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\begin{document}

\title{A Comparison of Climate-Driven Deep Learning Ensemble and SWAT+ Models
for Daily Streamflow Simulation in the Niger River Basin, West Africa}



\author[1]{Jefia Idolor}%
\author[1]{Aderemi Adediji}%
\author[1]{Peter Adebayo Idowu}%
\affil[1]{Obafemi Awolowo University}%


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  \date{June 13, 2024}


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\begin{abstract}
Streamflow monitoring is very important for planning and management of
water resources in watersheds, and their prediction accuracy is crucial
for decision-making. The Niger River Basin is a transboundary resource,
shared by nine West African Countries and Algeria and, a large portion
of the population rely on the basin for rain-fed agriculture and
hydropower. Over the years, the basin's streamflow regime has been
altered due to climate change, drought, desertification and
establishment of Dams. This research describes a novel Deep Learning
framework comprised of Bidirectional-Long Short-Term Memory (LSTM)
requiring Antecedent Precipitation Index (API) and meteorological
variables, preprocessed using Normal Quantile Transform (NQT) as input
drivers and, compared with the Soil and Water Assessment Tool (SWAT+)
for streamflow prediction. NQT-API-LSTM which considers catchment
wetness and seasonality, was forced with reanalyzed climate (1979--2021)
while, SWAT+ was driven with biophysical data and reanalyzed climate
(2010--2020). The very high performance of both NQT-API-LSTM and SWAT+
models showed the models were reliable and can predict regulated flows
with reasonable certainty. However, NQT-API-LSTM outperformed SWAT+ at
Lokoja watershed and, realistically captured the influence of seasonal
climate and regional groundwater dynamics from upstream catchments
including the Sahara Desert on the Benue, Guinean, Sahelian and Sudan
Flood. Overall, NQT-API-LSTM could be used successfully for
watershed-scale streamflow prediction without the need for continuous
ground support data, a benefit for sparsely gauged West African River
Basins, while SWAT+ could be used as an alternative, particularly, to
evaluate the watershed's response to land use/land cover changes.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Idolor Adediji Idowu Manuscript.docx`  available at \url{https://authorea.com/users/775215/articles/874202-a-comparison-of-climate-driven-deep-learning-ensemble-and-swat-models-for-daily-streamflow-simulation-in-the-niger-river-basin-west-africa}

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