Preprints
https://doi.org/10.5194/hess-2024-328
https://doi.org/10.5194/hess-2024-328
18 Nov 2024
 | 18 Nov 2024
Status: this preprint is currently under review for the journal HESS.

Improving large-scale river routing models for climate studies: the impact of ESA long-term CCI discharge products on correcting multi-model hydrological simulations

Malak Sadki, Gaëtan Noual, Simon Munier, Vanessa Pedinotti, Kaushlendra Verma, Clément Albergel, Sylvain Biancamaria, and Alice Andral

Abstract. Large scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages 20 years of high-resolution discharge data (2000–2020) from the ESA Climate Change Initiative (CCI) to enhance CTRIP and MGB models via ensemble Kalman Filter frameworks (HyDAS and HYFAA). Applied to the Niger and Congo basins, the models assimilate discharge data derived from altimetry and multispectral imagery, alongside water surface elevation (WSE) anomaly data, to evaluate their impact on model performance.

Discharge assimilation was more effective than WSE anomaly assimilation, as it provided a more direct input for improving model accuracy. Temporal data density was the key factor in reducing bias and enhancing the simulation of seasonal flow patterns, with spatial coverage and data quality also playing important roles. In the Niger Basin, the assimilation of denser discharge data resulted in a significant bias reduction, which should improve the representation of long-term climate trends. Furthermore, the higher temporal resolution allowed for better capture of flow variability, which is crucial for both seasonal climate studies and short-term predictions, such as extreme hydrological events.

The study also emphasizes the trade-offs between data resolution and quality, particularly in the Congo Basin. Future advancements include merging altimetry and multispectral discharge data, improving the discharge retrieval algorithms using SWOT data, and refining data assimilation techniques to improve climate studies and river system modeling in complex, climate-impacted basins.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Malak Sadki, Gaëtan Noual, Simon Munier, Vanessa Pedinotti, Kaushlendra Verma, Clément Albergel, Sylvain Biancamaria, and Alice Andral

Status: open (until 30 Dec 2024)

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Malak Sadki, Gaëtan Noual, Simon Munier, Vanessa Pedinotti, Kaushlendra Verma, Clément Albergel, Sylvain Biancamaria, and Alice Andral
Malak Sadki, Gaëtan Noual, Simon Munier, Vanessa Pedinotti, Kaushlendra Verma, Clément Albergel, Sylvain Biancamaria, and Alice Andral

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Short summary
This study explores how 20 years of remote-sensed discharge data from the ESA CCI improve large-scale hydrological models, CTRIP and MGB, through data assimilation. Using an EnKF framework across the Niger and Congo basins, it shows how assimilating denser temporal discharge data reduces biases and improves flow variability, enhancing accuracy. These findings underscore the role of long-term discharge data in refining models for climate assessments, water management, and forecasting.