EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Long Short-Term Memory Networks for Real-Time Runoff Forecasting using Remotely Sensed Data

Paul Muñoz1,2, David F. Muñoz3, Johanna Orellana-Alvear1, Hamed Moftakhari3, Hamid Moradkhani3, and Rolando Célleri1,2
Paul Muñoz et al.
  • 1Department of Water Resources and Environmental Sciences, Universidad de Cuenca, Cuenca, Ecuador.
  • 2Faculty of Engineering, Universidad de Cuenca, Cuenca, Ecuador.
  • 3Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, USA.

Current efforts on Deep Learning-based modeling are being put for solving real world problems with complex or even not-fully understood interactions between predictors and target variables. A special artificial neural network, the Long Short-Term Memory (LSTM) is a promising data-driven modeling approach for dynamic systems yet little has been explored in hydrological applications such as runoff forecasting. An aditional challenge to the forecasting task arises from the uncertainties generated when using readily-available Remote Sensing (RS) imagery aimed to overcome lack of in-situ data describing the runoff governing processes. Here, we proposed a runoff forecasting framework for a 300-kmmountain catchment located in the tropical Andes of Ecuador. The framework consists on real-time data acquisition, preprocessing and runoff forecasting for lead times between 1 and 12 hours. LSTM models were fed with 18 years of hourly runoff, and precipitation data from the novel PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now) product. Model efficiencies according to the NSE metric ranged from 0.959 to 0.554, for the 1- to 12-hour models, respectively. Considering that the concentration time of the catchment is approximately 4 hours, the proposed framework becomes a useful tool for delivering runoff forecasts to decision makers, stakeholders and the public. This study has shown the suitability of using the PDIR-Now product in a LSTM-modeling framework for real-time hydrological applications. Future endeavors must focus on improving data representation and data assimilation through feature engineering strategies.

Keywords: Long Short-Term Memory; PDIR-Now; Hydroinformatics; Runoff forecasting; Tropical Andes

How to cite: Muñoz, P., Muñoz, D. F., Orellana-Alvear, J., Moftakhari, H., Moradkhani, H., and Célleri, R.: Long Short-Term Memory Networks for Real-Time Runoff Forecasting using Remotely Sensed Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13900,, 2021.


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