A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: Combining the Glacio-Hydrological Degree-Day Model and Recurrent Neural Networks

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.

Downloads

Download Preprint

Supplementary Files
Authors

Dinesh Joshi, Rijan Bhakta Kayastha, Kundan Lal Shrestha, Rakesh Kayastha

Abstract

The Glacio-hydrological Degree-day Model (GDM) is a distributed model but prone to uncertainties due to its conceptual nature, parameter estimation, and limited data in the Himalayan basins. To enhance accuracy without sacrificing interpretability, we propose a hybrid model, GDM-RNNs, combining GDM with Recurrent Neural Networks (RNNs). Three RNN types (Simple RNN, GRU, LSTM) are integrated with the GDM. Rather than directly predicting streamflow, RNNs forecast GDM's residual errors. We assessed performance across different data availability scenarios, with promising results. In limited data conditions (one year), GDM-RNNs (GDM-SimpleRNN, GDM-LSTM, GDM-GRU) outperformed standalone GDM and machine learning models. For GDM-SimpleRNN, NSE, R2, and PBIAS were 0.85, 0.82, and -6.21, for GDM-LSTM 0.86, 0.79, and -6.37, and for GDM-GRU 0.85, 0.8, and -5.64, compared to GDM's 0.80, 0.63, and -4.78, respectively. Machine learning models yielded similar results, with SimpleRNN at 0.81, 0.7, and -16.6, LSTM at 0.79, 0.65, and -21.42, and GRU at 0.82, 0.75, and -12.29, respectively. Our study highlights the potential of machine learning in enhancing streamflow predictions in data-scarce Himalayan basins while preserving physical stream flow mechanisms.

DOI

https://doi.org/10.31223/X5GX1F

Subjects

Physical Sciences and Mathematics

Keywords

hydrological modelling, Recurrent neural networks, Residual Error modelling

Dates

Published: 2024-04-12 18:14

Last Updated: 2024-04-13 01:14

License

CC BY Attribution 4.0 International