Multi-gauge calibration comparison for simulating streamflow across the Major River Basins in Madagascar: SWAT + Toolbox, R-SWAT, and SWAT + Editor Hard calibration

Author:

Harifidy Rakotoarimanana Zy1ORCID,Hiroshi Ishidaira2,Kazuyoshi Souma2,Jun Magome2,Harivelo Rakotoarimanana Zy Misa3,Fernández-Palomino Carlos Antonio45

Affiliation:

1. a Integrated Graduate School of Medicine, Engineering and Agricultural Sciences, University of Yamanashi, Kofu 400-8511, Japan

2. b Interdisciplinary Centre for River Basin Environment, University of Yamanashi, Kofu 400-8511, Japan

3. c College of Civil Engineering, Tongji University, No. 1239, Si Ping Rd., Shanghai 200092, China

4. d Research Department II – Climate Resilience, Potsdam Institute for Climate Impact Research, Potsdam, Germany

5. e Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany

Abstract

Abstract This paper aims to improve the Soil and Water Assessment Tool (SWAT) model performance across the Major River Basins in Madagascar (MRBM), specifically for SWAT simulation in the Manambolo, Onilahy, Mananara, and Mandrare basins. A multi-gauge calibration was carried out to compare the performance of SWAT+ Toolbox, and R-SWAT, SWAT+ Editor Hard calibration on a monthly time step for the periods 1982–1999. We found that the SWAT+ model generated greater surface runoff, while the SWAT model resulted in higher groundwater flow in both CSFR and CHIRPS datasets. It has been demonstrated that the SWAT+ Toolbox had more potential in calibrating runoff across the MRBM compared to R-SWAT. Calibration in both methods led to a reduction in surface runoff, percolation, water yield, and curve number but increased the lateral flow, evapotranspiration (ET), and groundwater flow. The results showed that the multi-gauge calibrations did not significantly enhance simulation performance in the MRBM compared to single-site calibration. The performance of the SWAT+ model for runoff simulation within the SWAT+ Toolbox and R-SWAT was unsatisfactory for most basins (NSE < 0) except for Betsiboka, Mahavavy, Tsiribihina, Mangoro, and Mangoky basins (NSE = 0.40–0.70; R2 = 0.45–0.80, PBIAS≤ ±25), whether considering the CHIRPS or CSFR datasets. Further study is still required to address this issue.

Funder

Japan Science and Technology Corporation

Publisher

IWA Publishing

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