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A platform for research: civil engineering, architecture and urbanism
Hydrological Modeling Using a Multisite Stochastic Weather Generator
Weather data are usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multisite stochastic weather generator, which considers the spatial correlation of weather variables. Prior to using a multisite weather generator for hydrological modeling, its ability to adequately represent the proper hydrological response needs to be assessed. This study assesses the effectiveness of a new multisite weather generator (MulGETS) for hydrological modeling over a Canadian watershed in the Province of Québec. Prior to hydrological modeling, MulGETS is first evaluated with respect to reproducing the spatial correlation and statistical characteristics of precipitation and temperature for the studied watershed. Hydrological simulations obtained from MulGETS-generated precipitation and temperature are then compared with those obtained from a single-site weather generator (WeaGETS) and a WeaGETS-based lumped approach (WeaGETS-lumped) that averages the climate series over all stations in a watershed before running the single-site weather generator. The hydrology is simulated using two hydrological models: the conceptually lumped model HSAMI and the physically based distributed model CEQUEAU. When using the conceptually lumped model, the weather time series is first averaged over all stations in the watershed. The results show that the monthly mean discharge is accurately represented by both MulGETS-generated and WeaGETS-lumped-generated precipitation and temperature, whereas it is considerably underestimated by WeaGETS data for the snowmelt period. The MulGETS and WeaGETS-lumped data also show significant advantages in representing the monthly streamflow variability, which is underestimated by the WeaGETS outputs. Additionally, MulGETS and WeaGETS-lumped consistently perform better than WeaGETS for simulating extreme flows (snowmelt high flows and summer-autumn high and low flows). However, no obvious difference in performance was found between MulGETS and WeaGETS-lumped data for hydrological modeling. Moreover, the use of a physically based distributed model with MulGETS did not result in any significant performance gain compared with the much simpler combination of WeaGETS-lumped with a lumped hydrological model for the studied watershed. Overall, this study indicates that a single-site weather generator combined with a lumped hydrological model is sufficient for accurate hydrological simulations, even in the case of a large watershed.
Hydrological Modeling Using a Multisite Stochastic Weather Generator
Weather data are usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multisite stochastic weather generator, which considers the spatial correlation of weather variables. Prior to using a multisite weather generator for hydrological modeling, its ability to adequately represent the proper hydrological response needs to be assessed. This study assesses the effectiveness of a new multisite weather generator (MulGETS) for hydrological modeling over a Canadian watershed in the Province of Québec. Prior to hydrological modeling, MulGETS is first evaluated with respect to reproducing the spatial correlation and statistical characteristics of precipitation and temperature for the studied watershed. Hydrological simulations obtained from MulGETS-generated precipitation and temperature are then compared with those obtained from a single-site weather generator (WeaGETS) and a WeaGETS-based lumped approach (WeaGETS-lumped) that averages the climate series over all stations in a watershed before running the single-site weather generator. The hydrology is simulated using two hydrological models: the conceptually lumped model HSAMI and the physically based distributed model CEQUEAU. When using the conceptually lumped model, the weather time series is first averaged over all stations in the watershed. The results show that the monthly mean discharge is accurately represented by both MulGETS-generated and WeaGETS-lumped-generated precipitation and temperature, whereas it is considerably underestimated by WeaGETS data for the snowmelt period. The MulGETS and WeaGETS-lumped data also show significant advantages in representing the monthly streamflow variability, which is underestimated by the WeaGETS outputs. Additionally, MulGETS and WeaGETS-lumped consistently perform better than WeaGETS for simulating extreme flows (snowmelt high flows and summer-autumn high and low flows). However, no obvious difference in performance was found between MulGETS and WeaGETS-lumped data for hydrological modeling. Moreover, the use of a physically based distributed model with MulGETS did not result in any significant performance gain compared with the much simpler combination of WeaGETS-lumped with a lumped hydrological model for the studied watershed. Overall, this study indicates that a single-site weather generator combined with a lumped hydrological model is sufficient for accurate hydrological simulations, even in the case of a large watershed.
Hydrological Modeling Using a Multisite Stochastic Weather Generator
Chen, Jie (author) / Brissette, François P. (author) / Zhang, Xunchang J. (author)
2015-08-19
Article (Journal)
Electronic Resource
Unknown
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