Spatial interpolation of precipitation in a dense gauge network for monsoon storm eve
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Spatial interpolation of precipitation in a dense gauge network for monsoon storm eve
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Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States

Author: Matthew Garcia,Christa D. Peters-Lidard, and David C. Goodrich | Size: 900 KB | Format: PDF | Quality: Unspecified | Publisher: Wiley | Year: 2008 | pages: 14


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Inaccuracy in spatially distributed precipitation fields can contribute significantly to
the uncertainty of hydrological states and fluxes estimated from land surface models. This
paper examines the results of selected interpolation methods for both convective and
mixed/stratiform events that occurred during the North American monsoon season over a
dense gauge network at the U.S. Department of Agriculture Agricultural Research Service
Walnut Gulch Experimental Watershed in the southwestern United States. The spatial
coefficient of variation for the precipitation field is employed as an indicator of event
morphology, and a gauge clustering factor CF is formulated as a new, scale-independent
measure of network organization. We consider that CF < 0 (a more distributed gauge
network) will produce interpolation errors by reduced resolution of the precipitation field
and that CF > 0 (clustering in the gauge network) will produce errors because of
reduced areal representation of the precipitation field. Spatial interpolation is performed
using both inverse-distance-weighted (IDW) and multiquadric-biharmonic (MQB)
methods. We employ ensembles of randomly selected network subsets for the statistical
evaluation of interpolation errors in comparison with the observed precipitation. The
magnitude of interpolation errors and differences in accuracy between interpolation
methods depend on both the density and the geometrical organization of the gauge
network. Generally, MQB methods outperform IDW methods in terms of interpolation
accuracy under all conditions, but it is found that the order of the IDW method is
important to the results and may, under some conditions, be just as accurate as the
MQB method. In almost all results it is demonstrated that the inverse-distance-squared
method for spatial interpolation, commonly employed in operational analyses and for
engineering assessments, is inferior to the ID-cubed method, which is also more
computationally efficient than the MQB method in studies of large networks.

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