On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions

D. Del Giudice, Rebecca Muenich, M. McCahon Kalcic, N. S. Bosch, D. Scavia, A. M. Michalak

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Sophisticated methods for uncertainty quantification have been proposed for overcoming the pitfalls of simple statistical inference in hydrology. The implementation of such methods is conceptually and computationally challenging, however, especially for large-scale models. Here, we explore whether there are circumstances in which simple approaches, such as least squares, produce comparably accurate and reliable predictions. We do so using three case studies, with two involving a small sewer catchment with limited calibration data, and one an agricultural river basin with rich calibration data. We also review additional published case studies. We find that least squares performs similarly to more sophisticated approaches such as a Bayesian autoregressive error model in terms of both accuracy and reliability if calibration periods are long or if the input data and the model have minimal bias. Overall, we find that, when mindfully applied, simple statistical methods such as LS can still be useful for uncertainty quantification.

Original languageEnglish (US)
Pages (from-to)286-295
Number of pages10
JournalEnvironmental Modelling and Software
Volume105
DOIs
StatePublished - Jul 2018

Keywords

  • Least squares
  • Mechanistic modeling
  • Statistical inference
  • Surface hydrology
  • Uncertainty assessment
  • Water quality

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

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