Articles | Volume 25, issue 8
https://doi.org/10.5194/hess-25-4549-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4549-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
Michael Stoelzle
CORRESPONDING AUTHOR
Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
Lina Stein
Department of Civil Engineering, University of Bristol, Bristol, UK
Related authors
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Erik Tijdeman, Veit Blauhut, Michael Stoelzle, Lucas Menzel, and Kerstin Stahl
Nat. Hazards Earth Syst. Sci., 22, 2099–2116, https://doi.org/10.5194/nhess-22-2099-2022, https://doi.org/10.5194/nhess-22-2099-2022, 2022
Short summary
Short summary
We identified different drought types with typical hazard and impact characteristics. The summer drought type with compounding heat was most impactful. Regional drought propagation of this drought type exhibited typical characteristics that can guide drought management. However, we also found a large spatial variability that caused distinct differences among propagating drought signals. Accordingly, local multivariate drought information was needed to explain the full range of drought impacts.
Jost Hellwig, Michael Stoelzle, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 25, 1053–1068, https://doi.org/10.5194/hess-25-1053-2021, https://doi.org/10.5194/hess-25-1053-2021, 2021
Short summary
Short summary
Potential future groundwater and baseflow drought hazards depend on systems' sensitivity to altered recharge conditions. With three generic scenarios, we found different sensitivities across Germany driven by hydrogeology. While changes in drought hazard due to seasonal recharge shifts will be rather low, a lengthening of dry spells could cause stronger responses in regions with slow groundwater response to precipitation, urging local water management to prepare for more severe droughts.
Maria Staudinger, Stefan Seeger, Barbara Herbstritt, Michael Stoelzle, Jan Seibert, Kerstin Stahl, and Markus Weiler
Earth Syst. Sci. Data, 12, 3057–3066, https://doi.org/10.5194/essd-12-3057-2020, https://doi.org/10.5194/essd-12-3057-2020, 2020
Short summary
Short summary
The data set CH-IRP provides isotope composition in precipitation and streamflow from 23 Swiss catchments, being unique regarding its long-term multi-catchment coverage along an alpine–pre-alpine gradient. CH-IRP contains fortnightly time series of stable water isotopes from streamflow grab samples complemented by time series in precipitation. Sampling conditions, catchment and climate information, lab standards and errors are provided together with areal precipitation and catchment boundaries.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Veit Blauhut, Michael Stoelzle, Lauri Ahopelto, Manuela I. Brunner, Claudia Teutschbein, Doris E. Wendt, Vytautas Akstinas, Sigrid J. Bakke, Lucy J. Barker, Lenka Bartošová, Agrita Briede, Carmelo Cammalleri, Ksenija Cindrić Kalin, Lucia De Stefano, Miriam Fendeková, David C. Finger, Marijke Huysmans, Mirjana Ivanov, Jaak Jaagus, Jiří Jakubínský, Svitlana Krakovska, Gregor Laaha, Monika Lakatos, Kiril Manevski, Mathias Neumann Andersen, Nina Nikolova, Marzena Osuch, Pieter van Oel, Kalina Radeva, Renata J. Romanowicz, Elena Toth, Mirek Trnka, Marko Urošev, Julia Urquijo Reguera, Eric Sauquet, Aleksandra Stevkov, Lena M. Tallaksen, Iryna Trofimova, Anne F. Van Loon, Michelle T. H. van Vliet, Jean-Philippe Vidal, Niko Wanders, Micha Werner, Patrick Willems, and Nenad Živković
Nat. Hazards Earth Syst. Sci., 22, 2201–2217, https://doi.org/10.5194/nhess-22-2201-2022, https://doi.org/10.5194/nhess-22-2201-2022, 2022
Short summary
Short summary
Recent drought events caused enormous damage in Europe. We therefore questioned the existence and effect of current drought management strategies on the actual impacts and how drought is perceived by relevant stakeholders. Over 700 participants from 28 European countries provided insights into drought hazard and impact perception and current management strategies. The study concludes with an urgent need to collectively combat drought risk via a European macro-level drought governance approach.
