English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Groundwater hydraulic head (H) measurements and point-estimates of hydraulic conductivity (K) both contain information about the K field. There is no simple, a priori estimate of the relative worth of H and K data. Thus, there is a gap in our conceptual understanding of the value of the K inversion procedure. Here, using synthetic calibration experiments, we quantified the worth of H and K data in terms of reducing calibrated K errors. We found that normalized K error e K could be approximated by a polynomial function with first-order terms of H and K data densities μ H and μ K , which have been normalized by the correlation lengths of the K field, and a mutually inhibitive interaction term. This equation can be applied to obtain a rough estimate of the uncertainty prior to the inversion for a case with a similar configuration. The formulation suggests that the inversion is valuable even without K data. The relative worths of H and K depend heavily on existing data densities and heterogeneity. K can be ten times more informative when it is sparse. Noise perturbation experiments show that we should incorporate noisy K data when K is sparse, but not a large amount of low-quality K estimates. Our conclusions establish a crude, baseline estimate of the value of calibration. A similar assessment method for information content can be employed for more complex problems.

Toward a Priori Evaluation of Relative Worth of Head and Conductivity Data as Functions of Data Densities in Inverse Groundwater Modeling