Pia Ebeling, Rohini Kumar, S. Lutz, Tam V. Nguyen, F. Sarrazin, M. Weber, O. Büttner, S. Attinger, A. Musolff
{"title":"QUADICA - water quality, discharge and catchment attributes for large-sample studies in Germany","authors":"Pia Ebeling, Rohini Kumar, S. Lutz, Tam V. Nguyen, F. Sarrazin, M. Weber, O. Büttner, S. Attinger, A. Musolff","doi":"10.4211/hs.26e8238f0be14fa1a49641cd8a455e29","DOIUrl":null,"url":null,"abstract":"Abstract. Environmental data are the key to defining and addressing\nwater quality and quantity challenges at the catchment scale. Here, we present\nthe first large-sample water quality data set for 1386 German catchments\ncovering a large range of hydroclimatic, topographic, geologic, land use, and\nanthropogenic settings. QUADICA (water QUAlity, DIscharge and Catchment\nAttributes for large-sample studies in Germany) combines water quality with\nwater quantity data, meteorological and nutrient forcing data, and catchment\nattributes. The data set comprises time series of riverine macronutrient\nconcentrations (species of nitrogen, phosphorus, and organic carbon) and\ndiffuse nitrogen forcing data (nitrogen surplus,\natmospheric deposition, and fixation) at the catchment scale. Time series are generally aggregated\nto an annual basis; however, for 140 stations with long-term water quality\nand quantity data (more than 20 years), we additionally present monthly\nmedian discharge and nutrient concentrations, flow-normalized concentrations,\nand corresponding mean fluxes as outputs from Weighted Regressions on Time,\nDischarge, and Season (WRTDS). The catchment attributes include catchment\nnutrient inputs from point and diffuse sources and characteristics from\ntopography, climate, land cover, lithology, and soils. This comprehensive,\nfreely available data collection with a large spatial and temporal coverage\ncan facilitate large-sample data-driven water quality assessments at the\ncatchment scale as well as mechanistic modeling studies. QUADICA is\navailable at https://doi.org/10.4211/hs.0ec5f43e43c349ff818a8d57699c0fe1 (Ebeling et al., 2022b) and https://doi.org/10.4211/hs.88254bd930d1466c85992a7dea6947a4 (Ebeling et al., 2022a).\n","PeriodicalId":388186,"journal":{"name":"HydroShare Resources","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"HydroShare Resources","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4211/hs.26e8238f0be14fa1a49641cd8a455e29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Abstract. Environmental data are the key to defining and addressing
water quality and quantity challenges at the catchment scale. Here, we present
the first large-sample water quality data set for 1386 German catchments
covering a large range of hydroclimatic, topographic, geologic, land use, and
anthropogenic settings. QUADICA (water QUAlity, DIscharge and Catchment
Attributes for large-sample studies in Germany) combines water quality with
water quantity data, meteorological and nutrient forcing data, and catchment
attributes. The data set comprises time series of riverine macronutrient
concentrations (species of nitrogen, phosphorus, and organic carbon) and
diffuse nitrogen forcing data (nitrogen surplus,
atmospheric deposition, and fixation) at the catchment scale. Time series are generally aggregated
to an annual basis; however, for 140 stations with long-term water quality
and quantity data (more than 20 years), we additionally present monthly
median discharge and nutrient concentrations, flow-normalized concentrations,
and corresponding mean fluxes as outputs from Weighted Regressions on Time,
Discharge, and Season (WRTDS). The catchment attributes include catchment
nutrient inputs from point and diffuse sources and characteristics from
topography, climate, land cover, lithology, and soils. This comprehensive,
freely available data collection with a large spatial and temporal coverage
can facilitate large-sample data-driven water quality assessments at the
catchment scale as well as mechanistic modeling studies. QUADICA is
available at https://doi.org/10.4211/hs.0ec5f43e43c349ff818a8d57699c0fe1 (Ebeling et al., 2022b) and https://doi.org/10.4211/hs.88254bd930d1466c85992a7dea6947a4 (Ebeling et al., 2022a).
摘要环境数据是定义和解决集水区水质和水量挑战的关键。在这里,我们展示了1386个德国流域的第一个大样本水质数据集,涵盖了大范围的水文气候、地形、地质、土地利用和人为环境。QUADICA(德国用于大样本研究的水质、排放和集水区属性)将水质与水量数据、气象和养分强迫数据以及集水区属性结合起来。该数据集包括流域尺度的河流宏量养分浓度(氮、磷和有机碳的种类)和弥漫氮强迫数据(氮过剩、大气沉积和固定)的时间序列。时间序列通常按年汇总;然而,对于140个拥有长期水质和水量数据(超过20年)的站点,我们还提供了每月流量和营养物质浓度中位数、流量标准化浓度和相应的平均通量,作为时间、流量和季节加权回归(WRTDS)的输出。流域属性包括来自点源和扩散源的流域养分输入,以及地形、气候、土地覆盖、岩性和土壤的特征。这种全面、免费的数据收集具有大的空间和时间覆盖范围,可以促进大样本数据驱动的集水区水质评估以及机制建模研究。QUADICA可从https://doi.org/10.4211/hs.0ec5f43e43c349ff818a8d57699c0fe1 (Ebeling et al., 2022b)和https://doi.org/10.4211/hs.88254bd930d1466c85992a7dea6947a4 (Ebeling et al., 2022a)获得。