Effectiveness of Multi-Site Weather Generator for Hydrological Modeling

Authors

  • Malika Khalili,

    1. Respectively, Postdoctoral Fellow (Khalili), Department of Civil Engineering and Applied Mechanics, Macdonald Engineering Building, McGill University, 817 Sherbrooke Street West, Montreal, Quebec, Canada H3A 2K6 [Ph.D. Student at École de Technologie Supérieure at the time this paper was prepared]
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  • François Brissette,

    1. Professor (Brissette), Department of Construction Engineering, École de Technologie Supérieure, Montreal, Quebec, Canada
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  • Robert Leconte

    1. Professor (Leconte), Department of Civil Engineering, University of Sherbrooke, Sherbrooke, Quebec, Canada [Professor at École de Technologie Supérieure at the time this paper was prepared].
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  • Paper No. JAWRA-10-0040-P of the Journal of the American Water Resources Association (JAWRA). Discussions are open until six months from print publication.

(E-Mail/Khalili: malika.khalili@mail.mcgill.ca)

Abstract

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi-site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00514.x

Abstract:  A multi-site weather generator has been developed using the concept of spatial autocorrelation. The multi-site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi-site weather generator compared to a uni-site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in-depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi-site and uni-site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi-site weather generator significantly outperformed that using the uni-site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.

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