Towards Operational Streamflow Forecasting in the Limpopo River Basin using Long Short-Term Memory Networks

TL;DR

This paper explores the use of LSTM deep learning models for streamflow forecasting in the data-scarce Limpopo River Basin, highlighting challenges, potential solutions, and future directions for hydrological prediction in African regions.

Contribution

It demonstrates the application of LSTM models to African catchments with limited data and discusses strategies for model adaptation and improvement in data-scarce environments.

Findings

Data scarcity is the main obstacle for deep learning in African hydrology.

Human influence significantly affects data-driven hydrological models.

Recommendations for seasonal prediction and model architecture improvements are provided.

Abstract

Robust hydrological simulation is key for sustainable development, water management strategies, and climate change adaptation. In recent years, deep learning methods have been demonstrated to outperform mechanistic models at the task of hydrological discharge simulation. Adoption of these methods has been catalysed by the proliferation of large sample hydrology datasets, consisting of the observed discharge and meteorological drivers, along with geological and topographical catchment descriptors. Deep learning methods infer rainfall-runoff characteristics that have been shown to generalise across catchments, benefitting from the data diversity in large datasets. Despite this, application to catchments in Africa has been limited. The lack of adoption of deep learning methodologies is primarily due to sparsity or lack of the spatiotemporal observational data required to enable downstream model training. We therefore investigate the application of deep learning models, including LSTMs, for hydrological discharge simulation in the transboundary Limpopo River basin, emphasising application to data scarce regions. We conduct a number of computational experiments primarily focused on assessing the impact of varying the LSTM model input data on performance. Results confirm that data constraints remain the largest obstacle to deep learning applications across African river basins. We further outline the impact of human influence on data-driven modelling which is a commonly overlooked aspect of data-driven large-sample hydrology approaches and investigate solutions for model adaptation under smaller datasets. Additionally, we include recommendations for future efforts towards seasonal hydrological discharge prediction and direct comparison or inclusion of SWAT model outputs, as well as architectural improvements.