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Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed, Canada

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Version 2 2019-10-02, 11:04
Version 1 2019-08-29, 11:08
journal contribution
posted on 2019-10-02, 11:04 authored by C. Dai, X. S. Qin

To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlation matrix. Precipitation series from weather generators are used as input to the hydrological model, the soil and water assessment tool (SWAT), to generate runoff over the Red Deer watershed, Canada for further runoff analysis. The results indicate that the SWAT model using SWG-generated data accurately represents the mean monthly streamflow for most of the months. The multi-site generators were capable of better representing the monthly streamflow variability, which was notably underestimated by the single-site version. In terms of extreme flows, the proposed method reproduced the observed extreme flow with smaller bias than MBFA, while the single-site generator significantly underestimated the annual maximum flows due to its poor capability in addressing partial precipitation correlations.

Funding

This project was supported by Start-Up Grant [M4081327.030] from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore.

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