Models may fit data without supporting a reliable decision. This audit examines whether data quality, aquifer-test assumptions, delayed-response evidence, transformation uncertainty, and decision variables have been examined before the result enters a pumping, remediation, recovery, or subsurface-energy decision.
A model can reproduce a groundwater response while leaving the mechanism, analytical-model uncertainty, or decision variable insufficiently examined. The audit focuses on how the same data change when they are transformed into a decision.
The output is designed for internal technical use, client discussion, or the scoping stage of a larger project.
Review forcing history, response variables, simplifications, calibration targets, and validation gaps.
Identify which decision variable is exposed: allowable pumping, recovery time, remediation boundary, thermal design margin, or uncertainty buffer.
Screen whether non-instantaneous drawdown, recovery, head, or thermal-response evidence is strong enough to justify lag-aware analysis.
Define how model-choice and transformation uncertainty should move into the decision variable.
Specify the smallest next analysis that can determine whether the decision changes materially.
Summarize findings in language usable by engineers, managers, and technical reviewers.
Clarify the decision, data type, exposure, and whether a paid diagnostic audit is appropriate.
Four to six weeks of data/model assumption review, decision-variable mapping, and lagging-response relevance screening.
Compare conventional and lag-aware interpretation models on a selected site, dataset, or anonymized case.
Build a larger project around uncertainty propagation, reporting, review support, and possible publication.
Budget ranges should be matched to data access, confidentiality, reporting needs, and university contracting rules. A small diagnostic phase often provides a clearer basis for the first project.