An efficient framework for hydrologic model calibration on long data periods. Issue 12 (16th December 2013)

• Record Type: Journal Article
• Authors: Razavi, Saman; Tolson, Bryan A.
• Is Part Of: Water resources research. Volume 49:Issue 12(2013:Dec.)
• Page Start: 8418
• Page End: 8431
• Publication Date: 2013-12-16
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2012WR013442
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>[1] Long periods of hydrologic data records have become available in many watersheds around the globe. Hydrologic model calibration on such long, full‐length data periods is typically deemed the most robust approach for calibration but at larger computational costs. Determination of a representative short period as a "surrogate" of a long data period that sufficiently embeds its information content is not trivial and is a challenging research question. The representativeness of such a short period is not only a function of data characteristics but also model and calibration error function dependent. Unlike previous studies, this study goes beyond identifying the best surrogate data period to be used in model calibration and proposes an efficient framework that calibrates the hydrologic model to full‐length data while running the model only on a short period for the majority of the candidate parameter sets. To this end, a mapping system is developed to approximate the model performance on the full‐length data period based on the model performance for the short data period. The basic concepts and the promise of the framework are demonstrated through a computationally expensive hydrologic model case study. Three calibration approaches, namely calibration solely to a surrogate period, calibration to the full period, and calibration through the proposed framework, are evaluated and compared. Results show that within the same computational budget, the proposed framework leads to improved or equal calibration performance compared to the two conventional approaches. Results also indicate that model calibration solely to a short data period may lead to a range of performances from poor to very well depending on the representativeness of the short data period which is typically not known a priori.</p> </abstract>