Comment on ‘Siegel D. 2008. Reductionist hydrogeology: ten fundamental principles. Hydrological Processes 22: 4967– 4970’

✍ Scribed by Shlomo P. Neuman

• Book ID: 102266913
• Publisher: John Wiley and Sons
• Year: 2009
• Tongue: English
• Weight: 42 KB
• Volume: 23
• Category: Article
• ISSN: 0885-6087
• DOI: 10.1002/hyp.7280

✦ Synopsis

I offer two observations on

Siegel's (2008)

recent commentary:

1. In discussing his "Hydrogeologist's Credo" Siegel (2008) asserts: "Some argue that using random hydraulic conductivity distributions derived from scant data bases provide mathematical 'certainty' that professional judgment does not (Neuman and Wierenga, 2003;Neuman, 2007)." This is a misreading of the cited literature, which argues nothing of the sort. Instead of certainty this literature speaks of uncertainty; instead of devaluing professional judgment it advocates incorporating it in formal uncertainty assessments by means of well-established Bayesian methods. Siegel, on the other hand, advocates relying on subjective judgment while devaluing formal analysis; in his view, ". . . the metadata of experience leads to more success than sophisticated attempts at analysis, unwarranted by the field data and . . . difficult to explain to the non-specialists . . ." My 40 years of academic and professional experience have convinced me otherwise: rendering professional judgment without due support in theory and analysis runs the risk of misinterpreting site conditions, as illustrated by two notable case studies in Neuman and Wierenga (2003). Siegel ( 2008) notes correctly that ". . . under circumstances of scant data, many hydrogeologic conceptual and mathematical models fail because of 'surprises' unanticipated during the analysis, including errors in assumed distributions of hydraulic conductivity (Bredehoeft, 2004)." Siegel is, however, unaware that this same problem forms the central theme of the Neuman and Wierenga (2003) report he cites. As made clear by these authors and by Neuman (2003Neuman ( , 2004)), analyses based on a single hydrologic concept are prone to statistical bias and underestimation of uncertainty. To deal with this problem, Neuman