water-environment-agriculture cross-sector

Journal Article · Wed Mar 14 00:00:00 EDT 2018 · Mathematics for Industry

DOI:https://doi.org/10.1007/978-981-10-7811-8_15· OSTI ID:1429841

Jakeman, Anthony J. ^[1]; Jakeman, John Davis ^[2]

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1. Australian National Univ., Canberra, ACT (Australia)
2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Uncertainty pervades the representation of systems in the water–environment–agriculture cross-sector. Successful methods to address uncertainties have largely focused on standard mathematical formulations of biophysical processes in a single sector, such as partial or ordinary differential equations. More attention to integrated models of such systems is warranted. Model components representing the different sectors of an integrated model can have less standard, and different, formulations to one another, as well as different levels of epistemic knowledge and data informativeness. Thus, uncertainty is not only pervasive but also crosses boundaries and propagates between system components. Uncertainty assessment (UA) cries out for more eclectic treatment in these circumstances, some of it being more qualitative and empirical. Here in this paper, we discuss the various sources of uncertainty in such a cross-sectoral setting and ways to assess and manage them. We have outlined a fast-growing set of methodologies, particularly in the computational mathematics literature on uncertainty quantification (UQ), that seem highly pertinent for uncertainty assessment. There appears to be considerable scope for advancing UA by integrating relevant UQ techniques into cross-sectoral problem applications. Of course this will entail considerable collaboration between domain specialists who often take first ownership of the problem and computational methods experts.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1429841
• Report Number(s): SAND--2017-9715J; 656893
• Journal Information: Mathematics for Industry, Journal Name: Mathematics for Industry Vol. 28; ISSN 2198-350X
• Publisher: Springer
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Mathematics-for-Industry
Uncertainty quantification
Water resources Uncertainty assessment