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<p>The acidity and buffering capacity of inland waters are essential for biogeochemical processes and impose significant constraints on the distribution of freshwater species. Although many measurements exist worldwide, the data distribution is biased toward more-studied regions, and a global assessment of gradients and their spatial distribution is lacking. In the PHALK dataset, we compile alkalinity and pH values for continental surface waters worldwide, collating chemical data from 18 source databases and 55 scientific publications. A quality-control filter yielded high-quality alkalinity and pH datasets, including 50,916 and 107,896 sites, respectively. Based on the collated dataset and a random forest model, pH and alkalinity in surface waters were modeled worldwide at the basin scale (HydroBASINS v1 sub-basin level 12: 1,034,083 drainage basins) using 23 variables describing basin geological and hydrological characteristics. Each extrapolated value is accompanied by two uncertainty indicators: environmental differentiation, based on the similarity of the basin’s environmental conditions to those of basins with measured data, and upscaling confidence, based on the variation in the random forest’s internal bootstrap.</p>

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Global basin-scale mapping of pH and alkalinity in inland waters

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Meritxell Batalla,
  • Jordi Martínez-Artero,
  • Jordi Catalan

摘要

The acidity and buffering capacity of inland waters are essential for biogeochemical processes and impose significant constraints on the distribution of freshwater species. Although many measurements exist worldwide, the data distribution is biased toward more-studied regions, and a global assessment of gradients and their spatial distribution is lacking. In the PHALK dataset, we compile alkalinity and pH values for continental surface waters worldwide, collating chemical data from 18 source databases and 55 scientific publications. A quality-control filter yielded high-quality alkalinity and pH datasets, including 50,916 and 107,896 sites, respectively. Based on the collated dataset and a random forest model, pH and alkalinity in surface waters were modeled worldwide at the basin scale (HydroBASINS v1 sub-basin level 12: 1,034,083 drainage basins) using 23 variables describing basin geological and hydrological characteristics. Each extrapolated value is accompanied by two uncertainty indicators: environmental differentiation, based on the similarity of the basin’s environmental conditions to those of basins with measured data, and upscaling confidence, based on the variation in the random forest’s internal bootstrap.