Erik Tijdeman, Veit Blauhut, Michael Stoelzle, Lucas Menzel, and Kerstin Stahl
Nat. Hazards Earth Syst. Sci., 22, 2099–2116, https://doi.org/10.5194/nhess-22-2099-2022, https://doi.org/10.5194/nhess-22-2099-2022, 2022
Short summary
Short summary
We identified different drought types with typical hazard and impact characteristics. The summer drought type with compounding heat was most impactful. Regional drought propagation of this drought type exhibited typical characteristics that can guide drought management. However, we also found a large spatial variability that caused distinct differences among propagating drought signals. Accordingly, local multivariate drought information was needed to explain the full range of drought impacts.
Jost Hellwig, Michael Stoelzle, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 25, 1053–1068, https://doi.org/10.5194/hess-25-1053-2021, https://doi.org/10.5194/hess-25-1053-2021, 2021
Short summary
Short summary
Potential future groundwater and baseflow drought hazards depend on systems' sensitivity to altered recharge conditions. With three generic scenarios, we found different sensitivities across Germany driven by hydrogeology. While changes in drought hazard due to seasonal recharge shifts will be rather low, a lengthening of dry spells could cause stronger responses in regions with slow groundwater response to precipitation, urging local water management to prepare for more severe droughts.
Maria Staudinger, Stefan Seeger, Barbara Herbstritt, Michael Stoelzle, Jan Seibert, Kerstin Stahl, and Markus Weiler
Earth Syst. Sci. Data, 12, 3057–3066, https://doi.org/10.5194/essd-12-3057-2020, https://doi.org/10.5194/essd-12-3057-2020, 2020
Short summary
Short summary
The data set CH-IRP provides isotope composition in precipitation and streamflow from 23 Swiss catchments, being unique regarding its long-term multi-catchment coverage along an alpine–pre-alpine gradient. CH-IRP contains fortnightly time series of stable water isotopes from streamflow grab samples complemented by time series in precipitation. Sampling conditions, catchment and climate information, lab standards and errors are provided together with areal precipitation and catchment boundaries.
Cited articles
Albrecht, M.: Color blindness, Nat. Methods, 7, 775–775, https://doi.org/10.1038/nmeth1010-775a, 2010.
Borland, D. and Taylor, R.:
Rainbow Color Map (Still) Considered Harmful,
IEEE Comput. Graph.,
27, 14–17, https://doi.org/10.1109/MCG.2007.323435, 2007.
Brychtová, A. and Çöltekin, A.:
The effect of spatial distance on the discriminability of colors in maps,
Cartogr. Geogr. Inf. Sc.,
44, 229–245, https://doi.org/10.1080/15230406.2016.1140074, 2017.
Burden, C. M., Morgan, M. O., Hladun, K. R., Amdam, G. V., Trumble, J. J., and Smith, B. H.:
Acute sublethal exposure to toxic heavy metals alters honey bee (Apis mellifera) feeding behavior,
Sci. Rep.-UK,
9, 4253, https://doi.org/10.1038/s41598-019-40396-x, 2019.
Campbell, J. M., Jordan, P., and Arnscheidt, J.: Using high-resolution phosphorus data to investigate mitigation measures in headwater river catchments, Hydrol. Earth Syst. Sci., 19, 453–464, https://doi.org/10.5194/hess-19-453-2015, 2015.
Chang, W., Cheng, J., Allaire, J., Xie, Y., and McPherson, J.:
shiny: Web Application Framework for R, available at: https://cran.r-project.org/web/packages/shiny/index.html (last access: 30 July 2021), 2020.
Cheng, J., Karambelkar, B., and Xie, Y.:
leaflet: Create Interactive Web Maps with the JavaScript “Leaflet” Library, available at: https://cran.r-project.org/web/packages/leaflet/index.html, last access: 30 July 2021.
Coalter, J.:
ColorBrewer 2.0 and the Rainbow: Using Color Tools to Choose Appropriate Color Schema for your Data Visualization,
ISTL, 94, 1–12, https://doi.org/10.29173/istl63, 2020.
Crameri, F.:
The Rainbow Colour Map (repeatedly) considered harmful, EGU Blogs, available at: https://blogs.egu.eu/divisions/gd/2017/08/23/the-rainbow-colour-map/, (last access 30 July 2021), 2017.
