A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models c...

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Bibliographic Details
Published in:Hydrogeology journal 2012-08, Vol.20 (5), p.915-932
Main Authors: Guillaume, Joseph H. A., Qureshi, M. Ejaz, Jakeman, Anthony J.
Format: Article
Language:English
Subjects:
Aquatic Pollution
Biophysics
Decisions
Earth and Environmental Science
Earth Sciences
Economics
Geology
Geophysics/Geodesy
Groundwater
Hydrogeology
Hydrology/Water Resources
Management
Mathematical models
Regression
Sampling
Simulation
Socioeconomic factors
Uncertainty
Waste Water Technology
Water Management
Water Pollution Control
Water Quality/Water Pollution
Water resources management
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Summary:Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.
ISSN:1431-2174
1435-0157
DOI:10.1007/s10040-012-0864-0