dnk_lake_catchments

# Data and analysis products for manuscript: DOI: https://doi.org/10.1016/j.scitotenv.2022.158090 Journal: Science of the Total Environment Title: Predicting water quality from geospatial lake, catchment, and buffer zone characteristics in temperate lowland lakes Authors: Kenneth Thorø Martinsen* and Kaj Sand-Jensen Affiliations: Freshwater Biological Laboratory, Biological Institute, University of Copenhagen, Universitetsparken 4, 3rd floor, 2100 Copenhagen, Denmark *Corresponding author: Kenneth Thorø Martinsen, [e-mail] ## Contents: This repository contains files (GIS files available in both '.shp' and '.sqlite' formats in sub-folders) associated with the above manuscript: *catchments_simple* Catchment boundaries of 180.377 lakes identified by the "gml_id" feature. Catchments are derived from a high-resolution digital elevation model (1.6 m resolution) followed by simplification, retaining approx. 10% of the original points. *lakes* 180.378 lake polygons identified by the "gml_id" feature. *predictions.csv* CSV text file containing predictions of water quality (alk/alkalinity [meq L^-1], chl_a/chlorophyll a [ug L^-1], color [mg Pt L^-1], ph/pH [pH], tn/total nitrogen [mg L^-1], tp/total phosphorus [mg L^-1], secchi/Secchi depth [m], pco2/CO2 partial pressure [uatm]) for 180.378 lakes identified by the "gml_id" feature. Estimates represents annual averages of surface water concentrations. *models.rds* R-object file which can be read in R using the readRDS() function. The contains a nested R list object: The first level keys are "raw" or "mlr", and second level keys "alk", "chl_a", "color", "ph", "tn", "tp", "secchi", "pco2"). The list contains trained predictive models for each of the eight water quality variables, either as implemented in the MLR R-package ("mlr") or in the underlying R-packages ("raw"). ## Raw data Raw data sources are publicly available and cited in the main text. The lake polygons in this repository are included in the "INSPIRE - HYDROGRAFI" dataset from SDFE, Agency for Datasupply and Efficiency ("Styrelsen for Dataforsyning og Effektivisering" in Danish), downloaded October 2021. This dataset is derived from "GeoDanmark-data" ("Styrelsen for Dataforsyning og Effektivisering og Danske kommuner" in Danish): General terms: https://sdfe.dk/Media/637703280490685559/Vilkaar_for_brug_af_frie_geografiske_data_2021.pdf GeoDanmark-data terms: https://www.geodanmark.dk/wp-content/uploads/2020/03/Vilk%C3%A5r-for-brug-af-frie-geografiske-data.pdf