@article {Kurtzman2009,
	title = {Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators},
	journal = {Hydrol. Process},
	volume = {23},
	year = {2009},
	pages = {3281{\textendash}3291},
	abstract = {Detailed hydrologic models require high-resolution spatial and temporal data. This study aims at improving the spatial interpolation of daily precipitation for hydrologic models. Different parameterizations of (1) inverse distance weighted (IDW) interpolation and (2) A local weighted regression (LWR) method in which elevation is the explanatory variable and distance, elevation difference and aspect difference are weighting factors, were tested at a hilly setting in the eastern Mediterranean, using 16 years of daily data. The preferred IDW interpolation was better than the preferred LWR scheme in 27 out of 31 validation gauges (VGs) according to a criteria aimed at minimizing the absolute bias and the mean absolute error (MAE) of estimations. The choice of the IDW exponent was found to be more important than the choice of whether or not to use elevation as explanatory data in most cases. The rank of preferred interpolators in a specific VG was found to be a stable local characteristic if a sufficient number of rainy days are averaged. A spatial pattern of the preferred IDW exponents was revealed. Large exponents (3) were more effective closer to the coast line whereas small exponents (1) were more effective closer to the mountain crest. This spatial variability is consistent with previous studies that showed smaller correlation distances of daily precipitation closer to the Mediterranean coast than at the hills, attributed mainly to relatively warm sea-surface temperature resulting in more cellular convection coastward. These results suggest that spatially variable, physically based parameterization of the distance weighting function can improve the spatial interpolation of daily precipitation},
	keywords = {daily precipitation, interpolation methods, inverse distance weighted (IDW), local weighted regression (LWR), spatial variability},
	isbn = {1066800790},
	issn = {08856087},
	doi = {10.1002/hyp.7442},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/hyp.7442/abstract},
	author = {Kurtzman, Daniel and Navon, Shilo and Morin, Efrat}
}
