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Abstract Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones. 

On hillslopes with patchy vegetation cover, vegetation is a significant factor controlling surface hydraulic and hydrological properties.  Soil permeability is often greater within vegetated areas than in surrounding bare soil areas, leading to the redistribution of rainfall from bare, runoff-generating areas to permeable, vegetated areas. While many studies have examined the hydrological consequences of permeability contrasts, the hydrodynamic effects of greater surface roughness in vegetated patches compared to bare areas remain under-investigated. The role of roughness is not obvious: greater roughness in vegetated patches provides greater resistance to flow, slowing water movement and thus extending the time frame over which infiltration can occur. However, greater roughness may also cause partial blocking and flow diversion, reducing the volume of water traversing vegetated areas, a mechanism that could reduce rainfall redistribution to these sites. To differentiate the roles of spatially-varying roughness and permeability on rainfall redistribution, the two-dimensional Saint Venant Equations are employed to model the hydrologic outcomes of permeability and roughness contrasts under varying rainfall intensities.The simulations consider the dynamically interesting case of an idealized vegetated patch surrounded by runoff-generating unvegetated areas. The model results indicate that greater resistance causes flow diversion around vegetation. However, vegetative resistance only reduces rainfall redistributed to the vegetation under the specific conditions of low rainfall intensity and high soil permeability. Otherwise, prolonged ponding during the recession period, due to greater vegetative resistance, creates additional time for infiltration, compensating for increased flow diversion around the vegetation.  

Abstract Describing flow resistance from the properties of an underlying surface is a challenge in surface hydrology. Runoff models must specify a resistance formulation or “roughness scheme”—describing the functional relationship between flow resistance and flow depth/velocity—and its parameters. Uncertainty in runoff predictions derives from both the selected roughness scheme (e.g., Darcy Weisbach, Manning's, or laminar flow equations), and its parameterization with a roughness coefficient (e.g., Manning's ). Both choices are informed by model calibration to data, usually discharge, and, if available, velocity. In this study, a Saint Venant Equation‐based runoff model is calibrated to discharge and velocity data from 112 rainfall simulator experiments. The results are used to identify the optimal roughness scheme among four widely‐used options for each experiment, and to explore whether surface properties can be used to select the optimal roughness scheme and its coefficient. Among the tested roughness schemes, a transitional flow equation provided the best fit to the plurality of experiments. The most suitable roughness scheme for a given experiment was not related to measured surface properties. Regression models predicted the calibrated roughness coefficients with adjusted values between 0.48 and 0.54, depending on the roughness scheme used. Litter cover was the best predictor of the roughness coefficient, followed by soil cover and average canopy gap size. The results suggest that selection of an optimal roughness scheme based on surface properties alone remains difficult, but that once a scheme is selected, roughness coefficients can be estimated from surface properties. 

This project contains the Saint Venant Equation (SVE) simulation data needed to reproduce the figures and results of the manuscript: Roughness giving you the runaround? Investigating the interplay of infiltration and resistance on vegetated hillslopes, currently under review at Journal of Hydrology. 

This project contains the Saint Venant Equation (SVE) simulation data needed to reproduce the figures and results of the publication: Crompton, O., Katul, G., Lapides, D. A., & Thompson, S. E. (2023). Bridging structural and functional hydrological connectivity in dryland ecosystems. Catena, 231, 107322. 

This project contains the Saint Venant Equation (SVE) simulation data needed to reproduce the figures and results of the manuscript: Crompton, O., Katul, G., Lapides, D., & Thompson, S. (2023). Hydrologic Connectivity and Patch‐To‐Hillslope Scale Relations in Dryland Ecosystems. Geophysical Research Letters, 50(10), e2022GL101801. 

Ecohydrological phenomena are o ften multiscale in nature, with behavioTur that emerges from the interaction of tightly coupled systems having characteristic timescales that differ by orders of magnitude. Models address these differences using timescale separation methods, where each system is held in psuedo‐steady state while the other evolves. When the computational demands of solving the ‘fast’ system are large, this strategy can become numerically intractable. Here, we use emulation modelling to accelerate the simulation of a computationally intensive ‘fast’ system: overland flow. We focus on dryland ecosystems in which storms generate overland flow, on timescales of 101 − 2 s. In these ecosystems, overland flow delivers crucial water inputs to vegetation, which grows and disperses ‘slowly’, on timescales of 107 − 9 s. Emulation allows for a physically realistic treatment of flow, advancing on phenomenological descriptions used in previous studies. Resolving the within‐storm processes reveals novel dynamics, including new transition pathways from patchy vegetation to desertification, that are specifically controlled by storm processes.