DOI: 10.5281/zenodo.17953164
title: Human pressures intensify stochiometric nitrogen excess in flowing waters (water quality variables and catchment attributes)

description: Human pressures intensify stochiometric nitrogen excess in flowing waters (water quality variables and catchment attributes)

 Alexander Bartusch1, Felipe Saavedra2, Anika Große1,3, Carolin Winter4, Pia Ebeling5, Julia Pasqualini6, Michele Meyer6, Lindsey N. Aman Cromwell8, Linus Silvester Schauer5, Alexander Hubig5, Yao Li1, Andreas Musolff5, Rohini Kumar7, and Daniel Graeber1,✉

 

1 Dep. Aquatic Ecosystem Analysis, Helmholtz-Centre for Environmental Research – UFZ2 Dep. Catchment Hydrology, Helmholtz-Centre for Environmental Research – UFZ3 Department of Ecoscience, Aarhus University4 Chair of Environmental Hydrological Systems, University Freiburg5 Dep. Hydrogeology, Helmholtz-Centre for Environmental Research – UFZ6 Dep. River Ecology, Helmholtz-Centre for Environmental Research – UFZ7 Dep. Computational Hydrosystems, Helmholtz-Centre for Environmental Research – UFZ8 School of Forest, Fisheries, and Geomatics Sciences, University of Florida

✉ Correspondence: Daniel Graeber <daniel.graeber@ufz.de>

Description

This repository includes the following datasets:

Seven water quality datasets with observational data of C, N and P concentrations in streams were collected and downloaded from the original databases. Details regarding the corresponding dataset and original data source can be found below.

Additionally, the repository contains the HydroATLAS and the HydroRIVERS databases, both of which were downloaded from the HYDROSHEDS project website (www.hydrosheds.org) in May 2024. 

The global map of the topographic wetness index, was caclulated using a void-filled digital elevation model DEM (resolution 15 arc-seconds) and a flow accumulation map (ACA upstream area in hectares; 15 arc-seconds).  Both, on a global scale, were obtained from the core data products of the HydroSHEDS project1.

Finally, the repository provides the processed dataset of exported rC:rN:rP ratios from 3,496 hydrological catchments and their corresponding hydro-environmental attributes.  

Overview

Raw data:

1.              7 water quality datasets

2.              7 files containing the corresponding station IDs with coordinates

3.              HYDROATLAS: BasinATLAS_v10_lev12

4.              HYDRORIVERS: HydroRIVERS_v10

5.              Topographic wetness index (TWI): TWI_global.tif (calculated using QGIS)

Processed datasets and model parameters

1.              Stoichiometric rOC:rN:rP export from the hydrological catchments with the corresponding catchment attributes:

–       median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv

–       A table with full variable names and units of the processed data is contained within Bartusch_etal_Zenodo.pdf.

2.              The optimized model parameter set for the Gradient Boosting Regression Tree (GBRT) model:

–       optimized_model_parms_GBRT_models.csv

 

Detailed description of the 7 water quality datasets

Denmark

The dataset of Danish river water quality data was downloaded from the Overfladevandsdatabasen webpage (“Overfladevandsdatabasen”), and includes dissolved inorganic nitrogen (nitrate, nitrite and ammonium), organic carbon (total organic carbon TOC, dissolved organic carbon DOC) and phosphorus (total phosphorus TP, dissolved inorganic phosphorus DIP). Observations cover the period from 1970 until 2022 with different timespans and temporal resolution, depending on the parameter. The data contains NO3–N and NO2--N (combined).

Germany

The water quality database Germany 2.0 was collected and put together at the UFZ and constitutes an update of the QUADICA data set version 12. The dataset is compiled from water quality data provided by the German federal state authorities. The dataset provides DOC, NO3--N, and TP concentrations at 3,965 sites across Germany. All stations have at least these three fractions, but if available, NO2--N, NH4+-N, dissolved inorganic P (DIP) and TOC are additionally included. Samples cover the period from 1982 to 2020, but with varying temporal coverage between sites, ranging from 1 to 37 years.

