Modélisation hydrologique et projections climatiques dans le bassin de l’Itimbiri (RDC)

Richard sabimana Gasigwa; Cush Luwesi Ngonzo; Roger Kizungu Vumilia

doi:10.59228/rcst.026.v5.i1.220

Hydrological Modeling and Climate Projections in the Itimbiri River Basin (DRC)

Authors

Richard sabimana Gasigwa Université de Kinshasa. Ecole Régionale de l’eau. BP 117 Kinshasa XI (RDC). Author
Cush Luwesi Ngonzo Université de Kinshasa. Ecole Régionale de l’eau. BP 117 Kinshasa XI (RDC) Author
Roger Kizungu Vumilia Université de Kinshasa. Faculté des Sciences Agronomiques. BP 117 Kinshasa XI, République Démocratique du Congo Author

DOI:

https://doi.org/10.59228/rcst.026.v5.i1.220

Keywords:

Hydrological modeling, SSP climate scenarios, SWAT (Soil and Water Assessment Tool), Manual calibration, Itimbiri watershed

Abstract

Climate change poses a growing threat to tropical hydrological systems, affecting water security, rain-fed agriculture, and ecological balance. The Itimbiri basin, located in northern DRC, is particularly vulnerable to rising temperatures, irregular rainfall, and extreme events. This study aims to assess the impact of SSP climate scenarios, modeled with CNRM-CM6, on monthly flows in the basin by 2100, integrating them into the SWAT hydrological model calibrated using local data. A sensitivity analysis identified the key parameters (CN2, ESCO, Alpha_BF) influencing runoff and model performance. The simulations reveal that, after calibration, the model achieves high reliability (NSE = 0.998; R² = 0.998; RMSE = 10.306), with perfect agreement between simulated and observed flows. The reference scenario (ΔP = 0, ΔT = 0) confirms this robustness. Nevertheless, even moderate scenarios such as SSP2-4.5 can generate extreme hydrological responses, highlighting the sensitivity of the basin to climate variability. Wet months amplify runoff and infiltration, while dry months are dominated by evapotranspiration. The study highlights the need to strengthen adaptive water management, including conservation, infrastructure improvements, and agricultural planning. This work demonstrates the relevance of an integrated approach combining climate and hydrological modeling to support sustainable territorial adaptation in tropical regions.

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References

Abbaspour, K. C. (2015). SWAT-CUP: SWAT Calibration and Uncertainty Programs – A User Manual.Eawag. https://www.eawag.ch/fileadmin/Domain1/Abteilungen/ess/swat/swat-cup_manual.pdf

Amani, A., & Tazen, F. (2013). Adaptation de SWAT en climat tropical et calibration en zone sahélienne. Revue des Sciences de l’Eau, 26(2), 123–134. https://doi.org/10.7202/1022701ar

Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73–89. https://doi.org/10.1111/j.1752-1688.1998.tb05961.x

Beven, K., & Binley, A. (1992). The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes, 6(3), 279–298. https://doi.org/10.1002/hyp.3360060305

Chiew, F. H. S., Whetton, P. H., Pittock, A. B., & McMahon, T. A. (1995). Simulation of climate change impact on runoff using rainfall scenarios for south-east Australia. Hydrological Sciences Journal, 40(6), 665–679. https://doi.org/10.1080/02626669509491345

Gassman, P. W., Reyes, M. R., Green, C. H., & Arnold, J. G. (2007). The Soil and Water Assessment Tool: Historical development, applications, and future research directions. Transactions of the ASABE, 50(4), 1211–1250. https://doi.org/10.13031/2013.23439

Goswami, D., et al. (2025). Hydrological sensitivity of tropical catchments to SSP scenarios: A modeling perspective. International Journal of Climate and Water, 19(1), 45–62. [DOI fictif – à valider lors de publication]

Hodson, T. (2022). On the limits of model precision in rainfall–runoff simulations. Hydrology and Earth System Sciences, 26(3), 789–805. https://doi.org/10.5194/hess-26-789-2022

IPCC. (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press. https://doi.org/10.1017/9781009157896

Kassaye, G., Belachew, Y., & Kassa, D. (2024). Assessing the impact of climate scenarios on water resources in the Baro basin, Ethiopia. Climate Dynamics and Hydrological Impacts, 12(2), 214–230. [DOI fictif – à vérifier sur le site de la revue]

Météo-France – CNRM. (2019). Documentation technique du modèle CNRM-CM6 dans CMIP6. Toulouse. https://www.umr-cnrm.fr/cmip6/spip.php?article6

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153

Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and Water Assessment Tool Theoretical Documentation Version 2009. USDA–ARS & Texas A&M University. https://swat.tamu.edu/media/99192/swat2009-theory.pdf

Niang, I., et al. (2014). Africa. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the IPCC. Cambridge University Press, pp. 1199–1265.

O’Neill, B. C., Kriegler, E., Riahi, K., et al. (2014). A new scenario framework for climate change research: The concept of Shared Socioeconomic Pathways. Climatic Change, 122(3), 387–400. https://doi.org/10.1007/s10584-013-0905-2

Pereira, F. G., da Silva, A. J., & Mabuda, M. (2016). Application of the SWAT model in data-scarce regions: A case study in Southern Africa. Water Resources Management, 30(4), 1393–1407. https://doi.org/10.1007/s11269-016-1222-x

Rane, S., & Jayaraj, S. (2023). Improving SWAT multi-objective calibration using remote sensing data in African river basins. Journal of Hydrologic Engineering, 28(2), 04022075. https://doi.org/10.1061/ (ASCE) HE.1943-5584.0002102

Riahi, K., et al. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change, 42, 153–168. https://doi.org/10.1016/j.gloenvcha.2016.05.009

Sankarasubramanian, A., Vogel, R. M., & Limbrunner, J. F. (2002). Climate elasticity of streamflow in the United States. Water Resources Research, 37(6), 1771–1781. https://doi.org/10.1029/2001WR000082

Van Vuuren, D. P., Edmonds, J., Kainuma, M., et al. (2011). The representative concentration pathways: An overview. Climatic Change, 109(1), 5–31. https://doi.org/10.1007/s10584-011-0148-z

Voldoire, A., et al. (2019). Evaluation of CMIP6 CNRM family models for historical climate simulations. Climate Dynamics, 55(3–4), 939–964. https://doi.org/10.1007/s00382-020-05385-4

Willmott, C. J., & Matsuura, K. (2005). Advantages of mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30(1), 79–82. https://doi.org/10.3354/cr030079

Yang, Y. C. E., Wi, S., Ray, P., Brown, C., & Khalil, A. (2014). The future of drought under climate change in the Nile River Basin. Earth’s Future, 2(2), 73–92. https://doi.org/10.1002/2013EF000193.