# Thematically weighted regression models for identification of important drivers of environmental trends in lake survey data

## TL;DR

This paper introduces a new method to identify environmental change drivers in lake data by using thematically weighted regression models instead of traditional geographic approaches.

## Contribution

The novel approach uses thematic weighting based on PCA or PLS components to evaluate environmental trends in low-resolution monitoring data.

## Key findings

- The method successfully identified regions in Sweden where lake pH is decreasing.
- Changes in calcium levels are linked to the observed pH trends in these regions.

## Abstract

The processes that drive environmental change are complex and often interwoven. Monitoring such processes and their effects on environmental variables is, at best, spatially and temporally incomplete. The Swedish Lake survey consists of several thousands of randomly selected lakes stretching over a wide range of potential influences, including regional and local scale pressures, such as recovery from acidification, changes in climate, and effects from various catchment characteristics. The main drawback for this monitoring data is that the temporal resolution is low and does not allow for the use of traditional trend evaluation methods at individual stations, much less the attribution of important drivers. In this study, we present a method that enables an evaluation of important drivers of change by defining trend coefficients as smoothed estimates over lakes that exhibit similar attributes, e.g., comparable levels or similar temporal changes in selected explanatory variables. The principles are the same as geographically weighted regression but replace the geographic coordinate system with a thematic one, based on principal (PCA) or partial least squares (PLS) components. We illustrate this method by evaluating trends in pH in Sweden from 2012 to 2023 and detected several regions where pH is decreasing, mainly in relation to changes in calcium.

The online version contains supplementary material available at 10.1007/s10661-025-14611-4.