Comparison of climate datasets for lumped hydrological modeling over the continental United States

JOURNAL OF HYDROLOGY

卷:537

Article

Essou, Gilles R. C.¹  Arsenault, Richard¹  Brissette, Francois P.¹ 

[1] Ecole Technol Super, 1100 Rue Notre Dame Ouest, Montreal, PQ H3C 1K3, Canada

关键词: Interpolated data;    Bias;    Hydrological modeling;    Calibration;    Performance comparison;   

DOI  :  10.1016/j.jhydrol.2016.03.063

来源: Elsevier

【 摘 要 】

Climate data measured by weather stations are crucially important and regularly used in hydrologic modeling. However, they are not always available due to the low spatial density and short record history of many station networks. To overcome these limitations, gridded datasets have become increasingly available. They have excellent continuous spatial coverage and no missing data. However, these datasets are usually interpolated using station data, with little new information besides elevation. Furthermore, minimal validation has been done on most of these datasets. This study compares three such datasets covering the continental United States to evaluate their differences and their impact on lumped hydrological modeling. Three daily time step gridded datasets with resolutions varying between 0.25 degrees and 1 km were used in this study - Santa-Clara, Daymet and CPC. The hydrological modeling evaluation of these datasets was performed over 424 basins from the MOPEX database. Results show that there are significant differences between the datasets, even though they were essentially all interpolated from almost the same climate databases. Despite those differences, the hydrological model used in this study was able to perform equally well after a specific calibration to each dataset. While there were a few exceptions, by and large, Nash-Sutcliffe efficiency metrics obtained in validation were not statistically different from one database to the other for most basins. It appears that there are no reasons to favor one dataset versus another for lumped hydrological modeling, and that these datasets perform just as well as using the original station data. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free