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
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 km<sup>2</sup>) that spans a very wide range of hydroclimatic conditions. Area-averaged forcing data for the p...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-01-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/19/209/2015/hess-19-209-2015.pdf |
| Summary: | 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 km<sup>2</sup>) 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. |
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| ISSN: | 1027-5606 1607-7938 |