Human pressures reshape reactive carbon, nitrogen, and phosphorus imbalances in global freshwaters (water quality variables and catchment attributes)

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. 6 water quality datasets (no German raw data, see also below) 2. 7 files containing the corresponding station IDs with coordinates 3. HYDROATLAS: BasinATLAS_v10_lev12, packaged in HydroBasins.zip 4. HYDRORIVERS: HydroRIVERS_v10, packaged in HydrRIVERS.zip 5. Topographic wetness index (TWI): TWI_global.tif (calculated using QGIS), packaged in TWI_global.zip 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 raw data for Germany is not part of the published dataset due to license restrictions of the data owner. However, the aggregated German data is available in median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv. 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 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. 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.本仓库包含如下数据集： 7套河流水体碳（C）、氮（N）、磷（P）浓度观测水质数据集，均从原始数据库收集下载。各数据集详情与原始数据来源详见下文。 此外，本仓库还包含HydroATLAS与HydroRIVERS数据库，二者均于2024年5月从HYDROSHEDS项目官网（www.hydrosheds.org）下载获取。 全球地形湿度指数（Topographic Wetness Index, TWI）通过填洼数字高程模型（Digital Elevation Model, DEM，分辨率15弧秒）与汇流累积量图（上游集水区面积，单位公顷；15弧秒分辨率）计算得到。上述两类全球尺度数据均取自HYDROSHEDS项目核心数据产品1。 最后，本仓库提供了来自3496个水文集水区的输出rC:rN:rP比值的处理后数据集，及其对应的水文环境属性。 ## 数据集概览 ### 原始数据 1. 6套水质数据集（无德国原始数据，详见下文） 2. 7个包含对应站点ID与坐标的文件 3. HYDROATLAS：BasinATLAS_v10_lev12，打包于HydroBasins.zip中 4. HYDRO RIVERS：HydroRIVERS_v10，打包于HydrRIVERS.zip中 5. 地形湿度指数（Topographic Wetness Index, TWI）：TWI_global.tif（通过QGIS计算），打包于TWI_global.zip中 ### 处理后数据集与模型参数 1. 水文集水区的化学计量比rOC:rN:rP输出结果及对应集水区属性： – median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv – 处理后数据的完整变量名与单位表详见Bartusch_etal_Zenodo.pdf文件。 2. 梯度提升回归树（Gradient Boosting Regression Tree, GBRT）模型的优化参数集： – optimized_model_parms_GBRT_models.csv ## 7套水质数据集详细说明 ### 丹麦 丹麦河流水质数据集从Overfladevandsdatabasen网页下载获取，包含溶解无机氮（硝酸盐、亚硝酸盐与铵盐）、有机碳（总有机碳TOC、溶解有机碳DOC）与磷（总磷TP、溶解无机磷DIP）。观测时段为1970年至2022年，不同参数的时间跨度与时间分辨率存在差异。数据中NO3–N与NO2--N为合并统计项。 ### 德国 由于数据所有者的许可限制，德国原始数据未纳入本公开数据集。但聚合后的德国数据可从median_cnp_export_abs_conc_and_rfr_and_basin_feat.csv中获取。 德国水质数据库2.0由UFZ收集整理，是QUADICA数据集v12版本的更新版本。该数据集整合了德国联邦州政府机构提供的水质数据，涵盖德国境内3965个监测点的DOC、NO3--N与TP浓度。所有监测点均至少包含上述三项参数，若数据可用则额外包含NO2--N、NH4+-N、溶解无机磷DIP与总有机碳TOC。采样时段为1982年至2020年，不同监测点的时间覆盖范围各异，为1至37年不等。 ### 北极三角洲 ArcticGRO水质数据集3（2024版）是北极大河观测站数据库的子集，包含6个北极河流三角洲的水质监测数据。该数据集可在ArcticGRO官网（ArcticGRO Water Quality Dataset, 2024）免费获取并定期更新。数据集包含2003年至2021年间，每条北极大河沿线6个监测点的NO3--N、NH4+-N、DOC与PO43–-P浓度。年观测频次为5至7次，单个监测点的平均时间序列长度为17年。 ### 法国 法国数据集包含法国水质数据库中486个监测点的NO3--N、DOC与PO43--P观测数据。这些监测点经预先筛选，用于长期水质分析，与文献4、5中采用的研究站点一致。因此本数据集仅包含具备长期水质数据的监测点。采样时段为1969年至2016年，不同监测点的时间覆盖范围为17年至46年不等，单个监测点的平均时间序列长度为31年。 ### GRQA 全球河流水质档案库（Global River Water Quality Archive, GRQA）6（下载版本：GRQA v1.2，2022年3月11日）是一套经过标准化整合的水质数据集，基于5项国家级、大陆级与全球级数据集构建：CESI（加拿大环境可持续性指标计划）、GEMStat（全球淡水水质数据库）、GLORICH（全球河流化学数据库）、Waterbase与WQP（水质门户）。该数据集包含42项水质参数，本次研究选取其中的NO2--N、NO3--N、NH4--N、DOC、DIP、TOC与TP作为子集。该子集的采样时段为1900年至2020年。 ### 瑞典 该数据集源自瑞典CLEO数据库（Temnerud等，2014）（https://www.slu.se/cleo/data，原始链接现已失效）。观测水质数据来自瑞典 boreal Krycklan集水区内的森林源头溪流。数据集包含27个监测点的NO2--N与NO3-–N（合并统计）、NH4+-N、DIN、TOC、DOC、TP与DIP数据。观测时段为1985年至2022年，不同监测点的时间覆盖范围为4至35年不等，单个监测点的平均时间序列长度为14年。 ### USGS 美国地质调查局（United States Geological Survey, USGS）数据集于2017年随科学调查报告《全美河流与溪流水质趋势（1972-2012）》7发布。本次研究从其提供的参数中提取了NH4+-N、NO3-–N、PO43–-P与TOC。全美范围内共有764个监测点具备浓度观测数据。采样时段为1965年至2013年，不同监测点的时间覆盖范围为9年至49年不等，单个监测点的平均时间序列长度为25年。 ## 参考文献 1. Lehner, B., Verdin, K. & Jarvis, A. 基于星载高程数据构建的新全球水文地图. 《美国地球物理联合会汇刊》（Eos, Transactions American Geophysical Union）89, 93–94 (2008). 2. Ebeling, P. et al. QUADICA v2: 扩展德国水质、径流与集水区属性大样本数据集. 地球系统科学数据讨论（Earth System Science Data Discussions）1–37 (2025) doi:10.5194/essd-2025-450. 3. Holmes, R. M. et al. 气候变化对北极河流水文与生物地球化学的影响. 见《内陆水域的气候变化与全球变暖》1–26 (John Wiley & Sons, Ltd, 2012). doi:10.1002/9781118470596.ch1. 4. Ebeling, P. et al. 西欧集水区的长期硝酸盐轨迹随季节变化. 全球生物地球化学循环（Global Biogeochemical Cycles）35, e2021GB007050 (2021). 5. Ehrhardt, S. et al. 西欧集水区的硝酸盐输运与滞留受水文气候与地下属性调控. 水资源研究（Water Resources Research）57, e2020WR029469 (2021). 6. Virro, H., Amatulli, G., Kmoch, A., Shen, L. & Uuemaa, E. GRQA：全球河流水质档案库. 地球系统科学数据（Earth System Science Data）13, 5483–5507 (2021). 7. Oelsner, G. P. et al. 全美河流与溪流水质趋势（1972–2012）：数据准备、统计方法与趋势结果. 科学调查报告（Scientific Investigations Report）(2017) doi:10.3133/sir20175006.