Crameri, F.:
Scientific Colour Maps,
Zenodo,
https://doi.org/10.5281/zenodo.1243862, 2020.
Crameri, F., Shephard, G. E., and Heron, P. J.:
The misuse of colour in science communication,
Nat. Commun.,
11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020.
Garnier, S.:
viridis: Default Color Maps from “matplotlib”, available at: https://cran.r-project.org/web/packages/viridis/index.html (last access: 30 July 2021), 2018.
Gehlenborg, N. and Wong, B.:
Heat maps,
Nat. Methods,
9, 213–213, https://doi.org/10.1038/nmeth.1902, 2012a.
Gehlenborg, N. and Wong, B.:
Mapping quantitative data to color,
Nat. Methods,
9, 769–769, https://doi.org/10.1038/nmeth.2134, 2012b.
Gehlenborg, N. and Wong, B.:
Power of the plane,
Nat. Methods,
9, 935–935, https://doi.org/10.1038/nmeth.2186, 2012c.
Geissbuehler, M. and Lasser, T.:
How to display data by color schemes compatible with red-green color perception deficiencies,
Opt. Express,
21, 9862, https://doi.org/10.1364/OE.21.009862, 2013.
Gnann, S. J., Woods, R. A., and Howden, N. J. K.:
Is There a Baseflow Budyko Curve?,
Water Resour. Res.,
55, 2838–2855, https://doi.org/10.1029/2018WR024464, 2019.
Gnann, S. J., McMillan, H., Woods, R. A., and Howden, N. J. K.:
Including Regional Knowledge Improves Baseflow Signature Predictions in Large Sample Hydrology,
Water Resour. Res., 57, e2020WR028354, https://doi.org/10.1029/2020WR028354, 2020.
Gomis, M. I. and Pidcock, R.:
IPCC Visual Style Guide for Authors, availabe at: https://www.ipcc.ch/site/assets/uploads/2019/04/IPCC-visual-style-guide.pdf (last access: 30 July 2021), 28, 2018.
Harrower, M. and Brewer, C. A.:
ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps,
Cartogr. J.,
40, 27–37, https://doi.org/10.1179/000870403235002042, 2003.
Healey, C. G.:
Choosing effective colours for data visualization,
in: Proceedings of Seventh Annual IEEE Visualization '96, Seventh Annual IEEE Visualization '96, San Francisco, CA, USA, 27 October–1 November 1996, 263–270, https://doi.org/10.1109/VISUAL.1996.568118, 1996.
Hoellein, T. J., Shogren, A. J., Tank, J. L., Risteca, P., and Kelly, J. J.:
Microplastic deposition velocity in streams follows patterns for naturally occurring allochthonous particles,
Sci. Rep.-UK,
9, 3740, https://doi.org/10.1038/s41598-019-40126-3, 2019.
Hvitfeldt, E.:
paletteer: Comprehensive Collection of Color Palettes, available at: https://cran.r-project.org/web/packages/paletteer/index.html, last access: 30 July 2021.
Kelleher, C. and Braswell, A.:
Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets,
Environmental Modelling & Software, 143, 105113, https://doi.org/10.1016/j.envsoft.2021.105113, 2021.
Kelleher, C. and Wagener, T.:
Ten guidelines for effective data visualization in scientific publications,
Environ. Modell. Softw.,
26, 822–827, https://doi.org/10.1016/j.envsoft.2010.12.006, 2011.
Kingston, D. G. and Taylor, R. G.: Sources of uncertainty in climate change impacts on river discharge and groundwater in a headwater catchment of the Upper Nile Basin, Uganda, Hydrol. Earth Syst. Sci., 14, 1297–1308, https://doi.org/10.5194/hess-14-1297-2010, 2010.
Kovesi, P.:
Bad Colour Maps Hide Big Features and Create False Anomalies,
ASEG Extended Abstracts,
2015, 1–4, https://doi.org/10.1071/ASEG2015ab107, 2015.
Kreit, E., Mäthger, L. M., Hanlon, R. T., Dennis, P. B., Naik, R. R., Forsythe, E., and Heikenfeld, J.:
Biological versus electronic adaptive coloration: how can one inform the other?,
J. R. Soc. Interface, 10, 20120601, https://doi.org/10.1098/rsif.2012.0601, 2013.
Lee, B., Choe, E. K., Isenberg, P., Marriott, K., Stasko, J., and Rhyne, T.-M.:
Reaching Broader Audiences With Data Visualization,
IEEE Comput. Graph.,
40, 82–90, https://doi.org/10.1109/MCG.2020.2968244, 2020.
Light, A. and Bartlein, P. J.:
The end of the rainbow? Color schemes for improved data graphics,
EOS T. Am. Geophys. Un.,
85, 385, https://doi.org/10.1029/2004EO400002, 2004.
Liu, Y. and Heer, J.:
Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps,
in: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems – CHI '18, the 2018 CHI Conference, Montreal QC, Canada, 21–26 April 2018, 1–12, https://doi.org/10.1145/3173574.3174172, 2018.
McNeall, D.:
How many rainbows at EGU 2018?,
Better Figures, available at: https://betterfigures.org/2018/04/16/how-many-rainbows-at-egu-2018/ (last access: 30 July 2021), 2018.
McWhite, C. D. and Wilke, C. O.:
colorblindr: Simulate colorblindness in R figures, available at: https://github.com/clauswilke/colorblindr, last access: 30 July 2021.
Milly, P. C. D.:
Climate, soil water storage, and the average annual water balance, Water Resour. Res., 30, 2143–2156, https://doi.org/10.1029/94WR00586, 1994.
Moreland, K.:
Why We Use Bad Color Maps and What You Can Do About It,
Electronic Imaging,
2016, 1–6, https://doi.org/10.2352/ISSN.2470-1173.2016.16.HVEI-133, 2016.
Neuwirth, E.:
RColorBrewer: ColorBrewer Palettes, available at: https://cran.r-project.org/web/packages/RColorBrewer/index.html (last access: 30 July 2021), 2012.
Nuñez, J. R., Anderton, C. R., and Renslow, R. S.:
Optimizing colormaps with consideration for color vision deficiency to enable accurate interpretation of scientific data,
PLoS ONE,
13, e0199239, https://doi.org/10.1371/journal.pone.0199239, 2018.
Okabe, M. and Ito, K.:
Color universal design (CUD) – how to make figures and presentations that are friendly to colorblind people, available at: https://jfly.uni-koeln.de/color/ (last access: 30 July 2021), 2008.
Pedersen, T. L. and Crameri, F.:
scico: Colour Palettes. Based on the Scientific Colour-Maps, available at: https://github.com/thomasp85/scico (last access: 30 July 2021), 2020.
Pramanik, T., Khatiwada, B., and Pandit, R.: Color vision deficiency among a group of students of health sciences, Nepal Med. Coll. J., 14, 334–336, 2012.
Roa-García, M. C. and Weiler, M.: Integrated response and transit time distributions of watersheds by combining hydrograph separation and long-term transit time modeling, Hydrol. Earth Syst. Sci., 14, 1537–1549, https://doi.org/10.5194/hess-14-1537-2010, 2010.
Rogowitz, B. E. and Kalvin, A. D.:
The “Which Blair project”: a quick visual method for evaluating perceptual color maps,
in: Proceedings Visualization, 2001, VIS '01, IEEE Visualization 2001, October 21 - October 26, 2001, San Diego, CA, USA,
183–556, https://doi.org/10.1109/VISUAL.2001.964510, 2001.
Rogowitz, B. E., Treinish, L. A., and Bryson, S.:
How Not to Lie with Visualization,
Comput. Phys.,
10, 268, https://doi.org/10.1063/1.4822401, 1996.
Rougier, N. P., Droettboom, M., and Bourne, P. E.:
Ten Simple Rules for Better Figures,
PLoS Comput. Biol.,
10, e1003833, https://doi.org/10.1371/journal.pcbi.1003833, 2014.
Schaefli, B., Hingray, B., Niggli, M., and Musy, A.: A conceptual glacio-hydrological model for high mountainous catchments, Hydrol. Earth Syst. Sci., 9, 95–109, https://doi.org/10.5194/hess-9-95-2005, 2005.
Sharma, G. and Trussell, H. J.:
Digital color imaging,
IEEE T. Image Process.,
6, 901–932, https://doi.org/10.1109/83.597268, 1997.
Shoresh, N. and Wong, B.:
Data exploration,
Nat. Methods,
9, 5–5, https://doi.org/10.1038/nmeth.1829, 2012.
Stauffer, R., Mayr, G. J., Dabernig, M., and Zeileis, A.: Somewhere over the rainbow: How to make effective use of colors in meteorological visualizations, B. Am. Meteorol. Soc., 96, 203–216, 2015.
Stoelzle, M.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication (v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5145746, 2021a.
Stoelzle, M.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Kaggle Notebook, available at: https://www.kaggle.com/modche/rainbow-papersurvey-hydrology, last access: 29 July 2021b.
Stoelzle, M., Stahl, K., and Weiler, M.: Are streamflow recession characteristics really characteristic?, Hydrol. Earth Syst. Sci., 17, 817–828, https://doi.org/10.5194/hess-17-817-2013, 2013.
Streit, M. and Gehlenborg, N.:
Temporal data,
Nat. Methods,
12, 97–97, https://doi.org/10.1038/nmeth.3262, 2015.
Sunyer, M. A., Hundecha, Y., Lawrence, D., Madsen, H., Willems, P., Martinkova, M., Vormoor, K., Bürger, G., Hanel, M., Kriaučiūnienė, J., Loukas, A., Osuch, M., and Yücel, I.: Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe, Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, 2015.
Thyng, K., Greene, C., Hetland, R., Zimmerle, H., and DiMarco, S.:
True Colors of Oceanography: Guidelines for Effective and Accurate Colormap Selection,
Oceanography,
29, 9–13, https://doi.org/10.5670/oceanog.2016.66, 2016.
Tufte, E. R.:
The visual display of quantitative information,
CT Graphics, Cheshire, 1983.
Vandemeulebroecke, M., Baillie, M., Margolskee, A., and Magnusson, B.:
Effective Visual Communication for the Quantitative Scientist,
CPT Pharmacometrics Syst. Pharmacol.,
8, 705–719, https://doi.org/10.1002/psp4.12455, 2019.
Vanderkam, D., Allaire, J., Owen, J., Gromer, D., and Thieurmel, B.: dygraphs: Interface to “Dygraphs” Interactive Time Se-
ries Charting Library, available at: https://CRAN.R-project.org/package=dygraphs (last access: 2 August 2021), R package version 1.1.1.6, 2018.
Wanzer, D. L., Azzam, T., Jones, N. D., and Skousen, D.:
The role of titles in enhancing data visualization,
Eval. Program Plann.,
84, 101896, https://doi.org/10.1016/j.evalprogplan.2020.101896, 2021.
Waskom, M.:
seaborn: statistical data visualization,
JOSS,
6, 3021, https://doi.org/10.21105/joss.03021, 2021.
Wong, B.:
Design of data figures,
Nat. Methods,
7, 665–665, https://doi.org/10.1038/nmeth0910-665, 2010.
Wong, B.:
Avoiding color,
Nat. Methods,
8, 525–525, https://doi.org/10.1038/nmeth.1642, 2011a.
Wong, B.:
Points of view: Color blindness,
Nat. Methods,
8, 441–441, https://doi.org/10.1038/nmeth.1618, 2011b.
Wong, B.:
Points of view: Points of review (part 2),
Nat. Methods,
8, 189–189, https://doi.org/10.1038/nmeth0311-189, 2011c.
World Health Organization: World report on vision, 2019.
Zeileis, A., Fisher, J. C., Hornik, K., Ihaka, R., McWhite, C. D., Murrell, P., Stauffer, R., and Wilke, C. O.: colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes, J. Stat. Softw., 92, https://doi.org/10.18637/jss.v096.i01, 2020.
Short summary
We found with a scientific paper survey (~ 1000 papers) that 45 % of the papers used rainbow color maps or red–green visualizations. Those rainbow visualizations, although attracting the media's attention, will not be accessible for up to 10 % of people due to color vision deficiency. The rainbow color map distorts and misleads scientific communication. The study gives guidance on how to avoid, improve and trust color and how the flaws of the rainbow color map should be communicated in science.
We found with a scientific paper survey (~ 1000 papers) that 45 % of the papers used rainbow...