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 Citations (Scopus)

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 1 2018

Fingerprint

Water quality
Calibration
calibration
water quality
Catchments
prediction
Hydrology
Sewers
Statistical methods
hydrology
river basin
Rivers
catchment
Uncertainty
method

Keywords

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

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling

Cite this

On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions. / Del Giudice, D.; Muenich, Rebecca; McCahon Kalcic, M.; Bosch, N. S.; Scavia, D.; Michalak, A. M.

In: Environmental Modelling and Software, Vol. 105, 01.07.2018, p. 286-295.

Research output: Contribution to journalArticle

Del Giudice, D. ; Muenich, Rebecca ; McCahon Kalcic, M. ; Bosch, N. S. ; Scavia, D. ; Michalak, A. M. / On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions. In: Environmental Modelling and Software. 2018 ; Vol. 105. pp. 286-295.
@article{c63e4a8d14324faa8c794dfe12740715,
title = "On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions",
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.",
keywords = "Least squares, Mechanistic modeling, Statistical inference, Surface hydrology, Uncertainty assessment, Water quality",
author = "{Del Giudice}, D. and Rebecca Muenich and {McCahon Kalcic}, M. and Bosch, {N. S.} and D. Scavia and Michalak, {A. M.}",
year = "2018",
month = "7",
day = "1",
doi = "10.1016/j.envsoft.2018.03.009",
language = "English (US)",
volume = "105",
pages = "286--295",
journal = "Environmental Modelling and Software",
issn = "1364-8152",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - On the practical usefulness of least squares for assessing uncertainty in hydrologic and water quality predictions

AU - Del Giudice, D.

AU - Muenich, Rebecca

AU - McCahon Kalcic, M.

AU - Bosch, N. S.

AU - Scavia, D.

AU - Michalak, A. M.

PY - 2018/7/1

Y1 - 2018/7/1

N2 - 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.

AB - 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.

KW - Least squares

KW - Mechanistic modeling

KW - Statistical inference

KW - Surface hydrology

KW - Uncertainty assessment

KW - Water quality

UR - http://www.scopus.com/inward/record.url?scp=85047063844&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047063844&partnerID=8YFLogxK

U2 - 10.1016/j.envsoft.2018.03.009

DO - 10.1016/j.envsoft.2018.03.009

M3 - Article

AN - SCOPUS:85047063844

VL - 105

SP - 286

EP - 295

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -