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Hydrol. Earth Syst. Sci., 21, 3701-3713, 2017
https://doi.org/10.5194/hess-21-3701-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Review article
20 Jul 2017
Scaling, similarity, and the fourth paradigm for hydrology
Christa D. Peters-Lidard1, Martyn Clark2, Luis Samaniego3, Niko E. C. Verhoest4, Tim van Emmerik5, Remko Uijlenhoet6, Kevin Achieng7, Trenton E. Franz8, and Ross Woods9 1Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
2Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80301, USA
3UFZ-Helmholtz Centre for Environmental Research, Leipzig, 04318, Germany
4Laboratory of Hydrology and Water Management, Ghent University, Coupure links 653, 9000 Ghent, Belgium
5Water Resources Section, Delft University of Technology, Delft, 2628 CN, the Netherlands
6Hydrology and Quantitative Water Management Group, Wageningen University, 6700 AA Wageningen, the Netherlands
7Department of Civil and Architectural Engineering, University of Wyoming, Laramie, WY 82071, USA
8School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
9Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
Abstract. In this synthesis paper addressing hydrologic scaling and similarity, we posit that roadblocks in the search for universal laws of hydrology are hindered by our focus on computational simulation (the third paradigm) and assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological scaling and similarity hypotheses. We summarize important scaling and similarity concepts (hypotheses) that require testing; describe a mutual information framework for testing these hypotheses; describe boundary condition, state, flux, and parameter data requirements across scales to support testing these hypotheses; and discuss some challenges to overcome while pursuing the fourth hydrological paradigm. We call upon the hydrologic sciences community to develop a focused effort towards adopting the fourth paradigm and apply this to outstanding challenges in scaling and similarity.

Citation: Peters-Lidard, C. D., Clark, M., Samaniego, L., Verhoest, N. E. C., van Emmerik, T., Uijlenhoet, R., Achieng, K., Franz, T. E., and Woods, R.: Scaling, similarity, and the fourth paradigm for hydrology, Hydrol. Earth Syst. Sci., 21, 3701-3713, https://doi.org/10.5194/hess-21-3701-2017, 2017.
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In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology...
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