09 Mar 2021

09 Mar 2021

Review status: this preprint is currently under review for the journal HESS.

Rainbow colors distort and mislead research in hydrology – guidance for better visualizations and science communication

Michael Stoelzle1 and Lina Stein2 Michael Stoelzle and Lina Stein
  • 1Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
  • 2Department of Civil Engineering, University of Bristol, Bristol, UK

Abstract. Nowadays color in scientific visualizations is standard and extensively used to group, highlight or delineate different parts of data in visualizations. The rainbow color map (also known as jet color map) is famous for its appealing use of the full visual spectrum with impressive changes in chroma and luminance. Beside attracting attention, science has for decades criticized the rainbow color map for its non-linear and erratic change of hue and luminance along the data variation. The missed uniformity causes a misrepresentation of data values and flaws in science communication. The rainbow color map is scientifically incorrect and hardly decodable for a considerable number of people due to color-vision deficiency (CVD) or other vision impairments. Here we aim to raise awareness how widely used the rainbow color maps still is in hydrology. To this end we perform a paper survey scanning for color issues in around 1000 scientific publications in three different journals including papers published between 2005 and 2020. In this survey, depending on the journal, 16–24 % of the publications have a rainbow color map and around the same ratio of papers (18–29 %) use red-green elements often in a way that color is the only possibility to decode the visualized groups of data. Given these shares, there is a 99.6 % chance to pick at least one visual problematic publication in 10 randomly chosen papers from our survey. To overcome the use of the rainbow color maps in science, we propose some tools and techniques focusing on improvement of typical visualization types in hydrological science. Consequently, color should be used with more care to highlight most important aspects of a visualization and the identification of correct data types such as categorical or sequential data is essential to pick appropriate color maps. We give guidance how to avoid, improve and trust and color in a proper and scientific way. Finally, we sketch a way to improve the communication of rainbow flaws between different status groups in science, publishers, and the media.

Michael Stoelzle and Lina Stein

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-118', Fabio Crameri, 22 Mar 2021
    • AC2: 'Reply on RC1 (F. Crameri)', Michael Stoelzle, 22 Apr 2021
  • CC1: 'Comment on hess-2021-118', J.C. Refsgaard, 24 Mar 2021
    • AC1: 'Reply on CC1', Michael Stoelzle, 22 Apr 2021
  • RC2: 'Comment on hess-2021-118', Anonymous Referee #2, 26 Mar 2021
    • AC4: 'Reply on RC2', Michael Stoelzle, 06 May 2021
  • RC3: 'What role does human laziness play?', Thorsten Wagener, 29 Mar 2021
    • AC3: 'Reply on RC3 by T. Wagener', Michael Stoelzle, 29 Apr 2021

Michael Stoelzle and Lina Stein

Data sets

HESS Paper survey data Michael Stoelzle and Lina Stein

Michael Stoelzle and Lina Stein


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Short summary
Here, a paper survey scanning 1000 papers found that 45 % of the scientific papers used rainbow color maps or red-green visualizations. Those rainbow visualizations, although attracting media's attention, will not be accessible for up to 8–10 % of the people due to color vision deficiency (CVD) and distorts and misleads scientific communication. The study gives guidance how to avoid, improve and trust color and how the flaws of rainbow color maps could be communicated in science.