Scaling, Similarity, and the Fourth Paradigm for Hydrology

Abstract

In this review of 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 15 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 modelling, 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 20 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.

Cosmic‐ray Neutron Rover Surveys of Field Soil Moisture and the Influence of Roads

“…org/10.1029/2017WR021719 Gentine et al, 2012;Schulz et al, 2006;Vereecken et al, 2015). One of the grand challenges in soil moisture monitoring is the provision of parameters which describe these critical processes at the landscape scale and which represent the natural heterogeneity of the soil-hydrological system at scales of 1-1,000 m (Peters-Lidard et al, 2017;Robinson et al, 2008).…”

A Computationally Efficient and Physically Based Approach for Urban Flood Modeling Using a Flexible Spatiotemporal Structure

“…• Fully distributed hydraulics-hydrology, flexible surface mesh, and spatiotemporally adaptive hydraulic time stepping are incorporated • Unstructured mesh development is controlled by incorporating graphical features designed to maximize efficiency while maintaining accuracy • Proposed approach is 80% more efficient in simulating the flood hydrodynamics of Hurricane Harvey compared to a fixed-resolution model planning strategies (Dottori et al, 2016). Flooding in complex urban systems is affected by several physical factors interacting together at different spatiotemporal scales, including but not limited to rainfall intensity and magnitude, land use variability, storm surge in coastal regions, reservoir storage and release, riverine flow, infiltration induced by surface-subsurface interactions, longitudinal and lateral movement of riverfloodplain fluxes, water entrapment though topographic gradients, and levee overtopping (Fletcher et al, 2013;Hattermann et al, 2004;Peters-Lidard et al, 2017;Salvadore et al, 2015;Sulis et al, 2010;Vivoni et al, 2007). With the increasing intensity and spatial influence of extreme flood events affecting urban systems, the question that the research community needs to address is whether the existing techniques for flood prediction are capable of capturing the dynamic and compound nature of these events using the existing computational tools.…”

Response to Reviewer 3

“…Third, there is considerable scope to improve the way that multivariate data are used to constrain model parameter values. A key path forward is to identify different signatures from the data that can be used to improve parameter values in different parts of the model Yilmaz et al, 2008;Pokhrel et al, 2012;Vrugt and Sadegh, 2013;Rakovec et al, 2015). For example, Troy et al (2008) use regionalized estimates of the precipitation : runoff ratio to constrain the VIC model at the grid scale, and there is much more that can be done using such methods (e.g., see the approach of Yadav et al, 2007).…”

“…The challenge is to estimate spatial variations in the storage and transmission properties of the landscape. Advances are possible through the development of new data sources on geophysical attributes (Simard et al, 2011;Gleason and Smith, 2014;Fan et al, 2015;Chaney et al, 2016b;Pelletier et al, 2016;De Graaf et al, 2017), new approaches to link geophysical attributes to model parameters (Samaniego et al, 2010;Kumar et al, 2013;Rakovec et al, 2015), and new diagnostics to infer model parameters Yilmaz et al, 2008;Pokhrel et al, 2012). Such focus will give the parameter estimation problem the scientific attention that it deserves, rather than the far-too-common approach where parameter estimation is relegated to a "tuning exercise" in model applications.…”