Arctic deltas

The ArcticGRO Water Quality Dataset3 (version 2024) is a subset of the Arctic Great Rivers Observatory database and consists of water quality measurements from six Arctic river deltas. The data are freely available at the ArcticGRO webpage (ArcticGRO Water Quality Dataset, 2024) and frequently updated. The dataset provides NO3--N, NH4+-N, DOC, and PO43–-P concentrations for 6 stations along each of the Arctic great rivers from 2003-2021. The temporal resolution is 5–7 observations per year. The average time series length per site is 17 years.

France

The French dataset provides NO3--N, DOC and PO43--P observations for 486 French stations of the French water quality database. The stations were preselected for long-term water quality analysis, as for the work published in4 and5. Therefore, only stations with available long-term water quality data are included here. Samples cover the period from 1969 to 2016, but temporal coverage ranges between stations from at least 17 years up to maximum 46 years. The mean time series length per site is 31 years.

GRQA

The Global River Water Quality Archive (GRQA)6 (downloaded version: GRQA v1.2, March 11, 2022) is a harmonized and aggregated water quality dataset, based on five national, continental and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat (Global Freshwater Quality Database), GLORICH(GLObal RIver CHemistry), Waterbase and WQP (Water Quality Portal). The dataset contains 42 water quality parameters from which a subset of NO2--N, NO3--N, NH4--N, DOC, DIP, TOC and TP was selected. The samples of this subset were observed between 1900 and 2020.

Sweden

The dataset comprises data from the Swedish CLEO database (Temnerud et al. 2014)(https://www.slu.se/cleo/data, original link not active anymore). The observed water quality data originate from forested headwater streams within the boreal Krycklan catchment in Sweden. The dataset contains data on NO2--N and NO3-–N (combined), NH4+-N, DIN, TOC, DOC, TP and DIP for 27 sites. Observations cover the period from 1985 to 2022 with varying temporal coverage between four and 35 years per site. The average time series length is 14 years.

USGS

The United States Geological Survey (USGS) dataset was published in 2017 along with scientific investigations report ”Water-Quality Trends in the Nation’s Rivers and Streams, 1972-2012”7. From the provided parameters NH4+-N, NO3-–N, PO43–-P and TOC were extracted. Observed concentrations were available for 764 sites across the United States. Samples cover the period from 1965 to 2013, but temporal coverage ranges between stations from at least nine years up to maximum 49 years. The average time series length per site is 25 years.

References

1. Lehner, B., Verdin, K. & Jarvis, A. New Global Hydrography Derived From Spaceborne Elevation Data. Eos, Transactions American Geophysical Union 89, 93–94 (2008).

2. Ebeling, P. et al. QUADICA: Water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany. Earth System Science Data 14, 3715–3741 (2022).

3. Holmes, R. M. et al. Climate Change Impacts on the Hydrology and Biogeochemistry of Arctic Rivers. in Climatic Change and Global Warming of Inland Waters 1–26 (John Wiley & Sons, Ltd, 2012). doi:10.1002/9781118470596.ch1.

4. Ebeling, P. et al. Long-Term Nitrate Trajectories Vary by Season in Western European Catchments. Global Biogeochemical Cycles 35, e2021GB007050 (2021).

5. Ehrhardt, S. et al. Nitrate Transport and Retention in Western European Catchments Are Shaped by Hydroclimate and Subsurface Properties. Water Resources Research 57, e2020WR029469 (2021).

6. Virro, H., Amatulli, G., Kmoch, A., Shen, L. & Uuemaa, E. GRQA: Global River Water Quality Archive. Earth System Science Data 13, 5483–5507 (2021).

7. Oelsner, G. P. et al. Water-quality trends in the nation’s rivers and streams, 1972–2012—Data preparation, statistical methods, and trend results. Scientific Investigations Report (2017) doi:10.3133/sir20175006.