Comparing the hybrid-lumped-LSTM model with a semi-distributed model for improved hydrological modeling

Abstract

Accurate hydrological modeling is essential for understanding and managing water resources. This study conducts a comparative analysis of hydrological modeling strategies in a date-scarce region. This study examines lumped (IHACRES), semi-distributed (HEC-HMS), and hybrid-lumped/long short-term memory (LSTM) models, aiming to assess their performance and accuracy in a data-scarce region. It investigates whether lump models can accurately simulate flow and evaluates the impact of combining lump models with machine learning to enhance accuracy, compared to semi-distributed models. The IHACRES model underestimates discharge, but its commendable NSE during calibration (0.628) and validation (0.681) signifies reliable simulation. The HEC-HMS model accurately depicts daily streamflow but struggles with extreme events, showcasing limitations in predicting maximum flows. The hybrid-lumped/LSTM model exhibits improved accuracy over IHACRES. Despite some underestimation, it mitigates IHACRES limitations during extreme events. However, challenges persist in simulating high flows, emphasizing the necessity for further refinement. The findings contribute to the discourse on merging machine learning with traditional hydrological models in data-scarce regions. The hybrid model offers promise but underscores the need for ongoing research to optimize performance, especially during extreme events. This study provides valuable insights for advancing hydrological modeling capabilities in complex watersheds.