Lessons learnt from checking the quality of openly accessible river flow data worldwide

Louise Crochemore, Kristina Isberg, Rafael Pimentel, Luis Pineda, Abdulghani Hasan, Berit Arheimer

Research output: Contribution to journalArticlepeer-review

18 Citations (SciVal)


Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and to integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions.
Keywords: open data, river flow, global hydrology, quality control, time series
Original languageEnglish
JournalHydrological Sciences Journal
Publication statusPublished - 2019 Oct 1

Subject classification (UKÄ)

  • Earth and Related Environmental Sciences


  • open data
  • river flow
  • global hydrology
  • quality control
  • time series


Dive into the research topics of 'Lessons learnt from checking the quality of openly accessible river flow data worldwide'. Together they form a unique fingerprint.

Cite this