Human pressures intensify stochiometric nitrogen excess in streams and rivers (water quality variables and catchment attributes)

Human pressures intensify stoichiometric nitrogen 1 excess in streams and rivers (water quality2 variables and catchment attributes)3 Alexander Bartusch1Felipe Saavedra2Anika Große1,3 4 Carolin Winter4Pia Ebeling5Julia Pasqualini6Michele Meyer6 5 Lindsey N. Aman Cromwell8Linus Silvester Schauer5 6 Alexander Hubig5Yao Li1Andreas Musolff5Rohini Kumar7 7 Daniel Graeber1,* 8 1Dep. Aquatic Ecosystem Analysis, Helmholtz-Centre for Environmental Research – UFZ9 2Dep. Catchment Hydrology, Helmholtz-Centre for Environmental Research – UFZ10 3Department of Ecoscience, Aarhus University11 4Chair of Environmental Hydrological Systems, University Freiburg12 5Dep. Hydrogeology, Helmholtz-Centre for Environmental Research – UFZ13 6Dep. River Ecology, Helmholtz-Centre for Environmental Research – UFZ14 7Dep. Computational Hydrosystems, Helmholtz-Centre for Environmental Research – UFZ15 8School of Forest, Fisheries, and Geomatics Sciences, University of Florida16 *Correspondence: Daniel Graeber <daniel.graeb[email protected]>17 1 Description18 This repository includes the following datasets:19 Seven water quality datasets with observational data of C, N and P concentrations in streams were 20 collected and downloaded from the original databases. Details regarding the corresponding dataset 21 and original data source can be found below.22 Additionally, the repository contains the HydroATLAS and the HydroRIVERS databases, both of 23 which were downloaded from the HYDROSHEDS project website (www.hydrosheds.org) in May 24 2024.25 The global map of the topographic wetness index, was caclulated using a void-filled digital elevation 26 model DEM (resolution 15 arc-seconds) and a flow accumulation map (ACA upstream area in 27 hectares; 15 arc-seconds). Both, on a global scale, were obtained from the core data products of the 28 HydroSHEDS project1.29 Finally, the repository provides the processed dataset of exported rC:rN:rP ratios from 3,496 30 hydrological catchments and their corresponding hydro-environmental attributes.31 Overview32 Raw data:33 1. 6 water quality datasets (no German raw data, see also below)34 2. 7 files containing the corresponding station IDs with coordinates35 3. HYDROATLAS: BasinATLAS_v10_lev12, packaged in HydroBasins.zip36 4. HYDRORIVERS: HydroRIVERS_v10, packaged in HydrRIVERS.zip37 5. Topographic wetness index (TWI): TWI_global.tif (calculated using QGIS), packaged in 38 TWI_global.zip39 2 Processed datasets and model parameters40 1. Stoichiometric rOC:rN:rP export from the hydrological catchments with the corresponding 41 catchment attributes:42 •median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv43 • A table with full variable names and units of the processed data is contained within 44 Bartusch_etal_Zenodo.pdf.45 2. The optimized model parameter set for the Gradient Boosting Regression Tree (GBRT) model: 46 •optimized_model_parms_GBRT_models.csv47 Table 1: Variable ids, names and units of the processed dataset (median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv) Column header Description Unit HYBAS_ID unique basin identifier c_imbal Redfield normalized concentration of reactive OC (rOCRFR) % n_imbal Redfield normalized concentration of DIN (DINRFR) % p_imbal Redfield normalized concentration of SRP (SRPRFR) % median_DOC_TOC_bioav absolute concentration of reactive OC (rOC) mg L-1 median_DIN absolute concentration of DIN (rN) mg L-1 median_SRP absolute concentration of SRP (rP) mg L-1 3 Column header Description Unit UP_AREA total upstream area, calculated from the headwater to the polygon location (including the polygon). The upstream area only comprises the directly connected watershed area, i.e., it does not include endorheic regions that may be part of the larger basin through virtual connections km-2 twi90 90th percentile of the topographic wetness index - slp_dg_uav Terrain slope degrees (x10) for_pc_use Forest cover extent % crp_pc_use Cropland extent % pst_pc_use Pasture extent % ppd_pk_uav Population density people per km2 run_mm_syr Land surface runoff mm inu_pc_umn Inundation extent % dor_pc_pva Degree of regulation % (x10) ria_ha_usu_perkm2 River area in total watershed upstream of sub-basin pour point hectares per km2 riv_tc_usu_perkm2 River volume in total watershed upstream of sub-basin pour point thousand m3per km2 ele_mt_uav Elevation meters a.s.l. sgr_dk_sav Stream gradient dm km-1 tmp_dc_uyr Air temperature degrees C (x10) pre_mm_uyr Precipitation mm pet_mm_uyr Potential evapotranspiration mm aet_mm_uyr Actual evapotranspiration mm 4 Column header Description Unit snw_pc_uyr Snow cover extent % wet_pc_ug2 Wetland extent % ire_pc_use Irrigated area extent % gla_pc_use Glacier extent % prm_pc_use Permafrost extent % pac_pc_use Protected area extent % cly_pc_uav Clay fraction in soil % slt_pc_uav Silt fraction in soil % snd_pc_uav Sand fraction in soil % soc_th_uav Organic carbon content in soil tonnes ha-1 swc_pc_uyr Soil water content % kar_pc_use Karst area extent % ero_kh_uav Soil erosion kg ha-1 year-1 gdp_ud_usu Gross domestic product US Dollars