IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

A vision for human-water systems data

Heidi Kreibich1, David Yu2, Nick Brozović3, Serena Ceola4, Mariana Madruga de Brito5, Kimberly Quesnel6, Kai Schröter1, and David Walker7
Heidi Kreibich et al.
  • 1German Research Centre for Geosciences GFZ, Section Hydrology, Potsdam, Germany (heidi.kreibich@gfz-potsdam.de)
  • 2Purdue University, School of Civil Engineering & Department of Political Science, US
  • 3Agricultural Economics, University of Nebraska, US
  • 4University of Bologna, Department of Civil, Chemical, Environmental, and Materials Engineering, Italy
  • 5Helmholtz Centre for Environmental Research - UFZ, Department of Urban and Environmental Sociology, Germany
  • 6Stanford University, Woods Institute, US
  • 7Wageningen University & Research, Water Resources Management group, The Netherlands

Research on the co-evolution of water and society is essential for supporting policy decisions and for the development and implementation of water management and climate adaptation strategies. Findable Accessible Interoperable Reusable (FAIR) data on human-water systems is needed to understand, model and project how these systems co-evolve. Our aim is to characterize data that support quality action and research concerning human-water systems and present examples how data of novel and emerging data sources help to tacklel data challenges and gaps. Our vision is FAIR data that support quality action and research. Such data need to have the following characteristics:

⮚ Data on human-water systems encompassing hydrological, environmental and socio-economic data.

⮚ Essential variables and indicators, meaningful for decision-makers, including detailed observational data describing interactions and feedbacks between hydrology and society.

⮚ Data across a broad range of different temporal and spatial scales.

⮚ Good quality data, i.e. intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.

FAIR data principles that need to be followed comprise the following:

⮚ Data needs to be findable, i.e., registered or indexed in a searchable resource, with a globally unique and persistent identifier and described with rich metadata.

⮚ Data needs to be accessible, (meta)data must be retrievable by their identifier using a standardized communications protocol that is open, free, and universally implementable.

⮚ Data needs to be interoperable, that is (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation and include qualified references to other data.

⮚ Data needs to be reusable, meta(data) should be richly described with a plurality of accurate and relevant attributes meeting domain-relevant community standards; and data should be released with a clear and accessible data usage license and detailed provenance.

How to cite: Kreibich, H., Yu, D., Brozović, N., Ceola, S., Madruga de Brito, M., Quesnel, K., Schröter, K., and Walker, D.: A vision for human-water systems data, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-41, https://doi.org/10.5194/iahs2022-41, 2022.