Preprints
https://doi.org/10.5194/hess-2022-77
https://doi.org/10.5194/hess-2022-77
 
21 Mar 2022
21 Mar 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Using LSTM to monitor continuous discharge indirectly with electrical conductivity observations

Yong Chang1, Benjamin Mewes2, and Andreas Hartmann3,4 Yong Chang et al.
  • 1School of Earth Science and Engineering, Hohai University, Nanjing 210098, China
  • 2Ruhr-University Bochum, Institute of Hydrology, Water Resources and Environmental Engineering, Bochum, Germany
  • 3Institute of Groundwater Management, Technical University of Dresden, Dresden, Germany
  • 4Chair of Hydrological Modeling and Water Resources, Freiburg University, Freiburg, 79098, Germany

Abstract. Due to EC’s easy recordability and the existence of a strong correlation between EC (electrical conductivity) and discharge in certain catchments, EC is a potential predictor of discharge. This potential has not yet to be widely addressed. In this paper, we investigate the feasibility of using EC as a proxy for long-term discharge monitoring in a small karst catchment where EC always shows a negative correlation with the spring discharge. Given their complex relationship, a special machine learning architecture, LSTM (Long Short Term Memory), was used to handle the mapping from EC to discharge. LSTM results indicate that the spring discharge can be predicted well with EC, particularly in storms when the dilution dominates the EC dynamic; however, the prediction may have relatively large uncertainties in the small or middle recharge events. A small number of discharge observations are sufficient to obtain a robust LSTM for the long-term discharge prediction from EC, indicating the practicality of recording EC in ungauged catchments for indirect discharge monitoring. Our study also highlights that the random or fixed-interval discharge measurement strategy, which covers various climate conditions, is more informative for LSTM to give robust predictions than other strategies. While our study is implemented in a karst catchment, the method may be also suitable for non-karst catchments where there is a strong correlation between EC and discharge.

Yong Chang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-77', Anonymous Referee #1, 16 Apr 2022
    • AC1: 'Reply on RC1', Yong Chang, 16 Jul 2022
  • RC2: 'Comment on hess-2022-77', Anonymous Referee #2, 18 Jun 2022
    • AC2: 'Reply on RC2', Yong Chang, 16 Jul 2022

Yong Chang et al.

Yong Chang et al.

Viewed

Total article views: 594 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
465 111 18 594 6 7
  • HTML: 465
  • PDF: 111
  • XML: 18
  • Total: 594
  • BibTeX: 6
  • EndNote: 7
Views and downloads (calculated since 21 Mar 2022)
Cumulative views and downloads (calculated since 21 Mar 2022)

Viewed (geographical distribution)

Total article views: 532 (including HTML, PDF, and XML) Thereof 532 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Oct 2022
Download
Short summary
This study presents a work to investigate the feasibility of using EC to predict the discharge in a typical karst catchment. We found that the spring discharge can be well predicted by EC in storms using LSTM (Long Short Term Memory) model, while the prediction has relatively large uncertainties in small recharge events. To establish a roust LSTM model for long-term discharge prediction from EC in ungauged catchments, the random or fixed-interval discharge monitoring strategy is recommended.