Application of Inverse Path Distance Weighting for high-density spatial mapping of coastal water quality patterns

TL;DR

This paper introduces a non-Euclidean interpolation method called inverse path distance weighting (IPDW) for high-density coastal water quality mapping, improving accuracy over traditional Euclidean methods by accounting for landscape barriers.

Contribution

The paper develops and implements IPDW as an R package, demonstrating its effectiveness in accurately mapping water quality patterns in coastal environments with barriers.

Findings

IPDW outperforms Euclidean IDW in accuracy.

IPDW provides better estimates in areas with intense gradients.

High-density sampling combined with IPDW enhances spatial resolution.

Abstract

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.