Votre recherche
Résultats 2 ressources
-
Abstract Precipitation forcing is critical for hydrological modeling as it has a strong impact on the accuracy of simulated river flows. In general, precipitation data used in hydrological modeling are provided by weather stations. However, in regions with sparse weather station coverage, the spatial interpolation of the individual weather stations provides a rough approximation of the real precipitation fields. In such regions, precipitation from interpolated weather stations is generally considered unreliable for hydrological modeling. Precipitation estimates from reanalyses could represent an interesting alternative in regions where the weather station density is low. This article compares the performances of river flows simulated by a watershed model using precipitation and temperature estimates from reanalyses and gridded observations. The comparison was carried out based on the density of surface weather stations for 316 Canadian watersheds located in three climatic regions. Three state-of-the-art atmospheric reanalyses—ERA-Interim, CFSR, and MERRA—and one gridded observations database over Canada—Natural Resources Canada (NRCan)—were used. Results showed that the Nash–Sutcliffe values of simulated river flows using precipitation and temperature data from CFSR and NRCan were generally equivalent regardless of the weather station density. ERA-Interim and MERRA performed significantly better than NRCan for watersheds with weather station densities of less than 1 station per 1000 km2 in the mountainous region. Overall, these results indicate that for hydrological modeling in regions with high spatial variability of precipitation such as mountainous regions, reanalyses perform better than gridded observations when the weather station density is low.
-
Abstract This paper investigates the potential of reanalyses as proxies of observed surface precipitation and temperature to force hydrological models. Three global atmospheric reanalyses (ERA-Interim, CFSR, and MERRA), one regional reanalysis (NARR), and one global meteorological forcing dataset obtained by bias-correcting ERA-Interim [Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] were compared to one gridded observation database over the contiguous United States. Results showed that all temperature datasets were similar to the gridded observation over most of the United States. On the other hand, precipitation from all three global reanalyses was biased, especially in summer and winter in the southeastern United States. The regional reanalysis precipitation was closer to observations since it indirectly assimilates surface precipitation. The WFDEI dataset was generally less biased than the reanalysis datasets. All datasets were then used to force a global conceptual hydrological model on 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database. River flows were computed for each watershed, and results showed that the flows simulated using NARR and gridded observations forcings were very similar to the observed flows. The simulated flows forced by the global reanalysis datasets were also similar to the observations, except in the humid continental and subtropical climatic regions, where precipitation seasonality biases degraded river flow simulations. The WFDEI dataset led to better river flows than reanalysis in the humid continental and subtropical climatic regions but was no better than reanalysis—and sometimes worse—in other climatic zones. Overall, the results indicate that global reanalyses have good potential to be used as proxies to observations to force hydrological models, especially in regions with few weather stations.