hdi_ix_sav Human Development Index index value (x1000) Detailed description of the 7 water quality datasets48 Denmark49 The dataset of Danish river water quality data was downloaded from the Overfladevandsdatabasen 50 webpage (“Overfladevandsdatabasen”), and includes dissolved inorganic nitrogen (nitrate, nitrite 51 and ammonium), organic carbon (total organic carbon TOC, dissolved organic carbon DOC) and 52 phosphorus (total phosphorus TP, dissolved inorganic phosphorus DIP). Observations cover the 53 period from 1970 until 2022 with different timespans and temporal resolution, depending on the 54 parameter. The data contains NO3–N and NO2--N (combined).55 5 Germany56 The raw data for Germany is not part of the published dataset due to license re57 strictions of the data owner. However, the aggregated German data is available 58 in median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv.59 The water quality database Germany 2.0 was collected and put together at the UFZ and constitutes 60 an update of the QUADICA data set version 1 2 . The dataset is compiled from water quality data 61 provided by the German federal state authorities. The dataset provides DOC, NO 3- -N, and TP 62 concentrations at 3,965 sites across Germany. All stations have at least these three fractions, but 63 if available, NO 2- -N, NH 4+ -N, dissolved inorganic P (DIP) and TOC are additionally included. 64 Samples cover the period from 1982 to 2020, but with varying temporal coverage between sites, 65 ranging from 1 to 37 years.66 Arctic deltas67 The ArcticGRO Water Quality Dataset 3 (version 2024) is a subset of the Arctic Great Rivers 68 Observatory database and consists of water quality measurements from six Arctic river deltas. The 69 data are freely available at the ArcticGRO webpage (ArcticGRO Water Quality Dataset, 2024) and 70 frequently updated. The dataset provides NO 3- -N, NH 4+ -N, DOC, and PO 43– -P concentrations 71 for 6 stations along each of the Arctic great rivers from 2003-2021. The temporal resolution is 5–7 72 observations per year. The average time series length per site is 17 years.73 France74 The French dataset provides NO 3- -N, DOC and PO 43- -P observations for 486 French stations of the 75 French water quality database. The stations were preselected for long-term water quality analysis, 76 as for the work published in 4 and 5 . Therefore, only stations with available long-term water quality 77 data are included here. Samples cover the period from 1969 to 2016, but temporal coverage ranges 78 6 between stations from at least 17 years up to maximum 46 years. The mean time series length per 79 site is 31 years.80 GRQA81 The Global River Water Quality Archive (GRQA) 6 (downloaded version: GRQA v1.2, March 11, 82 2022) is a harmonized and aggregated water quality dataset, based on five national, continental 83 and global datasets: CESI (Canadian Environmental Sustainability Indicators program), GEMStat 84 (Global Freshwater Quality Database), GLORICH(GLObal RIver CHemistry), Waterbase and WQP 85 (Water Quality Portal). The dataset contains 42 water quality parameters from which a subset of 86 NO 2- -N, NO 3- -N, NH 4- -N, DOC, DIP, TOC and TP was selected. The samples of this subset were 87 observed between 1900 and 2020.88 Sweden89 The dataset comprises data from the Swedish CLEO database (Temnerud et al. 2014)(https://www.slu.se/cleo/data, 90 original link not active anymore). The observed water quality data originate from forested headwater 91 streams within the boreal Krycklan catchment in Sweden. The dataset contains data on NO 2- -N 92 and NO 3- –N (combined), NH 4+ -N, DIN, TOC, DOC, TP and DIP for 27 sites. Observations cover 93 the period from 1985 to 2022 with varying temporal coverage between four and 35 years per site. 94 The average time series length is 14 years.95 USGS96 The United States Geological Survey (USGS) dataset was published in 2017 along with scientific 97 investigations report ”Water-Quality Trends in the Nation’s Rivers and Streams, 1972-2012” 7 . 98 From the provided parameters NH 4+ -N, NO 3- –N, PO 43– -P and TOC were extracted. Observed 99 concentrations were available for 764 sites across the United States. Samples cover the period from 100 1965 to 2013, but temporal coverage ranges between stations from at least nine years up to maximum 101 49 years. The average time series length per site is 25 years.102 7 References103 1. Lehner, B., Verdin, K. & Jarvis, A. New Global Hydrography Derived From Spaceborne Elevation Data.Eos, Transactions American Geophysical Union 89, 93–94 (2008). 104 2. Ebeling, P. et al. QUADICA v2: Extending the large-sample data set for water QUAlity, DIscharge and Catchment Attributes in Germany. Earth System Science Data Discussions 1–37 (2025) doi:10.5194/essd-2025-450. 105 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. 106 4. Ebeling, P. et al. Long-Term Nitrate Trajectories Vary by Season in Western European Catchments.Global Biogeochemical Cycles 35, e2021GB007050 (2021). 107 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). 108 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). 109 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. 110 8