Big data and hydroinformatics

Yiheng Chen, Dawei Han

Research output: Contribution to journalArticle (Academic Journal)peer-review

26 Citations (Scopus)
416 Downloads (Pure)


Big data is popular in the areas of computer science, commerce and bioinformatics, but is in an early stage in hydroinformatics. Big data is originated from the extreme large datasets that cannot be processed in tolerable elapsed time with the traditional data processing methods. Using the analogy from the object-oriented programming, big data should be considered as objects encompassing the data, its characteristics and the processing methods. Hydroinformatics can benefit from the big data technology with newly emerged data, techniques and analytical tools to handle large datasets, from which creative ideas and new values could be mined. This paper provides a timely review on big data with its relevance to hydroinformatics. A further exploration on precipitation big data is discussed because estimation of precipitation is an important part of hydrology for managing floods and droughts, and understanding the global water cycle. It is promising that fusion of precipitation data from remote sensing, weather radar, rain gauge and numerical weather modelling could be achieved by parallel computing and distributed data storage, which will trigger a leap in precipitation estimation as the available data from multiple sources could be fused to generate a better product than those from single sources.
Original languageEnglish
Pages (from-to)599-614
Number of pages16
JournalJournal of Hydroinformatics
Issue number4
Early online date2 Mar 2016
Publication statusPublished - Jul 2016


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