Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.153
IF5.153
IF 5-year value: 5.460
IF 5-year
5.460
CiteScore value: 7.8
CiteScore
7.8
SNIP value: 1.623
SNIP1.623
IPP value: 4.91
IPP4.91
SJR value: 2.092
SJR2.092
Scimago H <br class='widget-line-break'>index value: 123
Scimago H
index
123
h5-index value: 65
h5-index65
Preprints
https://doi.org/10.5194/hess-2019-464
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-464
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Oct 2019

07 Oct 2019

Review status
A revised version of this preprint was accepted for the journal HESS and is expected to appear here in due course.

Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model

Aynom T. Teweldebrhan1, John F. Burkhart1, Thomas V. Schuler1, and Morten Hjorth-Jensen1,2 Aynom T. Teweldebrhan et al.
  • 1University of Oslo, Oslo, Norway
  • 2Michigan State University, USA

Abstract. Monte Carlo (MC) methods have been widely used in uncertainty analysis and parameter identification for hydrological models. The main challenge with these approaches is, however, the prohibitive number of model runs required to get an adequate sample size which may take from days to months especially when the simulations are run in distributed mode. In the past, emulators have been used to minimize the computational burden of the MC simulation through direct estimation of the residual based response surfaces. Here, we apply emulators of MC simulation in parameter identification for a distributed conceptual hydrological model using two likelihood measures, i.e. the absolute bias of model predictions (Score) and another based on the time relaxed limits of acceptability concept (pLoA). Three machine learning models (MLMs) were built using model parameter sets and response surfaces with limited number of model realizations (4000). The developed MLMs were applied to predict pLoA and Score for a large set of model parameters (95 000). The behavioural parameter sets were identified using a time relaxed limits of acceptability approach based on the predicted pLoA values; and applied to estimate the quantile streamflow predictions weighted by their respective Score. The three MLMs were able to adequately mimic the response surfaces directly estimated from MC simulations; and the models identified using the coupled ML emulators and the limits of acceptability approach have performed very well in reproducing the median streamflow prediction both during the calibration and validation periods.

Aynom T. Teweldebrhan et al.

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Aynom T. Teweldebrhan et al.

Aynom T. Teweldebrhan et al.

Viewed

Total article views: 584 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
390 177 17 584 29 29
  • HTML: 390
  • PDF: 177
  • XML: 17
  • Total: 584
  • BibTeX: 29
  • EndNote: 29
Views and downloads (calculated since 07 Oct 2019)
Cumulative views and downloads (calculated since 07 Oct 2019)

Viewed (geographical distribution)

Total article views: 484 (including HTML, PDF, and XML) Thereof 477 with geography defined and 7 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 19 Sep 2020
Publications Copernicus
Download
Citation