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<p>Protecting and improving surface water quality is contingent upon understanding the trends and spatial patterns in physical, biological, and chemical conditions and their underlying drivers. This requires observational data, spanning a diverse range of water quality constituents, coupled with contextual environmental data. Here we present the first global-scale integration of stream water quality into large-sample hydrology (named <i>Caravan-Qual</i>), combining ~96 million observations of 100 constituents with streamflow measurements, meteorological forcing and catchment attributes covering the period 1980–2025. We envisage that the dataset can facilitate a diverse range of empirical analyses (e.g. spatio-temporal analysis across diverse regions, quantification of pollutant loadings and exports, concentration-discharge analysis), in addition to supporting development and evaluation of process-based and data-driven models for water quality prediction and management.</p>

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Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Edward R. Jones,
  • Frederik Kratzert,
  • Michelle T. H. van Vliet

摘要

Protecting and improving surface water quality is contingent upon understanding the trends and spatial patterns in physical, biological, and chemical conditions and their underlying drivers. This requires observational data, spanning a diverse range of water quality constituents, coupled with contextual environmental data. Here we present the first global-scale integration of stream water quality into large-sample hydrology (named Caravan-Qual), combining ~96 million observations of 100 constituents with streamflow measurements, meteorological forcing and catchment attributes covering the period 1980–2025. We envisage that the dataset can facilitate a diverse range of empirical analyses (e.g. spatio-temporal analysis across diverse regions, quantification of pollutant loadings and exports, concentration-discharge analysis), in addition to supporting development and evaluation of process-based and data-driven models for water quality prediction and management.