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<p>Despite being the world’s second largest river basin by size and discharge after the Amazon, the Congo River Basin (CRB) remains comparatively understudied in the scientific literature. This situation is exacerbated by a drastic decline in in-situ gauges, from approximately 400 in the early 1900s to only a few today, severely limiting hydraulic and hydrological research. Consequently, our knowledge of surface water variations, including water level, width, and slope, is limited within the CRB. While the Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, promises comprehensive hydraulic variables, its data utilization for hydrological research is still preliminary. To address these gaps, this study monitors water surface slope variation in the middle CRB using SAR images and altimetry data from 2006 to 2010. Building on multi-temporal two-dimensional water level maps, we constructed and analyzed the river slope in the middle reach of the CRB. Our findings reveal that the slope generally ranged from 2.6 to 5.2&#xa0;cm/km. We also identified a negative correlation between river discharge and water slope, which is anomalous compared to conventional theories, potentially due to a constriction at around chainage 555&#xa0;km upstream from Kinshasa. These hydraulic variables, including water level and slope information, present promising potential for improving river discharge estimation using an ensemble model approach. To address the limitations of single-variable estimation, this study implemented an ensemble learning regression (ELQ) model by integrating water level, river width, and slope. The performance of the ELQ model was evaluated using Leave-One-Out Cross-Validation (LOOCV), which showed improved discharge estimation, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.88. This research is expected to improve our hydraulic knowledge in the middle CRB and will contribute to validating river slopes extracted from SWOT satellite data.</p>

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Water Surface Slope Variation and Its Potential to Estimate River Discharge Using an Ensemble Model: A Case Study of the Middle Congo River

  • 期刊论文

  • 发表日期：

    2026年6月18日

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  • Donghwan Kim

摘要

Despite being the world’s second largest river basin by size and discharge after the Amazon, the Congo River Basin (CRB) remains comparatively understudied in the scientific literature. This situation is exacerbated by a drastic decline in in-situ gauges, from approximately 400 in the early 1900s to only a few today, severely limiting hydraulic and hydrological research. Consequently, our knowledge of surface water variations, including water level, width, and slope, is limited within the CRB. While the Surface Water and Ocean Topography (SWOT) mission, launched in December 2022, promises comprehensive hydraulic variables, its data utilization for hydrological research is still preliminary. To address these gaps, this study monitors water surface slope variation in the middle CRB using SAR images and altimetry data from 2006 to 2010. Building on multi-temporal two-dimensional water level maps, we constructed and analyzed the river slope in the middle reach of the CRB. Our findings reveal that the slope generally ranged from 2.6 to 5.2 cm/km. We also identified a negative correlation between river discharge and water slope, which is anomalous compared to conventional theories, potentially due to a constriction at around chainage 555 km upstream from Kinshasa. These hydraulic variables, including water level and slope information, present promising potential for improving river discharge estimation using an ensemble model approach. To address the limitations of single-variable estimation, this study implemented an ensemble learning regression (ELQ) model by integrating water level, river width, and slope. The performance of the ELQ model was evaluated using Leave-One-Out Cross-Validation (LOOCV), which showed improved discharge estimation, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.88. This research is expected to improve our hydraulic knowledge in the middle CRB and will contribute to validating river slopes extracted from SWOT satellite data.