A methodology for quantifying the value of spatial information for dynamic Earth problems

• Published in: Stochastic environmental research and risk assessment 2013-05, Vol.27 (4), p.969-983
• Main Authors: Trainor-Guitton, Whitney J., Mukerji, Tapan, Knight, Rosemary
• Format: Article
• Language: English
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Geostatistics; Groundwater; Groundwater data; Heterogeneity; Hydrologic data; Hydrologic modeling; Math. Appl. in Environmental Science; Original Paper; Physics; Probability Theory and Stochastic Processes; Statistics for Engineering; Waste Water Technology; Water Management; Water Pollution Control; Water resources management
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-012-0619-4

We develop a methodology for assessing the value of information (VOI) from spatial data for groundwater decisions. Two sources of uncertainty are the focus of this VOI methodology: the spatial heterogeneity (how it influences the hydrogeologic response of interest) and the reliability of geophysical data (how they provide information about the spatial heterogeneity). An existing groundwater situation motivates and in turn determines the scope of this research. The objectives of this work are to (1) represent the uncertainty of the dynamic hydrogeologic response due to spatial heterogeneity, (2) provide a quantitative measure for how well a particular information reveals this heterogeneity (the uncertainty of the information) and (3) use both of these to propose a VOI workflow for spatial decisions and spatial data. The uncertainty of the hydraulic response is calculated using many Earth models that are consistent with the a priori geologic information. The information uncertainty is achieved quantitatively through Monte Carlo integration and geostatistical simulation. Two VOI results are calculated which demonstrate that a higher VOI occurs when the geophysical attribute (the data) better discriminates between geological indicators. Although geophysical data can only indirectly measure static properties that may influence the dynamic response, this transferable methodology provides a framework to estimate the value of spatial data given a particular decision scenario.