Temporal trend evaluation in monitoring programs with high spatial resolution and low temporal resolution using geographically weighted regression models.

ABSTRACT: Data from monitoring programs with high spatial resolution but low temporal resolution are often overlooked when assessing temporal trends, as the data structure does not permit the use of established trend analysis methods. However, the data include uniquely detailed information about geographically differentiated temporal trends driven by large-scale influences, such as climate or airborne deposition. In this study, we used geographically weighted regression models, extended with a temporal component, to evaluate linear and nonlinear trends in environmental monitoring data. To improve the results, we tested approaches for station-wise pre-processing of data and for validation of the resulting models. To illustrate the method, we used data on changes in total organic carbon (TOC) obtained

SUBMITTER: von Bromssen C 

PROVIDER: S-EPMC10083161 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature