  One of the primary goals of coastal water quality monitoring is to
characterize spatial variation. Generally, this monitoring takes place at a
limited number of fixed sampling points. The alternative sampling methodology
explored in this paper involves high density sampling from an onboard
flow-through water analysis system (Dataflow). Dataflow has the potential to
provide better spatial resolution of water quality features because it
generates many closely spaced (< 10 m) measurements. Regardless of the
measurement technique, parameter values at unsampled locations must be
interpolated from nearby measurement points in order to generate a
comprehensive picture of spatial variations. Standard Euclidean interpolations
in coastal settings tend to yield inaccurate results because they extend
through barriers in the landscape such as peninsulas, islands, and submerged
banks. We recently developed a method for non-Euclidean interpolation by
inverse path distance weighting (IPDW) in order to account for these barriers.
The algorithms were implemented as part of an R package and made available from
R repositories. The combination of IPDW with Dataflow provided more accurate
estimates of salinity patterning relative to Euclidean inverse distance
weighting (IDW). IPDW was notably more accurate than IDW in the presence of
intense spatial gradients.
