Performance of landscape composition metrics for predicting water quality in headwater catchments
datasetposted on 22.07.2019 by Linda Staponites
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
In this article, we sought to examine how landscape composition metrics influence water quality predictions in headwater catchments. The data shows the percent of each land use category (meadows and forests) within each catchment (identified by sample #) which was gathered via GIS analysis (vectorization and calculation) as well as the land use percentages when each weighting scheme was applied which was gathered via application of the python script found within the supplementary materials (Note: a complete list of landscape composition metric descriptions can be found within the manuscript), as well as the chemical concentrations for each water chemistry parameter at each sampling site (including outliers). In this data, you can see that the application of weighting schemes slightly modifies the percentage of each land use category. Each landscape composition metric and water parameter value can be applied to linear regression models and outcomes can be compared to determine how weighting the landscape influences water quality predictions.