Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa

• Published in: Journal of hydrology. Regional studies Vol. 27; p. 100655
• Main Authors: Adeyeri, O.E., Laux, P., Arnault, J., Lawin, A.E., Kunstmann, H.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2020; Elsevier
• Subjects: Base flow; GR5J; High flow; Lake Chad; Optimization techniques; Wavelet analysis; Optimization techniques; GR5J; Wavelet analysis; Base flow; Lake Chad; High flow
• ISSN: 2214-5818; 2214-5818
• DOI: 10.1016/j.ejrh.2019.100655

•Calibration methods showed reasonably simulate runoff satisfactorily.•Optimization techniques improve model parameters stability.•Improved accuracy of calibration algorithms towards runoff representativeness.
The discharge of the transboundary Komadugu-Yobe Basin, Lake Chad Area, West Africa is calibrated using multi-objective optimization techniques.
The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multi-objective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a time-frequency wavelet transform.
For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin.