globalchange  > 气候变化事实与影响
DOI: 10.5194/hess-19-209-2015
Scopus记录号: 2-s2.0-84920972955
论文题名:
Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance
作者: Newman A; J; , Clark M; P; , Sampson K; , Wood A; , Hay L; E; , Bock A; , Viger R; J; , Blodgett D; , Brekke L; , Arnold J; R; , Hopson T; , Duan Q
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2015
卷: 19, 期:1
起始页码: 209
结束页码: 223
语种: 英语
Scopus关键词: Calibration ; Global optimization ; Mean square error ; Meteorology ; Snow ; Soil moisture ; Hydroclimatic conditions ; Hydrologic response units ; Hydrometeorological data ; Root mean squared errors ; Shuffled Complex Evolution ; Spatial configuration ; United states geological surveys ; Water information system ; Benchmarking ; data set ; geological survey ; hydrological modeling ; hydrometeorology ; performance assessment ; sampling ; streamflow ; temporal period ; watershed ; United States
英文摘要: We present a community data set of daily forcing and hydrologic response data for 671 small-to medium-sized basins across the contiguous United States (median basin size of 336 km2) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the period 1980-2010 was generated for three basin spatial configurations-basin mean, hydrologic response units (HRUs) and elevation bands-by mapping daily, gridded meteorological data sets to the subbasin (Daymet) and basin polygons (Daymet, Maurer and NLDAS). Daily streamflow data was compiled from the United States Geological Survey National Water Information System. The focus of this paper is to (1) present the data set for community use and (2) provide a model performance benchmark using the coupled Snow-17 snow model and the Sacramento Soil Moisture Accounting Model, calibrated using the shuffled complex evolution global optimization routine. After optimization minimizing daily root mean squared error, 90% of the basins have Nash-Sutcliffe efficiency scores ≥ 0.55 for the calibration period and 34% ≥ 0.8. This benchmark provides a reference level of hydrologic model performance for a commonly used model and calibration system, and highlights some regional variations in model performance. For example, basins with a more pronounced seasonal cycle generally have a negative low flow bias, while basins with a smaller seasonal cycle have a positive low flow bias. Finally, we find that data points with extreme error (defined as individual days with a high fraction of total error) are more common in arid basins with limited snow and, for a given aridity, fewer extreme error days are present as the basin snow water equivalent increases. © 2015 Author(s).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78641
Appears in Collections:气候变化事实与影响

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作者单位: National Center for Atmospheric Research, Boulder, CO, United States; United States Geological Survey, Modeling of Watershed Systems, Lakewood, CO, United States; United States Geological Survey, Center for Integrated Data Analytics, Middleton, WI, United States; US Department of Interior, Bureau of Reclamation, Denver, CO, United States; US Army Corps of Engineers, Institute for Water Resources, Seattle, WA, United States; Beijing Normal University, Beijing, China

Recommended Citation:
Newman A,J,, Clark M,et al. Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: Data set characteristics and assessment of regional variability in hydrologic model performance[J]. Hydrology and Earth System Sciences,2015-01-01,19(1)
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