Using LSTM to monitor continuous discharge indirectly with electrical conductivity observations

Abstract

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.

Automated streamflow measurements in high‐elevation Alpine catchments

“…It is noticeable how EC T drops abruptly during larger precipitation events at all three measuring sites (Figure 3a). The natural background EC T in streams as a predictor for discharge was already observed in previous studies (Cano-Paoli et al, 2019;Chang et al, 2022;Weijs et al, 2013), and therefore, the collected continuous EC T values of the AutoSalt system and of the OTT CTD probe could be further used beyond the event-based observations to generate EC T -based gauging station.…”

Transformer Versus LSTM: A Comparison of Deep Learning Models for Karst Spring Discharge Forecasting

“…EC has generally such a strong correlation with discharge, that it can be used as proxy for discharge, as shown for example, by Chang et al. (2022) for a small karst catchment in China (Chang et al., 2022). In our study it improved four of the six models in the noQ scenario when used as input feature, particularly for the Aubach spring which has a short response time.…”

“…The physicochemical variables are well established for karst systems. They were found to correlate moderately with discharge at LKAS2 (correlation coefficients of 0.70 for EC, 0.67 for turbidity, and 0.66 for SAC, p-value < 0.05, Reischer et al, 2008), but also in other karst catchments (e.g., Chang et al, 2022). Meteorological variables were available from two stations in the catchment area at altitudes of 1,350 and 1,520 m a.s.l.…”