Articles | Volume 26, issue 7
https://doi.org/10.5194/hess-26-1727-2022
https://doi.org/10.5194/hess-26-1727-2022
Technical note
 | 
05 Apr 2022
Technical note |  | 05 Apr 2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen

Related authors

Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022,https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary

Related subject area

Subject: Groundwater hydrology | Techniques and Approaches: Stochastic approaches
An ensemble-based approach for pumping optimization in an island aquifer considering parameter, observation and climate uncertainty
Cécile Coulon, Jeremy T. White, Alexandre Pryet, Laura Gatel, and Jean-Michel Lemieux
Hydrol. Earth Syst. Sci., 28, 303–319, https://doi.org/10.5194/hess-28-303-2024,https://doi.org/10.5194/hess-28-303-2024, 2024
Short summary
Improving understanding of groundwater flow in an alpine karst system by reconstructing its geologic history using conduit network model ensembles
Chloé Fandel, Ty Ferré, François Miville, Philippe Renard, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 4205–4215, https://doi.org/10.5194/hess-27-4205-2023,https://doi.org/10.5194/hess-27-4205-2023, 2023
Short summary
The effects of rain and evapotranspiration statistics on groundwater recharge estimations for semi-arid environments
Tuvia Turkeltaub and Golan Bel
Hydrol. Earth Syst. Sci., 27, 289–302, https://doi.org/10.5194/hess-27-289-2023,https://doi.org/10.5194/hess-27-289-2023, 2023
Short summary
Characterization of the highly fractured zone at the Grimsel Test Site based on hydraulic tomography
Lisa Maria Ringel, Mohammadreza Jalali, and Peter Bayer
Hydrol. Earth Syst. Sci., 26, 6443–6455, https://doi.org/10.5194/hess-26-6443-2022,https://doi.org/10.5194/hess-26-6443-2022, 2022
Short summary
Influence of low-frequency variability on high and low groundwater levels: example of aquifers in the Paris Basin
Lisa Baulon, Nicolas Massei, Delphine Allier, Matthieu Fournier, and Hélène Bessiere
Hydrol. Earth Syst. Sci., 26, 2829–2854, https://doi.org/10.5194/hess-26-2829-2022,https://doi.org/10.5194/hess-26-2829-2022, 2022
Short summary

Cited articles

Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens Environ., 180, 137–145, 2016. a
Amaranto, A., Munoz-Arriola, F., Corzo, G., Solomatine, D. P., and Meyer, G.: Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland, J. Hydroinform., 20, 1227–1246, 2018. a
Amaranto, A., Munoz-Arriola, F., Solomatine, D., and Corzo, G.: A spatially enhanced data-driven multimodel to improve semiseasonal groundwater forecasts in the High Plains aquifer, USA, Water Resour. Res., 55, 5941–5961, 2019. a
Banerjee, S., Carlin, B. P., and Gelfand, A. E.: Hierarchical modeling and analysis for spatial data, CRC Press, https://doi.org/10.1201/9780203487808, 2014. a
Beckers, J.-M. and Rixen, M.: EOF calculations and data filling from incomplete oceanographic datasets, J. Atmos. Ocean. Tech., 20, 1839–1856, 2003. a
Download
Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.